“Integrating de novo and inherited variants in over 42,607 autism cases identifies mutations in new moderate risk genes”, Xueya Zhou, Pamela Feliciano, Tianyun Wang, Irina Astrovskaya, Chang Shu, Jacob B. Hall, Joseph U. Obiajulu, Jessica Wright, Shwetha Murali, Simon Xuming Xu, Leo Brueggeman, Taylor R. Thomas, Olena Marchenko, Christopher Fleisch, Sarah D. Barns, LeeAnne Green Snyder, Bing Han, Timothy S. Chang, Tychele N. Turner, William Harvey, Andrew Nishida, Brian J. O’Roak, Daniel H. Geschwind, The SPARK Consortium, Jacob J. Michaelson, Natalia Volfovsky, Evan E. Eichler, Yufeng Shen, Wendy K. Chung (2021-10-11):
Despite the known heritable nature of autism spectrum disorder (ASD), studies have primarily identified risk genes with de novo variants (DNVs). To capture the full spectrum of ASD genetic risk, we performed a two-stage analysis of rare de novo and inherited coding variants in 42,607 ASD cases, including 35,130 new cases recruited online bySPARK. In the first stage, we analyzed 19,843 cases with one or bothbiological parents and found that known ASD or neurodevelopmentaldisorder (NDD) risk genes explain nearly 70% of the genetic burdenconferred by DNVs. In contrast, less than 20% of genetic risk conferred by rare inherited loss-of-function (LoF) variants are explained by known ASD/NDD genes. We selected 404 genes based on the first stage of analysis and performed a meta-analysis with an additional 22,764 cases and 236,000 population controls. We identified 60 genes with exome-wide significance (p < 2.5e-6), including five new risk genes (NAV3, ITSN1, MARK2, SCAF1, and HNRNPUL2). Theassociation of NAV3 with ASD risk is entirely driven by rare inherited LoFs variants, with an average relative risk of 4, consistent with moderate effect. ASD individuals with LoF variants in the fourmoderate risk genes (NAV3, ITSN1, SCAF1, and HNRNPUL2, n = 95) have less cognitive impairment compared to 129 ASD individuals with LoFvariants in well-established, highly penetrant ASD risk genes (CHD8,SCN2A, ADNP, FOXP1, SHANK3) (59% vs. 88%, p = 1.9e-06) . These findings will guide future gene discovery efforts and suggest that much larger numbers of ASD cases and controls are needed to identifyadditional genes that confer moderate risk of ASD through rare, inherited variants.
Background: Genetic studies have implicated rare and common variations in liability for autism spectrum disorder (ASD). Of the discovered risk variants, those rare in the population invariably have large impact on liability, while common variants have small effects. Yet, collectively, common risk variants account for the majority of population-level variability. How these rare and common risk variants jointly affect liability for individuals requires further study.
Methods: To explore how common and rare variants jointly affect liability, we assessed 2 cohorts of ASD families characterized for rare and common genetic variations (Simons Simplex Collection and Population-Based Autism Genetics and Environment Study). We analyzed data from 3011 affected subjects, as well as 2 cohorts of unaffected individuals characterized for common genetic variation: 3011 subjects matched for ancestry to ASD subjects and 11,950 subjects for estimating allele frequencies. We used genetic scores, which assessed the relative burden of common genetic variation affecting risk of ASD (henceforth “burden”), and determined how this burden was distributed among 3 subpopulations: ASD subjects who carry a potentially damagingvariant implicated in risk of ASD (“PDV carriers”); ASD subjects who do not (“non-carriers”); and unaffected subjects who are assumed to be non-carriers.
Results: Burden harbored by ASD subjects is stochastically greaterthan that harbored by control subjects. For PDV carriers, theiraverage burden is intermediate between non-carrier ASD and controlsubjects. Both carrier and non-carrier ASD subjects have greater burden, on average, than control subjects. The effects of common and rare variants likely combine additively to determine individual-level liability.
Limitations: Only 305 ASD subjects were known PDV carriers. This relatively small subpopulation limits this study to characterizing general patterns of burden, as opposed to effects of specific PDVs or genes. Also, a small fraction of subjects that are categorized as non-carriers could be PDV carriers.
Conclusions: Liability arising from common and rare risk variations likely combines additively to determine risk of any individual diagnosed with ASD. On average, ASD subjects carry a substantial burden of common risk variation, even if they also carry a rare PDV affecting risk.
