The omnigenic model was proposed as a framework to understand the highly polygenic architecture of complex traits revealed by genome-wide association studies (GWASs).
I argue that this model also explains recent observations about cross-population genetic effects, specifically the low transferability of polygenic scores and the lack of clear evidence for polygenic selection. In particular, the omnigenic model explains why the effects of most GWAS variants vary between populations.
This interpretation has several consequences for the evolutionary interpretation and practical use of GWAS summary statistics and polygenic scores. First, some polygenic scores may be applicable only in populations of the same ancestry and environment as the discovery population. Second, most GWAS associations will have differing effects between populations and are unlikely to be robust clinical targets. Finally, it may not always be possible to detect polygenic selection from population genetic data.
These considerations make it difficult to interpret the clinical and evolutionary meanings of polygenic scores without an explicit model of genetic architecture.
The genetic effect-size distribution of a disease describes the number of risk variants, the range of their effect sizes and sample sizes that will be required to discover them. Accurate estimation has been a challenge.
Here I propose the method-of-momentsFourier Mixture Regression (FMR), validating that it accurately estimates real and simulated effect-size distributions. Applied to summary statistics for 10 diseases (average Neff = 169,000), FMR estimates that 100,000–1,000,000 cases will be required for genome-wide statistically-significantSNPs to explain 50% of SNP heritability. In such large studies, genome-wide statistical-significance becomes increasingly conservative, and less stringent thresholds achieve high true positive rates if confounding is controlled.
Across traits, polygenicity varies, but the range of their effect sizes is similar. Compared with effect sizes in the top 10% of heritability, including most discovered thus far, those in the bottom 10–50% are orders of magnitude smaller and more numerous, spanning a large fraction of the genome.
[Twitter] The X-chromosome has long been hypothesized to have a disproportionate influence on the brain based on its enrichment for genes that are expressed in the brain and associated with intellectual disability.
Here, we verify this hypothesis through partitioned heritability analysis of X-chromosome influences (XIs) on human brain anatomy in 32,256 individuals from the UK Biobank. We first establish evidence for dosage compensation in XIs on brain anatomy—reflecting larger XIs in males compared to females, which correlate with regional sex-biases in neuroanatomical variance. XIs are statistically-significantly larger than would be predicted from X-chromosome size for the relative surface area of cortical systems supporting attention, decision-making and motor control. Follow-up association analyses implicate X-linked genes with pleiotropic effects on cognition.
Our study reveals a privileged role for the X-chromosome in human neurodevelopment and urges greater inclusion of this chromosome in future genome-wide association studies.
This article explores the nature of psychiatric genetics research conducted in asylums in Western Europe in the mid-19th century through an examination of 4 studies published 1841 to 1864 from Great Britain, France, and Germany.
They all utilize asylum records to determine if patients had a hereditary predisposition (HP) to mental illness. A diverse range of topics were investigated, with most attention on whether men or women are more likely to transmit, or are more sensitive to the receipt of, an HP. When statistically-significant sex effects were seen, they consistently found women to be more likely to transmit and/or more sensitive to the receipt of an HP. Other questions explored included:
the relationship between an HP and recurrence rates;
the degree of homogeneity versus heterogeneity of transmission of specific mental illnesses in families;
the level of HP among different forms of mental illness; and
differences in the proportion of psychiatric patients with an HP as a function of their religion.
While the method of assessment of familial/genetic risk was relatively crude, even at this early stage in the history of psychiatric genetics, investigators were asking thoughtful questions about the nature and clinical impact of that risk.
2021-akbari.pdf: “Sequencing of 640,000 exomes identifies GPR75 variants associated with protection from obesity”, Parsa Akbari, Ankit Gilani, Olukayode Sosina, Jack A. Kosmicki, Lori Khrimian, Yi-Ya Fang, Trikaldarshi Persaud, Victor Garcia, Dylan Sun, Alexander Li, Joelle Mbatchou, Adam E. Locke, Christian Benner, Niek Verweij, Nan Lin, Sakib Hossain, Kevin Agostinucci, Jonathan V. Pascale, Ercument Dirice, Michael Dunn, Regeneron Genetics Center, DiscovEHR Collaboration, William E. Kraus, Svati H. Shah, Yii-Der I. Chen, Jerome I. Rotter, Daniel J. Rader, Olle Melander, Christopher D. Still, Tooraj Mirshahi, David J. Carey, Jaime Berumen-Campos, Pablo Kuri-Morales, Jesus Alegre-Díaz, Jason M. Torres, Jonathan R. Emberson, Rory Collins, Suganthi Balasubramanian, Alicia Hawes, Marcus Jones, Brian Zambrowicz, Andrew J. Murphy, Charles Paulding, Giovanni Coppola, John D. Overton, Jeffrey G. Reid, Alan R. Shuldiner, Michael Cantor, Hyun M. Kang, Goncalo R. Abecasis, Katia Karalis, Aris N. Economides, Jonathan Marchini, George D. Yancopoulos, Mark W. Sleeman, Judith Altarejos, Giusy Della Gatta, Roberto Tapia-Conyer, Michal L. Schwartzman, Aris Baras, Manuel A. R. Ferreira, Luca A. Lotta (2021-07-02):
Large-scale human exome sequencing can identify rare protein-coding variants with a large impact on complex traits such as body adiposity. We sequenced the exomes of 645,626 individuals from the United Kingdom, the United States, and Mexico and estimated associations of rare coding variants with body mass index (BMI). We identified 16 genes with an exome-wide statistically-significant association with BMI, including those encoding five brain-expressed G protein–coupled receptors (CALCR, MC4R, GIPR, GPR151, and GPR75). Protein-truncating variants in GPR75 were observed in ~4/10,000 sequenced individuals and were associated with 1.8 kilograms per square meter lower BMI and 54% lower odds of obesity in the heterozygous state. Knock out of Gpr75 in mice resulted in resistance to weight gain and improved glycemic control in a high-fat diet model. Inhibition of GPR75 may provide a therapeutic strategy for obesity.
Across species, offspring of related individuals often exhibit substantial reduction in fitness-related traits, known as inbreeding depression (ID), yet the genetic and molecular basis for ID remains elusive. Here, we develop a method to quantify enrichment of ID within specific genomic annotations and apply it to human data.
We analyzed the phenomes and genomes of ~350,000 unrelated participants of the UK Biobank and found, on average of over 11 traits, statistically-significant enrichment of ID within genomic regions with high recombination rates (>21×; p < 10−5), with conserved function across species (>19×; p < 10−4), and within regulatory elements such as DNase I hypersensitive sites (~5×; p = 8.9 × 10−7). We also quantified enrichment of ID within trait-associated regions and found suggestive evidence that genomic regions contributing to additive genetic variance in the population are enriched for ID signal. We find strong correlations between functional enrichment of SNP-based heritability and that of ID (r = 0.8, standard error: 0.1). These findings provide empirical evidence that ID is most likely due to many partially recessive deleterious alleles in low linkage disequilibrium regions of the genome.
Our study suggests that functional characterization of ID may further elucidate the genetic architectures and biological mechanisms underlying complex traits and diseases.
[Keywords: inbreeding depression, functional annotation, genomic partitioning, genome-wide association studies]
2021-owen.pdf: “Rapid Sequencing–Based Diagnosis of Thiamine Metabolism Dysfunction Syndrome”, Mallory J. Owen, Anna-Kaisa Niemi, David P. Dimmock, Mark Speziale, Mark Nespeca, Kevin K. Chau, Luca Van Der Kraan, Meredith S. Wright, Christian Hansen, Narayanan Veeraraghavan, Yan Ding, Jerica Lenberg, Shimul Chowdhury, Charlotte A. Hobbs, Sergey Batalov, Zhanyang Zhu, Shareef A. Nahas, Sheldon Gilmer, Gail Knight, Sebastien Lefebvre, John Reynders, Thomas Defay, Jacqueline Weir, Vicki S. Thomson, Louise Fraser, Bryan R. Lajoie, Tim K. McPhail, Shyamal S. Mehtalia, Chris M. Kunard, Kevin P. Hall, Stephen F. Kingsmore (2021-06-03; backlinks):
Approximately 30 years after the start of the Human Genome Project, we sequenced the genome of an infant with encephalopathy in just over 11 hours. The results led to a clinical diagnosis of thiamine metabolism dysfunction syndrome 2 (THMD2) 16.5 hours after a blood sample was obtained and 13 hours after we initiated sequencing, which informed treatment of the infant, thereby illustrating the fulfillment of the promise of the Human Genome Project to transform health care…Video electroencephalography showed numerous seizures occurring in the interim. Thiamine and biotin administration was started 37.5 hours after admission, and phenobarbital administration was started 2 hours later. One 15-second seizure was recorded thereafter. 6 hours later, the patient was alert, calm, and bottle feeding. Standard, trio genome sequencing confirmed the diagnosis. After a further 24 hours passed without seizures, the patient was discharged. He is now thriving at 7 months of age.
…Ten years earlier, his parents, who were first cousins, had had a child with a similar neurologic presentation that rapidly progressed to epileptic encephalopathy; the child died at 11 months of age without an etiologic diagnosis, despite extensive evaluation.
…This case illustrates the potential for decreased suffering and improved outcomes through the implementation of rapid genome sequencing in a multidisciplinary, integrated, precision medicine delivery system…Currently, rapid genome sequencing is being implemented in Australia, England, Germany, and Wales and in Medicaid pilot programs in California, Florida, and Michigan.
Mutations in the melanocortin 4 receptor gene (MC4R) are associated with obesity but little is known about the prevalence and impact of such mutations throughout human growth and development.
We examined the MC4R coding sequence in 5,724 participants from the Avon Longitudinal Study of Parents and Children, functionally characterized all nonsynonymous MC4R variants and examined their association with anthropometric phenotypes from childhood to early adulthood.
The frequency of heterozygous loss-of-function (LoF) mutations in MC4R was ~1 in 337 (0.30%), considerably higher than previous estimates. At age 18 years, mean differences in body weight, body mass index and fat mass between carriers and noncarriers of LoF mutations were 17.76 kg (95% CI 9.41, 26.10), 4.84 kg m−2 (95% CI 2.19, 7.49) and 14.78 kg (95% CI 8.56, 20.99), respectively.
MC4R LoF mutations may be more common than previously reported and carriers of such variants may enter adult life with a substantial burden of excess adiposity.
Major depressive disorder is the most common neuropsychiatric disorder, affecting 11% of veterans.
Here we report results of a large meta-analysis of depression using data from the Million Veteran Program, 23andMe, UK Biobank and FinnGen, including individuals of European ancestry (n = 1,154,267; 340,591 cases) and African ancestry (n = 59,600; 25,843 cases).
Transcriptome-wide association study analyses revealed statistically-significant associations with expression of NEGR1 in the hypothalamus and DRD2 in the nucleus accumbens, among others. We fine-mapped 178 genomic risk loci, and we identified likely pathogenicity in these variants and overlapping gene expression for 17 genes from our transcriptome-wide association study, including TRAF3. Finally, we were able to show substantial replications of our findings in a large independent cohort (n = 1,342,778) provided by 23andMe.
This study sheds light on the genetic architecture of depression and provides new insight into the interrelatedness of complex psychiatric traits.
2021-mullins.pdf: “Genome–wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology”, Niamh Mullins, Andreas J. Forstner, Kevin S. O’Connell, Brandon Coombes, Jonathan R. I. Coleman, Zhen Qiao, Thomas D. Als, Tim B. Bigdeli, Sigrid Børte, Julien Bryois, Alexander W. Charney, Ole Kristian Drange, Michael J. Gandal, Saskia P. Hagenaars, Masashi Ikeda, Nolan Kamitaki, Minsoo Kim, Kristi Krebs, Georgia Panagiotaropoulou, Brian M. Schilder, Laura G. Sloofman, Stacy Steinberg, Vassily Trubetskoy, Bendik S. Winsvold, Hong-Hee Won, Liliya Abramova, Kristina Adorjan, Esben Agerbo, Mariam Eissa, Diego Albani, Ney Alliey-Rodriguez, Adebayo Anjorin, Verneri Antilla, Anastasia Antoniou, Swapnil Awasthi, Ji Hyun Baek, Marie Bækvad-Hansen, Nicholas Bass, Michael Bauer, Eva C. Beins, Sarah E. Bergen, Armin Birner, Carsten Bøcker Pedersen, Erlend Bøen, Marco P. Boks, Rosa Bosch, Murielle Brum, Ben M. Brumpton, Nathalie Brunkhorst-Kanaan, Monika Budde, Jonas Bybjerg-Grauholm, William Byerley, Murray Cairns, Miquel Casas, Pablo Cervantes, Toni-Kim Clarke, Cristiana Cruceanu, Alfredo Cuellar-Barboza, Julie Cunningham, David Curtis, Piotr M. Czerski, Anders M. Dale, Nina Dalkner, Friederike S. David, Franziska Degenhardt, Srdjan Djurovic, Amanda L. Dobbyn, Athanassios Douzenis, Torbjørn Elvsåshagen, Valentina Escott-Price, I. Nicol Ferrier, Alessia Fiorentino, Tatiana M. Foroud, Liz Forty, Josef Frank, Oleksandr Frei, Nelson B. Freimer, Louise Frisén, Katrin Gade, Julie Garnham, Joel Gelernter, Marianne Giørtz Pedersen, Ian R. Gizer, Scott D. Gordon, Katherine Gordon-Smith, Tiffany A. Greenwood, Jakob Grove, José Guzman-Parra, Kyooseob Ha, Magnus Haraldsson, Martin Hautzinger, Urs Heilbronner, Dennis Hellgren, Stefan Herms, Per Hoffmann, Peter A. Holmans, Laura Huckins, Stéphane Jamain, Jessica S. Johnson, Janos L. Kalman, Yoichiro Kamatani, James L. Kennedy, Sarah Kittel-Schneider, James A. Knowles, Manolis Kogevinas, Maria Koromina, Thorsten M. Kranz, Henry R. Kranzler, Michiaki Kubo, Ralph Kupka, Steven A. Kushner, Catharina Lavebratt, Jacob Lawrence, Markus Leber, Heon-Jeong Lee, Phil H. Lee, Shawn E. Levy, Catrin Lewis, Calwing Liao, Susanne Lucae, Martin Lundberg, Donald J. MacIntyre, Sigurdur H. Magnusson, Wolfgang Maier, Adam Maihofer, Dolores Malaspina, Eirini Maratou, Lina Martinsson, Manuel Mattheisen, Steven A. McCarroll, Nathaniel W. McGregor, Peter McGuffin, James D. McKay, Helena Medeiros, Sarah E. Medland, Vincent Millischer, Grant W. Montgomery, Jennifer L. Moran, Derek W. Morris, Thomas W. Mühleisen, Niamh O’Brien, Claire O’Donovan, Loes M. Olde Loohuis, Lilijana Oruc, Sergi Papiol, Antonio F. Pardiñas, Amy Perry, Andrea Pfennig, Evgenia Porichi, James B. Potash, Digby Quested, Towfique Raj, Mark H. Rapaport, J. Raymond DePaulo, Eline J. Regeer, John P. Rice, Fabio Rivas, Margarita Rivera, Julian Roth, Panos Roussos, Douglas M. Ruderfer, Cristina Sánchez-Mora, Eva C. Schulte, Fanny Senner, Sally Sharp, Paul D. Shilling, Engilbert Sigurdsson, Lea Sirignano, Claire Slaney, Olav B. Smeland, Daniel J. Smith, Janet L. Sobell, Christine Søholm Hansen, Maria Soler Artigas, Anne T. Spijker, Dan J. Stein, John S. Strauss, Beata Świątkowska, Chikashi Terao, Thorgeir E. Thorgeirsson, Claudio Toma, Paul Tooney, Evangelia-Eirini Tsermpini, Marquis P. Vawter, Helmut Vedder, James T. R. Walters, Stephanie H. Witt, Simon Xi, Wei Xu, Jessica Mei Kay Yang, Allan H. Young, Hannah Young, Peter P. Zandi, Hang Zhou, Lea Zillich, Rolf Adolfsson, Ingrid Agartz, Martin Alda, Lars Alfredsson, Gulja Babadjanova, Lena Backlund, Bernhard T. Baune, Frank Bellivier, Susanne Bengesser, Wade H. Berrettini, Douglas H. R. Blackwood, Michael Boehnke, Anders D. Børglum, Gerome Breen, Vaughan J. Carr, Stanley Catts, Aiden Corvin, Nicholas Craddock, Udo Dannlowski, Dimitris Dikeos, Tõnu Esko, Bruno Etain, Panagiotis Ferentinos, Mark Frye, Janice M. Fullerton, Micha Gawlik, Elliot S. Gershon, Fernando S. Goes, Melissa J. Green, Maria Grigoroiu-Serbanescu, Joanna Hauser, Frans Henskens, Jan Hillert, Kyung Sue Hong, David M. Hougaard, Christina M. Hultman, Kristian Hveem, Nakao Iwata, Assen V. Jablensky, Ian Jones, Lisa A. Jones, René S. Kahn, John R. Kelsoe, George Kirov, Mikael Landén, Marion Leboyer, Cathryn M. Lewis, Qingqin S. Li, Jolanta Lissowska, Christine Lochner, Carmel Loughland, Nicholas G. Martin, Carol A. Mathews, Fermin Mayoral, Susan L. McElroy, Andrew M. McIntosh, Francis J. McMahon, Ingrid Melle, Patricia Michie, Lili Milani, Philip B. Mitchell, Gunnar Morken, Ole Mors, Preben Bo Mortensen, Bryan Mowry, Bertram Müller-Myhsok, Richard M. Myers, Benjamin M. Neale, Caroline M. Nievergelt, Merete Nordentoft, Markus M. Nöthen, Michael C. O’Donovan, Ketil J. Oedegaard, Tomas Olsson, Michael J. Owen, Sara A. Paciga, Chris Pantelis, Carlos Pato, Michele T. Pato, George P. Patrinos, Roy H. Perlis, Danielle Posthuma, Josep Antoni Ramos-Quiroga, Andreas Reif, Eva Z. Reininghaus, Marta Ribasés, Marcella Rietschel, Stephan Ripke, Guy A. Rouleau, Takeo Saito, Ulrich Schall, Martin Schalling, Peter R. Schofield, Thomas G. Schulze, Laura J. Scott, Rodney J. Scott, Alessandro Serretti, Cynthia Shannon Weickert, Jordan W. Smoller, Hreinn Stefansson, Kari Stefansson, Eystein Stordal, Fabian Streit, Patrick F. Sullivan, Gustavo Turecki, Arne E. Vaaler, Eduard Vieta, John B. Vincent, Irwin D. Waldman, Thomas W. Weickert, Thomas Werge, Naomi R. Wray, John-Anker Zwart, Joanna M. Biernacka, John I. Nurnberger, Sven Cichon, Howard J. Edenberg, Eli A. Stahl, Andrew McQuillin, Arianna Florio, Roel A. Ophoff, Ole A. Andreassen (2021-05-17):
Bipolar disorder is a heritable mental illness with complex etiology.
We performed a genome-wide association study of 41,917 bipolar disorder cases and 371,549 controls of European ancestry, which identified 64 associated genomic loci. Bipolar disorder risk alleles were enriched in genes in synaptic signaling pathways and brain-expressed genes, particularly those with high specificity of expression in neurons of the prefrontal cortex and hippocampus.
Statistically-significant signal enrichment was found in genes encoding targets of antipsychotics, calcium channel blockers, antiepileptics and anesthetics. Integrating expression quantitative trait locus data implicated 15 genes robustly linked to bipolar disorder via gene expression, encoding druggable targets such as HTR6,MCHR1, DCLK3 and FURIN. Analyses of bipolar disorder subtypes indicated high but imperfect genetic correlation between bipolar disorder type I and II and identified additional associated loci.
Together, these results advance our understanding of the biological etiology of bipolar disorder, identify novel therapeutic leads and prioritize genes for functional follow-up studies.
Background: A dominant feature of anxiety disorders is familial aggregation. However, the underlying mechanisms of between-generational and within-generational anxiety resemblance remain poorly understood. By disentangling the genetic vs environmental sources of familial resemblance in anxiety, we can help prevent within-family transmission of anxiety disorders. Therefore, data from both parents and twins are needed to obtain unbiased and detailed estimations of genetic and environmental sources of similarity between family members.
Methods: We examined data from 991 families with same-sex twins. Trait anxiety in twins was assessed via self-report and parent report, while parental trait anxiety was assessed via self-report. We established a nuclear twin family model and estimated genetic and environmental variances using 2 survey waves.
Results: The results suggested that additive genetic (A), dominant genetic (D), and non-shared environmental (E) influences statistically-significantly contributed to trait anxiety, whereas familial environmental influences (F) and passive gene-environment correlations (rGE) did not. Sibling environmental influences (S) were only found in self-report data, and increased when genetic influences decreased from Wave 1 to Wave 2.
Conclusions: Our study highlights the important role of broad heritability in intra-familial trait anxiety similarity. Parent-child resemblance occurred primarily due to shared genetic makeup rather than direct environmental transmission. Sibling-specific environments, as the only source of shared environments, need further investigation. These findings have both theoretical and practical importance for anxiety disorders. Future research can expand our understanding by examining the gene-environment interplay and sex differences.
UK Biobank is a major prospective epidemiological study, including multimodal brain imaging, genetics and ongoing health outcomes.
Previously, we published genome-wide associations of 3,144 brain imaging-derived phenotypes, with a discovery sample of 8,428 individuals. Here we present a new open resource of genome-wide association study summary statistics, using the 2020 data release, almost tripling the discovery sample size. We now include the X chromosome and new classes of imaging-derived phenotypes (subcortical volumes and tissue contrast). Previously, we found 148 replicated clusters of associations between genetic variants and imaging phenotypes; in this study, we found 692, including 12 on the X chromosome.
We describe some of the newly found associations, focusing on the X chromosome and autosomal associations involving the new classes of imaging-derived phenotypes. Our novel associations implicate, for example, pathways involved in the rare X-linked STAR (syndactyly, telecanthus and anogenital and renal malformations) syndrome, Alzheimer’s disease and mitochondrial disorders.
2021-belbin.pdf: “Toward a fine–scale population health monitoring system”, Gillian M. Belbin, Sinead Cullina, Stephane Wenric, Emily R. Soper, Benjamin S. Glicksberg, Denis Torre, Arden Moscati, Genevieve L. Wojcik, Ruhollah Shemirani, Noam D. Beckmann, Ariella Cohain, Elena P. Sorokin, Danny S. Park, Jose-Luis Ambite, Steve Ellis, Adam Auton, Erwin P. Bottinger, Judy H. Cho, Ruth J.F. Loos, Noura S. Abul-Husn, Noah A. Zaitlen, Christopher R. Gignoux, Eimear E. Kenny, CBIPM Genomics Team, Regeneron Genetics Center (2021-04-15):
Genomic data linked to health records capture demography in health systems
Genetic networks reveal recent common ancestry in diverse populations
Evidence of many founder populations in New York City
Fine-scale population structure impacts genetic risk predictions
Understanding population health disparities is an essential component of equitable precision health efforts. Epidemiology research often relies on definitions of race and ethnicity, but these population labels may not adequately capture disease burdens and environmental factors impacting specific sub-populations.
Here, we propose a framework for repurposing data from electronic health records (EHRs) in concert with genomic data to explore the demographic ties that can impact disease burdens. Using data from a diverse biobank in New York City, we identified 17 communities sharing recent genetic ancestry.
We observed 1,177 health outcomes that were statistically associated with a specific group and demonstrated statistically-significant differences in the segregation of genetic variants contributing to Mendelian diseases. We also demonstrated that fine-scale population structure can impact the prediction of complex disease risk within groups.
This work reinforces the utility of linking genomic data to EHRs and provides a framework toward fine-scale monitoring of population health.
[Keywords: electronic health records, computational genomics, genomic medicine, machine learning, biobanks, genetic ancestry, population health, health disparities]
Genetic influences on human behavior are increasingly well understood, but laypeople may endorse genetic attributions selectively; eg., they appear to make stronger genetic attributions for prosocial than for antisocial behavior.
We explored whether this could be accounted for by the relationship of genetic attributions to perceptions of naturalness. Participants read about positively or negatively valenced traits or behaviors and rated naturalness and genetic causation. Positively valenced phenotypes were rated statistically-significantly more natural and statistically-significantly more genetically influenced than negatively valenced phenotypes, and the former asymmetry statistically-significantly mediated the latter (Experiments 1 and 2). Participants’ interpretation of what “natural” meant was not synonymous with valence or genetic attributions (Experiment 3).
People ascribe differing degrees of genetic influence to the same phenotype depending on whether it is expressed in socially favored or disfavored ways, potentially representing an important threat to public understanding of genetics.
[Keywords: genetics, social cognition, causal attribution, motivated reasoning]
In 10 years, the ability to predict intelligence from DNA has gone from 0% to 10%.
Genome-wide polygenic scores (GPS) are transforming research on intelligence.
GPS will transport intelligence to many new areas of science.
The availability of GPS at birth, prenatally, and before conception will impact society.
We need to maximize benefits and minimize risks of DNA prediction of intelligence.
The DNA revolution made it possible to use DNA to predict intelligence. We argue that this advance will transform intelligence research and society.
Our paper has 3 objectives:
First, we review how the DNA revolution has transformed the ability to predict individual differences in intelligence. Thousands of DNA variants have been identified that—aggregatedinto genome-wide polygenic scores (GPS)—account for more than10% of the variancein phenotypic intelligence. The intelligence GPS is now one of the most powerful predictors in the behavioral sciences.
Second, we consider the impact of GPS on intelligence research.The intelligence GPS can be added as a genetic predictor of intelligence to any study without the need to assess phenotypic intelligence. This feature will help export intelligence to many new areas of science. Also, the intelligence GPS will help to address complex questions in intelligence research, in particular how the gene-environment interplay affects the development of individual differences in intelligence.
Third, we consider the societal impact of the intelligence GPS,focusing on DNA testing at birth, DNA testing before birth (eg.,embryo selection), and DNA testing before conception (eg., DNA dating).
The intelligence GPS represents a major scientific advance, and, like all scientific advances, it can be used for bad as well as good. We stress the need to maximize the considerable benefits and minimize the risks of our new ability to use DNA to predict intelligence.
Twin studies function as natural experiments that reveal political ideology’s substantial genetic roots, but how does that comport with research showing a largely nonideological public? This study integrates two important literatures and tests whether political sophistication—itself heritable—provides an “enriched environment” for genetic predispositions to actualize in political attitudes. Estimates from the Minnesota Twin Study show that sociopolitical conservatism is extraordinarily heritable (74%) for the most informed fifth of the public—much more so than population-level results (57%)—but with much lower heritability (29%) for the public’s bottom half. This heterogeneity is clearest in the Wilson–Patterson (W-P) index, with similar patterns for individual index items, an ideological constraint measure, and ideological identification. The results resolve tensions between two key fields by showing that political knowledge facilitates the expression of genetic predispositions in mass politics.
2021-schiele.pdf: “Therapygenetic effects of 5–HTTLPR on cognitive–behavioral therapy in anxiety disorders: A meta–analysis”, Miriam A. Schiele, Andreas Reif, Jiaxi Lin, Georg W. Alpers, Evelyn Andersson, Gerhard Andersson, Volker Arolt, Jan Bergström, Per Carlbring, Thalia C. Eley, Gabriel Esquivel, Tomas Furmark, Alexander L. Gerlach, Alfons Hamm, Sylvia Helbig-Lang, Jennifer L. Hudson, Thomas Lang, Kathryn J. Lester, Nils Lindefors, Tina B. Lonsdorf, Paul Pauli, Jan Richter, Winfried Rief, Susanna Roberts, Christian Rück, Koen R. J. Schruers, Christiane Thiel, Hans-Ulrich Wittchen, Katharina Domschke, Heike Weber, Ulrike Lueken (2021-03-01):
There is a recurring debate on the role of the serotonin transporter gene linked polymorphic region (5-HTTLPR) in the moderation of response to cognitive behavioral therapy (CBT) in anxiety disorders. Results, however, are still inconclusive. We here aim to perform a meta-analysison the role of 5-HTTLPR [candidate-gene] in the moderation of CBT outcome in anxiety disorders. We investigated both categorical (symptom reduction of at least 50%) and dimensional outcomes from baseline to post-treatment and follow-up.
Original data were obtained from 10 independent samples (including 3 unpublished samples) with a total of 2,195 patients with primary anxiety disorder. No statistically-significanteffects of 5-HTTLPR genotype on categorical or dimensional outcomes at post and follow-up were detected. We conclude that current evidence does not support the hypothesis of 5-HTTLPR as a moderator of treatment outcome forCBT in anxiety disorders.
Future research should address whether other factors such as long-term changes or epigenetic processes may explain further variance in these complex gene-environment interactions and molecular-genetic pathways that may confer behavioral change following psychotherapy.
The Classical Twin Method (CTM) compares the similarity of monozygotic (MZ) twins with that of dizygotic (DZ) twins to make inferences about the relative importance of genes and environment in the etiology of individual differences. The design has been applied to thousands of traits across the biomedical, behavioral and social sciences and is arguably the most widely used natural experiment known to science.
The fundamental assumption of the CTM is that trait relevant environmental covariation within MZ pairs is the same as that found within DZ pairs, so that zygosity differences in within-pair variance must be due to genetic factors uncontaminated by the environment. This equal environments assumption (EEA) has been, and still is hotly contested, and has been mentioned as a possible contributing factor to the missing heritability conundrum.
In this manuscript, we introduce a new model for testing the EEA, which we call the Augmented Classical Twin Design which uses identity by descent (IBD) sharing between DZ twin pairs to estimate separate environmental variance components for MZ and DZ twin pairs, and provides a test of whether these are equal. We show through simulation that given large samples of DZ twin pairs, the model provides unbiased estimates of variance componentsand valid tests of the EEA under strong assumptions (eg. no epistatic variance,IBD sharing in DZ twins estimated accurately etc.) which may not hold in reality. Sample sizes in excess of 50,000 DZ twin pairs with genome-wide genetic data are likely to be required in order to detect substantial violations of the EEA with moderate power.
Consequently, we recommend that the Augmented Classical Twin Design only be applied to datasets with very large numbers of DZ twin pairs (> 50 000 DZ twin pairs), and given the strong assumptions relating to the absence of epistatic variance, appropriate caution be exercised regarding interpretation of the results.
We show that family background matters statistically-significantly for children’s accumulation of wealth and investor behavior as adults, even when removing the genetic connection between children and the parents raising them. The analysis is made possible by linking Korean-born children who were adopted at infancy by Norwegian parents to a population panel data set with detailed information on wealth and socioeconomic characteristics. The mechanism by which these Korean-Norwegian adoptees were assigned to adoptive families is known and effectively random. This mechanism allows us to estimate the causal effects from an adoptee being raised in one type of family versus another.
…The linear rank correlations are 0.24 and 0.16 for the samples of non-adoptees and adoptees, respectively. This means that, on average, a 10 percentile increase in parent net wealth is associated with a 2.4 percentile increase in a biological child’s net wealth and a 1.6 percentile increase in an adoptee’s net wealth…On average, the adoptees accrue an extra US$2,250 of wealth if they are assigned to an adoptive family with US$10,000 of additional wealth. The magnitude of this estimate suggests that adoptees raised by parents with a wealth level that is 10% above the mean of the parent generation can expect to obtain a wealth level that is almost 3.7% above the mean of the child generation.
…We find that the indirect effects arising from changes in the observed mediator variables explain about 37% of the average causal effect from assignment to wealthier parents on children’s accumulation of wealth. Direct transfers of wealth are the most important mediator variable, accounting for almost 90% of the indirect effect.