Statistical challenges due to rarity and combinatorial explosion resulting from exhaustive evaluation of rare variant combinations have limited the study of oligogenic etiology for complex disorders. We present RareComb, a framework that combines apriori algorithm and statistical inference to identify specific combinations of mutated genes associated with complex phenotypes. Using RareComb on 6,189 affected individuals, we identified 718 combinations of mutated genes statistically-significantly associated with intellectual disability (ID), and carriers of these combinations showed lower IQ than expected in a replication cohort of 1,878 individuals. These combinations were enriched for nervous system genes, showed complex inheritance patterns, and were depleted in unaffected siblings. We further identified oligogenic combinations associated with multiple comorbid phenotypes, including COL28A1 and MFSD2B mutations for ID and schizophrenia. Our framework enables rare variant analysis in affected individuals lacking diagnosis based on de novo mutations, and provides a paradigm for dissecting the genetic basis of complex disorders.
Autism arises in high and low-risk families. De novo mutation contributes to autism incidence in low-risk families as there is a higher incidence in the affected of the simplex families than in their unaffected siblings. But the extent of contribution in low-risk families cannot be determined solely from simplex families as they are a mixture of low and high-risk. The rate of de novo mutation in nearly pure populations of high-risk families, the multiplex families, has not previously been rigorously determined. Moreover, rates of de novo mutation have been underestimated from studies based on low resolution microarrays and whole exome sequencing.
Here we report on findings from whole genome sequence (WGS) of bothsimplex families from the Simons Simplex Collection (SSC) andmultiplex families from the Autism Genetic Resource Exchange (AGRE). After removing the multiplex samples with excessive cell-line genetic drift, we find that the contribution of de novo mutation in multiplex is substantially smaller than the contribution in simplex. We use WGS to provide high resolution CNV profiles and to analyze more than coding regions, and revise upward the rate in simplex autism due to an excess of de novo events targeting introns.
Based on this study, we now estimate that de novo events contribute to 52–67% of cases of autism arising from low risk families, and 30–39% of cases of all autism.
Genome sequencing of tens of thousands of human individuals has recently enabled the measurement of large selective effects for mutations to protein-coding genes. Here we describe a new method, called ExtRaINSIGHT, for measuring similar selective effects at individual sites in noncoding as well as in coding regions of the human genome. ExtRaINSIGHT estimates the prevalance of strong purifying selection, or “ultraselection” (λs), as the fractional depletion of rare single-nucleotide variants (minor allele frequency < 0.1%) in a target set of genomic sites relative to matched sites that are putatively neutrally evolving, in a manner that controls for local variation and neighbor-dependence in mutation rate. We show using simulations that, above an appropriate threshold, λs is closely related to the average site-specific selection coefficient against heterozygous point mutations, as predicted at mutation-selection balance. Applying ExtRaINSIGHT to 71,702 whole genome sequences from gnomAD v3, we find particularly strong evidence of ultraselection in evolutionarily ancient miRNAs and neuronal protein-coding genes, as well as at splice sites. Moreover, our estimated selection coefficient against heterozygous amino-acid replacements across the genome (at 1.4%) is substantially larger than previous estimates based on smaller sample sizes. By contrast, we find weak evidence of ultraselection in other noncoding RNAs and transcription factor binding sites, and only modest evidence in ultraconserved elements and human accelerated regions. We estimate that ~0.3–0.5% of the human genome is ultraselected, with one third to one half of ultraselected sites falling in coding regions. These estimates suggest ~0.3–0.4 lethal or nearly lethal de novo mutations per potential human zygote, together with ~2 de novo mutations that are more weakly deleterious. Overall, our study sheds new light on the genome-wide distribution of fitness effects for new point mutations by combining deep new sequencing data sets and classical theory from population genetics.
Recent works have shown that SNP-heritability—which is dominated by low-effect common variants—may not be the most relevant quantity for localizing high-effect/critical disease genes. Here, we introduce methods to estimate the proportion of phenotypic variance explained by a given assignment of SNPs to a single gene (genelevel heritability). We partition gene-level heritability across minor allele frequency (MAF) classes to find genes whose gene-level heritability is explained exclusively by “low-frequency/rare” variants (0.5% ≤ MAF < 1%). Applying our method to ~17K protein-coding genes and 25 quantitative traits in the UK Biobank (n = 290K), we find that, on average across traits, ~2.5% of nonzero-heritability genes have a rare-variant component, and only ~0.8% (370 gene-trait pairs) have heritability exclusively from rare variants. Of these 370 gene-trait pairs, 37% were not detected by existing gene-level association testing methods, likely because existing methods combine signal from all variants in a region irrespective of MAF class. Many of the additional genes we identify are implicated in phenotypically related Mendelian disorders or congenital developmental disorders, providing further evidence of their trait-relevance. Notably, the rare-variant component of gene-level heritability exhibits trends different from those of common-variant gene-level heritability. For example, while total gene-level heritability increases with gene length, the rare-variant component is significantly larger among shorter genes; the cumulative distributions of gene-level heritability also vary across traits and reveal differences in the relative contributions of rare/common variants to overall gene-level polygenicity. We conclude that the proportion of gene-level heritability attributable to low-frequency/rare variation can yield novel insights into complex-trait genetic architecture.
Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder, with onset in childhood (“childhood ADHD”), and around two thirds of affected individuals will continue to have ADHD symptoms in adulthood (“persistent ADHD”). Age at first diagnosis can vary, and sometimes ADHD is first diagnosed in adulthood (“late-diagnosed ADHD”).
In this study, we analyzed a large Danish population-based case-cohort generated by iPSYCH in order to identify common genetic risk loci and perform in-depth characterization of the polygenic architecture of childhood (n = 14,878), persistent (n = 1,473) and late-diagnosed ADHD (n = 6,961) alongside 38,303 controls. Additionally, the burden of rare protein truncating variants in the three groups were evaluated in whole-exome sequencing data from a subset of the individuals (7,650 ADHD cases and 8,649 controls). We identified genome-wide statistically-significant loci associated with childhood ADHD (four loci) and late-diagnosed ADHD (one locus). In analyses of the polygenic architecture, we found higher polygenic score (PGS) of ADHD risk variants in persistent ADHD (mean PGS=0.41) compared to childhood (mean PGS=0.26) and late-diagnosed ADHD (mean PGS=0.27), and we found a significant decreased genetic correlationof late-diagnosed ADHD with inattention (rg = 0.57) compared to childhood ADHD (rg = 0.86). These results suggest that a higher ADHD polygenic risk burden is associated with persistence of symptoms, and that a later diagnosis of ADHD could be due in part to genetic factors. Additionally, childhood ADHD demonstrated both a significantly increased genetic overlap with autism compared to late-diagnosed ADHD as well as the highest burden of rare protein-truncating variants in highly constrained genes among ADHD subgroups (compared to controls: β = 0.13, p = 2.41×10−11). Late-diagnosed ADHD demonstrated significantly larger genetic overlap with depression than childhood ADHD and no increased burden in rare protein-truncating variants (compared to controls: β = 0.06). Overall, our study finds genetic heterogeneity among ADHD subgroups and suggests that genetic factors influence time of first ADHD diagnosis, persistence of ADHD and comorbidity patterns in the sub-groups.
“Genetic correlates of phenotypic heterogeneity in autism”, Varun Warrier, Xinhe Zhang, Patrick Reed, Alexandra Havdahl, Tyler M. Moore, Freddy Cliquet, Claire S. Leblond, Thomas Rolland, Anders Rosengren, EU-AIMS-LEAP, iPSYCH-Autism Working Group, Spectrum 10K, APEX Consortium, David H. Rowitch, Matthew E. Hurles, Daniel H. Geschwind, Anders D. Børglum, Elise B. Robinson, Jakob Grove, Hilary C. Martin, Thomas Bourgeron, Simon Baron-Cohen (2021-08-05):
The substantial phenotypic heterogeneity in autism limits our understanding of its genetic aetiology. To address this gap, we investigated genetic differences between autistic individuals (Nmax = 12,893) based on core (i.e., social communication difficulties, and restricted and repetitive behaviours) and associated features of autism, co-occurring developmental disabilities (e.g. language, motor, and intellectual developmental disabilities and delays), and sex. We conducted a comprehensive factor analysis of core autism features in autistic individuals and identified six factors. Common genetic variants including autism polygenic scores (PGS) were associated with the core factors but de novo variants were not, even though the latent factor structure was similar between carriers and non-carriers of de novo variants. We identify that increasing autism PGS decrease the likelihood of co-occurring developmental disabilities in autistic individuals, which reflects both a true protective effect and additivity between rare and common variants. Furthermore in autistic individuals without co-occurring intellectual disability (ID), autism PGS are overinherited by autistic females compared to males. Finally, we observe higher SNP heritability for males and autistic individuals without ID, but found no robust differences in SNP heritability by the level of core autism features. Deeper phenotypic characterisation will be critical to determining how the complex underlying genetics shapes cognition, behaviour, and co-occurring conditions in autism.