…Columns 1 and 2 in panel A of Table 5 suggest that both family environment and genetics are important in explaining the variation in children’s wealth accumulation. Shared environment accounts for about 16% (10%) of the variation in net (financial) wealth accumulation. Relative to shared environment, the genetic factors explain a larger portion (twice as much or more) of the variation in wealth accumulation (both net and financial wealth). These findings are consistent with the results in Table 3, showing statistically-significant but less wealth transmission from parents to adoptees as compared with non-adoptees.
As shown in column 3 in panel A of Table 5, shared environment is also important for explaining the variation in financial risk-taking, as measured by the risky share. By comparison, genetic factors explain little of the variation in this measure of financial risk-taking. In column 4 of Table 5, we report results for education as measured by years of schooling. These results are close to the American study of Korean adoptees by Sacerdote (2007), who finds that 9% of the variation in years of schooling can be explained by shared environment, while 60% is attributable to genes.
Age at first sexual intercourse (AFS) and age at first birth (AFB) have implications for health and evolutionary fitness. In the largest genome-wide association study to date (AFS, n = 387,338; AFB, n = 542,901), we identify 370 independent signals, 11 sex-specific, with a 5–6% polygenic score (PGS) prediction. Heritability of AFB shifted from 9% [CI = 4–14] for women born in 1940 to 22% [CI = 19–25] in 1965. Signals are driven by the genetics of reproductive biology and externalising behaviour, with key genes related to follicle stimulating hormone (FSHB), implantation (ESR1), infertility, and spermatid differentiation. Polycystic Ovarian Syndrome leads to later AFB, linking with infertility. Late AFB is protective against later-life disease and associated with parental longevity. Higher childhood socioeconomic circumstances and those in the highest PGS decile (90%+) experience markedly later reproductive onset. Results are relevant for improving teenage and late-life health, for understanding longevity, and guiding experimentation into mechanisms of infertility.
Heritability estimates vary widely across reading-related neurocognitive skills.
Age-specific and school-grade-specific genetic influences have been reported.
Previous meta-analyses focused on some reading skills without controlling for moderators.
Reading-related skills show moderate-to-substantial meta-heritability estimates.
School grade levels moderated the heritability of some reading-related skills.
Reading ability is a complex task requiring the integration of multiple cognitive and perceptual systems supporting language, visual and orthographic processes, working memory, attention, motor movements, and higher-level comprehension and cognition. Estimates of genetic and environmental influences for some of these reading-related neurocognitive components vary across reports.
By using a multi-level meta-analysis approach, we synthesized the results of behavioral genetic research on reading-related neurocognitive components (ie. general reading, letter-word knowledge, phonological decoding, reading comprehension, spelling, phonological awareness, rapid automatized naming, and language) of 49 twin studies spanning 4.1–18.5 years of age, with a total sample size of more than 38,000 individuals.
Except for language for which shared environment seems to play a more important role, the causal architecture across most of the reading-related neurocognitive components can be represented by the following equation a2 > e2 > c2. Moderators analysis revealed that sex and spoken language did not affect the heritability of any reading-related skills; school grade levels moderated the heritability of general reading, reading comprehension and phonological awareness.
A healthy diet is associated with the improvement or maintenance of health parameters, and several indices have been proposed to assess diet quality comprehensively. Twin studies have found that some specific foods, nutrients and food patterns have a heritable component; however, the heritability of overall dietary intake has not yet been estimated. Here, we compute heritability estimates of the nine most common dietary indices utilized in nutritional epidemiology. We analyzed 2590 female twins from TwinsUK (653 monozygotic [MZ] and 642 dizygotic [DZ] pairs) who completed a 131-item food frequency questionnaire (FFQ). Heritability estimates were computed using structural equation models (SEM) adjusting for body mass index (BMI), smoking status, Index of Multiple Deprivation (IMD), physical activity, menopausal status, energy and alcohol intake. The AE model was the best-fitting model for most of the analyzed dietary scores (seven out of nine), with heritability estimates ranging from 10.1% (95% CI[.02, .18]) for the Dietary Reference Values (DRV) to 42.7% (95% CI [.36, .49]) for the Alternative Healthy Eating Index (A-HEI). The ACE model was the best-fitting model for the Healthy DietIndicator (HDI) and Healthy Eating Index 2010 (HEI-2010) with heritability estimates of 5.4% (95% CI [−.17, .28]) and 25.4% (95% CI [.05, .46]), respectively. Here, we find that all analyzed dietary indices have a heritable component, suggesting that there is a genetic predisposition regulating what you eat. Future studies should explore genes underlying dietary indices to further understand the genetic disposition toward diet-related health parameters.
The developmental course of antisocial behavior is often described in terms of qualitatively distinct trajectories. However, the genetic etiology of various trajectories is not well understood.
We examined heterogeneity in the development of delinquent and aggressive behavior in 1532 twin youth using 4 waves of data collection, spanning ages 9–10 to 16–18. A latent class growth analysis was used to uncover relevant subgroups.
For delinquent behavior, 3 latent classes emerged: Non-Delinquent, Low-Level Delinquent, and Persistent Delinquent. Liability for persistent delinquency had a substantial genetic origin (heritability = 67%), whereas genetic influences were negligible for lower-risk subgroups. 3 classes of aggressive behavior were identified: Non-Aggressive, Moderate, and High. Moderate heritability spanned the entire continuum of risk for aggressive behavior.
Thus, there are differences between aggressive behavior and non-aggressive delinquency with respect to heterogeneity of etiology. We conclude that persistent delinquency represents an etiologically distinct class of rule-breaking with strong genetic roots.
Although germline de novo copy number variants (CNVs) are known causes of autism spectrum disorder (ASD), the contribution of mosaic(early-developmental) copy number variants (mCNVs) has not beenexplored. In this study, we assessed the contribution of mCNVs to ASDby ascertaining mCNVs in genotype array intensity data from 12,077probands with ASD and 5,500 unaffected siblings. We detected 46 mCNVsin probands and 19 mCNVs in siblings, affecting 2.8–73.8% of cells. Probands carried a statistically-significant burden of large (>4-Mb) mCNVs, which were detected in 25 probands but only one sibling (odds ratio = 11.4, 95% confidence interval = 1.5–84.2, p = 7.4 × 10−4). Event size positively correlated with severity of ASD symptoms (p = 0.016). Surprisingly, we did not observe mosaic analogues of the short de novo CNVsrecurrently observed in ASD (eg,16p11.2). We further experimentally validated two mCNVs in postmortem brain tissue from 59 additional probands. These results indicate that mCNVs contribute a previously unexplained component of ASD risk.
We characterize the landscape of somatic mutations—mutations occurring after fertilization—in the human brain using ultra-deep (~250×) whole-genome sequencing of prefrontal cortex from 59 donors with autism spectrum disorder (ASD) and 15 control donors. We observe a mean of 26 somatic single-nucleotide variants per brain present in ≥4% of cells, with enrichment of mutations in coding and putative regulatory regions. Our analysis reveals that the first cell division after fertilization produces ~3.4 mutations, followed by 2–3 mutations in subsequent generations. This suggests that a typical individual possesses ~80 somatic single-nucleotide variants present in ≥2% of cells—comparable to the number of de novo germline mutations per generation—with about half of individuals having at least one potentially function-altering somatic mutation somewhere in the cortex. ASD brains show an excess of somatic mutations in neural enhancer sequences compared with controls, suggesting that mosaic enhancer mutations may contribute to ASD risk.
2021-jonsson.pdf: “Differences between germline genomes of monozygotic twins”, Hakon Jonsson, Erna Magnusdottir, Hannes P. Eggertsson, Olafur A. Stefansson, Gudny A. Arnadottir, Ogmundur Eiriksson, Florian Zink, Einar A. Helgason, Ingileif Jonsdottir, Arnaldur Gylfason, Adalbjorg Jonasdottir, Aslaug Jonasdottir, Doruk Beyter, Thora Steingrimsdottir, Gudmundur L. Norddahl, Olafur Th. Magnusson, Gisli Masson, Bjarni V. Halldorsson, Unnur Thorsteinsdottir, Agnar Helgason, Patrick Sulem, Daniel F. Gudbjartsson, Kari Stefansson (2021-01-07):
Despite the important role that monozygotic twins have played in genetics research, little is known about their genomic differences. Here we show that monozygotic twins differ on average by 5.2 early developmental mutations and that approximately 15% of monozygotic twins have a substantial number of these early developmental mutations specific to one of them. Using the parents and offspring of twins, we identified pre-twinning mutations. We observed instances where a twin was formed from a single cell lineage in the pre-twinning cell mass and instances where a twin was formed from several cell lineages. CpG>TpG mutations increased in frequency with embryonic development, coinciding with an increase in DNA methylation. Our results indicate that allocations of cells during development shapes genomic differences between monozygotic twins.
Indirect genetic effects, the effects of the genotype of one individual on the phenotype of other individuals, are environmental factors associated with human disease and complex trait variation that could help to expand our understanding of the environment linked to complex traits. Here, we study indirect genetic effects in 80,889 human couples of European ancestry for 105 complex traits. Using a linear mixed model approach, we estimate partner indirect heritability and find evidence of partner heritability on ~50% of the analysed traits. Follow-up analysis suggests that in at least ~25% of these traits, the partner heritability is consistent with the existence of indirect genetic effects including a wide variety of traits such as dietary traits, mental health and disease. This shows that the environment linked to complex traits is partially explained by the genotype of other individuals and motivates the need to find new ways of studying the environment.
The Val66Met is a polymorphism of the brain-derived neurotrophic factor (BDNF) gene that encodes a substitution of a valine (Val) to methionine (Met) amino acid. Carrying this polymorphism reduces the activity-dependent secretion of the BDNF protein, which can potentially affect brain plasticity and cognition. We reviewed the biology of Val66Met and surveyed 26 studies (11,417 participants) that examined the role of this polymorphism in moderating the cognitive response to physical activity (PA) and exercise. Nine observational studies confirmed a moderating effect of Val66Met on the cognitive response to PA but differences between Val and Met carriers were inconsistent and only significant in some cognitive domains. Only five interventional studies found a moderating effect of Val66Met on the cognitive response to exercise, which was also inconsistent in its direction. Two studies showed a superior cognitive response in Val carriers and three studies showed a better response in Met carriers. These results do not support a general and consistent effect of Val66Met in moderating the cognitive response to PA or exercise. Both Val and Met carriers can improve specific aspects of cognition by increasing PA and engaging in exercise. Causes for discrepancies among studies, effect moderators, and future directions are discussed.
Objective: Leisure activity has been shown to be beneficial to mental health and cognitive aging. The biological basis of the correlation is, however, poorly understood. This study aimed at exploring the genetic and environmental impacts on correlation between leisure activities and cognitive function in the Chinese middle-aged and old-aged twins.
Methods: Cognition measured using a screening test (Montreal Cognitive Assessment, MoCA) and leisure activities including intellectual and social activity were investigated on 379 complete twin pairs of middle-aged and old-aged twins. Univariate and bivariate twin models were fitted to estimate the genetic and environmental components in their variance and covariance.
Results: Moderate heritability was estimated for leisure activities and cognition (0.44–0.53) but insignificant for social activity. Common environmental factors accounted for about 0.36 of the total variance to social activity with no statistically-significant contribution to leisure activity, intellectual activity and cognition. Unique environmental factors displayed moderate contributions (0.47–0.64) to leisure activities and cognition. Bivariate analysis showed highly and positively genetic correlations between leisure activities and cognition (rg = 0.80–0.96). Besides, intellectual activity and cognition presented low but statistically-significant unique environmental correlation (rE = 0.12).
Conclusions: Genetic factor had the moderate contribution to leisure activities and cognition. Cognitive function was highly genetically related to leisure activities. Intellectual activity and cognitive function may share some unique environmental basis.
2020-surendran.pdf: “Discovery of rare variants associated with blood pressure regulation through meta-analysis of 1.3 million individuals”, Praveen Surendran, Elena V. Feofanova, Najim Lahrouchi, Ioanna Ntalla, Savita Karthikeyan, James Cook, Lingyan Chen, Borbala Mifsud, Chen Yao, Aldi T. Kraja, James H. Cartwright, Jacklyn N. Hellwege, Ayush Giri, Vinicius Tragante, Gudmar Thorleifsson, Dajiang J. Liu, Bram P. Prins, Isobel D. Stewart, Claudia P. Cabrera, James M. Eales, Artur Akbarov, Paul L. Auer, Lawrence F. Bielak, Joshua C. Bis, Vickie S. Braithwaite, Jennifer A. Brody, E. Warwick Daw, Helen R. Warren, Fotios Drenos, Sune Fallgaard Nielsen, Jessica D. Faul, Eric B. Fauman, Cristiano Fava, Teresa Ferreira, Christopher N. Foley, Nora Franceschini, He Gao, Olga Giannakopoulou, Franco Giulianini, Daniel F. Gudbjartsson, Xiuqing Guo, Sarah E. Harris, Aki S. Havulinna, Anna Helgadottir, Jennifer E. Huffman, Shih-Jen Hwang, Stavroula Kanoni, Jukka Kontto, Martin G. Larson, Ruifang Li-Gao, Jaana Lindström, Luca A. Lotta, Yingchang Lu, Jian’an Luan, Anubha Mahajan, Giovanni Malerba, Nicholas G. D. Masca, Hao Mei, Cristina Menni, Dennis O. Mook-Kanamori, David Mosen-Ansorena, Martina Müller-Nurasyid, Guillaume Paré, Dirk S. Paul, Markus Perola, Alaitz Poveda, Rainer Rauramaa, Melissa Richard, Tom G. Richardson, Nuno Sepúlveda, Xueling Sim, Albert V. Smith, Jennifer A. Smith, James R. Staley, Alena Stanáková, Patrick Sulem, Sébastien Thériault, Unnur Thorsteinsdottir, Stella Trompet, Tibor V. Varga, Digna R. Velez Edwards, Giovanni Veronesi, Stefan Weiss, Sara M. Willems, Jie Yao, Robin Young, Bing Yu, Weihua Zhang, Jing-Hua Zhao, Wei Zhao, Wei Zhao, Evangelos Evangelou, Stefanie Aeschbacher, Eralda Asllanaj, Stefan Blankenberg, Lori L. Bonnycastle, Jette Bork-Jensen, Ivan Brandslund, Peter S. Braund, Stephen Burgess, Kelly Cho, Cramer Christensen, John Connell, Renée de Mutsert, Anna F. Dominiczak, Marcus Dörr, Gudny Eiriksdottir, Aliki-Eleni Farmaki, J. Michael Gaziano, Niels Grarup, Megan L. Grove, Göran Hallmans, Torben Hansen, Christian T. Have, Gerardo Heiss, Marit E. Jørgensen, Pekka Jousilahti, Eero Kajantie, Mihir Kamat, AnneMari Käräjämäki, Fredrik Karpe, Heikki A. Koistinen, Csaba P. Kovesdy, Kari Kuulasmaa, Tiina Laatikainen, Lars Lannfelt, I-Te Lee, Wen-Jane Lee, LifeLines Cohort Study, Allan Linneberg, Lisa W. Martin, Marie Moitry, Girish Nadkarni, Matt J. Neville, Colin N. A. Palmer, George J. Papanicolaou, Oluf Pedersen, James Peters, Neil Poulter, Asif Rasheed, Katrine L. Rasmussen, N. William Rayner, Reedik Mägi, Frida Renström, Rainer Rettig, Jacques Rossouw, Pamela J. Schreiner, Peter S. Sever, Emil L. Sigurdsson, Tea Skaaby, Yan V. Sun, Johan Sundstrom, Gudmundur Thorgeirsson, Tõnu Esko, Elisabetta Trabetti, Philip S. Tsao, Tiinamaija Tuomi, Stephen T. Turner, Ioanna Tzoulaki, Ilonca Vaartjes, Anne-Claire Vergnaud, Cristen J. Willer, Peter W. F. Wilson, Daniel R. Witte, Ekaterina Yonova-Doing, He Zhang, Naheed Aliya, Peter Almgren, Philippe Amouyel, Folkert W. Asselbergs, Michael R. Barnes, Alexandra I. Blakemore, Michael Boehnke, Michiel L. Bots, Erwin P. Bottinger, Julie E. Buring, John C. Chambers, Yii-Der Ida Chen, Rajiv Chowdhury, David Conen, Adolfo Correa, George Davey Smith, Rudolf A. de Boer, Ian J. Deary, George Dedoussis, Panos Deloukas, Emanuele Di Angelantonio, Paul Elliott, EPIC-CVD, EPIC-InterAct, Stephan B. Felix, Jean Ferrières, Ian Ford, Myriam Fornage, Paul W. Franks, Stephen Franks, Philippe Frossard, Giovanni Gambaro, Tom R. Gaunt, Leif Groop, Vilmundur Gudnason, Tamara B. Harris, Caroline Hayward, Branwen J. Hennig, Karl-Heinz Herzig, Erik Ingelsson, Jaakko Tuomilehto, Marjo-Riitta Järvelin, J. Wouter Jukema, Sharon L. R. Kardia, Frank Kee, Jaspal S. Kooner, Charles Kooperberg, Lenore J. Launer, Lars Lind, Ruth J. F. Loos, Abdulla al Shafi. Majumder, Markku Laakso, Mark I. McCarthy, Olle Melander, Karen L. Mohlke, Alison D. Murray, Børge Grønne Nordestgaard, Marju Orho-Melander, Chris J. Packard, Sandosh Padmanabhan, Walter Palmas, Ozren Polasek, David J. Porteous, Andrew M. Prentice, Michael A. Province, Caroline L. Relton, Kenneth Rice, Paul M. Ridker, Olov Rolandsson, Frits R. Rosendaal, Jerome I. Rotter, Igor Rudan, Veikko Salomaa, Nilesh J. Samani, Naveed Sattar, Wayne H.-H. Sheu, Blair H. Smith, Nicole Soranzo, Timothy D. Spector, John M. Starr, Sylvain Sebert, Kent D. Taylor, Timo A. Lakka, Nicholas J. Timpson, Martin D. Tobin, Understanding Society Scientific Group, Pim van der Harst, Peter van der Meer, Vasan S. Ramachandran, Niek Verweij, Jarmo Virtamo, Uwe Völker, David R. Weir, Eleftheria Zeggini, Fadi J. Charchar,Million Veteran Program, Nicholas J. Wareham, Claudia Langenberg, Maciej Tomaszewski, Adam S. Butterworth, Mark J. Caulfield, John Danesh, Todd L. Edwards, Hilma Holm, Adriana M. Hung, Cecilia M. Lindgren, Chunyu Liu, Alisa K. Manning, Andrew P. Morris, Alanna C. Morrison, Christopher J. O’Donnell, Bruce M. Psaty, Danish Saleheen, Kari Stefansson, Eric Boerwinkle, Daniel I. Chasman, Daniel Levy, Christopher Newton-Cheh, Patricia B. Munroe, Joanna M. M. Howson (2020-11-23; backlinks):
Genetic studies of blood pressure (BP) to date have mainly analyzed common variants (minor allele frequency > 0.05). In a meta-analysis of up to ~1.3 million participants, we discovered 106 new BP-associated genomic regions and 87 rare (minor allele frequency ≤ 0.01) variant BP associations (p <5 × 10−8), of which 32 were in new BP-associated loci and 55 were independent BP-associated single-nucleotide variants within known BP-associated regions. Average effects of rare variants (44% coding) were ~8 times larger than common variant effects and indicate potential candidate causal genes at new and known loci (for example, GATA5 and PLCB3). BP-associated variants (including rare and common) were enriched in regions of active chromatin in fetal tissues, potentially linking fetal development with BP regulation in later life. Multivariable Mendelian randomization suggested possible inverse effects of elevated systolic and diastolic BP on large artery stroke. Our study demonstrates the utility of rare-variant analyses for identifying candidate genes and the results highlight potential therapeutic targets.
Twin studies of insomnia exhibit heterogeneity in estimates of heritability. This heterogeneity is likely because of sex differences, age of the sample, the reporter and the definition of insomnia. The aim of the present study was to systematically search the literature for twin studies investigating insomnia disorder and insomnia symptoms and to meta-analyse the estimates of heritability derived from these studies to generate an overall estimate of heritability. We further examined whether heritability was moderated by sex, age, reporter and insomnia symptom. A systematic literature search of five online databases was completed on 24 January 2020. Two authors independently screened 5644 abstracts, and 160 complete papers for the inclusion criteria of twin studies from the general population reporting heritability statistics on insomnia or insomnia symptoms, written in English, reporting data from independent studies. We ultimately included 12 papers in the meta-analysis. The meta-analysis focussed on twin intra-class correlations for monozygotic and dizygotic twins. Based on these intra-class correlations, the meta-analytic estimate of heritability was estimated at 40%. Moderator analyses showed stronger heritability in females than males; and for parent-reported insomnia symptoms compared with self-reported insomnia symptoms. There were no other statistically-significant moderator effects, although this is likely because of the small number of studies that were comparable across levels of the moderators. Our meta-analysis provides a robust estimate of the heritability of insomnia, which can inform future research aiming to uncover molecular genetic factors involved in insomnia vulnerability.
A brief review of research findings regarding twins living apart is presented. This review is followed by a look into the lives of a pair of monozygotic male twins who have lived in different continents for many years, but who stay closely connected. The reasons behind their decision and its impact on their behavioral resemblance and social relationship quality are examined. The next section summarizes recent studies that address the management of monochorionic-diamniotic twin pregnancies, paternity testing in multiple pregnancies, trisomies in twin pregnancies and the roots of resilience. The final portion of this article presents human-interest stories involving reunited Brazilian twins, a new resource for twins with disabled co-twins, twins separated in the Secret of the Nile television series, a new book about Dr Josef Mengele and his horrific twin experiments conducted at the Auschwitz-Birkenau concentration camp, and a pair of twins dedicated to helping others.
Purpose: Identifying rare genetic causes of common diseases can improve diagnostic and treatment strategies, but incurs high costs. We tested whether individuals with common disease and low polygenic risk score (PRS) for that disease generated from less expensive genome-wide genotyping data are more likely to carry rare pathogenic variants.
Methods: We identified patients with one of five common complex diseases among 44,550 individuals who underwent exome sequencing in the UK Biobank. We derived PRS for these five diseases, and identified pathogenic rare variant heterozygotes. We tested whether individuals with disease and low PRS were more likely to carry rare pathogenic variants. Results: While rare pathogenic variants conferred, at most, 5.18-fold (95% confidence interval [CI]: 2.32–10.13) increased odds of disease, a standard deviation increase in PRS, at most, increased the odds of disease by 5.25-fold (95% CI: 5.06–5.45). Among diseased patients, a standard deviation decrease in the PRS was associated with, at most, 2.82-fold (95% CI: 1.14–7.46) increased odds of identifying rare variant heterozygotes.
Conclusion: Rare pathogenic variants were more prevalent among affected patients with a low PRS. Therefore, prioritizing individuals for sequencing who have disease but low PRS may increase the yield of sequencing studies to identify rare variant heterozygotes
Importance: Polygenic risk scores (PRS) are predictors of the genetic susceptibility to diseases, calculated for individuals as weighted counts of thousands of risk variants in which the risk variants and their weights have been identified in genome-wide association studies. Polygenic risk scores show promise in aiding clinical decision-making in many areas of medical practice. This review evaluates the potential use of PRS in psychiatry.
Observations: On their own, PRS will never be able to establish or definitively predict a diagnosis of common complex conditions (eg, mental health disorders), because genetic factors only contribute part of the risk and PRS will only ever capture part of the genetic contribution. Combining PRS with other risk factors has potential to improve outcome prediction and aid clinical decision-making (eg, determining follow-up options for individuals seeking help who are at clinical risk of future illness). Prognostication of adverse physical health outcomes or response to treatment in clinical populations are of great interest for psychiatric practice, but data from larger samples are needed to develop and evaluate PRS.
Conclusions and Relevance: Polygenic risk scores will contribute to risk assessment in clinical psychiatry as it evolves to combine information from molecular, clinical, and lifestyle metrics. The genome-wide genotype data needed to calculate PRS are inexpensive to generate and could become available to psychiatrists as a by-product of practices in other medical specialties. The utility of PRS in clinical psychiatry, as well as ethical issues associated with their use, should be evaluated in the context of realistic expectations of what PRS can and cannot deliver. Clinical psychiatry has lagged behind other fields of health care in its use of new technologies and routine clinical data for research. Now is the time to catch up.
Objective: College attainment is one of the few phenotypes to have substantial variance accounted for by environmental factors shared by reared-together relatives. The shared environment is implicated by the consistently strong parent-to-offspring transmission of college attainment. The mechanisms underlying this relationship remain unclear. We use genetically informative methods with a longitudinal, adoption sample to identify possible environmental mechanisms underlying parent-offspring college transmission.
Method: Data were drawn from the Sibling Interaction and Behavior Study (SIBS), which includes 409 adoptive and 208 nonadoptive families, consisting of two offspring followed from adolescence into young adulthood and their rearing parents. Four domains of environmental mechanisms were examined: (a) skill enhancement; (b) academic support; (c) material advantage; and (d) supportive family environment.
Results: Both shared environmental and genetic factors contributed to the parent-offspring transmission of college attainment. However, highly educated parents did not appear to be increasing their adopted offspring’s attainment through skill development. The environmental factors that were associated with increased odds of offspring college attainment were mother’s academic expectations and family income.
Conclusion: While complete mediation of the parent-offspring transmission of college attainment was not identified, the results shed light on some of the mechanisms associated with the common environment variance in the college attainment phenotype.
This study was carried out to assess the effect of amyloid and tau on Alzheimer’s disease using two-sample Mendelian randomization design. Genetic associations with plasma amyloid species (amyloid precursor protein, amyloid-like protein 2, serum amyloid P-component, amyloid beta peptide), cerebrospinal fluid (CSF) amyloid beta, total tau, and phosphorylated tau181 were extracted from the largest genome-wide association study (GWAS) available. Genetic associations with Alzheimer’s disease were obtained from a GWAS of proxy-cases based on family history of Alzheimer’s disease with 314,278 participants from the UK Biobank and a GWAS with clinically diagnosed Alzheimer’s disease from the International Genomics of Alzheimer’s Project (IGAP) with 21,982 cases and 41,944 controls. Estimates were obtained using inverse variance weighting with sensitivity analyses including MR-Egger, weighted median and MR-PRESSO. Presence of bias due to selective survival and competing risk was also considered. Plasma amyloid species, CSF total tau and phosphorylated tau181 were not associated with Alzheimer’s disease. For CSF Aβ42, no association was found using the proxy-cases but an inverse association was found after removing outliers with MR-PRESSO using IGAP. Higher genetically predicted (p <1 × 10−5) plasma amyloid species, CSF total tau and phosphorylated tau181 (based on sample sizes ~ 3300) were not associated with Alzheimer’s disease using family history or clinically diagnosed cases while effects of CSF Aβ42 were inconsistent between the family history and IGAPGWAS.
Our current society is characterized by an increased availability of industrially processed foods with high salt, fat and sugar content. How is it that some people prefer these unhealthy foods while others prefer more healthy foods? It is suggested that both genetic and environmental factors play a role. The aim of this study was to (1) identify food preference clusters in the largest twin-family study into food preference to date and (2) determine the relative contribution of genetic and environmental factors to individual differences in food preference in the Netherlands. Principal component analysis was performed to identify the preference clusters by using data on food liking/disliking from 16,541 adult multiples and their family members. To estimate the heritability of food preference, the data of 7833 twins were used in structural equation models. We identified seven food preference clusters (Meat, Fish, Fruits, Vegetables, Savory snacks, Sweet snacks and Spices) and one cluster with Drinks. Broad-sense heritability (additive [A] + dominant [D] genetic factors) for these clusters varied between 0.36 and 0.60. Dominant genetic effects were found for the clusters Fruit, Fish (males only) and Spices. Quantitative sex differences were found for Meat, Fish and Savory snacks and Drinks. To conclude, our study convincingly showed that genetic factors play a substantial role in food preference. A next important step is to identify these genes because genetic vulnerability for food preference is expected to be linked to actual food consumption and different diet-related disorders.
Twin studies show that political ideology is about 40% heritable.
More sophisticated designs also find a substantial genetic influence on ideology.
Recent studies have examined how genes connect to ideology, finding some evidence that psychological traits may link genes and ideology.
Genome-wide association studies have started to emerge, but findings should be taken as very preliminary at this point.
Future work will benefit from large samples that provide enough power to study genetic variants related to ideology.
Scholars have long been interested in the underpinnings of political ideology. Over the past fifteen years or so, political scientists, psychologists, sociologists, and economists have started to take seriously the idea that ideology might be influenced by genes. In this article, we review the literature on the genetics of ideology. We begin by describing twin studies and more sophisticated approaches that have now emerged, which consistently show that ideology is about 40% heritable. Next, we examine the state of research on genetic influences on ideology over the life cycle and mechanisms that could link genes and ideology. We conclude by discussing the preliminary genome-wide studies that have been conducted. Existing research has provided important insights into the link between biology and ideology, but additional research is needed in order to provide a more nuanced understanding of the role of biology in the formation of political ideology.
Recently, methods have been introduced using polygenic scores (PGS) to estimate the effects of genetic nurture, the environmentally-mediated effects of parental genotypes on the phenotype of their child above and beyond the effects of the alleles which are transmitted to the child. We introduce a simplified model for estimating genetic nurture effects and show, through simulation and analytical derivation, that our method provides unbiased estimates and offers an increase in power to detect genetic nurture of up to 1⁄3 greater than that of previous methods. Subsequently, we apply this method to data from the Avon Longitudinal Study of Parents and Children to estimate the effects of maternal genetic nurture on childhood body mass index (BMI) trajectories. Through mixed modeling, we observe a statistically-significant age-dependent effect of maternal PGS on child BMI, such that the influence of maternal genetic nurture appears to increase throughout development.
[Keywords: Genetic nurture, BMI, Polygenic score, Cultural transmission, ALSPAC]
Variance components estimates of political and social attitudes suggest a substantial level of genetic influence, but the results have been challenged because they rely on data from twins only. In this analysis, we include responses from parents and nontwin full siblings of twins, account for measurement error by using a panel design, and estimate genetic and environmental variance by maximum-likelihood structural equation modeling. By doing so, we address the central concerns of critics, including that the twin-only design offers no verification of either the equal environments or random mating assumptions. Moving beyond the twin-only design leads to the conclusion that for most political and social attitudes, genetic influences account for an even greater proportion of individual differences than reported by studies using more limited data and more elementary estimation techniques. These findings make it increasingly difficult to deny that—however indirectly—genetics plays a role in the formation of political and social attitudes.