“Recovery of trait heritability from whole genome sequence data”, Pierrick Wainschtein, Deepti Jain, Zhili Zheng, TOPMed Anthropometry Working Group, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, L. Adrienne Cupples, Aladdin H. Shadyab, Barbara McKnight, Benjamin M. Shoemaker, Braxton D. Mitchell, Bruce M. Psaty, Charles Kooperberg, Ching-Ti Liu, Christine M. Albert, Dan Roden, Daniel I. Chasman, Dawood Darbar, Donald M. Lloyd-Jones, Donna K. Arnett, Elizabeth A. Regan, Eric Boerwinkle, Jerome I. Rotter, Jeffrey R. O’Connell, Lisa R. Yanek, Mariza de Andrade, Matthew A. Allison, Merry-Lynn N. McDonald, Mina K. Chung, Myriam Fornage, Nathalie Chami, Nicholas L. Smith, Patrick T. Ellinor, Ramachandran S. Vasan, Rasika A. Mathias, Ruth J. F. Loos, Stephen S. Rich, Steven A. Lubitz, Susan R. Heckbert, Susan Redline, Xiuqing Guo, Y.-D Ida Chen, Cecelia A. Laurie, Ryan D. Hernandez, Stephen T. McGarvey, Michael E. Goddard, Cathy C. Laurie, Kari E. North, Leslie A. Lange, Bruce S. Weir, Loic Yengo, Jian Yang, Peter M. Visscher (2021-06-11):
Heritability, the proportion of phenotypic variance explained by genetic factors, can be estimated from pedigree data 1, but such estimates are uninformative with respect to the underlying genetic architecture. Analyses of data from genome-wide association studies (GWAS) on unrelated individuals have shown that for human traits and disease, approximately one-third to two-thirds of heritability is captured by common SNPs2–5. It is not known whether the remaining heritability is due to the imperfect tagging of causal variants by common SNPs, in particular if the causal variants are rare, or other reasons such as overestimation of heritability from pedigree data. Here we show that pedigree heritability for height and body mass index (BMI) appears to be largely recovered from whole-genome sequence (WGS) data on 25,465 unrelated individuals of European ancestry. We assigned 33.7 million genetic variants to groups based upon their minor allele frequencies (MAF) andlinkage disequilibrium (LD) with variants nearby, and estimated and partitioned genetic variance accordingly. The estimated heritability was 0.68 (SE 0.10) for height and 0.30 (SE 0.10) for BMI, with a range of ~0.60 – 0.71 for height and ~0.25 – 0.35 for BMI, depending on quality control and analysis strategies. Low-MAF variants in lowLD with neighbouring variants were enriched for heritability, to a greater extent for protein-altering variants, consistent with negative selection thereon. Cumulatively variants with 0.0001 < MAF < 0.1 explained 0.47 (SE 0.07) and 0.30 (SE 0.10) of heritability for height and BMI, respectively. Our results imply that rare variants, in particular those in regions of low LD, is a major source of the still missing heritability of complex traits and disease.
Both common and rare genetic variants (minor allele frequency > 1% and < 0.1% respectively) have been implicated in the aetiology of schizophrenia. In this study, we integrate single-cell gene expression data with publicly available Genome-Wide Association Study (GWAS) and exome sequenced data in order to investigate in parallel, the enrichment of common and (ultra-)rare variants related to schizophrenia in several functionally relevant gene sets. Four types of gene sets were constructed 1) protein-truncating variant (PTV)-intolerant (PI) genes 2) genes expressed in brain cell types and neurons ascertained from mouse and human brain tissue 3) genes defined by synaptic function and location and 4) intersection genes, i.e., PI genes that are expressed in the human and mouse brain cell gene sets. We show that common as well as (ultra-)rare schizophrenia-associated variants are overrepresented in PI genes, in excitatory neurons from the prefrontal cortex and hippocampus, medium spiny neurons, and genes enriched for synaptic processes. We also observed stronger enrichment in the intersection genes. Our findings suggest that across the allele frequency spectrum, genes and genetic variants likely to be under stringent selection, and those expressed in particular brain cell types, are involved in the same biological pathways influencing the risk for schizophrenia.
“The female protective effect against autism spectrum disorder”, Emilie M. Wigdor, Daniel J. Weiner, Jakob Grove, Jack M. Fu, Wesley K. Thompson, Caitlin E. Carey, Nikolas Baya, Celia van der Merwe, Raymond K. Walters, F. Kyle Satterstrom, Duncan S. Palmer, Anders Rosengren, Jonas Bybjerg-Grauholm, iPSYCH Consortium, David M. Hougaard, Preben Bo Mortensen, Mark J. Daly, Michael E. Talkowski, Stephan J. Sanders, Somer L. Bishop, Anders D. Børglum, Elise B. Robinson (2021-04-05):
Autism spectrum disorder (ASD) is diagnosed 3–4× more frequently in males than in females. Genetic studies of rare variants support a female protective effect (FPE) against ASD. However, sex differencesin common, inherited genetic risk for ASD are less studied. Leveragingthe nationally representative Danish iPSYCH resource, we foundsiblings of female ASD cases had higher rates of ASD than siblings ofmale ASD cases (P < 0.01). In the Simons Simplex and SPARKcollections, mothers of ASD cases carried more polygenic risk for ASDthan fathers of ASD cases (P = 7.0 × 10−7). Male unaffected siblings under-inherited polygenic risk (P = 0.03); female unaffected siblings did not. Further, female ASD cases without a high-impact de novo variant over-inherited nearly three-fold the polygenic risk of male cases with a high-impact de novo (P = 0.02). Our findings support a FPE against ASD that includes common, inherited genetic variation.
A complete characterization of genetic variation is a fundamental goal of human genome research. Long-read sequencing (LRS) improves the sensitivity for structural variant (SV) discovery and facilitates a better understanding of the SV spectrum in human genomes. Here, we conduct the first LRS-based SV analysis in Chinese population. Weperform whole-genome LRS for 405 unrelated Chinese, with 68 phenotypic and clinical measurements. We discover a complex landscape of 132,312 non-redundant SVs, of which 53.3% are novel. The identified SVs are of high-quality validated by the PacBio high-fidelity sequencing and PCR experiments. The total length of SVs represents approximately 13.2% of the human reference genome. We annotate 1,929 loss-of-function SVs affecting the coding sequences of 1,681 genes. We discover new associations of SVs with phenotypes and diseases, such as rare deletions in HBA1/HBA2/HBB associated with anemia and common deletions in GHR associated with body height. Furthermore, we identify SV candidates related to human immunity that differentiate sub-populations of Chinese. Our study reveals the complex landscape of human SVs in unprecedented detail and provides new insights into their roles contributing to phenotypes, diseases and evolution. The genotypic and phenotypic resource is freely available to the scientific community.
Copy number variants (CNVs) are associated with syndromic and severe neurological and psychiatric disorders (SNPDs), such as intellectual disability,epilepsy, schizophrenia, and bipolar disorder. Although considered high-impact, CNVs are also observed in the general population. This presents a diagnostic challenge in evaluating their clinical-significance.
To estimate the phenotypic differences between CNV carriers and non-carriers regarding general health and well-being, we compared the impact of SNPD-associatedCNVs on health, cognition, and socioeconomic phenotypes to the impact of three genome-wide polygenic risk score (PRS) in two Finnish cohorts (FINRISK, n = 23,053 and NFBC1966, n = 4895). The focus was on CNV carriers and PRS extremes who do not have an SNPD diagnosis.
We identified high-risk CNVs(DECIPHERCNVs, risk gene deletions, or large [>1 Mb] CNVs) in 744 study participants (2.66%), 36 (4.8%) of whom had a diagnosed SNPD. In the remaining 708 unaffected carriers, we observed lower educational attainment (EA; OR = 0.77 [95% CI 0.66–0.89]) and lower household income (OR = 0.77 [0.66–0.89]). Income-associated CNVs also lowered household income (OR = 0.50 [0.38–0.66]), and CNVs with medical consequences lowered subjective health (OR = 0.48 [0.32–0.72]). The impact of PRSs was broader. At the lowest extreme of PRS for EA, we observed lower EA (OR = 0.31 [0.26–0.37]), lower-income (OR = 0.66 [0.57–0.77]), lower subjective health (OR = 0.72 [0.61–0.83]), and increased mortality (Cox’s HR = 1.55 [1.21–1.98]). PRS for intelligence had a similar impact, whereas PRS for schizophrenia did not affect these traits.