2020-richter.pdf: “Genomic analyses implicate noncoding de novo variants in congenital heart disease”, Felix Richter, Sarah U. Morton, Seong Won Kim, Alexander Kitaygorodsky, Lauren K. Wasson, Kathleen M. Chen, Jian Zhou, Hongjian Qi, Nihir Patel, Steven R. DePalma, Michael Parfenov, Jason Homsy, Joshua M. Gorham, Kathryn B. Manheimer, Matthew Velinder, Andrew Farrell, Gabor Marth, Eric E. Schadt, Jonathan R. Kaltman, Jane W. Newburger, Alessandro Giardini, Elizabeth Goldmuntz, Martina Brueckner, Richard Kim, George A. Porter, Daniel Bernstein, Wendy K. Chung, Deepak Srivastava, Martin Tristani-Firouzi, Olga G. Troyanskaya, Diane E. Dickel, Yufeng Shen, Jonathan G. Seidman, Christine E. Seidman, Bruce D. Gelb (2020-06-29; backlinks):
A genetic etiology is identified for one-third of patients with congenital heart disease (CHD), with 8% of cases attributable tocoding de novo variants (DNVs). To assess the contribution ofnoncoding DNVs to CHD, we compared genome sequences from 749 CHD probands and their parents with those from 1,611 unaffected trios. Neural network prediction of noncoding DNV transcriptional impactidentified a burden of DNVs in individuals with CHD (n = 2,238 DNVs) compared to controls (n = 4,177; p = 8.7 × 10−4). Independent analyses of enhancers showed an excess of DNVs in associated genes (27 genes versus 3.7 expected, p = 1 × 10−5). We observed statistically-significant overlap between these transcription-based approaches (odds ratio (OR) = 2.5, 95% confidence interval (CI) 1.1–5.0, p = 5.4 × 10−3). CHD DNVs altered transcription levelsin 5 of 31 enhancers assayed. Finally, we observed a DNV burden inRNA-binding-protein regulatory sites (OR = 1.13, 95%CI 1.1–1.2, p = 8.8 × 10−5). Our findings demonstrate an enrichment of potentially disruptive regulatory noncoding DNVs in a fraction of CHD at least ashigh as that observed for damaging coding DNVs.
Background: Genetically informed studies have provided mixed findings as to what extent parental substance misuse is associated with offspring substance misuse and antisocial behavior due to shared environmental and genetic factors.
Methods: We linked data from nationwide registries for a cohort of 2 476 198 offspring born in Sweden 1958–1995 and their parents. Substance misuse was defined as International Classification of Diseases diagnoses of alcohol/drug use disorders or alcohol/drug-related criminal convictions. Quantitative genetic offspring-of-siblings analyses in offspring of monozygotic and dizygotic twin, full-sibling, and half-sibling parents were conducted.
Results: Both maternal and paternal substance misuse were robustly associated with offspring substance misuse [maternal adjusted hazard ratio (aHR) = 1.83 (95% confidence interval (CI) 1.80–1.87); paternal aHR = 1.96 (1.94–1.98)] and criminal convictions [maternal aHR = 1.56 (1.54–1.58); paternal aHR = 1.66 (1.64–1.67)]. Additive genetic effects explained 42% (95% CI 25–56%) and 46% (36–55%) of the variance in maternal and paternal substance misuse, respectively, and between 36 and 44% of the variance in substance misuse and criminality in offspring. The associations between parental substance misuse and offspring outcomes were mostly due to additive genetic effects, which explained 54–85% of the parent-offspring covariance. However, both nuclear and extended family environmental factors also contributed to the associations, especially with offspring substance misuse.
Conclusions: Our findings from a large offspring-of-siblings study indicate that shared genetic influences mostly explain the associations between parental substance misuse and both offspring substance misuse and criminality, but we also found evidence for the contribution of environmental factors shared by members of nuclear and extended families.
The Roma Diaspora—traditionally known as Gypsies—remains among the least explored population migratory events in historical times. It involved the migration of Roma ancestors out-of-India through the plateaus of Western Asia ultimately reaching Europe. The demographic effects of the Diaspora—bottlenecks, endogamy, and gene flow—might have left marked molecular traces in the Roma genomes. Here, we analyze the whole-genome sequence of 46 Roma individuals pertaining to four migrant groups in six European countries. Our analyses revealed a strong, early founder effect followed by a drastic reduction of ∼44% in effective population size. The Roma common ancestors split from the Punjabi population, from Northwest India, some generations before the Diaspora started, <2,000 years ago. The initial bottleneck and subsequent endogamy are revealed by the occurrence of extensive runs of homozygosity and identity-by-descent segments in all Roma populations. Furthermore, we provide evidence of gene flow from Armenian and Anatolian groups in present-day Roma, although the primary contribution to Roma gene pool comes from non-Roma Europeans, which accounts for >50% of their genomes. The linguistic and historical differentiation of Roma in migrant groups is confirmed by the differential proportion, but not a differential source, of European admixture in the Roma groups, which shows a westward cline. In the present study, we found that despite the strong admixture Roma had in their diaspora, the signature of the initial bottleneck and the subsequent endogamy is still present in Roma genomes.
[Keywords: Roma Diaspora, Gypsies, complete genomes, demographic history, endogamy, admixture]
Ageing may be due to mutation accumulation across the lifespan, leading to tissue dysfunction, disease, and death. We tested whether germline autosomal mutation rates in young adults predict their remaining survival, and, for women, their reproductive lifespans. Age-adjusted mutation rates (AAMRs) in 61 women and 61 men from theUtah CEPH (Centre d’Etude du Polymorphisme Humain) families were determined. Age at death, cause of death, all-site cancer incidence, and reproductive histories were provided by the Utah Population Database, Utah Cancer Registry, and Utah Genetic Reference Project. Higher AAMRs were statistically-significantly associated with higher all-cause mortality in both sexes combined. Subjects in the top quartile of AAMRs experienced more than twice the mortality of bottom quartile subjects (hazard ratio [HR], 2.07; 95% confidence interval [CI], 1.21–3.56; p = 0.008; median survival difference = 4.7 years). Fertility analyses were restricted to women whose age at last birth (ALB) was ≥ 30 years, the age when fertility begins to decline.Women with higher AAMRs had statistically-significantly fewer livebirths and a younger ALB. Adult germline mutation accumulation rates are established in adolescence, and later menarche in women is associated with delayed mutation accumulation. We conclude that germline mutation rates in healthy young adults may provide a measure of both reproductive and systemic ageing. Puberty may induce the establishment of adult mutation accumulation rates, just when DNA repair systems begin their lifelong decline.
2020-vujkovic.pdf: “Discovery of 318 new risk loci for type 2 diabetes and related vascular outcomes among 1.4 million participants in a multi-ancestry meta-analysis”, Marijana Vujkovic, Jacob M. Keaton, Julie A. Lynch, Donald R. Miller, Jin Zhou, Catherine Tcheandjieu, Jennifer E. Huffman, Themistocles L. Assimes, Kimberly Lorenz, Xiang Zhu, Austin T. Hilliard, Renae L. Judy, Jie Huang, Kyung M. Lee, Derek Klarin, Saiju Pyarajan, John Danesh, Olle Melander, Asif Rasheed, Nadeem H. Mallick, Shahid Hameed, Irshad H. Qureshi, Muhammad Naeem Afzal, Uzma Malik, Anjum Jalal, Shahid Abbas, Xin Sheng, Long Gao, Klaus H. Kaestner, Katalin Susztak, Yan V. Sun, Scott L. DuVall, Kelly Cho, Jennifer S. Lee, J. Michael Gaziano, Lawrence S. Phillips, James B. Meigs, Peter D. Reaven, Peter W. Wilson, Todd L. Edwards, Daniel J. Rader, Scott M. Damrauer, Christopher J. O''Donnell, Philip S. Tsao, Mark A. Atkinson, Al C. Powers, Ali Naji, Klaus H. Kaestner, Goncalo R. Abecasis, Aris Baras, Michael N. Cantor, Giovanni Coppola, Aris N. Economides, Luca A. Lotta, John D. Overton, Jeffrey G. Reid, Alan R. Shuldiner, Christina Beechert, Caitlin Forsythe, Erin D. Fuller, Zhenhua Gu, Michael Lattari, Alexander E. Lopez, Thomas D. Schleicher, Maria Sotiropoulos Padilla, Karina Toledo, Louis Widom, Sarah E. Wolf, Manasi Pradhan, Kia Manoochehri, Ricardo H. Ulloa, Xiaodong Bai, Suganthi Balasubramanian, Leland Barnard, Andrew L. Blumenfeld, Gisu Eom, Lukas Habegger, Alicia Hawes, Shareef Khalid, Evan K. Maxwell, William J. Salerno, Jeffrey C. Staples, Ashish Yadav, Marcus B. Jones, Lyndon J. Mitnaul, Samuel M. Aguayo, Sunil K. Ahuja, Zuhair K. Ballas, Sujata Bhushan, Edward J. Boyko, David M. Cohen, John Concato, Joseph I. Constans, Louis J. Dellitalia, Joseph M. Fayad, Ronald S. Fernando, Hermes J. Florez, Melinda A. Gaddy, Saib S. Gappy, Gretchen Gibson, Michael Godschalk, Jennifer A. Greco, Samir Gupta, Salvador Gutierrez, Kimberly D. Hammer, Mark B. Hamner, John B. Harley, Adriana M. Hung, Mostaqul Huq, Robin A. Hurley, Pran R. Iruvanti, Douglas J. Ivins, Frank J. Jacono, Darshana N. Jhala, Laurence S. Kaminsky, Scott Kinlay, Jon B. Klein, Suthat Liangpunsakul, Jack H. Lichy, Stephen M. Mastorides, Roy O. Mathew, Kristin M. Mattocks, Rachel McArdle, Paul N. Meyer, Laurence J. Meyer, Jonathan P. Moorman, Timothy R. Morgan, Maureen Murdoch, Xuan-Mai T. Nguyen, Olaoluwa O. Okusaga, Kris-Ann K. Oursler, Nora R. Ratcliffe, Michael I. Rauchman, R. Brooks Robey, George W. Ross, Richard J. Servatius, Satish C. Sharma, Scott E. Sherman, Elif Sonel, Peruvemba Sriram, Todd Stapley, Robert T. Striker, Neeraj Tandon, Gerardo Villareal, Agnes S. Wallbom, John M. Wells, Jeffrey C. Whittle, Mary A. Whooley, Junzhe Xu, Shing-Shing Yeh, Michaela Aslan, Jessica V. Brewer, Mary T. Brophy, Todd Connor, Dean P. Argyres, Nhan V. Do, Elizabeth R. Hauser, Donald E. Humphries, Luis E. Selva, Shahpoor Shayan, Brady Stephens, Stacey B. Whitbourne, Hongyu Zhao, Jennifer Moser, Jean C. Beckham, Jim L. Breeling, J. P. Casas Romero, Grant D. Huang, Rachel B. Ramoni, Saiju Pyarajan, Yan V. Sun, Kelly Cho, Peter W. Wilson, Christopher J. O''Donnell, Philip S. Tsao, Kyong-Mi Chang, J. Michael Gaziano, Sumitra Muralidhar, Kyong-Mi Chang, Benjamin F. Voight, Danish Saleheen (2020-06-15):
We investigated type 2 diabetes (T2D) genetic susceptibility via multi-ancestry meta-analysis of 228,499 cases and 1,178,783 controls in the Million Veteran Program(MVP), DIAMANTE,Biobank Japan and other studies. We report 568 associations, including 286 autosomal, 7 X-chromosomal and 25 identified in ancestry-specific analyses that were previously unreported. Transcriptome-wide association analysis detected 3,568 T2D associations with genetically predicted gene expression in 687 novel genes; of these, 54 are known to interact with FDA-approved drugs. Apolygenic risk score (PRS) was strongly associated with increased risk of T2D-related retinopathy and modestly associated with chronic kidney disease (CKD), peripheral arterydisease (PAD) and neuropathy. We investigated the genetic etiology ofT2D-related vascular outcomes in the MVP and observed statisticalSNP-T2D interactions at 13 variants, including coronary heart disease(CHD), CKD, PAD and neuropathy. These findings may help to identify potential therapeutic targets for T2D and genomic pathways that link T2D to vascular outcomes.
Schizophrenia is a severe psychiatric disorder with considerable morbidity and mortality. Although the past two decades have seen limited improvement in the treatment of schizophrenia, research into the genetic causes of this condition has made important advances that offer new insights into the aetiology of schizophrenia. This Review summarizes the evidence for a polygenic architecture of schizophrenia that involves a large number of risk alleles across the whole range of population frequencies. These genetic risk loci implicate biological processes related to neurodevelopment, neuronal excitability, synaptic function and the immune system in the pathogenesis of schizophrenia. Mathematical models also suggest a substantial overlap between schizophrenia and psychiatric, behavioural and cognitive traits, a situation that has implications for understanding its clinical epidemiology, psychiatric nosology and pathobiology. Looking ahead, further genetic discoveries are expected to lead to clinically relevant predictive approaches for identifying high-risk individuals, improved diagnostic accuracy, increased yield from drug development programmes and improved stratification strategies to address the heterogeneous disease course and treatment responses observed among affected patients.
Schizophrenia is characterized by ‘positive’ psychotic symptoms (including hallucinations and delusions) and ‘negative’ symptoms (including blunted affect, apathy and social impairment); this disorder is associated with considerable morbidity and mortality.
In the past decade, important advances have been made in our understanding of the genetics of schizophrenia.
The polygenic architecture of schizophrenia is accounted for by thousands of common genetic variants with small effect sizes and a few rare variants with large effect sizes.
These genetic risk variants implicate dysregulation of biological processes linked to neurodevelopment, neuronal excitability, synaptic function and the immune system in schizophrenia.
Genetic risk factors associated with schizophrenia transcend diagnostic boundaries and form a continuum with normal psychosocial traits, which challenges current psychiatric nosology.
Although increasingly larger sample sizes will accelerate the discovery of genetic variants, novel statistical methodologies could also improve the efficiency of analyses, render discoveries clinically relevant and facilitate precision medicine approaches.
2020-berry.pdf: “Human postprandial responses to food and potential for precision nutrition”, Sarah E. Berry, Ana M. Valdes, David A. Drew, Francesco Asnicar, Mohsen Mazidi, Jonathan Wolf, Joan Capdevila, George Hadjigeorgiou, Richard Davies, Haya Al Khatib, Christopher Bonnett, Sajaysurya Ganesh, Elco Bakker, Deborah Hart, Massimo Mangino, Jordi Merino, Inbar Linenberg, Patrick Wyatt, Jose M. Ordovas, Christopher D. Gardner, Linda M. Delahanty, Andrew T. Chan, Nicola Segata, Paul W. Franks, Tim D. Spector (2020-06-11; backlinks):
Metabolic responses to food influence risk of cardiometabolic disease, but large-scale high-resolution studies are lacking. We recruited n = 1,002 twins and unrelated healthy adults in the United Kingdom to the PREDICT 1 study and assessed postprandial metabolic responses in a clinical setting and at home. We observed large inter-individual variability (as measured by the population coefficient of variation, s.d./mean, n%) in postprandial responses of blood triglyceride (103%), glucose (68%) and insulin (59%) following identical meals. Person-specific factors, such as gut microbiome, had a greater influence (7.1% of variance) than did meal macronutrients (3.6%) for postprandial lipemia, but not for postprandial glycemia (6.0% and 15.4%, respectively); genetic variants had a modest impact on predictions (9.5% for glucose, 0.8% for triglyceride, 0.2% for C-peptide). Findings were independently validated in a US cohort (n = 100 people). We developed a machine-learning model that predicted both triglyceride (r = 0.47) and glycemic (r = 0.77) responses to food intake. These findings may be informative for developing personalized diet strategies. The ClinicalTrials.gov registration identifier is NCT03479866.
…The heritability of postprandial responses in the UK cohort was examined using classical twin methods (variance components analyses) to establish the upper bound of what might be predicted by directly measured genetic variation. Two-thirds of the cohort was recruited from the TwinsUK registry16, of which 230 twin pairs (n = 460; 183 monozygotic and 47 dizygotic) were studied for heritability. Additive genetic factors explained 48% of the variance in glucoseiAUC0–2h, whereas 0% of the variance in triglyceride6h-rise and 9% of the variance in insulin2h-rise were explained in this way (Figure 3b). The estimated genetic variances in insulin1h-rise and C-peptide1h-rise were close to 0 (Supplementary Table 4).
The genetic contribution to psychiatric disorders is observed through the increased rates of disorders in the relatives of those diagnosed with disorders. These increased rates are observed to be nonspecific; for example, children of those with schizophrenia have increased rates of schizophrenia but also a broad range of other psychiatric diagnoses. While many factors contribute to risk, epidemiological evidence suggests that the genetic contribution carries the highest risk burden. The patterns of inheritance are consistent with a polygenic architecture of many contributing risk loci. The genetic studies of the past decade have provided empirical evidence identifying thousands of DNA variants associated with psychiatric disorders. Here, we describe how these latest results are consistent with observations from epidemiology. We provide an R tool (CHARRGe) to calculate genetic parameters from epidemiological parameters and vice versa. We discuss how the single nucleotide polymorphism-based estimates of heritability and genetic correlation relate to those estimated from family records.
[Keywords: Family register data, Genetic correlation, GWAS, Heritability, Psychiatric genetics, Risk in relatives]
The overwhelming majority of participants in current genetic studies are of European ancestry. To elucidate disease biology in the East Asian population, we conducted a genome-wide association study (GWAS) with 212,453 Japanese individuals across 42 diseases. We detected 320 independent signals in 276 loci for 27 diseases, with 25 novel loci (p < 9.58 × 10−9). East Asian-specific missense variants were identified as candidate causal variants for three novel loci, and we successfully replicated two of them by analyzing independent Japanese cohorts; p.R220W of ATG16L2 (associated with coronary artery disease)and p.V326A of POT1 (associated with lung cancer). We further investigated enrichment of heritability within 2,868 annotations of genome-wide transcription factor occupancy, and identified 378 statistically-significant enrichments across nine diseases (false discovery rate < 0.05) (for example, NKX3-1 for prostate cancer). This large-scale GWAS in a Japanese population provides insights into the etiology of complex diseases and highlights the importance of performing GWAS in non-European populations.
Observed genetic associations with educational attainment may be due to direct or indirect genetic influences. Recent work highlights genetic nurture, the potential effect of parents’ genetics on their child’s educational outcomes via rearing environments. To date, few mediating childhood environments have been tested. We used a large sample of genotyped mother-child dyads (n = 2,077) to investigate whether genetic nurture occurs via the prenatal environment. We found that mothers with more education-related genes are generally healthier and more financially stable during pregnancy. Further, measured prenatal conditions explain up to one third of the associations between maternal genetics and children’s academic and developmental outcomes at the ages of 4 to 7 years. By providing the first evidence of prenatal genetic nurture and showing that genetic nurture is detectable in early childhood, this study broadens our understanding of how parental genetics may influence children and illustrates the challenges of within-person interpretation of existing genetic associations.
Sociological research has traditionally emphasized the importance of post-birth factors (ie., social, economic, and cultural capital) in the intergenerational transmission of educational advantages, to the neglect of potentially consequential pre-birth endowments (eg., heritable traits) that are passed from parent to child. In this study, we leverage an experiment of nurture—children who were adopted at birth into nonrelative families—in an effort to simultaneously model the effects associated with both pathways. To do so, we fit a series of simple linear regression models that relate the academic achievement of adopted children to the educational attainments of their adoptive and biological parents, using U.S. data from a recent nationwide sample of birth and adoptive families (the Early Growth and Development Study). Because our dataset includes both “genetic” and “environmental” relatives, but not “genetic-and-environmental” relatives, the separate contributions of each pathway can be identified, as well as possible interactions between the two. Our results show that children’s early achievements are influenced not only by the attainments of their adoptive parents, but also the attainments of their birth parents—suggesting the presence of environmental and genetically mediated effects. Supplementary analyses provide little evidence of effect moderation, using both distal and proximate measures of the childhood environment to model gene-by-environment interactions. These findings are robust to a variety of parameterizations, withstand a series of auxiliary checks, and remain intact even after controlling for intrauterine exposures and other measurable variables that could compromise our design. The implications of our results for theory and research in the stratification literature, and for those interested in educational mobility, are discussed.
Prior research conducted within the Uses and Gratifications paradigm has considered the contribution of numerous background social and psychological characteristics to motives for media use and media consumption patterns. In this study, we explore the extent to which far more fundamental characteristics—genes—explain, in part, motives to use news media and frequency of news use. Utilizing original data collected on identical and fraternal twins (n = 334), we find that latent genetic traits explain a nontrivial amount of variance in two unique news use motives, surveillance and entertainment, as well as frequency of consumption across multiple news sources. Genetic traits were particularly influential in explaining the frequency of using sources commonly characterized as ideological, such as Fox News and CNN.
Identifying genetic determinants of reproductive success may highlight mechanisms underlying fertility and also identify alleles under present-day selection. Using data in 785,604 individuals of European ancestry, we identify 43 genomic loci associated with either number of children ever born (NEB) or childlessness. These loci span diverse aspects of reproductive biology across the life course, including puberty timing, age at first birth, sex hormone regulation and age at menopause. Missense alleles in ARHGAP27 were associated with increased NEB but reduced reproductive lifespan, suggesting atrade-off between reproductive ageing and intensity. As NEB is one component of evolutionary fitness, our identified associations indicate loci under present-day natural selection. Accordingly, we find that NEB-increasing alleles have increased in frequency over the past two generations. Furthermore, integration with data from ancient selection scans identifies a unique example of an allele—FADS1/2 gene locus—that has been under selection for thousands of years and remains under selection today. Collectively, our findings demonstrate that diverse biological mechanisms contribute to reproductive success, implicating both neuro-endocrine and behavioural influences.
The classical twin design relies on a number of strong number of assumptions in order to yield unbiased estimates of heritability. This includes the equal environments assumption—that monozygotic and dizygotic twins experience similar degrees of environmental similarity—an assumption that is likely to be violated in practice for many traits of interest. An alternative method of estimating heritability that does not suffer from many of these limitations is to model trait similarity between sibling pairs as a function of their empirical genome-wide identity by descent sharing, estimated from genetic markers. In this review, I recount the story behind Nick Martin’s and my development of this method, our first attempts at applying it in a human population and more recent studies using the original and related methods to estimate trait heritability.
[Keywords: Linkage; identity by descent; heritability; height; equal environments assumption]
Using a twin study design, we explored the extent to which affectionate communication is a heritable behavioral trait. Participants (n = 928) were 464 adult twin pairs (229 monozygotic, 235 dizygotic) who provided data on their affectionate communication behaviors. Through ACE modeling, we determined that approximately 45% of the variance in trait expressed affectionate communication is heritable, whereas 21% of the variance in trait received affection was heritable. A bivariate Cholesky decomposition model also revealed that almost 26% of the covariation in expressed and received affection is attributable to additive genetic factors. These estimates were driven primarily by females and those 50 years of age and older. The results suggest the utility of giving greater attention to genetic and biological influences on communicative behaviors by expanding the scope of communication theory beyond consideration of only environmental influences.
Recent years have seen the birth of sociogenomics via the infusion of molecular genetic data. We chronicle the history of genetics, focusing particularly on post-2005 genome-wide association studies, the post-2015 big data era, and the emergence of polygenic scores. We argue that understanding polygenic scores, including their genetic correlations with each other, causation, and underlying biological architecture, is vital. We show how genetics can be introduced to understand a myriad of topics such as fertility, educational attainment, intergenerational social mobility, well-being, addiction, risky behavior, and longevity. Although models of gene-environment interaction and correlation mirror agency and structure models in sociology, genetics is yet to be fully discovered by this discipline. We conclude with a critical reflection on the lack of diversity, nonrepresentative samples, precision policy applications, ethics, and genetic determinism. We argue that sociogenomics can speak to long-standing sociological questions and that sociologists can offer innovative theoretical, measurement, and methodological innovations to genetic research.
Social science genetics is concerned with understanding whether, how and why genetic differences between human beings are linked to differences in behaviours and socioeconomic outcomes. Our review discusses the goals, methods, challenges and implications of this research endeavour. We survey how the recent developments in genetics are beginning to provide social scientists with a powerful new toolbox they can use to better understand environmental effects, and we illustrate this with several substantive examples. Furthermore, we examine how medical research can benefit from genetic insights into social-scientific outcomes and vice versa. Finally, we discuss the ethical challenges of this work and clarify several common misunderstandings and misinterpretations of genetic research on individual differences.
The timing of reproductive behaviour—age at first sexual intercourse (AFS) and age at first birth (AFB)—has implications for reproductive health, adolescent development and evolutionary fitness. In the largest genome-wide association studyto date (AFS, n = 387,338; AFB, n = 542,901), we identify 370 independent signals, 11 which are sex-specific, with a 5–6% polygenic score prediction. Heritability shifted from 10% for those born in 1940 to 23% for the 1965 birth cohort. Using Genomic SEM, we show that signals are largely driven by the genetics of reproductive biology and externalizing behaviour. This is supported by extensive biological follow-up that isolates key genes related to follicle stimulating hormone (FSHB), implantation (ESR1), infertility (endometriosis, spontaneous abortion) and spermatid differentiation, morphogenesis and binding (KLF17, ZPBP). Later AFB is protective against later-life disease (type 2 diabetes, cardiovascular) and associated with longevity. Those from higher childhood socioeconomic circumstances and polygenic scores in the highest deciles (90%+) experience markedly later reproductive onset. Results are relevant for interventions in teenage sexual, reproductive and mental health, deepen our understanding of the drivers of later-life health and longevity, and fuel infertility and functional follow-up experiments.
Question: Has association between genetic factors and autism spectrum disorders (ASDs) changed over time?
Findings: In this study, data were available from 2 twin cohorts, one born between 1982 and 2008 (n = 22 678 pairs) and the other between 1992 and 2008 (n = 15 279 pairs). Genetic factors were associated with ASD and autistic traits and the relative importance of these factors was consistent over time, whereas environmental factors played a smaller role.
Meaning: Environmental factors associated with ASD have not increased in importance over time and are unlikely to explain the apparent increase in the prevalence of ASD.
Importance: The frequency with which autism spectrum disorders (ASDs) are diagnosed has shown a marked increase in recent years. One suggestion is that this is partly because of secular changes in the environment, yet to our knowledge this hypothesis lacks evidence.
Objective: To assess whether the relative importance of genetic and environmental associations with ASD and autistic traits has changed over a 16-year and 26-year period.
Design, Setting, and Participants: A twin design was used to assess whether the heritability of ASD and autistic traits has changed over time. Data from 2 nationwide Swedish twin cohorts was used: the Swedish Twin Registry (STR; participants born between January 1982 and December 2008) and the Child and Adolescent Twin Study in Sweden (CATSS; participants born between January 1992 and December 2008). Autism spectrum disorder diagnoses were identified for twins in the STR, with follow-up to 2013. Questionnaires assigned screeningdiagnoses of ASD to CATSS participants and assessed autistic traits. Analyses were performed from September 1, 2018, to March 31, 2019.
Exposures: Each sample was divided into several birth cohorts covering 1982 to 1991 (for the STR only), 1992–1995, 1996–1999, 2000–2003, and 2004–2008.
Outcomes: We assessed whether the genetic and environment variance underlying autistic traits changed across birth cohorts and examined whether the relative contribution of genetics and environment to liability for autism changed across birth cohorts.
Results: Data were available for 22 678 twin pairs (5922 female same-sex pairs [26.1%], 5563 male same-sex pairs [24.5%], and 11193 opposite-sex pairs [49.4%]) in the STR and 15 280 pairs (4880 female same-sex pairs [31.9%], 5092 male same-sex pairs [33.3%], and 5308 opposite-sex pairs [34.7%]) in CATSS. The heritability ofASD diagnoses in the STR ranged from 0.88 (95%CI, 0.74–0.96) to 0.97 (95% CI, 0.89–0.99). The heritability of screening diagnoses in CATSS varied from 0.75 (95% CI, 0.58–0.87) to 0.93 (95% CI, 0.84–0.98). Autistic traits showed a modest variance increase over time that was associated with increases in genetic and environmental variance, with the total variance increasing from 0.95 (95% CI, 0.92–0.98) to 1.17 (95% CI, 1.13–1.21) over time.
Conclusions and Relevance: Weak evidence was found for changes in the genetic and environmental factors underlying ASD and autistic traits over time. Genetic factors played a consistently larger role than environmental factors. Environmental factors are thus unlikely to explain the increase in the prevalence of ASD.
Across the animal kingdom, males tend to exhibit more behavioural and morphological variability than females, consistent with the ‘greater male variability hypothesis’. This may reflect multiple mechanisms operating at different levels, including selective mechanisms that produce and maintain variation, extended male development, and X chromosome effects. Interestingly, human neuroanatomy shows greater male variability, but this pattern has not been demonstrated in any other species. To address this issue, we investigated sex-specific neuroanatomical variability in chimpanzees by examining relative and absolute surface areas of 23 cortical sulci across 226 individuals (135F/91M), using permutation tests of the male-to-female variance ratio of residuals from MCMC generalized linear mixed models controlling for relatedness. We used these models to estimate sulcal size heritability, simulations to assess the significance of heritability, and Pearson correlations to examine inter-sulcal correlations. Our results show that: (i) male brain structure is relatively more variable; (ii) sulcal surface areas are heritable and therefore potentially subject to selection; (iii) males exhibit lower heritability values, possibly reflecting longer development; and (iv) males exhibit stronger inter-sulcal correlations, providing indirect support for sex chromosome effects. These results provide evidence that greater male neuroanatomical variability extends beyond humans, and suggest both evolutionary and developmental explanations for this phenomenon.
Structural variants and presence/absence polymorphisms are common in plant genomes, yet they are routinely overlooked in genome-wide association studies (GWAS). Here, we expand the type of genetic variants detected in GWAS to include major deletions, insertions and rearrangements. We first use raw sequencing data directly to derive short sequences, k-mers, that mark a broad range of polymorphisms independently of a reference genome. We then link k-mers associated with phenotypes to specific genomic regions. Using this approach, we reanalyzed 2,000 traits in Arabidopsis thaliana, tomato and maize populations. Associations identified with k-mers recapitulate those found with SNPs, but with stronger statistical support. Importantly, we discovered new associations with structural variants and with regions missing from reference genomes. Our results demonstrate the power of performing GWAS before linking sequence reads to specific genomic regions, which allows the detection of a wider range of genetic variants responsible for phenotypic variation.
I write this commentary as a part of a special issue published in this journal to celebrate Nick Martin’s contribution to the field of human genetics. In this commentary, I briefly describe the background of the Yang et al. (2010) study and show some of the unpublished details of this study, its contribution to tackling the missing heritability problem and Nick’s contribution to the work.
The extended twin model is a unique design in the genetic epidemiology toolbox that allows to simultaneously estimate multiple causes of variation such as genetic and cultural transmission, genotype-environment covariance and assortative mating, among others. Nick Martin has played a key role in the conception of the model, the collection of substantially large data sets to test the model, the application of the model to a range of phenotypes, the publication of the results including cross-cultural comparisons, the evaluation of bias and power of the design and the further elaborations of the model, such as the children-of-twins design.
Nick Martin was a doctoral student of mine at the University of Birmingham in the mid 1970s. In this review, I discuss two of Nick’s earliest and most seminal contributions to the field of behavior genetics. First, Martin and Eaves’ (1977) extension of the model-fitting approach to multivariate data, which laid the theoretical groundwork for a generation of multivariate behavior genetic studies. Second, the Martin et al.’s (1978) manuscript on the power of the classical twin design, which showed that thousands of twin pairs would be required in order to reliably estimate components of variance, and has served as impetus for the formation of large-scale twin registries across the world. I discuss these contributions against the historical backdrop of a time when we and others were struggling with the challenge of figuring out how to incorporate gene-by-environment interaction, gene-environment correlation, mate selection and cultural transmission into more complex genetic models of human behavior.