We conclude that the majority of working-age individuals carrying high-risk CNVswithout SNPD diagnosis have a modest impact on morbidity and mortality, as well as the limited impact on income and educational attainment, compared to individuals at the extreme end of common genetic variation. Our findings highlight that the contribution of traditional high-risk variants such as CNVs should be analyzed in a broader genetic context, rather than evaluated in isolation.
Hundreds of the proteins encoded in human genomes contain domains that vary in size or copy number due to variable numbers of tandem repeats (VNTRs) in proteincoding exons. VNTRs have eluded analysis by themolecular methods—SNP arrays and high-throughput sequencing—used in large-scale human genetic studies to date; thus, the relationships of VNTRs to most human phenotypes are unknown. We developed ways toestimate VNTR lengths from whole-exome sequencing data, identify theSNP haplotypes on which VNTR alleles reside, and use imputation to project these haplotypes into abundant SNP data. We analyzed 118 protein-altering VNTRs in 415,280UK Biobank participants for association with 791 phenotypes. Analysis revealed some of the strongest associations of common variants with human phenotypes including height, hair morphology, and biomarkers of human health; for example, a VNTR encoding 13–44 copies of a 19-amino-acid repeat inthe chondroitin sulfate domain of aggrecan (ACAN) associated with height variation of 3.4 centimeters (s.e. 0.3 cm). Incorporating large-effect VNTRs into analysis also made it possible to map many additional effects at the same loci: for the blood biomarker lipoprotein(a), for example, analysis of the kringle IV-2 VNTR within the LPA gene revealed that 18 coding SNPs and the VNTR in LPA explained 90% of lipoprotein(a) heritability in Europeans, enabling insights about population differences and epidemiological significance of this clinical biomarker. These results point to strong, cryptic effects of highly polymorphic common structural variants that have largely eluded molecular analyses to date.
“Long read sequencing of 3,622 Icelanders provides insight into the role of structural variants in human diseases and other traits”, Doruk Beyter, Helga Ingimundardottir, Asmundur Oddsson, Hannes P. Eggertsson, Eythor Bjornsson, Hakon Jonsson, Bjarni A. Atlason, Snaedis Kristmundsdottir, Svenja Mehringer, Marteinn T. Hardarson, Sigurjon A. Gudjonsson, Droplaug N. Magnusdottir, Aslaug Jonasdottir, Adalbjorg Jonasdottir, Ragnar P. Kristjansson, Sverrir T. Sverrisson, Guillaume Holley, Gunnar Palsson, Olafur A. Stefansson, Gudmundur Eyjolfsson, Isleifur Olafsson, Olof Sigurdardottir, Bjarni Torfason, Gisli Masson, Agnar Helgason, Unnur Thorsteinsdottir, Hilma Holm, Daniel F. Gudbjartsson, Patrick Sulem, Olafur T. Magnusson, Bjarni V. Halldorsson, Kari Stefansson (2020-12-14):
Long-read sequencing (LRS) promises to improve characterization of structural variants (SVs), a major source of genetic diversity. We generated LRS data on 3,622 Icelanders using Oxford Nanopore Technologies, and identified a median of 22,636 SVs per individual (a median of 13,353 insertions and 9,474 deletions), spanning a median of 10 Mb per haploid genome. We discovered a set of 133,886 reliably genotyped SV alleles and imputed them into 166,281 individuals to explore their effects on diseases and other traits. We discovered an association with a rare (AF = 0.037%) deletion of the first exon of PCSK9. Carriers of this deletion have 0.93 mmol/L (1.31 SD) lower LDL cholesterol levels than the population average (p-value = 7.0·10−20). We also discovered an association with a multi-allelic SV inside a large repeat region, contained within single long reads, in an exon of ACAN. Within this repeat region we found 11 alleles that differ in the number of a 57 bp-motif repeat, and observed a linear relationship (0.016 SD per motif inserted, p = 6.2·10−18) between the number of repeats carried and height. These results show that SVs can be accurately characterized at population scale using long read sequence data in a genome-wide non-targeted approach and demonstrate how SVs impact phenotypes.
Asthma risk is a complex interplay between genetic susceptibility and environment. Despite many significantly-associated common variants, the contribution of rarer variants with potentially greater effect sizes has not been as extensively studied. We present an exome-based study adopting 24,576 cases and 120,530 controls to assess the contribution of rare protein-coding variants to the risk of early-onset or all-comer asthma.
Methods: We performed case-control analyses on three genetic units: variant-level, gene-level and pathway-level, using sequence data from the Scandinavian Asthma Genetic Study and UK Biobank participants with asthma. Cases were defined as all-comer asthma (n = 24,576) and early-onset asthma (n = 5,962). Controls were 120,530 UK Biobank participants without reported history of respiratory illness.
Results: Variant-level analyses identified statistically-significant variants at moderate-to-common allele frequency, including protein-truncating variants in FLG and IL33. Asthma risk was significantly increased not only by individual, common FLG protein-truncating variants, but also among the collection of rare-to-private FLG protein-truncating variants (p = 6.8×10−7). This signal was driven by early-onset asthma and did not correlate with circulating eosinophil levels. In contrast, a single splice variant in IL33 was significantly protective (p = 8.0×10−10), while the collection of remaining IL33 protein-truncating variants showed no class effect (p = 0.54). A pathway-based analysis identified that protein-truncating variants in loss-of-function intolerant genes were statistically-significantly enriched among individuals with asthma.
Conclusion: Access to the full allele frequency spectrum of protein-coding variants provides additional clarity about the potential mechanisms of action for FLG and IL33. Beyond these two significant drivers, we detected a significant enrichment of protein-truncating variants in loss-of-function intolerant genes.
Background: Many human diseases are known to have a genetic contribution. While genome-wide studies have identified many disease-associated loci, it remains challenging to elucidate causal genes. In contrast, exome sequencing provides an opportunity to identify new disease genes and large-effect variants of clinical relevance. We therefore sought to determine the contribution of rare genetic variation in a curated set of human diseases and traits using a unique resource of 200,000 individuals with exome sequencing data from the UK Biobank.
Methods and Results: We included 199,832 participants with a mean age of 68 at follow-up. Exome-wide gene-based tests were performed for 64 diseases and 23 quantitative traits using a mixed-effects model, testing rare loss-of-function and damaging missense variants. We identified 51 known and 23 novel associations with 26 diseases and traits at a false-discovery-rate of 1%. There was a striking risk associated with many Mendelian disease genes including: MYPBC3 with over a 100-fold increased odds of hypertrophic cardiomyopathy, PKD1 with a greater than 25-fold increased odds of chronic kidney disease, and BRCA2, BRCA1, ATM and PALB2 with 3 to 10-fold increased odds of breast cancer. Notable novel findings included an association between GIGYF1 and type 2 diabetes (OR 5.6, p = 5.35×10−8), elevated blood glucose, and lower insulin-like-growth-factor-1 levels. Rare variants in CCAR2 were also associated with diabetes risk (OR 13, p = 8.5×10−8), while COL9A3 was associated with cataract (OR 3.4, p = 6.7×10−8). Notable associations for blood lipids and hypercholesterolemia included NR1H3, RRBP1, GIGYF1, SCGN, APH1A,PDE3B and ANGPTL8. A number of novel genes were associated with height, including DTL, PIEZO1, SCUBE3, PAPPA and ADAMTS6, while BSN was associated with body-mass-index. We further assessed putatively pathogenic variants in known Mendelian cardiovascular disease genes and found that between 1.3 and 2.3% of the population carried likely pathogenic variants in known cardiomyopathy, arrhythmia or hypercholesterolemia genes.
Conclusions: Large-scale population sequencing identifies known and novel genes harboring high-impact variation for human traits and diseases. A number of novel findings, including GIGYF1, represent interesting potential therapeutic targets. Exome sequencing at scale can identify a meaningful proportion of the population that carries a pathogenic variant underlying cardiovascular disease.
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):
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× 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.