Professor Nicholas (Nick) Martin spearheaded initial investigations into the genetic basis of political attitudes and behaviors, demonstrating that behaviors that are perceived as socially constructed could have a biological basis. As he showed, the typical mode of inheritance for political attitudes consists of approximately equal proportions of variance from additive genetic, shared environmental and unique environmental sources. This differs from other psychological variables, such as personality traits, which tend to be characterized by genetic and unique environmental sources of variation. By treating political attitudes as a model phenotype, Nick Martin was able to leverage the unique pattern of observed intergenerational transmission for political attitudes to reexamine the quintessential assumptions of the classical twin model. Specifically, by creatively leveraging the nuances of the genetic architecture of political attitudes, he was able to demonstrate the robustness of the equal environments assumption and suggest corrections to account for assortative mating. These advances have had a substantial impact on both the fields of political science, as well as behavioral and quantitative genetics.
Nicholas Martin’s contribution to science is well known. This article reviews one small part of his pioneering work that integrated political and social attitudes with behavior genetics. Nick Martin, in part, led to a paradigm shift in the social sciences, and in political science in particular. These fields were previously wed to behavioralist approaches and now routinely include genetic influences in both theoretical and empirical study. This article also celebrates a part of Nick’s contribution that many do not know. Nick Martin does not just build science, he builds scientists. There are many who would not be academics or scholars without Nick’s guidance, mentorship and friendship. This review was written to express the deepest appreciation for what he has done and continues to do for science and the scientist.
2020-ruth.pdf: “Using human genetics to understand the disease impacts of testosterone in men and women”, Katherine S. Ruth, Felix R. Day, Jessica Tyrrell, Deborah J. Thompson, Andrew R. Wood, Anubha Mahajan, Robin N. Beaumont, Laura Wittemans, Susan Martin, Alexander S. Busch, A. Mesut Erzurumluoglu, Benjamin Hollis, Tracy A. Oamp#x02019;Mara, Mark I. McCarthy, Claudia Langenberg, Douglas F. Easton, Nicholas J. Wareham, Stephen Burgess, Anna Murray, Ken K. Ong, Timothy M. Frayling, John R. B. Perry
Women’s opportunities have been profoundly altered over the past century by reductions in the social and structural constraints that limit women’s educational attainment. Do social constraints manifest as a suppressing influence on genetic indicators of potential, and if so, did equalizing opportunity mean equalizing the role of genetics? We address this with three cohort studies: the Wisconsin Longitudinal Study (WLS; birth years 1939 to 1940), the Health and Retirement Study, and the National Longitudinal Study of Adolescent Health (Add Health; birth years 1975 to 1982). These studies include a “polygenic score” for educational attainment, providing a novel opportunity to explore this question. We find that within the WLS cohort, the relationship between genetics and educational outcomes is weaker for women than for men. However, as opportunities changed in the 1970s and 1980s, and many middle-aged women went back to school, the relationship between genetic factors and education strengthened for women as they aged. Furthermore, utilizing the HRS and Add Health, we find that as constraints limiting women’s educational attainment declined, gender differences in the relationship between genetics and educational outcomes weakened. We demonstrate that genetic influence must be understood through the lens of historical change, the life course, and social structures like gender.
2019-khera.pdf: “Rare Genetic Variants Associated With Sudden Cardiac Death in Adults”, Amit V. Khera, Heather Mason-Suares, Deanna Brockman, Minxian Wang, Martin J. VanDenburgh, Ozlem Senol-Cosar, Candace Patterson, Christopher Newton-Cheh, Seyedeh M. Zekavat, Julie Pester, Daniel I. Chasman, Christopher Kabrhel, Majken K. Jensen, JoAnn E. Manson, J. Michael Gaziano, Kent D. Taylor, Nona Sotoodehnia, Wendy S. Post, Stephen S. Rich, Jerome I. Rotter, Eric S. Lander, Heidi L. Rehm, Kenney Ng, Anthony Philippakis, Matthew Lebo, Christine M. Albert, Sekar Kathiresan (2019-11-18; backlinks):
Background: Sudden cardiac death occurs in ~220,000 U.S. adults annually, the majority of whom have no prior symptoms or cardiovascular diagnosis. Rare pathogenic DNA variants in any of 49 genes can pre-dispose to 4 important causes of sudden cardiac death: cardiomyopathy, coronary artery disease, inherited arrhythmia syndrome, and aortopathy or aortic dissection.
Objectives: This study assessed the prevalence of rare pathogenic variants in sudden cardiac death cases versus controls, and the prevalence and clinical importance of such mutations in an asymptomatic adult population.
Methods: The authors performed whole-exome sequencing in a case-control cohort of 600 adult-onset sudden cardiac death cases and 600 matched controls from 106,098 participants of 6 prospective cohort studies. Observed DNA sequence variants in any of 49 genes with known association to cardiovascular disease were classified as pathogenic or likely pathogenic by a clinical laboratory geneticist blinded to case status. In an independent population of 4,525 asymptomatic adult participants of a prospective cohort study, the authors performed whole-genome sequencing and determined the prevalence of pathogenic or likely pathogenic variants and prospective association with cardiovascular death.
Results: Among the 1,200 sudden cardiac death cases and controls, the authors identified 5,178 genetic variants and classified 14 as pathogenic or likely pathogenic. These 14 variants were present in 15 individuals, all of whom had experienced sudden cardiac death—corresponding to a pathogenic variant prevalence of 2.5% in cases and 0% in controls (p < 0.0001). Among the 4,525 participants of the prospective cohort study, 41 (0.9%) carried a pathogenic or likely pathogenic variant and these individuals had 3.24-fold higher risk of cardiovascular death over a median follow-up of 14.3 years (p = 0.02).
Conclusions: Gene sequencing identifies a pathogenic or likely pathogenic variant in a small but potentially important subset of adults experiencing sudden cardiac death; these variants are present in ~1% of asymptomatic adults.
Socio-genomics offers insight into gene-environment interplay.
We construct a genome-wide measure of genetic propensity for aggressive behavior.
Males with higher genetic propensity were more likely to experience incarceration.
But gene-environment interaction (G × E) was observed
Genetic propensity was not predictive for males raised in high education homes.
Incarceration is a disruptive event that is experienced by a considerable proportion of the United States population. Research has identified social factors that predict incarceration risk, but scholars have called for a focus on the ways that individual differences combine with social factors to affect incarceration risk. Our study is an initial attempt to heed this call using whole-genome data.
We use data from the Health and Retirement Study (HRS) (n = 6716) to construct a genome-wide measure of genetic propensity for aggressive behavior and use it to predict lifetime incarceration risk. We find that participants with a higher genetic propensity for aggression are more likely to experience incarceration, but the effect is stronger for males than females. Importantly, we identify a gene-environment interaction (G × E)—genetic propensity is reduced, substantively and statistically, to a non-significant predictor for males raised in homes where at least one parent graduated high school.
We close by placing these findings in the broader context of concerns that have been raised about genetics research in criminology.
“Associations of autozygosity with a broad range of human phenotypes”, David W. Clark, Yukinori Okada, Kristjan H. S. Moore, Dan Mason, Nicola Pirastu, Ilaria Gandin, Hannele Mattsson, Catriona L. K. Barnes, Kuang Lin, Jing Hua Zhao, Patrick Deelen, Rebecca Rohde, Claudia Schurmann, Xiuqing Guo, Franco Giulianini, Weihua Zhang, Carolina Medina-Gomez, Robert Karlsson, Yanchun Bao, Traci M. Bartz, Clemens Baumbach, Ginevra Biino, Matthew J. Bixley, Marco Brumat, Jin-Fang Chai, Tanguy Corre, Diana L. Cousminer, Annelot M. Dekker, David A. Eccles, Kristel R. van Eijk, Christian Fuchsberger, He Gao, Marine Germain, Scott D. Gordon, Hugoline G. de Haan, Sarah E. Harris, Edith Hofer, Alicia Huerta-Chagoya, Catherine Igartua, Iris E. Jansen, Yucheng Jia, Tim Kacprowski, Torgny Karlsson, Marcus E. Kleber, Shengchao Alfred Li, Ruifang Li-Gao, Anubha Mahajan, Koichi Matsuda, Karina Meidtner, Weihua Meng, May E. Montasser, Peter J. van der Most, Matthias Munz, Teresa Nutile, Teemu Palviainen, Gauri Prasad, Rashmi B. Prasad, Tallapragada Divya Sri Priyanka, Federica Rizzi, Erika Salvi, Bishwa R. Sapkota, Daniel Shriner, Line Skotte, Melissa C. Smart, Albert Vernon Smith, Ashley van der Spek, Cassandra N. Spracklen, Rona J. Strawbridge, Salman M. Tajuddin, Stella Trompet, Constance Turman, Niek Verweij, Clara Viberti, Lihua Wang, Helen R. Warren, Robyn E. Wootton, Lisa R. Yanek, Jie Yao, Noha A. Yousri, Wei Zhao, Adebowale A. Adeyemo, Saima Afaq, Carlos Alberto Aguilar-Salinas, Masato Akiyama, Matthew L. Albert, Matthew A. Allison, Maris Alver, Tin Aung, Fereidoun Azizi, Amy R. Bentley, Heiner Boeing, Eric Boerwinkle, Judith B. Borja, Gert J. de Borst, Erwin P. Bottinger, Linda Broer, Harry Campbell, Stephen Chanock, Miao-Li Chee, Guanjie Chen, Yii-Der I. Chen, Zhengming Chen, Yen-Feng Chiu, Massimiliano Cocca, Francis S. Collins, Maria Pina Concas, Janie Corley, Giovanni Cugliari, Rob M. van Dam, Anna Damulina, Maryam S. Daneshpour, Felix R. Day, Graciela E. Delgado, Klodian Dhana, Alexander S. F. Doney, Marcus Dörr, Ayo P. Doumatey, Nduna Dzimiri, S. Sunna Ebenesersdóttir, Joshua Elliott, Paul Elliott, Ralf Ewert, Janine F. Felix, Krista Fischer, Barry I. Freedman, Giorgia Girotto, Anuj Goel, Martin Gögele, Mark O. Goodarzi, Mariaelisa Graff, Einat Granot-Hershkovitz, Francine Grodstein, Simonetta Guarrera, Daniel F. Gudbjartsson, Kamran Guity, Bjarni Gunnarsson, Yu Guo, Saskia P. Hagenaars, Christopher A. Haiman, Avner Halevy, Tamara B. Harris, Mehdi Hedayati, David A. van Heel, Makoto Hirata, Imo Höfer, Chao Agnes Hsiung, Jinyan Huang, Yi-Jen Hung, M. Arfan Ikram, Anuradha Jagadeesan, Pekka Jousilahti, Yoichiro Kamatani, Masahiro Kanai, Nicola D. Kerrison, Thorsten Kessler, Kay-Tee Khaw, Chiea Chuen Khor, Dominique P. V. de Kleijn, Woon-Puay Koh, Ivana Kolcic, Peter Kraft, Bernhard K. Krämer, Zoltán Kutalik, Johanna Kuusisto, Claudia Langenberg, Lenore J. Launer, Deborah A. Lawlor, I-Te Lee, Wen-Jane Lee, Markus M. Lerch, Liming Li, Jianjun Liu, Marie Loh, Stephanie J. London, Stephanie Loomis, Yingchang Lu, Jian’an Luan, Reedik Mägi, Ani W. Manichaikul, Paolo Manunta, Gísli Másson, Nana Matoba, Xue W. Mei, Christa Meisinger, Thomas Meitinger, Massimo Mezzavilla, Lili Milani, Iona Y. Millwood, Yukihide Momozawa, Amy Moore, Pierre-Emmanuel Morange, Hortensia Moreno-Macías, Trevor A. Mori, Alanna C. Morrison, Taulant Muka, Yoshinori Murakami, Alison D. Murray, Renée de Mutsert, Josyf C. Mychaleckyj, Mike A. Nalls, Matthias Nauck, Matt J. Neville, Ilja M. Nolte, Ken K. Ong, Lorena Orozco, Sandosh Padmanabhan, Gunnar Pálsson, James S. Pankow, Cristian Pattaro, Alison Pattie, Ozren Polasek, Neil Poulter, Peter P. Pramstaller, Lluis Quintana-Murci, Katri Räikkönen, Sarju Ralhan, Dabeeru C. Rao, Wouter van Rheenen, Stephen S. Rich, Paul M. Ridker, Cornelius A. Rietveld, Antonietta Robino, Frank J. A van Rooij, Daniela Ruggiero, Yasaman Saba, Charumathi Sabanayagam, Maria Sabater-Lleal, Cinzia Felicita Sala, Veikko Salomaa, Kevin Sandow, Helena Schmidt, Laura J. Scott, William R. Scott, Bahareh Sedaghati-Khayat, Bengt Sennblad, Jessica van Setten, Peter J. Sever, Wayne H-H Sheu, Yuan Shi, Smeeta Shrestha, Sharvari Rahul Shukla, Jon K. Sigurdsson, Timo Tonis Sikka, Jai Rup Singh, Blair H. Smith, Alena Stančáková, Alice Stanton, John M. Starr, Lilja Stefansdottir, Leon Straker, Patrick Sulem, Gardar Sveinbjornsson, Morris A. Swertz, Adele M. Taylor, Kent D. Taylor, Natalie Terzikhan, Yih-Chung Tham, Gudmar Thorleifsson, Unnur Thorsteinsdottir, Annika Tillander, Russell P. Tracy, Teresa Tusié-Luna, Ioanna Tzoulaki, Simona Vaccargiu, Jagadish Vangipurapu, Jan H. Veldink, Veronique Vitart, Uwe Völker, Eero Vuoksimaa, Salma M. Wakil, Melanie Waldenberger, Gurpreet S. Wander, Ya Xing Wang, Nicholas J. Wareham, Sarah Wild, Chittaranjan S. Yajnik, Jian-Min Yuan, Lingyao Zeng, Liang Zhang, Jie Zhou, Najaf Amin, Folkert W. Asselbergs, Stephan J. L. Bakker, Diane M. Becker, Benjamin Lehne, David A. Bennett, Leonard H. van den Berg, Sonja I. Berndt, Dwaipayan Bharadwaj, Lawrence F. Bielak, Murielle Bochud, Mike Boehnke, Claude Bouchard, Jonathan P. Bradfield, Jennifer A. Brody, Archie Campbell, Shai Carmi, Mark J. Caulfield, David Cesarini, John C. Chambers, Giriraj Ratan Chandak, Ching-Yu Cheng, Marina Ciullo, Marilyn Cornelis, Daniele Cusi, George Davey Smith, Ian J. Deary, Rajkumar Dorajoo, Cornelia M. van Duijn, David Ellinghaus, Jeanette Erdmann, Johan G. Eriksson, Evangelos Evangelou, Michele K. Evans, Jessica D. Faul, Bjarke Feenstra, Mary Feitosa, Sylvain Foisy, Andre Franke, Yechiel Friedlander, Paolo Gasparini, Christian Gieger, Clicerio Gonzalez, Philippe Goyette, Struan F. A Grant, Lyn R. Griffiths, Leif Groop, Vilmundur Gudnason, Ulf Gyllensten, Hakon Hakonarson, Anders Hamsten, Pim van der Harst, Chew-Kiat Heng, Andrew A. Hicks, Hagit Hochner, Heikki Huikuri, Steven C. Hunt, Vincent W. V. Jaddoe, Philip L. De Jager, Magnus Johannesson, Åsa Johansson, Jost B. Jonas, J. Wouter Jukema, Juhani Junttila, Jaakko Kaprio, Sharon L. R. Kardia, Fredrik Karpe, M. Kumari, Markku Laakso, Sander W. van der Laan, Jari Lahti, Matthias Laudes, Rodney A. Lea, Wolfgang Lieb, Thomas Lumley, Nicholas G. Martin, Winfried März, Giuseppe Matullo, Mark I. McCarthy, Sarah E. Medland, Tony R. Merriman, Andres Metspalu, Brian F. Meyer, Karen L. Mohlke, Grant W. Montgomery, Dennis Mook-Kanamori, Patricia B. Munroe, Kari E. North, Dale R. Nyholt, Jeffery R. O’connell, Carole Ober, Albertine J. Oldehinkel, Walter Palmas, Colin Palmer, Gerard G. Pasterkamp, Etienne Patin, Craig E. Pennell, Louis Perusse, Patricia A. Peyser, Mario Pirastu, Tinca J. C. Polderman, David J. Porteous, Danielle Posthuma, Bruce M. Psaty, John D. Rioux, Fernando Rivadeneira, Charles Rotimi, Jerome I. Rotter, Igor Rudan, Hester M. Den Ruijter, Dharambir K. Sanghera, Naveed Sattar, Reinhold Schmidt, Matthias B. Schulze, Heribert Schunkert, Robert A. Scott, Alan R. Shuldiner, Xueling Sim, Neil Small, Jennifer A. Smith, Nona Sotoodehnia, E-Shyong Tai, Alexander Teumer, Nicholas J. Timpson, Daniela Toniolo, David-Alexandre Tregouet, Tiinamaija Tuomi, Peter Vollenweider, Carol A. Wang, David R. Weir, John B. Whitfield, Cisca Wijmenga, Tien-Yin Wong, John Wright, Jingyun Yang, Lei Yu, Babette S. Zemel, Alan B. Zonderman, Markus Perola, Patrik K. E. Magnusson, André G. Uitterlinden, Jaspal S. Kooner, Daniel I. Chasman, Ruth J. F. Loos, Nora Franceschini, Lude Franke, Chris S. Haley, Caroline Hayward, Robin G. Walters, John R. B. Perry, Tōnu Esko, Agnar Helgason, Kari Stefansson, Peter K. Joshi, Michiaki Kubo, James F. Wilson (2019-10-31; backlinks; genetics / selection / dysgenics):
In many species, the offspring of related parents suffer reduced reproductive success, a phenomenon known as inbreeding depression. In humans, the importance of this effect has remained unclear, partly because reproduction between close relatives is both rare and frequently associated with confounding social factors. Here, using genomic inbreeding coefficients (FROH) for >1.4 million individuals, we show that FROH is statistically-significantly associated (p < 0.0005) with apparently deleterious changes in 32 out of 100 traits analysed. These changes are associated with runs of homozygosity (ROH), but not with common varianthomozygosity, suggesting that genetic variants associated with inbreeding depression are predominantly rare. The effect on fertility is striking: FROH equivalent to the offspring of first cousins is associated with a 55% decrease [95% CI 44–66%] in the odds of having children. Finally, the effects of FROH are confirmed within full-sibling pairs, where the variation in FROH is independent of all environmental confounding.
2019-gurdasani.pdf: “Uganda Genome Resource Enables Insights into Population History and Genomic Discovery in Africa”, Deepti Gurdasani, Tommy Carstensen, Segun Fatumo, Guanjie Chen, Chris S. Franklin, Javier Prado-Martinez, Heleen Bouman, Federico Abascal, Marc Haber, Ioanna Tachmazidou, Iain Mathieson, Kenneth Ekoru, Marianne K. DeGorter, Rebecca N. Nsubuga, Chris Finan, Eleanor Wheeler, Li Chen, David N. Cooper, Stephen Schiffels, Yuan Chen, Graham R. S. Ritchie, Martin O. Pollard, Mary D. Fortune, Alex J. Mentzer, Erik Garrison, Anders Bergström, Konstantinos Hatzikotoulas, Adebowale Adeyemo, Ayo Doumatey, Heather Elding, Louise V. Wain, George Ehret, Paul L. Auer, Charles L. Kooperberg, Alexander P. Reiner, Nora Franceschini, Dermot P. Maher, Stephen B. Montgomery, Carl Kadie, Chris Widmer, Yali Xue, Janet Seeley, Gershim Asiki, Anatoli Kamali, Elizabeth H. Young, Cristina Pomilla, Nicole Soranzo, Eleftheria Zeggini, Fraser Pirie, Andrew P. Morris, David Heckerman, Chris Tyler-Smith, Ayesha Motala, Charles Rotimi, Pontiano Kaleebu, Ines Barroso, Manj S. Sandhu
2019-peterson.pdf: “Genome-wide Association Studies in Ancestrally Diverse Populations: Opportunities, Methods, Pitfalls, and Recommendations”, Roseann E. Peterson, Karoline Kuchenbaecker, Raymond K. Walters, Chia-Yen Chen, Alice B. Popejoy, Sathish Periyasamy, Max Lam, Conrad Iyegbe, Rona J. Strawbridge, Leslie Brick, Caitlin E. Carey, Alicia R. Martin, Jacquelyn L. Meyers, Jinni Su, Junfang Chen, Alexis C. Edwards, Allan Kalungi, Nastassja Koen, Lerato Majara, Emanuel Schwarz, Jordan W. Smoller, Eli A. Stahl, Patrick F. Sullivan, Evangelos Vassos, Bryan Mowry, Miguel L. Prieto, Alfredo Cuellar-Barboza, Tim B. Bigdeli, Howard J. Edenberg, Hailiang Huang, Laramie E. Duncan (backlinks)
Sleep is a crucial physiological process for our survival and cognitive performance, yet the factors controlling human sleep regulation remain poorly understood. Here, we identified a missense mutation in a G protein–coupled neuropeptide S receptor 1 (NPSR1) that is associated with a natural short sleep phenotype in humans. Mice carrying the homologous mutation exhibited less sleep time despite increased sleep pressure. These animals were also resistant to contextual memory deficits associated with sleep deprivation. In vivo, the mutant receptors showed increased sensitivity to neuropeptide S exogenous activation. These results suggest that the NPS/NPSR1 pathway might play a critical role in regulating human sleep duration and in the link between sleep homeostasis and memory consolidation.
The Study of Personality Architecture and Dynamics (SPeADy) is a German research project that aims to investigate the sources of interindividual differences in intraindividual personality development. The main focus lies in the dynamic interplay between more stable core characteristics and more environmentally malleable surface characteristics, as well as between personality and life experiences over time. SPeADy includes a twin family study encompassing data from 1962 individuals (age: 14–94) of 682 families, including 570 complete twin pairs (plus 1 triplet set), 327 parents, 236 spouses and 145 children of twins. Data collection started in 2016 and data from the first wave are currently obtainable as open source. Available data comprise a broad range of personality variables, such as personality trait constructs, motives, interests, values, moral foundations, religiosity and self-related concepts. For the currently ongoing second wave of data collection, we added retrospective reports on major life events. Special features of this genetically informative study are the extended twin family data and its longitudinal design. Three assessment waves in 2 years’ intervals are planned until 2022. In this article, we briefly describe the design and contents of the SPeADy twin family study as well as some recent findings, future plans and open science issues.
The Children of the Twins Early Development Study (CoTEDS) is a new prospective children-of-twins study in the UK, designed to investigate intergenerational associations across child developmental stages. CoTEDS will enable research on genetic and environmental factors that underpin parent–child associations, with a focus on mental health and cognitive-related traits. Through CoTEDS, we will have a new lens to examine the roles that parents play in influencing child development, as well as the genetic and environmental factors that shape parenting behavior and experiences. Recruitment is ongoing from the sample of approximately 20,000 contactable adult twins who have been enrolled in the Twins Early Development Study (TEDS) since infancy. TEDS twins areinvited to register all offspring to CoTEDS at birth, with 554 children registered as of May 2019. By recruiting the second generation of TEDS participants, CoTEDS will include information on adult twins and their offspring from infancy. Parent questionnaire-based data collection is now underway for 1- and 2-year-old CoTEDS infants, with further waves of data collection planned. Current data collection includes the following primary constructs: child mental health, temperament, language and cognitive development; parent mental health and social relationships; parenting behaviors and feelings; and other socioecological factors. Measurement tools have been selected with reference to existing genetically informative cohort studies to ensure overlap in phenotypes measured at corresponding stages of development. This built-in study overlap is intended to enable replication and triangulation of future analyses across samples and research designs. Here, we summarize study protocols and measurement procedures and describe future plans.
2019-saunders.pdf: “Leveraging European infrastructures to access 1 million human genomes by 2022”, Gary Saunders, Michael Baudis, Regina Becker, Sergi Beltran, Christophe Bamp#x000E9;roud, Ewan Birney, Cath Brooksbank, Samp#x000F8;ren Brunak, Marc Bulcke, Rachel Drysdale, Salvador Capella-Gutierrez, Paul Flicek, Francesco Florindi, Peter Goodhand, Ivo Gut, Jaap Heringa, Petr Holub, Jef Hooyberghs, Nick Juty, Thomas M. Keane, Jan O. Korbel, Ilkka Lappalainen, Brane Leskosek, Gert Matthijs, Michaela Th. Mayrhofer, Andres Metspalu, Arcadi Navarro, Steven Newhouse, Tommi Nyramp#x000F6;nen, Angela Page, Bengt Persson, Aarno Palotie, Helen Parkinson, Jordi Rambla, David Salgado, Erik Steinfelder, Morris A. Swertz, Alfonso Valencia, Susheel Varma, Niklas Blomberg, Serena Scollen
This study takes a socio-genomic approach to examine the complex relationships among 3 important socioeconomic outcomes: educational attainment, occupational status, and wealth.
Using more than 8,000 genetic samples from the Health and Retirement (HRS) study, it first estimates the collective influence of genetic variants across the whole human genome to each of the 3 socioeconomic outcomes. It then tests genetic correlations among 3 socioeconomic outcomes, and examines the extent to which genetic influences on occupational status and wealth are mediated by educational attainment.
Analyses using the genomic-relatedness-matrix restricted maximum likelihood method show statistically-significant genetic correlations among the 3 outcomes, and provide evidence for both mediated and independent genetic influences. A polygenic score analysis demonstrates the utility of findings in socio-genomic studies to address genetic confounding in causal relationships among the 3 socioeconomic outcomes.
Thousands of genes responsible for many diseases and other common traits in humans have been detected by Genome Wide Association Studies (GWAS) in the last decade. However, candidate causal variants found so far usually explain only a small fraction of the heritability estimated by family data. The most common explanation for this observation is that the missing heritability corresponds to variants, either rare or common, with very small effect, which pass undetected due to a lack of statistical power. We carried out a meta-analysis using data from the NHGRI-EBIGWAS Catalog in order to explore the observed distribution of locus effects for a set of 42 complex traits and to quantify their contribution to narrow-sense heritability. With the data at hand, we were able to predict the expected distribution of locus effects for 16 traits and diseases, their expected contribution to heritability, and the missing number of loci yet to be discovered to fully explain the familial heritability estimates. Our results indicate that, for 6 out of the 16 traits, the additive contribution of a great number of loci is unable to explain the familial (broad-sense) heritability, suggesting that the gap between GWAS and familial estimates of heritability may not ever be closed for these traits. In contrast, for the other 10 traits, the additive contribution of hundreds or thousands of loci yet to be found could potentially explain the familial heritability estimates, if this were the case. Computer simulations are used to illustrate the possible contribution from nonadditive genetic effects to the gap between GWAS and familial estimates of heritability.
Heritable variation in fitness—survival and reproduction—is the fuel of evolution by natural selection. Many human societies have dramatically reduced mortality before and during the prime reproductive years, making fertility a reasonably good proxy for the whole of fitness in much of our species. For this reason, empirical knowledge regarding the genetics of fertility must be an essential part of any framework for understanding past and ongoing trends in human adaptive evolution. Here we use R. A. Fisher’s analysis of human fertility as a starting point and find strong support from more recent research for his main contentions: fertility is a moderately heritable trait, where much of the genetic influences are shared with psychological characteristics.
Genetic variants affecting gene regulation contribute to human cognitive traits.
Functional genomic annotations in human brain tissue complement GWAS data.
Interdisciplinary teams are increasingly necessary when studying various aspects of human genetics.
Social scientists and natural scientists will benefit from close collaboration.
Virtually all human psychological and behavioral traits are at least partially heritable. For nearly a century, classical genetic studies have sought to understand how genetic variation contributes to human variation in these traits. More recently, genome-wide association studies have identified large numbers of specific genetic variants linked with complex traits.
Many of these variants fall outside of protein-coding genes, in putative gene regulatory elements. This suggests that some fraction of causal human genetic variation acts through gene regulation. New developments in the field of regulatory genomics offer resources and methods to understand how genetic variants that alter gene expression contribute to human psychology and risk for psychiatric disease.
The present study explores whether genetic factors explain variation in the levels of apostasy — defined as a disengagement from religious belief, identity and/or practice — in a US-based sample during the transition from adolescence to early adulthood. I posit that genetic factors at least partially explain the variance of three measures of apostasy: disengagement from religious institutions, cessation of prayer and religious disaffiliation. I argue that genetic factors associated with risk-taking behaviors, externalizing behaviors and/or correlates of apostasy may all influence the likelihood of becoming an apostate during the transition from adolescence to early adulthood in the USA. Results reveal that genetic factors explain approximately 34% of the variance in cessation of prayer and 75% of the variance in religious disaffiliation. However, genetic factors do not influence disengagement from religious institutions. This study advances our knowledge of the etiology of apostasy and highlights the need to incorporate genetic data into social scientific research.
We analysed a large health insurance dataset to assess the genetic and environmental contributions of 560 disease-related phenotypes in 56,396 twin pairs and 724,513 sibling pairs out of 44,859,462 individuals that live in the United States. We estimated the contribution of environmental risk factors (socioeconomic status (SES), air pollution and climate) in each phenotype. Mean heritability (h2 = 0.311) and shared environmental variance (c2 = 0.088) were higher than variance attributed to specific environmental factors such as zip-code-level SES (varSES = 0.002), daily air quality (var~AQI` = 0.0004), and average temperature (vartemp = 0.001) overall, as well as for individual phenotypes. We found statistically-significant heritability and shared environment for a number of comorbidities (h2 = 0.433, c2 = 0.241) and average monthly cost (h2 = 0.290, c2 = 0.302). All results are available using our Claims Analysis of Twin Correlation and Heritability (CaTCH) web application.
Genomic estimated breeding values (GEBVs) in livestock and polygenic risk scores (PRS) in humans are conceptually similar; however, the between-species differences in linkage disequilibrium (LD) provide a fundamental point of distinction that impacts approaches to data analyses…
In this Review, we focus on the similarity of the concepts underlying prediction of estimated breeding values (EBVs) in livestock and polygenic risk scores (PRS) in humans. Our research spans both fields and so we recognize factors that are very obvious for those in one field, but less so for those in the other. Differences in family size between species is the wedge that drives the different viewpoints and approaches. Large family size achievable in nonhuman species accompanied by selection generates a smaller effective population size, increased linkage disequilibrium and a higher average genetic relationship between individuals within a population.
In human genetic analyses, we select individuals unrelated in the classical sense (coefficient of relationship <0.05) to estimate heritability captured by common SNPs. In livestock data, all animals within a breed are to some extent “related”, and so it is not possible to select unrelated individuals and retain a data set of sufficient size to analyze. These differences directly or indirectly impact the way data analyses are undertaken. In livestock, genetic segregation variance exposed through samplings of parental genomes within families is directly observable and taken for granted. In humans, this genomic variation is under-recognized for its contribution to variation in polygenic risk of common disease, in both those with and without family history of disease.
We explore the equation that predicts the expected proportion of variance explained using PRS, and quantify how GWAS sample size is the key factor for maximizing accuracy of prediction in both humans and livestock. Last, we bring together the concepts discussed to address some frequently asked questions.
2019-warrington.pdf: “Maternal and fetal genetic effects on birth weight and their relevance to cardio-metabolic risk factors”, Nicole M. Warrington, Robin N. Beaumont, Momoko Horikoshi, Felix R. Day, amp#x000D8;yvind Helgeland, Charles Laurin, Jonas Bacelis, Shouneng Peng, Ke Hao, Bjarke Feenstra, Andrew R. Wood, Anubha Mahajan, Jessica Tyrrell, Neil R. Robertson, N. William Rayner, Zhen Qiao, Gunn-Helen Moen, Marc Vaudel, Carmen J. Marsit, Jia Chen, Michael Nodzenski, Theresia M. Schnurr, Mohammad H. Zafarmand, Jonathan P. Bradfield, Niels Grarup, Marjolein N. Kooijman, Ruifang Li-Gao, Frank Geller, Tarunveer S. Ahluwalia, Lavinia Paternoster, Rico Rueedi, Ville Huikari, Jouke-Jan Hottenga, Leo-Pekka Lyytikamp#x000E4;inen, Alana Cavadino, Sarah Metrustry, Diana L. Cousminer, Ying Wu, Elisabeth Thiering, Carol A. Wang, Christian T. Have, Natalia Vilor-Tejedor, Peter K. Joshi, Jodie N. Painter, Ioanna Ntalla, Ronny Myhre, Niina Pitkamp#x000E4;nen, Elisabeth M. Leeuwen, Raimo Joro, Vasiliki Lagou, Rebecca C. Richmond, Ana Espinosa, Sheila J. Barton, Hazel M. Inskip, John W. Holloway, Loreto Santa-Marina, Xavier Estivill, Wei Ang, Julie A. Marsh, Christoph Reichetzeder, Letizia Marullo, Berthold Hocher, Kathryn L. Lunetta, Joanne M. Murabito, Caroline L. Relton, Manolis Kogevinas, Leda Chatzi, Catherine Allard, Luigi Bouchard, Marie-France Hivert, Ge Zhang, Louis J. Muglia, Jani Heikkinen, Camilla S. Morgen, Antoine H. C. Kampen, Barbera D. C. Schaik, Frank D. Mentch, Claudia Langenberg, Jianamp#x02019;an Luan, Robert A. Scott, Jing Hua Zhao, Gibran Hemani, Susan M. Ring, Amanda J. Bennett, Kyle J. Gaulton, Juan Fernandez-Tajes, Natalie R. Zuydam, Carolina Medina-Gomez, Hugoline G. Haan, Frits R. Rosendaal, Zoltamp#x000E1;n Kutalik, Pedro Marques-Vidal, Shikta Das, Gonneke Willemsen, Hamdi Mbarek, Martina Mamp#x000FC;ller-Nurasyid, Marie Standl, Emil V. R. Appel, Cilius E. Fonvig, Caecilie Trier, Catharina E. M. Beijsterveldt, Mario Murcia, Mariona Bustamante, Samp#x000ED;lvia Bonas-Guarch, David M. Hougaard, Josep M. Mercader, Allan Linneberg, Katharina E. Schraut, Penelope A. Lind, Sarah E. Medland, Beverley M. Shields, Bridget A. Knight, Jin-Fang Chai, Kalliope Panoutsopoulou, Meike Bartels, Friman Samp#x000E1;nchez, Jakob Stokholm, David Torrents, Rebecca K. Vinding, Sara M. Willems, Mustafa Atalay, Bo L. Chawes, Peter Kovacs, Inga Prokopenko, Marcus A. Tuke, Hanieh Yaghootkar, Katherine S. Ruth, Samuel E. Jones, Po-Ru Loh, Anna Murray, Michael N. Weedon, Anke Tamp#x000F6;njes, Michael Stumvoll, Kim F. Michaelsen, Aino-Maija Eloranta, Timo A. Lakka, Cornelia M. Duijn, Wiel, Kiess, Antje Kamp#x000F6;rner, Harri Niinikoski, Katja Pahkala, Olli T. Raitakari, Bo Jacobsson, Eleftheria Zeggini, George V. Dedoussis, Yik-Ying Teo, Seang-Mei Saw, Grant W. Montgomery, Harry Campbell, James F. Wilson, Tanja G. M. Vrijkotte, Martine Vrijheid, Eco J. C. N. Geus, M. Geoffrey Hayes, Haja N. Kadarmideen, Jens-Christian Holm, Lawrence J. Beilin, Craig E. Pennell, Joachim Heinrich, Linda S. Adair, Judith B. Borja, Karen L. Mohlke, Johan G. Eriksson, Elisabeth E. Widamp#x000E9;n, Andrew T. Hattersley, Tim D. Spector, Mika Kamp#x000E4;hamp#x000F6;nen, Jorma S. Viikari, Terho Lehtimamp#x000E4;ki, Dorret I. Boomsma, Sylvain Sebert, Peter Vollenweider, Thorkild I. A. Samp#x000F8;rensen, Hans Bisgaard, Klaus Bamp#x000F8;nnelykke, Jeffrey C. Murray, Mads Melbye, Ellen A. Nohr, Dennis O. Mook-Kanamori, Fernando Rivadeneira, Albert Hofman, Janine F. Felix, Vincent W. V. Jaddoe, Torben Hansen, Charlotta Pisinger, Allan A. Vaag, Oluf Pedersen, Andramp#x000E9, G. Uitterlinden, Marjo-Riitta Jamp#x000E4;rvelin, Christine Power, Elina Hyppamp#x000F6;nen, Denise M. Scholtens, William L. Lowe, George Davey Smith, Nicholas J. Timpson, Andrew P. Morris, Nicholas J. Wareham, Hakon Hakonarson, Struan F. A. Grant, Timothy M. Frayling, Debbie A. Lawlor, Pamp#x000E5;l R. Njamp#x000F8;lstad, Stefan Johansson, Ken K. Ong, Mark I. McCarthy, John R. B. Perry, David M. Evans, Rachel M. Freathy
The genetics and evolution of complex traits, including quantitative traits and disease, have been hotly debated ever since Darwin. A century ago, a paper from R.A. Fisher reconciled Mendelian and biometrical genetics in a landmark contribution that is now accepted as the main foundation stone of the field of quantitative genetics. Here, we give our perspective on Fisher’s 1918 paper in the context of how and why it is relevant in today’s genome era. We mostly focus on human trait variation, in part because Fisher did so too, but the conclusions are general and extend to other natural populations, and to populations undergoing artificial selection.
During the last decade, there have been substantial advances in the identification and characterization of DNA sequence variants associated with individual predisposition to type 1 and type 2 diabetes. As well as providing insights into the molecular, cellular, and physiological mechanisms involved in disease pathogenesis, these risk variants, when combined into a polygenic score, capture information on individual patterns of disease predisposition that have the potential to influence clinical management. In this review, we describe the various opportunities that polygenic scores provide: to predict diabetes risk, to support differential diagnosis, and to understand phenotypic and clinical heterogeneity. We also describe the challenges that will need to be overcome if this potential is to be fully realized.
Humans use a family of more than 400 olfactory receptors (ORs) to detect odors, but there is currently no model that can predict olfactory perception from receptor activity patterns. Genetic variation in human ORs is abundant and alters receptor function, allowing us to examine the relationship between receptor function and perception. We sequenced the OR repertoire in 332 individuals and examined how genetic variation affected 276 olfactory phenotypes, including the perceived intensity and pleasantness of 68 odorants at two concentrations, detection thresholds of three odorants, and general olfactory acuity. Genetic variation in a single OR was frequently associated with changes in odorant perception, and we validated 10 cases in which in vitro OR function correlated with in vivo odorant perception using a functional assay. In 8 of these 10 cases, reduced receptor function was associated with reduced intensity perception. In addition, we used participant genotypes to quantify genetic ancestry and found that, in combination with single OR genotype, age, and gender, we can explain between 10% and 20% of the perceptual variation in 15 olfactory phenotypes, highlighting the importance of single OR genotype, ancestry, and demographic factors in the variation of olfactory perception.
Human skin and hair color are visible traits that can vary dramatically within and across ethnic populations. The genetic makeup of these traits—including polymorphisms in the enzymes and signaling proteins involved in melanogenesis, and the vital role of ion transport mechanisms operating during the maturation and distribution of the melanosome—has provided new insights into the regulation of pigmentation. A large number of novel loci involved in the process have been recently discovered through four large-scale genome-wide association studies in Europeans, two large genetic studies of skin color in Africans, one study in Latin Americans, and functional testing in animal models. The responsible polymorphisms within these pigmentation genes appear at different population frequencies, can be used as ancestry-informative markers, and provide insight into the evolutionary selective forces that have acted to create this human diversity.
2019-locke.pdf: “Exome sequencing of Finnish isolates enhances rare-variant association power”, Adam E. Locke, Karyn Meltz Steinberg, Charleston W. K. Chiang, Susan K. Service, Aki S. Havulinna, Laurel Stell, Matti Pirinen, Haley J. Abel, Colby C. Chiang, Robert S. Fulton, Anne U. Jackson, Chul Joo Kang, Krishna L. Kanchi, Daniel C. Koboldt, David E. Larson, Joanne Nelson, Thomas J. Nicholas, Arto Pietilamp#x000E4;, Vasily Ramensky, Debashree Ray, Laura J. Scott, Heather M. Stringham, Jagadish Vangipurapu, Ryan Welch, Pranav Yajnik, Xianyong Yin, Johan G. Eriksson, Mika Ala-Korpela, Marjo-Riitta Jamp#x000E4;rvelin, Minna Mamp#x000E4;nnikkamp#x000F6;, Hannele Laivuori, Susan K. Dutcher, Nathan O. Stitziel, Richard K. Wilson, Ira M. Hall, Chiara Sabatti, Aarno Palotie, Veikko Salomaa, Markku Laakso, Samuli Ripatti, Michael Boehnke, Nelson B. Freimer
Prediction of disease risk is an essential part of preventative medicine, often guiding clinical management. Risk prediction typically includes risk factors such as age, sex, family history of disease and lifestyle (e.g. smoking status); however, in recent years, there has been increasing interest to include genomic information into risk models. Polygenic risk scores (PRS) aggregate the effects of many genetic variants across the human genome into a single score and have recently been shown to have predictive value for multiple common diseases. In this review, we summarize the potential use cases for seven common diseases (breast cancer, prostate cancer, coronary artery disease, obesity, type 1 diabetes, type 2 diabetes and Alzheimer’s disease) where PRS has or could have clinical utility. PRS analysis for these diseases frequently revolved around (i) risk prediction performance of a PRS alone and in combination with other non-genetic risk factors, (ii) estimation of lifetime risk trajectories, (iii) the independent information of PRS and family history of disease or monogenic mutations and (iv) estimation of the value of adding a PRS to specific clinical risk prediction scenarios. We summarize open questions regarding PRS usability, ancestry bias and transferability, emphasizing the need for the next wave of studies to focus on the implementation and health-economic value of PRS testing. In conclusion, it is becoming clear that PRS have value in disease risk prediction and there are multiple areas where this may have clinical utility.
The same mutation can have different effects in different individuals. One important reason for this is that the outcome of a mutation can depend on the genetic context in which it occurs. This dependency is known as epistasis. In recent years, there has been a concerted effort to quantify the extent of pairwise and higher-order genetic interactions between mutations through deep mutagenesis of proteins and RNAs. This research has revealed two major components of epistasis: nonspecific genetic interactions caused by nonlinearities in genotype-to-phenotype maps, and specific interactions between particular mutations. Here, we provide an overview of our current understanding of the mechanisms causing epistasis at the molecular level, the consequences of genetic interactions for evolution and genetic prediction, and the applications of epistasis for understanding biology and determining macromolecular structures.
Genome-wide association studies (GWASs) have identified specific genetic variants associated with complex human traits and behaviors, such as educational attainment, mental disorders, and personality. However, small effect sizes for individual variants, uncertainty regarding the biological function of discovered genotypes, and potential “outside-the-skin” environmental mechanisms leave a translational gulf between GWAS results and scientific understanding that will improve human health and well-being. We propose a set of social, behavioral, and brain-science research activities that map discovered genotypes to neural, developmental, and social mechanisms and call this research program phenotypic annotation. Phenotypic annotation involves (a) elaborating the nomological network surrounding discovered genotypes, (b) shifting focus from individual genes to whole genomes, and (c) testing how discovered genotypes affect life-span development. Phenotypic-annotation research is already advancing the understanding of GWAS discoveries for educational attainment and schizophrenia. We review examples and discuss methodological considerations for psychologists taking up the phenotypic-annotation approach.
2019-bai.pdf: “Association of Genetic and Environmental Factors With Autism in a 5-Country Cohort”, Dan Bai, Benjamin Hon Kei Yip, Gayle C. Windham, Andre Sourander, Richard Francis, Rinat Yoffe, Emma Glasson, Behrang Mahjani, Auli Suominen, Helen Leonard, Mika Gissler, Joseph D. Buxbaum, Kingsley Wong, Diana Schendel, Arad Kodesh, Michaeline Breshnahan, Stephen Z. Levine, Erik T. Parner, Stefan N. Hansen, Christina Hultman, Abraham Reichenberg, Sven Sandin
Organisms are faced with variable environments and one of the most common solutions to cope with such variability is phenotypic plasticity, a modification of the phenotype to the environment. These modifications are commonly modelled in evolutionary theories as adaptive, influencing ecological and evolutionary processes. If plasticity is adaptive, we would predict that the closer to fitness a trait is, the less plastic it would be. To test this hypothesis, we conducted a meta-analysis of 213 studies and measured the plasticity of each reported trait as a coefficient of variation. Traits were categorized as closer to fitness—life-history traits including reproduction and survival related traits, and farther from fitness—non-life-history traits including traits related to development, metabolism and physiology, morphology and behaviour. Our results showed, unexpectedly, that although traits differed in their amounts of plasticity, trait plasticity was not related to its proximity to fitness. These findings were independent of taxonomic groups or environmental types assessed. We caution against general expectations that plasticity is adaptive, as assumed by many models of its evolution. More studies are needed that test the adaptive nature of plasticity, and additional theoretical explorations on adaptive and non-adaptive plasticity are encouraged.
The heritability of exercise behavior (EB) in young adults is substantial (60%–81%).
Several parameters measured in adolescence were correlated with adult EB.
These correlates showed statistically-significant genetic associations with adult EB.
A large part of the covariation between EB and the correlates was due to genetic causes.
Objectives: To improve the success of interventions aimed to increase moderate to vigorous physical activity, we need to better understand the correlates of the extensive individual differences in voluntary exercise activities. Starting in adolescence, genetic effects become a dominant factor in explaining individual differences in voluntary exercise behavior. Here we aim to establish the prospective contribution of potential correlates of voluntary exercise behavior to its heritability.
Design: In a sample of adolescent and young adult twins, data on potential correlates of exercise behavior were collected using surveys (time point 1, n = 373) and a laboratory study (time point 2, n = 499). Information on personality, perceived barriers & benefits, subjective and objective exercise ability and the affective response to exercise were collected in a set of healthy adolescent twin pairs (16–18y) and their non-twin siblings (12–25y). Almost 3 years later, the subjects were sent an online follow-up survey on their current exercise status (time point 3, n = 423).
Methods: In bivariate models, the phenotypic (co)variance in these correlates and exercise behavior at all time points were decomposed in sources of genetic (co)variance and environmental (co)variance. The correlates that were statistically-significant associated with exercise behavior at time point 1 or 2 and showed statistically-significant genetic correlations to exercise behavior at time point 3 were used in 2 further analyses: Multiple regression analysis to predict exercise behavior at time point 3, and a genetic analysis in a common 2-factor model, that tested the overlap in genetic factors influencing these correlates and exercise behavior.
Results: Personality (Extraversion), perceived benefits and barriers, exercise-induced affective response (Energy measured after the cycling test), and subjective and objective exercise ability (VO2max) showed statistically-significant phenotypic and genetic association with exercise behavior at time point 3. The genetic correlation between the 2 latent factors in the common 2-factor model was 0.51, indicating that part of the heritability in exercise behavior derives from genetic variants that also influence these correlates.
Conclusions: Given their shared genetic basis and predictive power we assert that individual differences in extraversion, perceived benefits and barriers, exercise-induced feelings of energy, and subjective and objective exercise ability can be used to develop stratified interventions for adolescent and young adult exercise behavior. In addition, our results provide the first clues on ‘where to look’ for specific genetic variants for voluntary exercise behavior.
Except for drinking water, most beverages taste bitter or sweet. Taste perception and preferences are heritable and determinants of beverage choice and consumption. Consumption of several bitter-tasting and sweet-tasting beverages has been implicated in development of major chronic diseases. We performed a genome-wide association study (GWAS) of self-reported bitter and sweet beverage consumption among ~370,000 participants of European ancestry, using a two-staged analysis design. Bitter beverages included coffee, tea, grapefruit juice, red wine, liquor and beer. Sweet beverages included artificially and sugar sweetened beverages (SSBs) and non-grapefruit juices. Five loci associated with total bitter beverage consumption were replicated (in/near GCKR, ABCG2, AHR, POR and CYP1A1/2). No locus was replicated for total sweet beverage consumption. Sub-phenotype analyses targeting the alcohol, caffeine and sweetener components of beverages yielded additional loci: (1) four loci for bitter alcoholic beverages (GCKR,KLB, ADH1B and AGBL2); (2) five loci for bitter non-alcoholicbeverages (ANXA9, AHR, POR, CYP1A1/2 and CSDC2); (3) 10 loci forcoffee; six novel loci (SEC16B, TMEM18, OR8U8, AKAP6, MC4R andSPECC1L-ADORA2A); (4) FTO for SSBs. Of these 17 replicated loci, 12 have been associated with total alcohol consumption, coffee consumption, plasma caffeine metabolites or BMI in previous GWAS; none was involved in known sweet and bitter taste transduction pathways. Our study suggests that genetic variants related to alcohol consumption, coffee consumption and obesity were primary genetic determinants of bitter and sweet beverage consumption. Whether genetic variants related to taste perception are associated with beverage consumption remains to be determined.
Virtual twins (VTs) are defined as same-age unrelated siblings raised together from early infancy. This special class of adoptive siblings replays the rearing situation of twins, absent genetic relatedness. The first such pair was identified and studied in 1990 at the University of Minnesota, leading to the creation of the Fullerton Virtual Twin Study (FVTS) at California State University, Fullerton(CSUF) the following year. The registry currently includes 169 VT pairs, mostly children, with new pairs identified on a regular basis. These sibling sets provide a direct estimate of environmental influences on developmental traits and, as such, offer informative comparisons with ordinary monozygotic and dizygotic twins, full siblings and adoptive brothers and sisters. The sample characteristics, assessment battery and findings to date are summarized in this 2019 update.
This study was designed to provide detailed estimates of genetic and environmental sources of variance in the HEXACO personality traits. For this purpose, we analyzed data from a German extended twin family study including 573 pairs of twins as well as 208 mothers, 119 fathers, 228 spouses, and 143 offspring of twins. All participants provided self-reports on the HEXACO-60.
Extended twin family analyses using structural equation modeling (SEM) yielded that additive and nonadditive genetic influences accounted for about 50% of the variance in personality traits. The remaining variance was primarily due to individual-specific environmental sources and random measurement error. Spousal similarity in Openness was attributable to assortative mating, whereas spousal similarity in Honesty-Humility was attributable to environmental circumstances, partly due to a shared social background and spouse-specific effects.
Our analyses yielded specifics for different personality traits. However, transmission of trait similarity from one generation to the next was primarily genetic.
Study Objectives: Report the first prevalence estimates of advanced sleep phase (ASP), familial advanced sleep phase (FASP), andadvanced sleep-wake phase disorder (ASWPD). This can guide clinicians on the utility of screening for extreme chronotypes both for clinical decision-making and to flag prospective participants in the study of the genetics and biology of FASP.
Methods: Data on morning or evening sleep schedule preference (chronotype) were collected from 2422 new patients presenting to a North American sleep center over 9.8 years. FASP was determined using a severity criterion that has previously identified dominant circadian mutations in humans. All patients were personally seen and evaluated by one of the authors (C. R. J.).
Results: Our results demonstrate an ASP prevalence of 0.33%, anFASP prevalence of 0.21%, and an ASWPD prevalence of at least 0.04%.Most cases of young-onset ASP were familial.
Conclusions: Among patients presenting to a sleep clinic, conservatively 1 out of every 300 patients will have ASP, 1 out ofevery 475 will have FASP, and 1 out of every 2500 will have ASWPD. This supports obtaining a routine circadian history and, for those with extreme chronotypes, obtaining a family history of circadian preference. This can optimize treatment for evening sleepiness and early morning awakening and lead to additional circadian gene discovery. We hope these findings will lead to improved treatment options for a wide range of sleep and medical disorders in the future.
Objective: Interest in candidate gene and candidate gene-by-environment interaction hypotheses regarding major depressive disorder remains strong despite controversy surrounding the validity of previous findings. In response to this controversy, the present investigation empirically identified 18 candidate genes for depression that have been studied 10 or more times and examined evidence for their relevance to depression phenotypes.
Methods: Utilizing data from large population-based and case-control samples (_n_s ranging from 62,138 to 443,264 across subsamples), the authors conducted a series of preregistered analyses examining candidate gene polymorphism main effects, polymorphism-by-environment interactions, and gene-level effects across a number of operational definitions of depression (eg., lifetime diagnosis, current severity, episode recurrence) and environmental moderators (eg., sexual or physical abuse during childhood, socioeconomic adversity).
Results: No clear evidence was found for any candidate gene polymorphism associations with depression phenotypes or any polymorphism-by-environment moderator effects. As a set, depression candidate genes were no more associated with depression phenotypes than non-candidate genes. The authors demonstrate that phenotypic measurement error is unlikely to account for these null findings.
Conclusions: The study results do not support previous depression candidate gene findings, in which large genetic effects are frequently reported in samples orders of magnitude smaller than those examined here. Instead, the results suggest that early hypotheses about depression candidate genes were incorrect and that the large number of associations reported in the depression candidate gene literature are likely to be false positives.
More than one in three adults worldwide is either overweight or obese. Epidemiological studies indicate that the location and distribution of excess fat, rather than general adiposity, are more informative for predicting risk of obesity sequelae, including cardiometabolic disease and cancer. We performed a genome-wide association study meta-analysis of body fat distribution, measured by waist-to-hip ratio (WHR) adjusted for body mass index(WHRadjBMI), and identified 463 signals in 346 loci. Heritability and variant effects were generally stronger in women than men, and we found approximately one-third of all signals to be sexually dimorphic. The 5% of individuals carrying the most WHRadjBMI-increasing alleles were 1.62 times more likely than thebottom 5% to have a WHR above the thresholds used for metabolic syndrome. These data, made publicly available, will inform the biology of body fat distribution and its relationship with disease.
Recent advances have led to the discovery of specific genetic variants that predict educational attainment. We study how these variants, summarized as a linear index—known as a polygenic score—are associated with human capital accumulation and labor market outcomes in the Health and Retirement Study (HRS). We present two main sets of results. First, we find evidence that the genetic factors measured by this score interact strongly with childhood socioeconomic status in determining educational outcomes. In particular, while the polygenic score predicts higher rates of college graduation on average, this relationship is substantially stronger for individuals who grew up in households with higher socioeconomic status relative to those who grew up in poorer households. Second, the polygenic score predicts labor earnings even after adjusting for completed education, with larger returns in more recent decades. These patterns suggest that the genetic traits that promote education might allow workers to better accommodate ongoing skill biased technological change. Consistent with this interpretation, we find a positive association between the polygenic score and non-routine analytic tasks that have benefited from the introduction of new technologies. Nonetheless, the college premium remains the dominant determinant of earnings differences at all levels of the polygenic score. Given the role of childhood SES in predicting college attainment, this raises concerns about wasted potential arising from limited household resources.
There is a great deal of interest in examining the genetic and environmental architecture to aggression, violence, and antisocial behaviors. This interest has resulted in hundreds of studies being published that estimate genetic and environmental effects on antisocial phenotypes. The results generated from these studies have been remarkably consistent and have contributed greatly to the knowledge base on the etiology of antisocial behavior. This chapter reviews the research on the genetic basis to antisocial phenotypes by presenting the results related to the heritability of antisocial phenotypes. It also discusses some of the molecular genetic association studies as well as genome-wide association studies that focus on the development of antisocial behaviors. In doing so, it also reviews findings related to gene-environment interactions. The chapter concludes by discussing some of the ways in which these findings could be used for intervention and prevention programs.
Research on environmental and genetic pathways to complex traits such as educational attainment (EA) is confounded by uncertainty over whether correlations reflect effects of transmitted parental genes, causal family environments, or some, possibly interactive, mixture of both. Thus, an aggregate of thousands of alleles associated with EA (a polygenic risk score; PRS) may tap parental behaviors and home environments promoting EA in the offspring. New methods for unpicking and determining these causal pathways are required. Here, we utilize the fact that parents pass, at random, 50% of their genome to a given offspring to create independent scores for the transmitted alleles (conventional EA PRS) and a parental score based on alleles not transmitted to the offspring (EA VP_PRS). The formal effect of non-transmitted alleles on offspring attainment was tested in 2,333 genotyped twins for whom high-quality measures of EA, assessed at age 17 years, were available, and whose parents were also genotyped. Four key findings were observed. First, the EA PRS and EA VP_PRS were empirically independent, validating the virtual-parent design. Second, in this family-based design, children’s own EA PRS statistically-significantly predicted their EA (β = 0.15), ruling out stratification confounds as a cause of the association of attainment with the EA PRS. Third, parental EA PRS predicted the SES environment parents provided to offspring (β = 0.20), and parental SES and offspring EA were statistically-significantly associated (β = 0.33). This would suggest that the EA PRS is at least as strongly linked to social competence as it is to EA, leading to higher attained SES in parents and, therefore, a higher experienced SES for children. In a full structural equation model taking account of family genetic relatedness across multiple siblings the non-transmitted allele effects were estimated at similar values; but, in this more complex model, confidence intervals included zero. A test using the forthcoming EA3 PRS may clarify this outcome. The virtual-parent method may be applied to clarify causality in other phenotypes where observational evidence suggests parenting may moderate expression of other outcomes, for instance in psychiatry.
Family trees have vast applications in multiple fields from genetics to anthropology and economics. However, the collection of extended family trees is tedious and usually relies on resources with limited geographical scope and complex data usage restrictions. Here, we collected 86 million profiles from publicly-available online data shared by genealogy enthusiasts. After extensive cleaning and validation, we obtained population-scale family trees, including a single pedigree of 13 million individuals. We leveraged the data to partition the genetic architecture of longevity by inspecting millions of relative pairs and to provide insights into the geographical dispersion of families. We also report a simple digital procedure to overlay other datasets with our resource in order to empower studies with population-scale genealogical data.
Over its 30 or so years of existence, the genomic commons—the worldwide collection of publicly accessible repositories of human and nonhuman genomic data—has enjoyed remarkable, perhaps unprecedented, success. Thanks to the rapid public data release policies initiated by the Human Genome Project, free access to a vast array of scientific data is now the norm, not only in genomics, but in scientific disciplines of all descriptions. And far from being a monolithic creation of bureaucratic fiat, the genomic commons is an exemplar of polycentric, multistakeholder governance. But like all dynamic and rapidly evolving systems, the genomic commons is not without its challenges. Issues involving scientific priority, intellectual property, individual privacy, and informed consent, in an environment of data sets of exponentially expanding size and complexity, must be addressed in the near term. In this review, we describe the characteristics and unique history of the genomic commons, then address some of the trends, challenges, and opportunities that we envision for this valuable public resource in the years to come.
2018-zengini.pdf: “Genome-wide analyses using UK Biobank data provide insights into the genetic architecture of osteoarthritis”, Eleni Zengini, Konstantinos Hatzikotoulas, Ioanna Tachmazidou, Julia Steinberg, Fernando P. Hartwig, Lorraine Southam, Sophie Hackinger, Cindy G. Boer, Unnur Styrkarsdottir, Arthur Gilly, Daniel Suveges, Britt Killian, Thorvaldur Ingvarsson, Helgi Jonsson, George C. Babis, Andrew McCaskie, Andre G. Uitterlinden, Joyce B. J. Meurs, Unnur Thorsteinsdottir, Kari Stefansson, George Davey Smith, Jeremy M. Wilkinson, Eleftheria Zeggini
2018-werling.pdf: “An analytical framework for whole-genome sequence association studies and its implications for autism spectrum disorder”, Donna M. Werling, Harrison Brand, Joon-Yong An, Matthew R. Stone, Lingxue Zhu, Joseph T. Glessner, Ryan L. Collins, Shan Dong, Ryan M. Layer, Eirene Markenscoff-Papadimitriou, Andrew Farrell, Grace B. Schwartz, Harold Z. Wang, Benjamin B. Currall, Xuefang Zhao, Jeanselle Dea, Clif Duhn, Carolyn A. Erdman, Michael C. Gilson, Rachita Yadav, Robert E. Handsaker, Seva Kashin, Lambertus Klei, Jeffrey D. Mandell, Tomasz J. Nowakowski, Yuwen Liu, Sirisha Pochareddy, Louw Smith, Michael F. Walker, Matthew J. Waterman, Xin He, Arnold R. Kriegstein, John L. Rubenstein, Nenad Sestan, Steven A. McCarroll, Benjamin M. Neale, Hilary Coon, A. Jeremy Willsey, Joseph D. Buxbaum, Mark J. Daly, Matthew W. State, Aaron R. Quinlan, Gabor T. Marth, Kathryn Roeder, Bernie Devlin, Michael E. Talkowski, Stephan J. Sanders
2018-ursini.pdf: “Convergence of placenta biology and genetic risk for schizophrenia”, Gianluca Ursini, Giovanna Punzi, Qiang Chen, Stefano Marenco, Joshua F. Robinson, Annamaria Porcelli, Emily G. Hamilton, Marina Mitjans, Giancarlo Maddalena, Martin Begemann, Jan Seidel, Hidenaga Yanamori, Andrew E. Jaffe, Karen F. Berman, Michael F. Egan, Richard E. Straub, Carlo Colantuoni, Giuseppe Blasi, Ryota Hashimoto, Dan Rujescu, Hannelore Ehrenreich, Alessandro Bertolino, Daniel R. Weinberger
2018-tedja.pdf: “Genome-wide association meta-analysis highlights light-induced signaling as a driver for refractive error”, Milly S. Tedja, Robert Wojciechowski, Pirro G. Hysi, Nicholas Eriksson, Nicholas A. Furlotte, Virginie J. M. Verhoeven, Adriana I. Iglesias, Magda A. Meester-Smoor, Stuart W. Tompson, Qiao Fan, Anthony P. Khawaja, Ching-Yu Cheng, Renamp#x000E9, Hamp#x000F6;hn, Kenji Yamashiro, Adam Wenocur, Clare Grazal, Toomas Haller, Andres Metspalu, Juho Wedenoja, Jost B. Jonas, Ya Xing Wang, Jing Xie, Paul Mitchell, Paul J. Foster, Barbara E. K. Klein, Ronald Klein, Andrew D. Paterson, S. Mohsen Hosseini, Rupal L. Shah, Cathy Williams, Yik Ying Teo, Yih Chung Tham, Preeti Gupta, Wanting Zhao, Yuan Shi, Woei-Yuh Saw, E-Shyong Tai, Xue Ling Sim, Jennifer E. Huffman, Ozren Polaamp#x00161;ek, Caroline Hayward, Goran Bencic, Igor Rudan, James F. Wilson, Tin Aung, Amutha B. Veluchamy, Kathryn P. Burdon, Harry Campbell, Li Jia Chen, Peng Chen, Wei Chen, Emily Chew, Margaret M. Deangelis, Xiaohu Ding, Angela Damp#x000F6;ring, David M. Evans, Sheng Feng, Brian Fleck, Rhys D. Fogarty, Jeremy R. Fondran, Maurizio Fossarello, Xiaobo Guo, Annet E. G. Haarman, Mingguang He, Laura D. Howe, Sarayut Janmahasatian, Vishal Jhanji, Mika Kamp#x000E4;hamp#x000F6;nen, Jaakko Kaprio, John P. Kemp, Kay-Tee Khaw, Chiea-Chuen Khor, Eva Krapohl, Jean-Franamp#x000E7;ois Korobelnik, Kris Lee, Shi-Ming Li, Yi Lu, Robert N. Luben, Kari-Matti Mamp#x000E4;kelamp#x000E4;, George McMahon, Akira Meguro, Evelin Mihailov, Masahiro Miyake, Nobuhisa Mizuki, Margaux Morrison, Vinay Nangia, Konrad Oexle, Songhomitra Panda-Jonas, Chi Pui Pang, Mario Pirastu, Robert Plomin, Taina Rantanen, Maria Schache, Ilkka Seppamp#x000E4;lamp#x000E4;, George D. Smith, Beate St Pourcain, Pancy O. Tam, J. Willem L. Tideman, Nicholas J. Timpson, Simona Vaccargiu, Zoran Vatavuk, Jie Jin Wang, Ningli Wang, Nick J. Wareham, Alan F. Wright, Liang Xu, Maurice K. H. Yap, Seyhan Yazar, Shea Ping Yip, Nagahisa Yoshimura, Alvin L. Young, Jing Hua Zhao, Xiangtian Zhou, Michelle Agee, Babak Alipanahi, Adam Auton, Robert K. Bell, Katarzyna Bryc, Sarah L. Elson, Pierre Fontanillas, David A. Hinds, Jennifer C. McCreight, Karen E. Huber, Aaron Kleinman, Nadia K. Litterman, Matthew H. McIntyre, Joanna L. Mountain, Elizabeth S. Noblin, Carrie A. M. Northover, Steven J. Pitts, J. Fah Sathirapongsasuti, Olga V. Sazonova, Janie F. Shelton, Suyash Shringarpure, Chao Tian, Vladimir Vacic, Catherine H. Wilson, Tariq M. Aslam, Sarah A. Barman, Jenny H. Barrett, Paul N. Bishop, Peter Blows, Catey Bunce, Roxana O. Carare, Usha Chakravarthy, Michelle Chan, Sharon Chua, David Crabb, Alexander Day, Parul Desai, Bal Dhillon, Andrew D. Dick, Cathy A. Egan, Sarah Ennis, Marcus Fruttiger, John Gallacher, David F. Garway-Heath, Jane Gibson, Dan M. Gore, Alison Hardcastle, Simon P. Harding, Ruth E. Hogg, Pearse A. Keane, Peng Tee Khaw, Gerassimos Lascaratos, Andrew Lotery, Phil J. Luthert, Tom J. MacGillivray, Sarah L. Mackie, Keith R. Martin, Michelle McGaughey, Bernadette McGuinness, Gareth J. McKay, Martin McKibbin, Danny Mitry, Tony Moore, James E. Morgan, Zaynah A. Muthy, Eoin Oamp#x02019;Sullivan, Chris Owen, Praveen J. Patel, Euan N. Paterson, Tunde Peto, Axel Petzold, Alicja R. Rudnicka, Jay E. Self, Sobha Sivaprasad, David H. W. Steel, Irene M. Stratton, Nicholas Strouthidis, Cathie L. M. Sudlow, Caroline Thaung, Dhanes Thomas, Emanuele Trucco, Adnan Tufail, Stephen A. Vernon, Ananth C. Viswanathan, Jayne V. Woodside, Max Yates, Jennifer L. Y. Yip, Yalin Zheng, Peter K. Joshi, Akitaka Tsujikawa, Fumihiko Matsuda, Kristina N. Whisenhunt, Tanja Zeller, Peter J. Spek, Roxanna Haak, Hanne Meijers-Heijboer, Elisabeth M. van Leeuwen, Sudha K. Iyengar, Jonathan H. Lass, Albert Hofman, Fernando Rivadeneira, Andramp#x000E9, G. Uitterlinden, Johannes R. Vingerling, Terho Lehtimamp#x000E4;ki, Olli T. Raitakari, Ginevra Biino, Maria Pina Concas, Tae-Hwi Schwantes-An, Robert P. Igo, Gabriel Cuellar-Partida, Nicholas G. Martin, Jamie E. Craig, Puya Gharahkhani, Katie M. Williams, Abhishek Nag, Jugnoo S. Rahi, Phillippa M. Cumberland, Camp#x000E9;cile Delcourt, Camp#x000E9;line Bellenguez, Janina S. Ried, Arthur A. Bergen, Thomas Meitinger, Christian Gieger, Tien Yin Wong, Alex W. Hewitt, David A. Mackey, Claire L. Simpson, Norbert Pfeiffer, Olavi Pamp#x000E4;rssinen, Paul N. Baird, Veronique Vitart, Najaf Amin, Cornelia M. Duijn, Joan E. Bailey-Wilson, Terri L. Young, Seang-Mei Saw, Dwight Stambolian, Stuart MacGregor, Jeremy A. Guggenheim, Joyce Y. Tung, Christopher J. Hammond, Caroline C. W. Klaver (backlinks)
We used classical and extended adoption designs in Swedish registries to disentangle genetic and rearing-environment influences on the intergenerational transmission of divorce. In classical adoption analyses, adoptees (n = 19,715) resembled their biological parents, rather than their adoptive parents, in their history of divorce. In extended adoption analyses, offspring (n = 82,698) resembled their not-lived-with fathers and their lived-with mothers. There was stronger resemblance to lived-with mothers, providing indirect evidence of rearing-environment influences on the intergenerational transmission of divorce. The heritability of divorce assessed across generations was 0.13. We attempted to replicate our findings using within-generation data from adoptive and biological siblings (ns = 8,523–53,097). Adoptees resembled their biological, not adoptive, siblings in their history of divorce. Thus, there was consistent evidence that genetic factors contributed to the intergenerational transmission of divorce but weaker evidence for a rearing-environment effect of divorce. Within-generation data from siblings supported these conclusions.
2018-roselli.pdf: “Multi-ethnic genome-wide association study for atrial fibrillation”, Carolina Roselli, Mark D. Chaffin, Lu-Chen Weng, Stefanie Aeschbacher, Gustav Ahlberg, Christine M. Albert, Peter Almgren, Alvaro Alonso, Christopher D. Anderson, Krishna G. Aragam, Dan E. Arking, John Barnard, Traci M. Bartz, Emelia J. Benjamin, Nathan A. Bihlmeyer, Joshua C. Bis, Heather L. Bloom, Eric Boerwinkle, Erwin B. Bottinger, Jennifer A. Brody, Hugh Calkins, Archie Campbell, Thomas P. Cappola, John Carlquist, Daniel I. Chasman, Lin Y. Chen, Yii-Der Ida Chen, Eue-Keun Choi, Seung Hoan Choi, Ingrid E. Christophersen, Mina K. Chung, John W. Cole, David Conen, James Cook, Harry J. Crijns, Michael J. Cutler, Scott M. Damrauer, Brian R. Daniels, Dawood Darbar, Graciela Delgado, Joshua C. Denny, Martin Dichgans, Marcus Damp#x000F6;rr, Elton A. Dudink, Samuel C. Dudley, Nada Esa, Tonu Esko, Markku Eskola, Diane Fatkin, Stephan B. Felix, Ian Ford, Oscar H. Franco, Bastiaan Geelhoed, Raji P. Grewal, Vilmundur Gudnason, Xiuqing Guo, Namrata Gupta, Stefan Gustafsson, Rebecca Gutmann, Anders Hamsten, Tamara B. Harris, Caroline Hayward, Susan R. Heckbert, Jussi Hernesniemi, Lynne J. Hocking, Albert Hofman, Andrea R. V. R. Horimoto, Jie Huang, Paul L. Huang, Jennifer Huffman, Erik Ingelsson, Esra Gucuk Ipek, Kaoru Ito, Jordi Jimenez-Conde, Renee Johnson, J. Wouter Jukema, Stefan Kamp#x000E4;amp#x000E4;b, Mika Kamp#x000E4;hamp#x000F6;nen, Yoichiro Kamatani, John P. Kane, Adnan Kastrati, Sekar Kathiresan, Petra Katschnig-Winter, Maryam Kavousi, Thorsten Kessler, Bas L. Kietselaer, Paulus Kirchhof, Marcus E. Kleber, Stacey Knight, Jose E. Krieger, Michiaki Kubo, Lenore J. Launer, Jari Laurikka, Terho Lehtimamp#x000E4;ki, Kirsten Leineweber, Rozenn N. Lemaitre, Man Li, Hong Euy Lim, Henry J. Lin, Honghuang Lin, Lars Lind, Cecilia M. Lindgren, Marja-Liisa Lokki, Barry London, Ruth J. F. Loos, Siew-Kee Low, Yingchang Lu, Leo-Pekka Lyytikamp#x000E4;inen, Peter W. Macfarlane, Patrik K. Magnusson, Anubha Mahajan, Rainer Malik, Alfredo J. Mansur, Gregory M. Marcus, Lauren Margolin, Kenneth B. Margulies, Winfried Mamp#x000E4;rz, David D. McManus, Olle Melander, Sanghamitra Mohanty, Jay A. Montgomery, Michael P. Morley, Andrew P. Morris, Martina Mamp#x000FC;ller-Nurasyid, Andrea Natale, Saman Nazarian, Benjamin Neumann, Christopher Newton-Cheh, Maartje N. Niemeijer, Kjell Nikus, Peter Nilsson, Raymond Noordam, Heidi Oellers, Morten S. Olesen, Marju Orho-Melander, Sandosh Padmanabhan, Hui-Nam Pak, Guillaume Paramp#x000E9;, Nancy L. Pedersen, Joanna Pera, Alexandre Pereira, David Porteous, Bruce M. Psaty, Sara L. Pulit, Clive R. Pullinger, Daniel J. Rader, Lena Refsgaard, Marta Ribasamp#x000E9;s, Paul M. Ridker, Michiel Rienstra, Lorenz Risch, Dan M. Roden, Jonathan Rosand, Michael A. Rosenberg, Natalia Rost, Jerome I. Rotter, Samir Saba, Roopinder K. Sandhu, Renate B. Schnabel, Katharina Schramm, Heribert Schunkert, Claudia Schurman, Stuart A. Scott, Ilkka Seppamp#x000E4;lamp#x000E4;, Christian Shaffer, Svati Shah, Alaa A. Shalaby, Jaemin Shim, M. Benjamin Shoemaker, Joylene E. Siland, Juha Sinisalo, Moritz F. Sinner, Agnieszka Slowik, Albert V. Smith, Blair H. Smith, J. Gustav Smith, Jonathan D. Smith, Nicholas L. Smith, Elsayed Z. Soliman, Nona Sotoodehnia, Bruno H. Stricker, Albert Sun, Han Sun, Jesper H. Svendsen, Toshihiro Tanaka, Kahraman Tanriverdi, Kent D. Taylor, Maris Teder-Laving, Alexander Teumer, Samp#x000E9;bastien Thamp#x000E9;riault, Stella Trompet, Nathan R. Tucker, Arnljot Tveit, Andre G. Uitterlinden, Pim Van Der Harst, Isabelle C. Van Gelder, David R. Van Wagoner, Niek Verweij, Efthymia Vlachopoulou, Uwe Vamp#x000F6;lker, Biqi Wang, Peter E. Weeke, Bob Weijs, Raul Weiss, Stefan Weiss, Quinn S. Wells, Kerri L. Wiggins, Jorge A. Wong, Daniel Woo, Bradford B. Worrall, Pil-Sung Yang, Jie Yao, Zachary T. Yoneda, Tanja Zeller, Lingyao Zeng, Steven A. Lubitz, Kathryn L. Lunetta, Patrick T. Ellinor
2018-martin-2.pdf: “Quantifying the contribution of recessive coding variation to developmental disorders”, Hilary C. Martin1, Wendy D. Jones, Rebecca McIntyre, Gabriela Sanchez-Andrade, Mark Sanderson, James D. Stephenson, Carla P. Jones, Juliet Handsaker, Giuseppe Gallone, Michaela Bruntraeger, Jeremy F. McRae, Elena Prigmore, Patrick Short, Mari Niemi, Joanna Kaplanis, Elizabeth J. Radford, Nadia Akawi, M. Balasubramanian, John Dean, Rachel Horton, Alice Hulbert, Diana S. Johnson, Katie Johnson, Dhavendra Kumar, Sally Ann Lynch, Sarju G. Mehta, Jenny Morton, Michael J. Parker, Miranda Splitt, Peter D. Turnpenny, Pradeep C. Vasudevan, Michael Wright, Andrew Bassett, Sebastian S. Gerety, Caroline F. Wright, David R. FitzPatrick, Helen V. Firth, Matthew E. Hurles, Jeffrey C. Barrett (the Deciphering Developmental Disorders Study) (2018-01-01):
We estimated the genome-wide contribution of recessive coding variation in 6040 families from the Deciphering Developmental Disorders study. The proportion of cases attributable to recessive coding variants was 3.6% in patients of European ancestry, compared with 50% explained by de novo coding mutations. It was higher (31%) in patients with Pakistani ancestry, owing to elevated autozygosity. Half of this recessive burden is attributable to known genes. We identified two genes not previously associated with recessive developmental disorders, KDM5B and EIF3F, and functionally validated them with mouse and cellular models. Our results suggest that recessive coding variants account for a small fraction of currently undiagnosed nonconsanguineous individuals, and that the role of noncoding variants, incomplete penetrance, and polygenic mechanisms need further exploration.
2018-mahajan.pdf: “Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes”, Anubha Mahajan, Jennifer Wessel, Sara M. Willems, Wei Zhao, Neil R. Robertson, Audrey Y. Chu, Wei Gan, Hidetoshi Kitajima, Daniel Taliun, N. William Rayner, Xiuqing Guo, Yingchang Lu, Man Li, Richard A. Jensen, Yao Hu, Shaofeng Huo, Kurt K. Lohman, Weihua Zhang, James P. Cook, Bram Peter Prins, Jason Flannick, Niels Grarup, Vassily Vladimirovich Trubetskoy, Jasmina Kravic, Young Jin Kim, Denis V. Rybin, Hanieh Yaghootkar, Martina Mamp#x000FC;ller-Nurasyid, Karina Meidtner, Ruifang Li-Gao, Tibor V. Varga, Jonathan Marten, Jin Li, Albert Vernon Smith, Ping An, Symen Ligthart, Stefan Gustafsson, Giovanni Malerba, Ayse Demirkan, Juan Fernandez Tajes, Valgerdur Steinthorsdottir, Matthias Wuttke, Camp#x000E9;cile Lecoeur, Michael Preuss, Lawrence F. Bielak, Marielisa Graff, Heather M. Highland, Anne E. Justice, Dajiang J. Liu, Eirini Marouli, Gina Marie Peloso, Helen R. Warren, Saima Afaq, Shoaib Afzal, Emma Ahlqvist, Peter Almgren, Najaf Amin, Lia B. Bang, Alain G. Bertoni, Cristina Bombieri, Jette Bork-Jensen, Ivan Brandslund, Jennifer A. Brody, Noamp#x000EB;l P. Burtt, Mickaamp#x000EB;l Canouil, Yii-Der Ida Chen, Yoon Shin Cho, Cramer Christensen, Sophie V. Eastwood, Kai-Uwe Eckardt, Krista Fischer, Giovanni Gambaro, Vilmantas Giedraitis, Megan L. Grove, Hugoline G. Haan, Sophie Hackinger, Yang Hai, Sohee Han, Anne Tybjamp#x000E6;rg-Hansen, Marie-France Hivert, Bo Isomaa, Susanne Jamp#x000E4;ger, Marit E. Jamp#x000F8;rgensen, Torben Jamp#x000F8;rgensen, Annemari Kamp#x000E4;ramp#x000E4;jamp#x000E4;mamp#x000E4;ki, Bong-Jo Kim, Sung Soo Kim, Heikki A. Koistinen, Peter Kovacs, Jennifer Kriebel, Florian Kronenberg, Kristi Lamp#x000E4;ll, Leslie A. Lange, Jung-Jin Lee, Benjamin Lehne, Huaixing Li, Keng-Hung Lin, Allan Linneberg, Ching-Ti Liu, Jun Liu, Marie Loh, Reedik Mamp#x000E4;gi, Vasiliki Mamakou, Roberta McKean-Cowdin, Girish Nadkarni, Matt Neville, Sune F. Nielsen, Ioanna Ntalla, Patricia A. Peyser, Wolfgang Rathmann, Kenneth Rice, Stephen S. Rich, Line Rode, Olov Rolandsson, Sebastian Schamp#x000F6;nherr, Elizabeth Selvin, Kerrin S. Small, Alena Stanamp#x0010D;amp#x000E1;kovamp#x000E1;, Praveen Surendran, Kent D. Taylor, Tanya M. Teslovich, Barbara Thorand, Gudmar Thorleifsson, Adrienne Tin, Anke Tamp#x000F6;njes, Anette Varbo, Daniel R. Witte, Andrew R. Wood, Pranav Yajnik, Jie Yao, Loamp#x000EF;c Yengo, Robin Young, Philippe Amouyel, Heiner Boeing, Eric Boerwinkle, Erwin P. Bottinger, Rajiv Chowdhury, Francis S. Collins, George Dedoussis, Abbas Dehghan, Panos Deloukas, Marco M. Ferrario, Jean Ferriamp#x000E8;res, Jose C. Florez, Philippe Frossard, Vilmundur Gudnason, Tamara B. Harris, Susan R. Heckbert, Joanna M. M. Howson, Martin Ingelsson, Sekar Kathiresan, Frank Kee, Johanna Kuusisto, Claudia Langenberg, Lenore J. Launer, Cecilia M. Lindgren, Satu Mamp#x000E4;nnistamp#x000F6;, Thomas Meitinger, Olle Melander, Karen L. Mohlke, Marie Moitry, Andrew D. Morris, Alison D. Murray, Renamp#x000E9;e Mutsert, Marju Orho-Melander, Katharine R. Owen, Markus Perola, Annette Peters, Michael A. Province, Asif Rasheed, Paul M. Ridker, Fernando Rivadineira, Frits R. Rosendaal, Anders H. Rosengren, Veikko Salomaa, Wayne H.-H. Sheu, Rob Sladek, Blair H. Smith, Konstantin Strauch, Andramp#x000E9, G. Uitterlinden, Rohit Varma, Cristen J. Willer, Matthias Blamp#x000FC;her, Adam S. Butterworth, John Campbell Chambers, Daniel I. Chasman, John Danesh, Cornelia Duijn, Josamp#x000E9;e Dupuis, Oscar H. Franco, Paul W. Franks, Philippe Froguel, Harald Grallert, Leif Groop, Bok-Ghee Han, Torben Hansen, Andrew T. Hattersley, Caroline Hayward, Erik Ingelsson, Sharon L. R. Kardia, Fredrik Karpe, Jaspal Singh Kooner, Anna Kamp#x000F6;ttgen, Kari Kuulasmaa, Markku Laakso, Xu Lin, Lars Lind, Yongmei Liu, Ruth J. F. Loos, Jonathan Marchini, Andres Metspalu, Dennis Mook-Kanamori, Bamp#x000F8;rge G. Nordestgaard, Colin N. A. Palmer, James S. Pankow, Oluf Pedersen, Bruce M. Psaty, Rainer Rauramaa, Naveed Sattar, Matthias B. Schulze, Nicole Soranzo, Timothy D. Spector, Kari Stefansson, Michael Stumvoll, Unnur Thorsteinsdottir, Tiinamaija Tuomi, Jaakko Tuomilehto, Nicholas J. Wareham, James G. Wilson, Eleftheria Zeggini, Robert A. Scott, Inamp#x000EA;s Barroso, Timothy M. Frayling, Mark O. Goodarzi, James B. Meigs, Michael Boehnke, Danish Saleheen, Andrew P. Morris, Jerome I. Rotter, Mark I. McCarthy (backlinks)
2018-kong.pdf: “The nature of nurture: Effects of parental genotypes”, Augustine Kong, Gudmar Thorleifsson, Michael L. Frigge, Bjarni J. Vilhjalmsson, Alexander I. Young, Thorgeir E. Thorgeirsson, Stefania Benonisdottir, Asmundur Oddsson, Bjarni V. Halldorsson, Gisli Masson, Daniel F. Gudbjartsson, Agnar Helgason, Gyda Bjornsdottir, Unnur Thorsteinsdottir, Kari Stefansson (backlinks)
2018-klarin.pdf: “Genetics of blood lipids among ~300,000 multi-ethnic participants of the Million Veteran Program”, Derek Klarin, Scott M. Damrauer, Kelly Cho, Yan V. Sun, Tanya M. Teslovich, Jacqueline Honerlaw, David R. Gagnon, Scott L. DuVall, Jin Li, Gina M. Peloso, Mark Chaffin, Aeron M. Small, Jie Huang, Hua Tang, Julie A. Lynch, Yuk-Lam Ho, Dajiang J. Liu, Connor A. Emdin, Alexander H. Li, Jennifer E. Huffman, Jennifer S. Lee, Pradeep Natarajan, Rajiv Chowdhury, Danish Saleheen, Marijana Vujkovic, Aris Baras, Saiju Pyarajan, Emanuele Angelantonio, Benjamin M. Neale, Aliya Naheed, Amit V. Khera, John Danesh, Kyong-Mi Chang, Gonamp#x000E7;alo Abecasis, Cristen Willer, Frederick E. Dewey, David J. Carey, John Concato, J. Michael Gaziano, Christopher J. Oamp#x02019;Donnell, Philip S. Tsao, Sekar Kathiresan, Daniel J. Rader, Peter W. F. Wilson, Themistocles L. Assimes
2018-hysi.pdf: “Genome-wide association meta-analysis of individuals of European ancestry identifies new loci explaining a substantial fraction of hair color variation and heritability”, Pirro G. Hysi, Ana M. Valdes, Fan Liu, Nicholas A. Furlotte, David M. Evans, Veronique Bataille, Alessia Visconti, Gibran Hemani, George McMahon, Susan M. Ring, George Davey Smith, David L. Duffy, Gu Zhu, Scott D. Gordon, Sarah E. Medland, Bochao D. Lin, Gonneke Willemsen, Jouke Hottenga, Dragana Vuckovic, Giorgia Girotto, Ilaria Gandin, Cinzia Sala, Maria Pina Concas, Marco Brumat, Paolo Gasparini, Daniela Toniolo, Massimiliano Cocca, Antonietta Robino, Seyhan Yazar, Alex W. Hewitt, Yan Chen, Changqing Zeng, Andre G. Uitterlinden, M. Arfan Ikram, Merel A. Hamer, Cornelia M. Duijn, Tamar Nijsten, David A. Mackey, Mario Falchi, Dorret I. Boomsma, Nicholas G. Martin, David A. Hinds, Manfred Kayser, Timothy D. Spector (backlinks)
2018-evangelou.pdf: “Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits”, Evangelos Evangelou, Helen R. Warren, David Mosen-Ansorena, Borbala Mifsud, Raha Pazoki, He Gao, Georgios Ntritsos, Niki Dimou, Claudia P. Cabrera, Ibrahim Karaman, Fu Liang Ng, Marina Evangelou, Katarzyna Witkowska, Evan Tzanis, Jacklyn N. Hellwege, Ayush Giri, Digna R. Velez Edwards, Yan V. Sun, Kelly Cho, J. Michael Gaziano, Peter W. F. Wilson, Philip S. Tsao, Csaba P. Kovesdy, Tonu Esko, Reedik Mamp#x000E4;gi, Lili Milani, Peter Almgren, Thibaud Boutin, Stamp#x000E9;phanie Debette, Jun Ding, Franco Giulianini, Elizabeth G. Holliday, Anne U. Jackson, Ruifang Li-Gao, Wei-Yu Lin, Jianamp#x02019;an Luan, Massimo Mangino, Christopher Oldmeadow, Bram Peter Prins, Yong Qian, Muralidharan Sargurupremraj, Nabi Shah, Praveen Surendran, Samp#x000E9;bastien Thamp#x000E9;riault, Niek Verweij, Sara M. Willems, Jing-Hua Zhao, Philippe Amouyel, John Connell, Renamp#x000E9;e Mutsert, Alex S. F. Doney, Martin Farrall, Cristina Menni, Andrew D. Morris, Raymond Noordam, Guillaume Paramp#x000E9;, Neil R. Poulter, Denis C. Shields, Alice Stanton, Simon Thom, Gonamp#x000E7;alo Abecasis, Najaf Amin, Dan E. Arking, Kristin L. Ayers, Caterina M. Barbieri, Chiara Batini, Joshua C. Bis, Tineka Blake, Murielle Bochud, Michael Boehnke, Eric Boerwinkle, Dorret I. Boomsma, Erwin P. Bottinger, Peter S. Braund, Marco Brumat, Archie Campbell, Harry Campbell, Aravinda Chakravarti, John C. Chambers, Ganesh Chauhan, Marina Ciullo, Massimiliano Cocca, Francis Collins, Heather J. Cordell, Gail Davies, Martin H. de Borst, Eco J. de Geus, Ian J. Deary, Joris Deelen, Fabiola Del Greco M., Cumhur Yusuf Demirkale, Marcus Damp#x000F6;rr, Georg B. Ehret, Roberto Elosua, Stefan Enroth, A. Mesut Erzurumluoglu, Teresa Ferreira, Mattias Framp#x000E5;nberg, Oscar H. Franco, Ilaria Gandin, Paolo Gasparini, Vilmantas Giedraitis, Christian Gieger, Giorgia Girotto, Anuj Goel, Alan J. Gow, Vilmundur Gudnason, Xiuqing Guo, Ulf Gyllensten, Anders Hamsten, Tamara B. Harris, Sarah E. Harris, Catharina A. Hartman, Aki S. Havulinna, Andrew A. Hicks, Edith Hofer, Albert Hofman, Jouke-Jan Hottenga, Jennifer E. Huffman, Shih-Jen Hwang, Erik Ingelsson, Alan James, Rick Jansen, Marjo-Riitta Jarvelin, Roby Joehanes, amp#x000C5;sa Johansson, Andrew D. Johnson, Peter K. Joshi, Pekka Jousilahti, J. Wouter Jukema, Antti Jula, Mika Kamp#x000E4;hamp#x000F6;nen, Sekar Kathiresan, Bernard D. Keavney, Kay-Tee Khaw, Paul Knekt, Joanne Knight, Ivana Kolcic, Jaspal S. Kooner, Seppo Koskinen, Kati Kristiansson, Zoltan Kutalik, Maris Laan, Marty Larson, Lenore J. Launer, Benjamin Lehne, Terho Lehtimamp#x000E4;ki, David C. M. Liewald, Li Lin, Lars Lind, Cecilia M. Lindgren, YongMei Liu, Ruth J. F. Loos, Lorna M. Lopez, Yingchang Lu, Leo-Pekka Lyytikamp#x000E4;inen, Anubha Mahajan, Chrysovalanto Mamasoula, Jaume Marrugat, Jonathan Marten, Yuri Milaneschi, Anna Morgan, Andrew P. Morris, Alanna C. Morrison, Peter J. Munson, Mike A. Nalls, Priyanka Nandakumar, Christopher P. Nelson, Teemu Niiranen, Ilja M. Nolte, Teresa Nutile, Albertine J. Oldehinkel, Ben A. Oostra, Paul F. Oamp#x02019;Reilly, Elin Org, Sandosh Padmanabhan, Walter Palmas, Aarno Palotie, Alison Pattie, Brenda W. J. H. Penninx, Markus Perola, Annette Peters, Ozren Polasek, Peter P. Pramstaller, Quang Tri Nguyen, Olli T. Raitakari, Meixia Ren, Rainer Rettig, Kenneth Rice, Paul M. Ridker, Janina S. Ried, Harriamp#x000EB;tte Riese, Samuli Ripatti, Antonietta Robino, Lynda M. Rose, Jerome I. Rotter, Igor Rudan, Daniela Ruggiero, Yasaman Saba, Cinzia F. Sala, Veikko Salomaa, Nilesh J. Samani, Antti-Pekka Sarin, Reinhold Schmidt, Helena Schmidt, Nick Shrine, David Siscovick, Albert V. Smith, Harold Snieder, Siim Samp#x000F5;ber, Rossella Sorice, John M. Starr, David J. Stott, David P. Strachan, Rona J. Strawbridge, Johan Sundstramp#x000F6;m, Morris A. Swertz, Kent D. Taylor, Alexander Teumer, Martin D. Tobin, Maciej Tomaszewski, Daniela Toniolo, Michela Traglia, Stella Trompet, Jaakko Tuomilehto, Christophe Tzourio, Andramp#x000E9, G. Uitterlinden, Ahmad Vaez, Peter J. Most, Cornelia M. Duijn, Anne-Claire Vergnaud, Germaine C. Verwoert, Veronique Vitart, Uwe Vamp#x000F6;lker, Peter Vollenweider, Dragana Vuckovic, Hugh Watkins, Sarah H. Wild, Gonneke Willemsen, James F. Wilson, Alan F. Wright, Jie Yao, Tatijana Zemunik, Weihua Zhang, John R. Attia, Adam S. Butterworth, Daniel I. Chasman, David Conen, Francesco Cucca, John Danesh, Caroline Hayward, Joanna M. M. Howson, Markku Laakso, Edward G. Lakatta, Claudia Langenberg, Olle Melander, Dennis O. Mook-Kanamori, Colin N. A. Palmer, Lorenz Risch, Robert A. Scott, Rodney J. Scott, Peter Sever, Tim D. Spector, Pim Harst, Nicholas J. Wareham, Eleftheria Zeggini, Daniel Levy, Patricia B. Munroe, Christopher Newton-Cheh, Morris J. Brown, Andres Metspalu, Adriana M. Hung, Christopher J. Oamp#x02019;Donnell, Todd L. Edwards, Bruce M. Psaty, Ioanna Tzoulaki, Michael R. Barnes, Louise V. Wain, Paul Elliott, Mark J. Caulfield (backlinks)
Genotype imputation has become a standard tool in genome-wide association studies because it enables researchers to inexpensively approximate whole-genome sequence data from genome-wide single-nucleotide polymorphism array data. Genotype imputation increases statistical power, facilitates fine mapping of causal variants, and plays a key role in meta-analyses of genome-wide association studies. Only variants that were previously observed in a reference panel of sequenced individuals can be imputed. However, the rapid increase in the number of deeply sequenced individuals will soon make it possible to assemble enormous reference panels that greatly increase the number of imputable variants. In this review, we present an overview of genotype imputation and describe the computational techniques that make it possible to impute genotypes from reference panels with millions of individuals.
2018-claes.pdf: “Genome-wide mapping of global-to-local genetic effects on human facial shape”, Peter Claes, Jasmien Roosenboom, Julie D. White, Tomek Swigut, Dzemila Sero, Jiarui Li, Myoung Keun Lee, Arslan Zaidi, Brooke C. Mattern, Corey Liebowitz, Laurel Pearson, Tomamp#x000E1;s Gonzamp#x000E1;lez, Elizabeth J. Leslie, Jenna C. Carlson, Ekaterina Orlova, Paul Suetens, Dirk Vandermeulen, Eleanor Feingold, Mary L. Marazita, John R. Shaffer, Joanna Wysocka, Mark D. Shriver, Seth M. Weinberg
2018-bouwman.pdf: “Meta-analysis of genome-wide association studies for cattle stature identifies common genes that regulate body size in mammals”, Aniek C. Bouwman, Hans D. Daetwyler, Amanda J. Chamberlain, Carla Hurtado Ponce, Mehdi Sargolzaei, Flavio S. Schenkel, Goutam Sahana, Armelle Govignon-Gion, Simon Boitard, Marlies Dolezal, Hubert Pausch, Rasmus F. Bramp#x000F8;ndum, Phil J. Bowman, Bo Thomsen, Bernt Guldbrandtsen, Mogens S. Lund, Bertr, Servin, Dorian J. Garrick, James Reecy, Johanna Vilkki, Alessandro Bagnato, Min Wang, Jesse L. Hoff, Robert D. Schnabel, Jeremy F. Taylor, Anna A. E. Vinkhuyzen, Frank Panitz, Christian Bendixen, Lars-Erik Holm, Birgit Gredler, Chris Hozamp#x000E9;, Mekki Boussaha, Marie-Pierre Sanchez, Dominique Rocha, Aurelien Capitan, Thierry Tribout, Anne Barbat, Pascal Croiseau, Cord Dramp#x000F6;gemamp#x000FC;ller, Vidhya Jagannathan, Christy Vander Jagt, John J. Crowley, Anna Bieber, Deirdre C. Purfield, Donagh P. Berry, Reiner Emmerling, Kay-Uwe Gamp#x000F6;tz, Mirjam Frischknecht, Ingolf Russ, Johann Samp#x000F6;lkner, Curtis P. Van Tassell, Ruedi Fries, Paul Stothard, Roel F. Veerkamp, Didier Boichard, Mike E. Goddard, Ben J. Hayes
2018-abulhusn.pdf: “A Protein-Truncating HSD17B13 Variant and Protection from Chronic Liver Disease”, Noura S. Abul-Husn, Xiping Cheng, Alexander H. Li, Yurong Xin, Claudia Schurmann, Panayiotis Stevis, Yashu Liu, Julia Kozlitina, Stefan Stender, G. Craig Wood, Ann N. Stepanchick, Matthew D. Still, Shane McCarthy, Colm O’Dushlaine, Jonathan S. Packer, Suganthi Balasubramanian, Nehal Gosalia, David Esopi, Sun Y. Kim, Semanti Mukherjee, Alexander E. Lopez, Erin D. Fuller, John Penn, Xin Chu, Jonathan Z. Luo, Uyenlinh L. Mirshahi, David J. Carey, Christopher D. Still, Michael D. Feldman, Aeron Small, Scott M. Damrauer, Daniel J. Rader, Brian Zambrowicz, William Olson, Andrew J. Murphy, Ingrid B. Borecki, Alan R. Shuldiner, Jeffrey G. Reid, John D. Overton, George D. Yancopoulos, Helen H. Hobbs, Jonathan C. Cohen, Omri Gottesman, Tanya M. Teslovich, Aris Baras, Tooraj Mirshahi, Jesper Gromada, Frederick E. Dewey
Existing literature connects military service to regional characteristics and family traditions, creating real distinctions between those who serve and those who do not. We engage this discussion by examining military service as a function of personality. In the second portion, we examine military service as predisposed by genetics. Our findings indicate there is a statistically-significant heritability component of serving in the military. We find a statistically-significant genetic correlation between personality traits associated with progressive political ambition and military service, suggesting that military service represents a different form of political participation to which individuals are genetically predisposed. We discuss the long-term implications of our findings for policy makers and recruiters.
While philosophers emphasize the distinction between description and prescription, in practice people’s beliefs about contentious issues seem to reflect their normative commitments. Less is known about the way that people infer others’ ideology from their reports about matters of fact. In the context of scientific research on the heritability of intelligence, scientists’ normative views (Study 1a) and motives (Study 2) are inferred from the evidence they report—independently of their stated research objectives. Two preregistered replications (Studies 1b and 3) revealed that these effects generalize to other contentious domains of behavioral and social science research. Thus, laypeople view social scientific inquiry as (partly) a guided pursuit of evidence in favor of scientists’ personal ideology.
Background: Twin studies have provided evidence that both genetic and environmental factors contribute to schizophrenia (SZ) risk. Heritability estimates of SZ in twin samples have varied methodologically. This study provides updated heritability estimates based on nationwide twin data and an improved statistical methodology.
Methods: Combining 2 nationwide registers, the Danish Twin Register and the Danish Psychiatric Research Register, we identified a sample of twins born between 1951 and 2000 (n = 31,524 twin pairs). Twins were followed until June 1, 2011. Liability threshold models adjusting for censoring with inverse probability weighting were used to estimate proband-wise concordance rates and heritability of the diagnoses of SZ and SZ spectrum disorders.
Results: The proband-wise concordance rate of SZ is 33% in monozygotic twins and 7% in dizygotic twins. We estimated the heritability of SZ to be 79%. When expanding illness outcome to include SZ spectrum disorders, the heritability estimate was almost similar (73%).
Conclusions: The key strength of this study is the application of a novel statistical method accounting for censoring in the follow-up period to a nationwide twin sample. The estimated 79% heritability of SZ is congruent with previous reports and indicates a substantial genetic risk. The high genetic risk also applies to a broader phenotype of SZ spectrum disorders. The low concordance rate of 33% in monozygotic twins demonstrates that illness vulnerability is not solely indicated by genetic factors.
Application of the experimental design of genome-wide association studies (GWASs) is now 10 years old (young), and here we review the remarkable range of discoveries it has facilitated in population and complex-trait genetics, the biology of diseases, and translation toward new therapeutics. We predict the likely discoveries in the next 10 years, when GWASs will be based on millions of samples with array data imputed to a large fully sequenced reference panel and on hundreds of thousands of samples with whole-genome sequencing data.
Previous research has found a genetic component of human reproduction and childlessness. Others have argued that the heritability of reproduction is counterintuitive due to a frequent misinterpretation that additive genetic variance in reproductive fitness should be close to zero. Yet it is plausible that different genetic loci operate in male and female fertility in the form of sexual dimorphism and that these genes are passed on to the next generation. This study examines the extent to which genetic factors influence childlessness and provides an empirical test of genetic sexual dimorphism. Data from the Swedish Twin Register (n = 9942) is used to estimate a classical twin model, a genomic-relatedness-matrix restricted maximum likelihood (GREML) model on twins and estimates polygenic scores of age at first birth on childlessness. Results show that the variation in individual differences in childlessness is explained by genetic differences for 47% in the twin model and 59% for women and 56% for men using the GREML model. Using apolygenic score (PGS) of age at first birth (AFB), the odds of remaining childless are around 1.25 higher forindividuals with 1 SD higher score on the AFBPGS, but only for women. We find that different sets of genes influence childlessness in men and in women. These findings provide insight into why people remain childless and give evidence of genetic sexual dimorphism.
2017-turcot.pdf: “Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity”, Valérie Turcot, Yingchang Lu, Heather M. Highland, Claudia Schurmann, Anne E. Justice, Rebecca S. Fine, Jonathan P. Bradfield, Tõnu Esko, Ayush Giri, Mariaelisa Graff, Xiuqing Guo, Audrey E. Hendricks, Tugce Karaderi, Adelheid Lempradl, Adam E. Locke, Anubha Mahajan, Eirini Marouli, Suthesh Sivapalaratnam, Kristin L. Young, Tamuno Alfred, Mary F. Feitosa, Nicholas G. D. Masca, Alisa K. Manning, Carolina Medina-Gomez, Poorva Mudgal, Maggie C. Y. Ng, Alex P. Reiner, Sailaja Vedantam, Sara M. Willems, Thomas W. Winkler, Gonçalo Abecasis, Katja K. Aben, Dewan S. Alam, Sameer E. Alharthi, Matthew Allison, Philippe Amouyel, Folkert W. Asselbergs, Paul L. Auer, Beverley Balkau, Lia E. Bang, Inês Barroso, Lisa Bastarache, Marianne Benn, Sven Bergmann, Lawrence F. Bielak, Matthias Blüher, Michael Boehnke, Heiner Boeing, Eric Boerwinkle, Carsten A. Böger, Jette Bork-Jensen, Michiel L. Bots, Erwin P. Bottinger, Donald W. Bowden, Ivan Brandslund, Gerome Breen, Murray H. Brilliant, Linda Broer, Marco Brumat, Amber A. Burt, Adam S. Butterworth, Peter T. Campbell, Stefania Cappellani, David J. Carey, Eulalia Catamo, Mark J. Caulfield, John C. Chambers, Daniel I. Chasman, Yii-Der I. Chen, Rajiv Chowdhury, Cramer Christensen, Audrey Y. Chu, Massimiliano Cocca, Francis S. Collins, James P. Cook, Janie Corley, Jordi Corominas Galbany, Amanda J. Cox, David S. Crosslin, Gabriel Cuellar-Partida, Angela D’Eustacchio, John Danesh, Gail Davies, Paul I. W. Bakker, Mark C. H. Groot, Renée Mutsert, Ian J. Deary, George Dedoussis, Ellen W. Demerath, Martin Heijer, Anneke I. Hollander, Hester M. Ruijter, Joe G. Dennis, Josh C. Denny, Emanuele Angelantonio, Fotios Drenos, Mengmeng Du, Marie-Pierre Dubé, Alison M. Dunning, Douglas F. Easton, Todd L. Edwards, David Ellinghaus, Patrick T. Ellinor, Paul Elliott, Evangelos Evangelou, Aliki-Eleni Farmaki, I. Sadaf Farooqi, Jessica D. Faul, Sascha Fauser, Shuang Feng, Ele Ferrannini, Jean Ferrieres, Jose C. Florez, Ian Ford, Myriam Fornage, Oscar H. Franco, Andre Franke, Paul W. Franks, Nele Friedrich, Ruth Frikke-Schmidt, Tessel E. Galesloot, Wei Gan, Ilaria Gandin, Paolo Gasparini, Jane Gibson, Vilmantas Giedraitis, Anette P. Gjesing, Penny Gordon-Larsen, Mathias Gorski, Hans-Jörgen Grabe, Struan F. A. Grant, Niels Grarup, Helen L. Griffiths, Megan L. Grove, Vilmundur Gudnason, Stefan Gustafsson, Jeff Haessler, Hakon Hakonarson, Anke R. Hammerschlag, Torben Hansen, Kathleen Mullan Harris, Tamara B. Harris, Andrew T. Hattersley, Christian T. Have, Caroline Hayward, Liang He, Nancy L. Heard-Costa, Andrew C. Heath, Iris M. Heid, Øyvind Helgeland, Jussi Hernesniemi, Alex W. Hewitt, Oddgeir L. Holmen, G. Kees Hovingh, Joanna M. M. Howson, Yao Hu, Paul L. Huang, Jennifer E. Huffman, M. Arfan Ikram, Erik Ingelsson, Anne U. Jackson, Jan-Håkan Jansson, Gail P. Jarvik, Gorm B. Jensen, Yucheng Jia, Stefan Johansson, Marit E. Jørgensen, Torben Jørgensen, J. Wouter Jukema, Bratati Kahali, René S. Kahn, Mika Kähönen, Pia R. Kamstrup, Stavroula Kanoni, Jaakko Kaprio, Maria Karaleftheri, Sharon L. R. Kardia, Fredrik Karpe, Sekar Kathiresan, Frank Kee, Lambertus A. Kiemeney, Eric Kim, Hidetoshi Kitajima, Pirjo Komulainen, Jaspal S. Kooner, Charles Kooperberg, Tellervo Korhonen, Peter Kovacs, Helena Kuivaniemi, Zoltán Kutalik, Kari Kuulasmaa, Johanna Kuusisto, Markku Laakso, Timo A. Lakka, David Lamparter, Ethan M. Lange, Leslie A. Lange, Claudia Langenberg, Eric B. Larson, Nanette R. Lee, Terho Lehtimäki, Cora E. Lewis, Huaixing Li, Jin Li, Ruifang Li-Gao, Honghuang Lin, Keng-Hung Lin, Li-An Lin, Xu Lin, Lars Lind, Jaana Lindström, Allan Linneberg, Ching-Ti Liu, Dajiang J. Liu, Yongmei Liu, Ken S. Lo, Artitaya Lophatananon, Andrew J. Lotery, Anu Loukola, Jian’an Luan, Steven A. Lubitz, Leo-Pekka Lyytikäinen, Satu Männistö, Gaëlle Marenne, Angela L. Mazul, Mark I. McCarthy, Roberta McKean-Cowdin, Sarah E. Medland, Karina Meidtner, Lili Milani, Vanisha Mistry, Paul Mitchell, Karen L. Mohlke, Leena Moilanen, Marie Moitry, Grant W. Montgomery, Dennis O. Mook-Kanamori, Carmel Moore, Trevor A. Mori, Andrew D. Morris, Andrew P. Morris, Martina Müller-Nurasyid, Patricia B. Munroe, Mike A. Nalls, Narisu Narisu, Christopher P. Nelson, Matt Neville, Sune F. Nielsen, Kjell Nikus, Pål R. Njølstad, Børge G. Nordestgaard, Dale R. Nyholt, Jeffrey R. O’Connel, Michelle L. O’Donoghue, Loes M. Olde Loohuis, Roel A. Ophoff, Katharine R. Owen, Chris J. Packard, Sandosh Padmanabhan, Colin N. A. Palmer, Nicholette D. Palmer, Gerard Pasterkamp, Aniruddh P. Patel, Alison Pattie, Oluf Pedersen, Peggy L. Peissig, Gina M. Peloso, Craig E. Pennell, Markus Perola, James A. Perry, John R. B. Perry, Tune H. Pers, Thomas N. Person, Annette Peters, Eva R. B. Petersen, Patricia A. Peyser, Ailith Pirie, Ozren Polasek, Tinca J. Polderman, Hannu Puolijoki, Olli T. Raitakari, Asif Rasheed, Rainer Rauramaa, Dermot F. Reilly, Frida Renström, Myriam Rheinberger, Paul M. Ridker, John D. Rioux, Manuel A. Rivas, David J. Roberts, Neil R. Robertson, Antonietta Robino, Olov Rolandsson, Igor Rudan, Katherine S. Ruth, Danish Saleheen, Veikko Salomaa, Nilesh J. Samani, Yadav Sapkota, Naveed Sattar, Robert E. Schoen, Pamela J. Schreiner, Matthias B. Schulze, Robert A. Scott, Marcelo P. Segura-Lepe, Svati H. Shah, Wayne H.-H. Sheu, Xueling Sim, Andrew J. Slater, Kerrin S. Small, Albert V. Smith, Lorraine Southam, Timothy D. Spector, Elizabeth K. Speliotes, John M. Starr, Kari Stefansson, Valgerdur Steinthorsdottir, Kathleen E. Stirrups, Konstantin Strauch, Heather M. Stringham, Michael Stumvoll, Liang Sun, Praveen Surendran, Amy J. Swift, Hayato Tada, Katherine E. Tansey, Jean-Claude Tardif, Kent D. Taylor, Alexander Teumer, Deborah J. Thompson, Gudmar Thorleifsson, Unnur Thorsteinsdottir, Betina H. Thuesen, Anke Tönjes, Gerard Tromp, Stella Trompet, Emmanouil Tsafantakis, Jaakko Tuomilehto, Anne Tybjaerg-Hansen, Jonathan P. Tyrer, Rudolf Uher, André G. Uitterlinden, Matti Uusitupa, Sander W. Laan, Cornelia M. Duijn, Nienke Leeuwen, Jessica van Setten, Mauno Vanhala, Anette Varbo, Tibor V. Varga, Rohit Varma, Digna R. Velez Edwards, Sita H. Vermeulen, Giovanni Veronesi, Henrik Vestergaard, Veronique Vitart, Thomas F. Vogt, Uwe Völker, Dragana Vuckovic, Lynne E. Wagenknecht, Mark Walker, Lars Wallentin, Feijie Wang, Carol A. Wang, Shuai Wang, Yiqin Wang, Erin B. Ware, Nicholas J. Wareham, Helen R. Warren, Dawn M. Waterworth, Jennifer Wessel, Harvey D. White, Cristen J. Willer, James G. Wilson, Daniel R. Witte, Andrew R. Wood, Ying Wu, Hanieh Yaghootkar, Jie Yao, Pang Yao, Laura M. Yerges-Armstrong, Robin Young, Eleftheria Zeggini, Xiaowei Zhan, Weihua Zhang, Jing Hua Zhao, Wei Zhao, Wei Zhou, Krina T. Zondervan, Jerome I. Rotter, John A. Pospisilik, Fernando Rivadeneira, Ingrid B. Borecki, Panos Deloukas, Timothy M. Frayling, Guillaume Lettre, Kari E. North, Cecilia M. Lindgren, Joel N. Hirschhorn, Ruth J. F. Loos
Utilizing a newly released cognitive Polygenic Score (PGS) from Wave IV of Add Health (n = 1,886), structural equation models (SEMs) examining the relationship between PGS and fertility (which is approximately 50% complete in the present sample), utilizing measures of verbal IQ and educational attainment as potential mediators, were estimated. The results of indirect pathway models revealed that verbal IQ mediates the positive relationship between PGS and educational attainment, and educational attainment in turn mediates the negative relationship between IQ and a latent fertility measure. The direct path from PGS to fertility was non-significant. The model was robust to controlling for age, sex and race, furthermore the results of a multi-group SEM revealed no statistically-significant differences in the estimated path coefficients across sex. These results indicate that those predisposed towards higher IQ by virtue of higher PGS values are also predisposed towards trading fertility against time spent in education, which contributes to those with higher PGS values producing fewer offspring.
2016-bagnall.pdf: “A Prospective Study of Sudden Cardiac Death among Children and Young Adults”, Richard D. Bagnall, Robert G. Weintraub, Jodie Ingles, Johan Duflou, Laura Yeates, Lien Lam, Andrew M. Davis, Tina Thompson, Vanessa Connell, Jennie Wallace, Charles Naylor, Jackie Crawford, Donald R. Love, Lavinia Hallam, Jodi White, Christopher Lawrence, Matthew Lynch, Natalie Morgan, Paul James, Desirée du Sart, Rajesh Puranik, Neil Langlois, Jitendra Vohra, Ingrid Winship, John Atherton, Julie McGaughran, Jonathan R. Skinner, Christopher Semsarian (2016-06-23; backlinks):
Background: Sudden cardiac death among children and young adults is a devastating event. We performed a prospective, population-based, clinical and genetic study of sudden cardiac death among children and young adults.
Methods: We prospectively collected clinical, demographic, and autopsy information on all cases of sudden cardiac death among children and young adults 1 to 35 years of age in Australia and New Zealand from 2010 through 2012. In cases that had no cause identified after a comprehensive autopsy that included toxicologic and histologic studies (unexplained sudden cardiac death), at least 59 cardiac genes were analyzed for a clinically relevant cardiac gene mutation.
Results: A total of 490 cases of sudden cardiac death were identified. The annual incidence was 1.3 cases per 100,000 persons 1 to 35 years of age; 72% of the cases involved boys or young men. Persons 31 to 35 years of age had the highest incidence of sudden cardiac death (3.2 cases per 100,000 persons per year), and persons 16 to 20 years of age had the highest incidence of unexplained sudden cardiac death (0.8 cases per 100,000 persons per year). The most common explained causes of sudden cardiac death were coronary artery disease (24% of cases) and inherited cardiomyopathies (16% of cases). Unexplained sudden cardiac death (40% of cases) was the predominant finding among persons in all age groups, except for those 31 to 35 years of age, for whom coronary artery disease was the most common finding. Younger age and death at night were independently associated with unexplained sudden cardiac death as compared with explained sudden cardiac death. A clinically relevant cardiac gene mutation was identified in 31 of 113 cases (27%) of unexplained sudden cardiac death in which genetic testing was performed. During follow-up, a clinical diagnosis of an inherited cardiovascular disease was identified in 13% of the families in which an unexplained sudden cardiac death occurred.
Conclusions: The addition of genetic testing to autopsy investigation substantially increased the identification of a possible cause of sudden cardiac death among children and young adults.
Family and twin studies suggest that up to 50% of individual differences in human fertility within a population might be heritable. However, it remains unclear whether the genes associated with fertility outcomes such as number of children ever born (NEB) or ageat first birth (AFB) are the same across geographical and historical environments. By not taking this into account, previous genetic studies implicitly assumed that the genetic effects are constant across time and space. We conduct a mega-analysis applying whole genome methods on 31,396 unrelated men and women from six Western countries. Across all individuals and environments, common single-nucleotide polymorphisms (SNPs) explained only ~4% of thevariance in NEB and AFB. We then extend these models to test whether genetic effects are shared across different environments or unique to them. For individuals belonging to the same population and demographic cohort (born before or after the 20th century fertility decline), SNP-based heritability was almost five times higher at 22% for NEB and19% for AFB. We also found no evidence suggesting that genetic effects on fertility are shared across time and space. Our findings imply that the environment strongly modifies genetic effects on the tempo and quantum of fertility, that currently ongoing natural selection is heterogeneous across environments, and that gene-environment interactions may partly account for missing heritability in fertility. Future research needs to combine efforts from genetic research and from the social sciences to better understand human fertility.
Fertility behavior—such as age at first birth and number of children—varies strongly across historical time and geographical space. Yet, family and twin studies, which suggest that up to 50% of individual differences in fertility are heritable, implicitly assume that the genes important for fertility are the same across both time and space. Using molecular genetic data (SNPs) from over 30,000 unrelated individuals from six different countries, we show that different genes influence fertility in different time periods and different countries, and that the genetic effects consistently related to fertility are presumably small. The fact that genetic effects on fertility appear not to be universal could have tremendous implications for research in the area of reproductive medicine, social science and evolutionary biology alike.
Mortality selection is a general concern in the social and health sciences. Recently, existing health and social science cohorts have begun to collect genomic data. Causes of selection into a genomic dataset can influence results from genomic analyses. Selective non-participation, which is specific to a particular study and its participants, has received attention in the literature. But mortality selection—the very general phenomenon that genomic data collected at a particular age represents selective participation by only the subset of birth cohort members who have survived to the time of data collection—has been largely ignored. Here we test the hypothesis that such mortality selection may significantly alter estimates in polygenetic association studies of both health and non-health traits. We demonstrate mortality selection into genome-wide SNP data collection at older ages using the U.S.-based Health and Retirement Study (HRS). We then model the selection process. Finally, we test whether mortality selection alters estimates from genetic association studies. We find evidence for mortality selection. Healthier and more socioeconomically advantaged individuals are more likely to survive to be eligible to participate in the genetic sample of the HRS. Mortality selection leads to modest drift in estimating time-varying genetic effects, a drift that is enhanced when estimates are produced from data that has additional mortality selection. There is no general solution for correcting for mortality selection in a birth cohort prior to entry into a longitudinal study. We illustrate how genetic association studies using HRS data can adjust for mortality selection from study entry to time of genetic data collection by including probability weights that account for mortality selection. Mortality selection should be investigated more broadly in genetically-informed samples from other cohort studies.
Higher paternal age at offspring conception increases de novo genetic mutations (Kong et al., 2012). Based on evolutionary genetic theory we predicted that the offspring of older fathers would be less likely to survive and reproduce, i.e. have lower fitness. In a sibling control study, we find clear support for negative paternal age effects on offspring survival, mating and reproductive success across four large populations with an aggregate N > 1.3 million in main analyses. Compared to a sibling born when the father was 10 years younger, individuals had 4–13% fewer surviving children in the four populations. Three populations were pre-industrial (1670-1850) Western populations and showed a pattern of paternal age effects across the offspring’s lifespan. In 20th-century Sweden, we found no negative paternal age effects on child survival or marriage odds. Effects survived tests for competing explanations, including maternal age and parental loss. To the extent that we succeeded in isolating a mutation-driven effect of paternal age, our results can be understood to show that de novo mutations reduce offspring fitness across populations and time. We can use this understanding to predict the effect of increasingly delayed reproduction on offspring genetic load, mortality and fertility.
2016-liu.pdf: “The MC1R Gene and Youthful Looks”, Fan Liu, Merel A. Hamer, Joris Deelen, Japal S. Lall, Leonie Jacobs, Diana van Heemst, Peter G. Murray, Andreas Wollstein, Anton J. M. de Craen, Hae-Won Uh, Changqing Zeng, Albert Hofman, André G. Uitterlinden, Jeanine J. Houwing-Duistermaat, Luba M. Pardo, Marian Beekman, P. Eline Slagboom, Tamar Nijsten, Manfred Kayser, David A. Gunn (backlinks)
“Genome-wide analysis identifies 12 loci influencing human reproductive behavior”, Barban, Nicola Jansen, Rick de Vlaming, Ronald Vaez, Ahmad Mandemakers, Jornt J. Tropf, Felix C. Shen, Xia Wilson, James F. Chasman, Daniel I. Nolte, Ilja M. Tragante, Vinicius van der Laan, Sander W. Perry, John R. B Kong, Augustine Ahluwalia, Tarunveer S. Albrecht, Eva Yerges-Armstrong, Laura Atzmon, Gil Auro, Kirsi Ayers, Kristin Bakshi, Andrew Ben-Avraham, Danny Berger, Klaus Bergman, Aviv Bertram, Lars Bielak, Lawrence F. Bjornsdottir, Gyda Bonder, Marc Jan Broer, Linda Bui, Minh Barbieri, Caterina Cavadino, Alana Chavarro, Jorge E. Turman, Constance Concas, Maria Pina Cordell, Heather J. Davies, Gail Eibich, Peter Eriksson, Nicholas Esko, Tõnu Eriksson, Joel Falahi, Fahimeh Felix, Janine F. Fontana, Mark Alan Franke, Lude Gandin, Ilaria Gaskins, Audrey J. Gieger, Christian Gunderson, Erica P. Guo, Xiuqing Hayward, Caroline He, Chunyan Hofer, Edith Huang, Hongyan Joshi, Peter K. Kanoni, Stavroula Karlsson, Robert Kiechl, Stefan Kifley, Annette Kluttig, Alexander Kraft, Peter Lagou, Vasiliki Lecoeur, Cecile Lahti, Jari Li-Gao, Ruifang Lind, Penelope A. Liu, Tian Makalic, Enes Mamasoula, Crysovalanto Matteson, Lindsay Mbarek, Hamdi McArdle, Patrick F. McMahon, George Meddens, S. Fleur W. Mihailov, Evelin Miller, Mike Missmer, Stacey A. Monnereau, Claire van der Most, Peter J. Myhre, Ronny Nalls, Mike A. Nutile, Teresa Kalafati, Ioanna Panagiota Porcu, Eleonora Prokopenko, Inga Rajan, Kumar B. Rich-Edwards, Janet Rietveld, Cornelius A. Robino, Antonietta Rose, Lynda M. Rueedi, Rico Ryan, Kathleen A. Saba, Yasaman Schmidt, Daniel Smith, Jennifer A. Stolk, Lisette Streeten, Elizabeth Tönjes, Anke Thorleifsson, Gudmar Ulivi, Sheila Wedenoja, Juho Wellmann, Juergen Willeit, Peter Yao, Jie Yengo, Loic Zhao, Jing Hua Zhao, Wei Zhernakova, Daria V. Amin, Najaf Andrews, Howard Balkau, Beverley Barzilai, Nir Bergmann, Sven Biino, Ginevra Bisgaard, Hans Bønnelykke, Klaus Boomsma, Dorret I. Buring, Julie E. Campbell, Harry Cappellani, Stefania Ciullo, Marina Cox, Simon R. Cucca, Francesco Toniolo, Daniela Davey-Smith, George Deary, Ian J. Dedoussis, George Deloukas, Panos van Duijn, Cornelia M. de Geus, Eco J. C Eriksson, Johan G. Evans, Denis A. Faul, Jessica D. Sala, Cinzia Felicita Froguel, Philippe Gasparini, Paolo Girotto, Giorgia Grabe, Hans-Jörgen Greiser, Karin Halina Groenen, Patrick J. F de Haan, Hugoline G. Haerting, Johannes Harris, Tamara B. Heath, Andrew C. Heikkilä, Kauko Hofman, Albert Homuth, Georg Holliday, Elizabeth G. Hopper, John Hyppönen, Elina Jacobsson, Bo Jaddoe, Vincent W. V Johannesson, Magnus Jugessur, Astan, Kähönen, Mika Kajantie, Eero Kardia, Sharon L. R Keavney, Bernard Kolcic, Ivana Koponen, Päivikki Kovacs, Peter Kronenberg, Florian Kutalik, Zoltan La Bianca, Martina Lachance, Genevieve Iacono, William G. Lai, Sandra Lehtimäki, Terho Liewald, David C. Lindgren, Cecilia M. Liu, Yongmei Luben, Robert Lucht, Michael Luoto, Riitta Magnus, Per Magnusson, Patrik K. E Martin, Nicholas G. McGue, Matt McQuillan, Ruth Medland, Sarah E. Meisinger, Christa Mellström, Dan Metspalu, Andres Traglia, Michela Milani, Lili Mitchell, Paul Montgomery, Grant W. Mook-Kanamori, Dennis de Mutsert, Renée Nohr, Ellen A. Ohlsson, Claes Olsen, Jørn Ong, Ken K. Paternoster, Lavinia Pattie, Alison Penninx, Brenda W. J H. Perola, Markus Peyser, Patricia A. Pirastu, Mario Polasek, Ozren Power, Chris Kaprio, Jaakko Raffel, Leslie J. Räikkönen, Katri Raitakari, Olli Ridker, Paul M. Ring, Susan M. Roll, Kathryn Rudan, Igor Ruggiero, Daniela Rujescu, Dan Salomaa, Veikko Schlessinger, David Schmidt, Helena Schmidt, Reinhold Schupf, Nicole Smit, Johannes Sorice, Rossella Spector, Tim D. Starr, John M. Stöckl, Doris Strauch, Konstantin Stumvoll, Michael Swertz, Morris A. Thorsteinsdottir, Unnur Thurik, A. Roy Timpson, Nicholas J. Tung, Joyce Y. Uitterlinden, André G. Vaccargiu, Simona Viikari, Jorma Vitart, Veronique Völzke, Henry Vollenweider, Peter Vuckovic, Dragana Waage, Johannes Wagner, Gert G. Wang, Jie Jin Wareham, Nicholas J. Weir, David R. Willemsen, Gonneke Willeit, Johann Wright, Alan F. Zondervan, Krina T. Stefansson, Kari Krueger, Robert F. Lee, James J. Benjamin, Daniel J. Cesarini, David Koellinger, Philipp D. den Hoed, Marcel Snieder, Harold Mills, Melinda C (2016; backlinks; genetics / selection / dysgenics):
The genetic architecture of human reproductive behavior-age at first birth (AFB) and number of children ever born (NEB)-has a strong relationship with fitness, human development, infertility and risk of neuropsychiatric disorders. However, very few genetic loci have been identified, and the underlying mechanisms of AFB and NEB are poorly understood. We report a large genome-wide association study of both sexes including 251,151 individuals for AFB and 343,072 individualsfor NEB. We identified 12 independent loci that arestatistically-significantly associated with AFB and/or NEB in aSNP-basedgenome-wide association study and 4 additional loci associated in a gene-based effort. These loci harbor genes that are likely to have a role, either directly or by affecting non-local gene expression, in human reproduction and infertility, thereby increasing understanding of these complex traits.
The social sciences have been reticent to integrate a biodemographic approach to the study of fertility choice and behaviour, resulting in theories and findings that are largely socially-deterministic. The aim of this paper is to first reflect on reasons for this lack of integration, provide a review of previous examinations, take stock of what we have learned until now and propose future research frontiers. We review the early foundations of proximate determinants followed by behavioural genetic (family and twin) studies that isolated the extent of genetic influence on fertility traits. We then discuss research that considers gene and environment interaction and the importance of cohort and country-specific estimates, followed by multivariate models that explore motivational precursors to fertility and education. The next section on molecular genetics reviews fertility-related candidate gene studies and their shortcomings and on-going work on genome wide association studies. Work in evolutionary anthropology and biology is then briefly examined, focusing on evidence for natural selection. Biological and genetic factors are relevant in explaining and predicting fertility traits, with socio-environmental factors and their interaction still key in understanding outcomes. Studying the interplay between genes and the environment, new data sources and integration of new methods will be central to understanding and predicting future fertility trends.
[Keywords: fertility, age at first birth, number of children ever born, genetics, behavioural genetics, molecular genetics, natural selection]
We propose a method (GREML-LDMS) to estimate heritability for human complex traits in unrelated individuals using whole-genome sequencing data. We demonstrate using simulations based on whole-genome sequencing data that ~97% and ~68% of variation at common and rare variants, respectively, can be captured by imputation. Using the GREML-LDMS method, we estimate from 44,126 unrelated individuals that all ~17 million imputed variants explain 56% (standard error (s.e.) = 2.3%) of variance for height and 27% (s.e. = 2.5%) of variance for body mass index (BMI), and we find evidence that height-associated and BMI-associated variants have been undernatural selection. Considering the imperfect tagging of imputation and potential overestimation of heritability from previous family-based studies, heritability is likely to be 60–70% for height and 30–40% for BMI. Therefore, the missing heritability is small for both traits. For further discovery of genes associated with complex traits, a study design with SNP arrays followed by imputation is more cost-effective than whole-genome sequencing at current prices.
Despite a century of research on complex traits in humans, the relative importance and specific nature of the influences of genes and environment on human traits remain controversial. We report a meta-analysis of twin correlations and reported variance components for 17,804 traits from 2,748 publications including 14,558,903 partly dependent twin pairs, virtually all published twin studies of complex traits. Estimates of heritability cluster strongly within functional domains, and across all traits the reported heritability is 49%. For a majority (69%) of traits, the observed twin correlations are consistent with a simple and parsimonious model where twin resemblance is solely due to additive genetic variation. The data are inconsistent with substantial influences from shared environment or non-additive genetic variation. This study provides the most comprehensive analysis of the causes of individual differences in human traits thus far and will guide future gene-mapping efforts. All the results can be visualized using the MaTCH webtool.
The relative importance of genetic and environmental influences on obesity-related phenotypes remains unclear, and few studies have targeted the Chinese population. Here, we used Chinese twins reared apart and together to explore genetic and environmental influences on body mass index (BMI), waist circumference (WC) and waist-height ratio (WHtR), further to differentiate phenotype heritability between different age groups and genders separately and to differentiate influences of rearing environment and correlated environment.
Phenotype heritability was calculated using the structural equation model in 11,401 twin pairs aged 25–85 years. BMI (0.70, 95% confidence interval (CI) 0.66–0.74) of the total population was highly heritable, while WC (0.53, 95% CI 0.50–0.57) and WHtR (0.48, 95% CI 0.45–0.51) were moderately heritable. Age and gender stratified analyses found higher heritability in the younger group and males than the older group and females.
The correlated environment had a greater influence on the phenotypes than the rearing environment, especially on WC and WHtR, indicating that more correlated environment actions should be taken to prevent the rising trend of abdominal obesity.
Objective: Clinically, attention-deficit/hyperactivity disorder (ADHD) is characterized by hyperactivity, impulsivity, and inattention and is among the most common childhood disorders. These same traits that define ADHD are variable in the general population, and the clinical diagnosis may represent the extreme end of a continuous distribution of inattentive and hyperactive behaviors. This hypothesis can be tested by assessing the predictive value of polygenic risk scores derived from a discovery sample of ADHD patients in a target sample from the general population with continuous scores of inattention and hyperactivity. In addition, the genetic overlap between ADHD and continuous ADHD scores can be tested across rater and age.
Method: The Psychiatric Genomics Consortium has performed the largest genome-wide analysis (GWA) study of ADHD so far, including 5,621 clinical patients and 13,589 controls. The effects sizes of single nucleotide polymorphisms (SNPs) estimated in this meta-analysis were used to obtain individual polygenic risk scores in an independent population-based cohort of 2,437 children from the Netherlands Twin Register. The variance explained in Attention Problems (AP) scale scores by the polygenic risk scores was estimated by linear mixed modeling.
Results: The ADHD polygenic risk scores statistically-significantly predicted both parent and teacher ratings of AP in preschool-aged and school-aged children.
Conclusion: These results indicate genetic overlap between a diagnosis of ADHD and AP scale scores across raters and age groups and provides evidence for a dimensional model of ADHD. Future GWA studies on ADHD can likely benefit from the inclusion of population-based cohorts and the analysis of continuous scores.
Our goal was to estimate genetic and environmental sources of influence on adolescent problematic internet use, and whether these individual differences can be explained by effortful control, an important aspect of self-regulation. A sample of 825 pairs of Chinese adolescent twins and their parents provided reports of problematic internet use and effortful control. Univariate analysis revealed that genetic factors explained 58–66% of variance in problematic internet use, with the rest explained by non-shared environmental factors. Sex difference was found, suggesting boys’ problematic internet use was more influenced by genetic influences than girls’ problematic internet use. Bivariate analysis indicated that effortful control accounted for a modest portion of the genetic and non-shared environmental variance in problematic internet use among girls. In contrast, among boys, effortful control explained between 6% (parent report) and 20% (self-report) of variance in problematic internet use through overlapping genetic pathways. Adolescent problematic internet use is heritable, and poor effortful control can partly explain adolescent problematic internet use, with effects stronger for boys. Implications for future research are discussed.
Although creative achievement is a subject of much attention to lay people, the origin of individual differences in creative accomplishments remain poorly understood.
This study examined genetic and environmental influences on creative achievement in an adult sample of 338 twins (mean age = 26.3 years; SD = 6.6 years). Twins completed the Creative Achievement Questionnaire (CAQ) that assesses observable creative accomplishments in variousdomains. The CAQ includes Artistic Creative Achievement (ACA),Scientific Creative Achievement (SCA), and the Total CreativeAchievement (TCA) scales.
Across all 3 scales, monozygotic twin correlations were consistently and substantially higher than dizygotic twin correlations, suggesting the importance of genetic influences on creative achievements. Heritability estimates for the 3 scales ranged from 43% to 67%, with the remaining variance being attributable to nonshared environmental influences plus measurement error. The effects of shared environmental factors were negligible.
These results were in contrast with those of early twin studies of creativity, which yielded a statistically-significant amount of shared family environmental influences. Discrepancies in findings between this study and prior investigations may be due in part to the differences in ages of twins and measures.
A great deal of scholarly work has explored the motivations behind media consumption and other various communication traits. However, little research has investigated the sources of these motivations and virtually no research considers their potential genetic underpinnings. Drawing on the field of behavior genetics, we use a classical twin design study to examine the genetic and environmental influences on nine communication behaviors. Our findings indicate a substantial portion of the total variance in media habits can be attributed to genes, as much as one-third of the variance in some instances. Mass communication scholars would benefit by paying closer attention to heritability when thinking about the causes as well as the consequences of media traits in contemporary society.
Unfortunately, the nature-versus-nurture debate continues in criminology. Over the past 5 years, the number of heritability studies in criminology has surged. These studies invariably report sizeable heritability estimates (~50%) and minimal effects of the so-called shared environment for crime and related outcomes. Reports of such high heritabilities for such complex social behaviors are surprising, and findings indicating negligible shared environmental influences (usually interpreted to include parenting and community factors) seem implausible given extensive criminological research demonstrating their importance. Importantly, however, the models on which these estimates are based have fatal flaws for complex social behaviors such as crime. Moreover, the goal of heritability studies—partitioning the effects of nature and nurture—is misguided given the bidirectional, interactional relationship among genes, cells, organisms, and environments. This study provides a critique of heritability study methods and assumptions to illuminate the dubious foundations of heritability estimates and questions the rationale and utility of partitioning genetic and environmental effects. After critiquing the major models, we call for an end to heritability studies. We then present what we perceive to be a more useful biosocial research agenda that is consonant with and informed by recent advances in our understanding of gene function and developmental plasticity.
In 1998, Robert Plomin and his Colorado Adoption Project (CAP) colleagues published the results of a longitudinal adoption study of personality. They found an average personality test score correlation of only 0.01 between birth-parents and their 240 adopted-away 16-year-old biological offspring, suggesting no genetic influences on personality. However, the researchers interpreted their results in the context of previous twin studies, produced an average 14% heritability estimate, and concluded that nonadditive genetic factors underlie personality traits. The author challenges these conclusions and notes that the near-zero correlation stands in contrast to other types of behavioral genetic methods, such as twin studies, that are more vulnerable to environmental confounds and other biases. The author shows that authoritative psychology texts frequently fail to mention this 1998 CAP study. When it is mentioned, the original researchers’ conclusions are usually accepted without critical analysis. The author also assesses the results in the context of the 20-year failure to discover the genes that behavioral geneticists believe underlie personality traits. He concludes that this 1998 investigation is a “lost study” in the sense that, although it is one of the most methodologically sound behavioral genetic studies ever performed, its results are largely unknown.
Claims of extreme survival of DNA have emphasized the need forreliable models of DNA degradation through time. By analysingmitochondrial DNA (mtDNA) from 158 radiocarbon-dated bones of the extinct New Zealand moa, we confirm empirically a long-hypothesized exponential decay relationship. The average DNA half-life within this geographically constrained fossil assemblage was estimated to be 521 years for a 242 bp mtDNA sequence, corresponding to a per nucleotide fragmentation rate ( k
) of 5.50 × 10 –6
per year. With an effective burial temperature of 13.1°C, the rate is almost 400 times slower than predicted from published kinetic data of in vitro DNA depurination at pH 5. Although best described by an exponential model ( R 2
= 0.39), considerable sample-to-sample variancein DNA preservation could not be accounted for by geologic age. This variation likely derives from differences in taphonomy and bone diagenesis, which have confounded previous, less spatially constrained attempts to study DNAdecay kinetics. Lastly, by calculating DNA fragmentation rates onIllumina HiSeq data, we show that nuclear DNA has degraded at leasttwice as fast as mtDNA. These results provide a baseline forpredicting long-term DNA survival in bone.
The classical twin study has been a powerful heuristic in biomedical, psychiatric and behavioural research for decades. Twin registries worldwide have collected biological material and longitudinal phenotypic data on tens of thousands of twins, providing a valuable resource for studying complex phenotypes and their underlying biology. In this Review, we consider the continuing value of twin studies in the current era of molecular genetic studies. We conclude that classical twin methods combined with novel technologies represent a powerful approach towards identifying and understanding the molecular pathways that underlie complex traits.
Personality psychology aims to explain the causes and the consequences of variation in behavioural traits. Because of the observational nature of the pertinent data, this endeavour has provoked many controversies. In recent years, the computer scientist Judea Pearl has used a graphical approach to extend the innovations in causal inference developed by Ronald Fisher and Sewall Wright. Besides shedding much light on the philosophical notion of causality itself, this graphical framework now contains many powerful concepts of relevance to the controversies just mentioned. In this article, some of these concepts are applied to areas of personality research where questions of causation arise, including the analysis of observational data and the genetic sources of individual differences.
Virtual twins (VTs) are same-age unrelated siblings reared together from early infancy. These unique sibling sets replicate twinship, but without the genetic link. The first VT pair was identified and studied at the University of Minnesota in 1990, launching the development of the Fullerton Virtual Twin Study at California State University, Fullerton (CSUF) in 1991. The registry currently includes 151 pairs, mostly children, with new pairs identified on a continuous basis. Research with VTs includes studies of general intelligence, body size, interpersonal trust, social coordination, social networks, and parenting. In some cases, VTs have been studied in conjunction with pairs of monozygotic twins, dizygotic twins, full siblings, and friends as part of TAPS (Twins, Adoptees, Peers and Siblings), acollaborative project conducted between CSUF and the University of San Francisco, 2002–2006. VTs will also serve as a comparison group for epigenetic analyses of young Chinese twins reared apart and together.
2012-rauch.pdf: “Range of genetic mutations associated with severe non-syndromic sporadic intellectual disability: an exome sequencing study”, Anita Rauch, Dagmar Wieczorek, Elisabeth Graf, Thomas Wiel, Sabine Endele, Thomas Schwarzmayr, Beate Albrecht, Deborah Bartholdi, Jasmin Beygo, Nataliya Di Donato, Andreas Dufke, Kirsten Cremer, Maja Hempel, Denise Horn, Juliane Hoyer, Pascal Joset, Albrecht Röpke, Ute Moog, Angelika Riess, Christian T. Thiel, Andreas Tzschach, Antje Wiesener, Eva Wohlleber, Christiane Zweier, Arif B. Ekici, Alexander M. Zink, Andreas Rump, Christa Meisinger, Harald Grallert, Heinrich Sticht, Annette Schenck, Hartmut Engels, Gudrun Rappold, Evelin Schröck, Peter Wieacker, Olaf Riess, Thomas Meitinger, André Reis, Tim M. Strom (backlinks)
There has been almost no overlap between behavior genetics and consumer behavior research, despite each field’s importance in understanding society. In particular, both have neglected to study genetic influences on consumer adoption and usage of new technologies—even technologies as important as the mobile phone, now used by 5.8 out of 7.0 billion people on earth. To start filling this gap, we analyzed self-reported mobile phone use, intelligence, and personality traits in two samples of Australian teenaged twins (mean ages 14.2 and 15.6 years), totaling 1,036 individuals. ACE modeling using Mx software showed substantial heritabilities for how often teens make voice calls (.60 and .34 in samples 1 and 2, respectively) and for how often they send text messages (.53 and. 50). Shared family environment—including neighborhood, social class, parental education, and parental income (i.e., the generosity of calling plans that parents can afford for their teens)—had much weaker effects. Multivariate modeling based on cross-twin, cross-trait correlations showed negative genetic correlations between talking/texting frequency and intelligence (around –.17), and positive genetic correlations between talking/texting frequency and extraversion (about .20 to .40). Our results have implications for assessing the risks of mobile phone use such as radiofrequency field (RF) exposure and driving accidents, for studying adoption and use of other emerging technologies, for understanding the genetic architecture of the cognitive and personality traits that predict consumer behavior, and for challenging the common assumption that consumer behavior is shaped entirely by culture, media, and family environment.
Are highly heritable attitudes more or less complex than less heritable attitudes? Over 2,000 participant responses on topics varying in heritability were coded for overall integrative complexity and its 2 subcomponents (dialectical complexity and elaborative complexity). Across different heritability sets drawn from 2 separate prior twin research programs, the present results yielded a consistent pattern: Heritability was always statistically-significantly positively correlated with integrative complexity. Further analyses of the subcomponents suggested that the manner in which complexity was expressed differed by topic type: For societal topics, heritable attitudes were more likely to be expressed in dialectically complex terms, whereas for personally involving topics, heritable attitudes were more likely to be expressed in elaboratively complex terms. Most of these relationships remained statistically-significant even when controlling for measurements of attitude strength. The authors discuss the genetic roots of complex versus simple attitudes, implications for understanding attitude development more broadly, and the contribution of these results to previous work on both heritability and complexity.
Individual differences in adolescent exercise behavior are to a large extent explained by shared environmental factors. The aim of this study was to explore to what extent this shared environment represents effects of cultural transmission of parents to their offspring, generation specific environmental effects or assortative mating. Survey data on leisure-time exercise behavior were available from 3,525 adolescent twins and their siblings (13–18 years) and 3,138 parents from 1,736 families registered at the Netherlands Twin Registry. Data were also available from 5,471 adult twins, their siblings and spouses similar in age to the parents. Exercise participation (No/Yes, using a cut-off criterion of 4 metabolic equivalents and 60 min weekly) was based on questions on type, frequency and duration of exercise. A model to analyze dichotomous data from twins, siblings and parents including differences in variance decomposition across sex and generation was developed. Data from adult twins and their spouses were used to investigate the causes of assortative mating (correlation between spouses = 0.41, due to phenotypic assortment). The heritability of exercise in the adult generation was estimated at 42%. The shared environment for exercise behavior in adolescents mainly represents generation specific shared environmental influences that seem somewhat more important in explaining familial clustering in girls than in boys (52 versus 41%). A small effect of vertical cultural transmission was found for boys only (3%). The remaining familial clustering for exercise behavior was explained by additive genetic factors (42% in boys and 36% in girls). Future studies on adolescent exercise behavior should focus on identification of the generation specific environmental factors.
The purpose of this study was to examine changes in the contribution of genetic and environmental influences to leisure time physical activity among male and female twins over a 6-year follow-up. At baseline the sample comprised 4,280 monozygotic and 9,276 dizygotic twin individuals, and at follow-up 4,383 monozygotic and 9,439 dizygotic twin individuals. Participants were aged 18–54 years at baseline. Genetic modeling results showed that genetic influences on leisure time physical activity declined from baseline (44%) to follow-up (34%). Most of the genetic influences identified at baseline were present at followup (rg= 0.72). Specific environmental influences increased from baseline (56%) to follow-up (66%) while at follow-up new environmental time-specific influences were observed (re= 0.23). The model with sex differences showed a higher estimate of genetic influences for men than women both at baseline (men 47% vs. women 42%) and at follow-up (men 38% vs. women 31%). The additive genetic correlation for this phenotype was greater for men (rg= 0.79) than women (rg= 0.64). The specific environmental influences were corresponding; at baseline men 53% and women 56% and at follow-up men 62% and women 69%. The environmental correlations between the two time points were similar for men (re= 0.21) and for women (re= 0.24). In conclusion, in a sample of healthy twins most of the genetic influences on leisure time physical activity expressed at baseline were present at 6 years of follow-up. New specific environmental factors underlying follow-up leisure time physical activity were observed.
The biological and social transmission of attitudes toward abortion and gay rights are analyzed in a large sample of adult twins, siblings, and their parents. We present a linear model for family resemblance allowing for both genetic and cultural transmission of attitudes from parents to offspring, as well as phenotypic assortative mating (the tendency to marry like) and other environmental sources of twin and sibling resemblance that do not depend on the attitudes of their parents. The model gives a close fit to the patterns of similarity between relatives for the two items. Results are consistent with a substantial role of genetic liability in the transmission of both attitudes. Contrary to the dominant paradigm of the social and political sciences, the kinship data are consistent with a relatively minor non-genetic impact of parental attitudes on the development of adult attitudes in their children. By contrast, the choice of mate is a social action that has a marked impact on the polarization of social attitudes and on the long-term influence that parents exert upon the next generation.
[Keywords: Abortion, Gay rights, Assortative mating, Political and social attitudes]
In this study, heritabilities of several measures of aggression were estimated in a group of 325 Golden Retrievers, using the Restricted Maximum Likelihood method. The studied measures were obtained either through owner opinions or by using the Canine Behavioural Assessment and Research Questionnaire (CBARQ). The aim of the study was to determine which of the aggression measures showed sufficient genetic variation to be useful as phenotypes for future molecular genetic studies on aggression in this population.
The most reliable heritability estimates seemed to be those for simple dog owner impressions of human-directed and dog-directed aggression, with heritability estimates of 0.77 (S.E. 0.09) and 0.81 (S.E. 0.09), respectively. In addition, several CBARQ-derived measures related to human-directed aggression showed clear genetic differences between the dogs. The correlation between the estimated breeding values for owner impressions on human-directed and dog-directed aggression was relatively low. The low correlation suggests that these two traits have a partially different genetic background. They will therefore have to be treated as separate traits in further genetic studies.
I analyze a new set of data on Korean American adoptees who were quasi-randomly assigned to adoptive families.
I find large effects on adoptees’ education, income, and health from assignment to parents with more education and from assignment to smaller families. Parental education and family size are statistically-significantly more correlated with adoptee outcomes than are parental income or neighborhood characteristics. Outcomes such as drinking, smoking, and the selectivity of college attended are more determined by nurture than is educational attainment.
Using the standard behavioral genetics variance decomposition, I find that shared family environment explains 14% of the variation in educational attainment, 35% of the variation in college selectivity, and 33% of the variation in drinking behavior.
This article notes 5 reasons why a correlation between a risk (or protective) factor and some specified outcome might not reflect environmental causation. In keeping with numerous other writers, it is noted that a causal effect is usually composed of a constellation of components acting in concert. The study of causation, therefore, will necessarily be informative on only one or more subsets of such components. There is no such thing as a single basic necessary and sufficient cause. Attention is drawn to the need (albeit unobservable) to consider the counterfactual (ie., what would have happened if the individual had not had the supposed risk experience). 15 possible types of natural experiments that may be used to test causal inferences with respect to naturally occurring prior causes (rather than planned interventions) are described. These comprise 5 types of genetically sensitive designs intended to control for possible genetic mediation (as well as dealing with other issues), 6 uses of twin or adoptee strategies to deal with other issues such as selection bias or the contrasts between different environmental risks, 2 designs to deal with selection bias, regression discontinuity designs to take into account unmeasured confounders, and the study of contextual effects. It is concluded that, taken in conjunction, natural experiments can be very helpful in both strengthening and weakening causal inferences.
A comprehensive evolutionary framework for understanding the maintenance of heritable behavioral variation in humans is yet to be developed. Some evolutionary psychologists have argued that heritable variation will not be found in important, fitness-relevant characteristics because of the winnowing effect of natural selection. This article propounds the opposite view. Heritable variation is ubiquitous in all species, and there are a number of frameworks for understanding its persistence. The author argues that each of the Big Five dimensions of human personality can be seen as the result of a trade-off between different fitness costs and benefits. As there is no unconditionally optimal value of these trade-offs, it is to be expected that genetic diversity will be retained in the population.
Background: Considerable evidence from twin and adoption studies indicates that genetic and shared environmental factors play a statistically-significant role in the initiation of smoking behavior. Although twin and adoption designs are powerful to detect genetic and environmental influences, they do not provide information on the processes of assortative mating and parent-offspring transmission and their contribution to the variability explained by genetic and/or environmental factors.
Methods: We examined the role of genetic and environmental factors for smoking initiation using an extended kinship design. This design allows the simultaneous testing of additive and non-additive genetic, shared and individual-specific environmental factors, as well as sex differences in the expression of genes and environment in the presence of assortative mating and combined genetic and cultural transmission. A dichotomous lifetime smoking measure was obtained from twins and relatives in the Virginia 30,000 sample.
Results: Results demonstrate that both genetic and environmental factors play a statistically-significant role in the liability to smoking initiation. Major influences on individual differences appeared to be additive genetic and unique environmental effects, with smaller contributions from assortative mating, shared sibling environment, twin environment, cultural transmission and resulting genotype-environment covariance. The finding of negative cultural transmission without dominance led us to investigate more closely two possible mechanisms for the lower parent-offspring correlations compared to the sibling and DZ twin correlations in subsets of the data: (1) age × gene interaction, and (2) social homogamy. Neither mechanism provided a statistically-significantly better explanation of the data, although age regression was statistically-significant.
Conclusions: This study showed statistically-significant heritability, partly due to assortment, and statistically-significant effects of primarily non-parental shared environment on smoking initiation.
The appropriate conjunction between the words nature and nurture is not versus but and. There is increasing acceptance of the evidence for substantial genetic influence on many behavioral traits, but the same research also provides the best available evidence for the importance of environmental influence and important clues about how the environment works. Because much developmental action is at the interface between genes and environment, genetic research needs to incorporate measures of the environment, and environmental research will be enhanced by collecting DNA.
Background: Substantial interindividual variation is observed in sports participation and physical activity levels in youth. This study aimed to (1) estimate the relative contribution of genes, along with shared and nonshared environmental factors, to variation in sports participation index (SPI) and leisure-time physical activity (LTPA); and (2) test differences in those factors in males and females.
Methods: The sample was comprised of 411 Portuguese twin pairs of different zygosity aged 12 to 25 years. The SPI and LTPA were assessed with the Baecke questionnaire. Quantitative genetic modeling was used to test alternative models for the presence of additive gene effects (a2), common or shared environment within the family (c2), and unique environmental factors (e2).
Results: The best-fitting models showed sex-specific effects for the two phenotypes. Variance componentsfor SPI in males were a2 = 68.4%, c2 = 20%, and e2 = 11.6%; and in females, a2 = 39.8%, c2 = 28.4%, and e2 = 31.8%. For variation in LTPA, genetic factors in males explained 63%, common environment was not statistically-significant, and unique environment explained 37%. In females, contributing factors were a2 = 32%, c2 = 38%, and e2 = 30%.
Conclusions: Genetic effects explained a considerable amount of variation in SPI and LTPA, which were greater in males than in females. The relevance of shared environmental factors (family and peers) and nonshared environmental factors in SPI and LTPA is particularly evident in females.
Purpose: The purpose of this review was to address the question of interindividual variation in responsiveness to regular exercise training and to define the contributions of age, sex, race, and pretraining phenotype level to this variability.
Methods: A literature review was conducted of the studies reporting interindividual variation in responsiveness to standardized and controlled exercise-training programs, and included an analysis of the contribution of age, sex, race, and initial phenotype values to the heterogeneity in VO2max, high-density lipoprotein (HDL)-C and submaximal exercise, heart rate (HR), and systolic blood pressure (SBP) training responses in subjects from the HERITAGE Family Study.
Results: Several studies have shown marked individual differences in responsiveness to exercise training. For example, VO2max responses to standardized training programs have ranged from almost no gain up to 100% increase in large groups of sedentary individuals. A similar pattern of heterogeneity has been observed for other phenotypes. Data from the HERITAGE Family Study show that age, sex, and race have little impact on interindividual differences in training responses. On the other hand, the initial level of a phenotype is a major determinant of training response for some traits, such as submaximal exercise heart rate and blood pressure (BP) but has only a minor effect on others (eg., VO2max, HDL-C). The contribution of familial factors (shared environment and genetic factors) is supported by data on substantial familial aggregation of training response phenotypes.
Conclusions: There is strong evidence for considerable heterogeneity in the responsiveness to regular physical activity. Age, sex, and ethnic origin are not major determinants of human responses to regular physical activity, whereas the pretraining level of a phenotype has a considerable impact in some cases. Familial factors also contribute substantially to variability in training response.
Group and individual-difference adoption designs lead to opposite conclusions concerning the importance of shared environment (SE) for the child outcomes of IQ and antisocial behavior. This paradox could be due to the range restriction (RR) of family environments (FE) that goes with adoption studies. Measures of FE from 2 of the most recent adoption studies indicate that RR is substantial, about 67%, which corresponds to the top half of a normal FE distribution. FE of 57% cuts effect sizes and R2 statistics by factors of 3 and 2–2.5, respectively. Because selection into an option study is inherently a between-family process and assuming that comparable restriction of genetic (G) influences are absent, estimates of SE, G, and nonshared influences will be substantially biased, respectively, down, up, and up by RR. Corrections for RR applied to adoption studies indicate that SE could account for as much as 50% of the variance in IQ.
[Keywords: restricted range of family environments, estimates of heritability & nonshared environment for child outcomes of IQ & antisocial behavior in behavior-genetic adoption studies]
It has long been recognized that both smoking and sports participation tend to cluster in families. In this chapter, we first describe the current status of smoking and sports participation as cardiovascular risk factors. After an outline of the principles of the quantitative genetic approaches to the analysis of individual differences in behaviour, we will review the literature on genetic and environmental determinants of smoking and sports participation. In the second half of this chapter, results from the Dutch Twin/Family Study of Health-Related Behavior are presented.
The authors administered inventories of vocational and recreational interests and talents to 924 pairs of twins who had been reared together and to 92 pairs separated in infancy and reared apart. Factor analysis of all 291 items yielded 39 identifiable factors and 11 superfactors. The data indicated that about 50% of interests variance (about two thirds of the stable variance) was associated with genetic variation. The authors show that heritability can be conservatively estimated from the within-pair correlations of adult monozygotic twins reared together. Evidence for nonadditive genetic effects on interests may explain why heritability estimates based on family studies are so much lower. The authors propose a model in which precursor traits of aptitude and personality, in part genetically determined, guide the development of interests through the mechanisms of gene-environment correlation and interaction.
During the twentieth century, geneticists have dramatically changed their assessments of the biological and social consequences of human race differences and race crossing.
In the first quarter of the century, most geneticists thought that human races differed hereditarily by important mental as well as physical differences and that wide race crosses were biologically and socially harmful. The period from 1925 to the outbreak of World War II saw no change in geneticists’ views on hereditary mental differences between human races, but a shift to agnosticism on the issue of wide race crosses. By the early 1950s, geneticists generally argued that wide race crosses were at worst biologically harmless, but still held to earlier beliefs about hereditary mental differences between races. The final period from 1951 to the present has witnessed the shift to agnosticism on the issue of hereditary mental differences between races. The changes in geneticists’ assessments of race differences and race crossing were caused by increased understanding of the complex relationship between genes and environment and by cultural changes.
This volume reports on a study of 850 pairs of twins who were tested to determine the influence of heredity and environment on individual differences in personality, ability, and interests. It presents the background, research design, and procedures of the study, a complete tabulation of the test results, and the authors’ extensive analysis of their findings. Based on one of the largest studies of twin behavior ever conducted, the book challenges a number of traditional beliefs about genetic and environmental contributions to personality development.
The subjects were chosen from participants in the National Merit Scholarship Qualifying Test of 1962 and were mailed a battery of personality and interest questionnaires. In addition, parents of the twins were sent questionnaires asking about the twins’ early experiences. A similar sample of nontwin students who had taken the merit exam provided a comparison group. The questions investigated included how twins are similar to or different from non-twins, how identical twins are similar to or different from fraternal twins, how the personalities and interests of twins reflect genetic factors, how the personalities and interests of twins reflect early environmental factors, and what implications these questions have for the general issue of how heredity and environment influence the development of psychological characteristics. In attempting to answer these questions, the authors shed new light on the importance of both genes and environment and have formed the basis for new approaches in behavior genetic research.
The concept of heritability in quantitative genetics is defined and discussed in terms of its implications for individual and group differences in behavioral traits, with particular reference to studies of the heritability of IQ.
Common misconceptions concerning the relevance of heritability analysis for individual scores and the roles of genotype x environment covariance and interaction are clarified.
Some of the popular criticisms of heritability analysis as applied to mental ability are shown to be misconceived.
Classic study of dog behavior, the authoritative information from 20 years of research at the Jackson Laboratory. The authors synthesize developmental problems and canine genetics, based on study of 470 dogs. Central to the book is the role heredity plays in the development of behavior. Giving puppies an environment designed on the principles of a well-run school, Scott and Fuller tested five breeds representing the major dog groups and carried out a Mendelian experiment with two of the most different breeds: The basenji and the cocker spaniel. They found that heredity affects almost every trait tested; that gender affects aggressiveness and the dominance order, but not trainability and problem-solving; that emotional traits profoundly influence performance; that, although breeds differ widely in emotional and motivational characteristics, none shows distinct superiority in problem solving; and that detailed statistical analyses indicate a highly complex pathway between primary gene action and its final effect on behavior. Includes important information on rearing methods, the origin and history of dog breeds, basic behavior patterns and the psychological and behavioral development of puppies. Their careful scientific work demonstrated the importance and existence of time limited phases in the early life of dogs within which certain experiences need to occur or the dogs may be forever deficient. Their work (with that of Eckhard Hess’s on ducks and geese) demonstrated that these critical or sensitive periods in early development could be scientifically studied in ways compatible with a scientific psychology. This book will always be especially valuable to dog breeders and trainers; its last chapters summarize in very clear terms the particular phases in early development and experiences the dog needs to be adequately socialized. The reader can refer back to earlier chapters to get more information on how the experiments were conducted and the distribution of results. It answers questions on proper age that puppies can be separated from their mothers, what experiences are important to provide at what age, etc. Originally published in 1965. [ISBN: 0-226-74335-7]