By meta-analyzing the whole-exomes of 24,248 cases and 97,322 controls, we implicate ultra-rare coding variants (URVs) in ten genes as conferring substantial risk for schizophrenia (odds ratios 3—50, p < 2.14 × 10−6), and 32 genes at a FDR < 5%. These genes have the greatest expression in central nervous system neurons and have diverse molecular functions that include the formation, structure, and function of the synapse. The associations of NMDA receptor subunitGRIN2A and AMPA receptor subunit GRIA3 provide support for the dysfunction of the glutamatergic system as a mechanistic hypothesis in the pathogenesis of schizophrenia. We find significant evidence for an overlap of rare variant risk between schizophrenia, autism spectrum disorders (ASD), and severe neurodevelopmental disorders (DD/ID), supporting a neurodevelopmental etiology for schizophrenia. We show that protein-truncating variants in GRIN2A, TRIO, and CACNA1G confer risk for schizophrenia whereas specific missense mutations in these genes confer risk for DD/ID. Nevertheless, few of the strongly associated schizophrenia genes appear to confer risk for DD/ID. We demonstrate that genes prioritized from common variant analyses of schizophrenia are enriched in rare variant risk, suggesting that common and rare genetic risk factors at least partially converge on the same underlying pathogenic biological processes. Even after excluding statistically-significantly associated genes, schizophrenia cases still carry a substantial excess of URVs, implying that more schizophrenia risk genes await discovery using this approach.
“Exome sequencing identifies rare coding variants in 10 genes which confer substantial risk for schizophrenia”, Tarjinder Singh, Timothy Poterba, David Curtis, Huda Akil, Mariam Al Eissa, Jack D. Barchas, Nicholas Bass, Tim B. Bigdeli, Gerome Breen, Evelyn J. Bromet, Peter F. Buckley, William E. Bunney, Jonas Bybjerg-Grauholm, William F. Byerley, Sinead B. Chapman, Wei J. Chen, Claire Churchhouse, Nicholas Craddock, Charles Curtis, Caroline M. Cusick, Lynn DeLisi, Sheila Dodge, Michael A. Escamilla, Saana Eskelinen, Ayman H. Fanous, Stephen V. Faraone, Alessia Fiorentino, Laurent Francioli, Stacey B. Gabriel, Diane Gage, Sarah A. Gagliano Taliun, Andrea Ganna, Giulio Genovese, David C. Glahn, Jakob Grove, Mei-Hua Hall, Eija Hamalainen, Henrike O. Heyne, Matti Holi, David M. Hougaard, Daniel P. Howrigan, Hailiang Huang, Hai-Gwo Hwu, Rene S. Kahn, Hyun Min Kang, Konrad Karczewski, George Kirov, James A. Knowles, Francis S. Lee, Douglas S. Lehrer, Francesco Lescai, Dolores Malaspina, Stephen R. Marder, Steven A. McCarroll, Helena Medeiros, Lili Milani, Christopher P. Morley, Derek W. Morris, Preben Bo Mortensen, Richard M. Myers, Merete Nordentoft, Niamh L. O'Brien, Ana Maria Olivares, Dost Ongur, Willem Hendrik Ouwehand, Duncan S. Palmer, Tiina Paunio, Digby Quested, Mark H. Rapaport, Elliott Rees, Brandi Rollins, F. Kyle Satterstrom, Alan Schatzberg, Edward Scolnick, Laura Scott, Sally I. Sharp, Pamela Sklar, Jordan W. Smoller, Janet L. Sobell, Matthew Solomonson, Christine R. Stevens, Jaana Suvisaari, Grace Tiao, Stanley J. Watson, Nicholas A. Watts, Douglas H. Blackwood, Anders Borglum, Bruce M. Cohen, Aiden P. Corvin, Tonu Esko, Nelson B. Freimer, Stephen J. Glatt, Christina M. Hultman, Andrew McQuillin, Aarno Palotie, Carlos N. Pato, Michele T. Pato, Ann E. Pulver, David St. Clair, Ming T. Tsuang, Marquis P. Vawter, James T. Walters, Thomas Werge, Roel A. Ophoff, Patrick F. Sullivan, Michael J. Owen, Michael Boehnke, Michael O'Donovan, Benjamin M. Neale, Mark J. Daly (2020-09-18; psychiatry / schizophrenia):
By meta-analyzing the whole-exomes of 24,248 cases and 97,322 controls, we implicate ultra-rare coding variants (URVs) in ten genes as conferring substantial risk for schizophrenia (odds ratios 3–50, p <2.14×10-6), and 32 genes at an FDR < 5%. These genes have the greatest expression in central nervous system neurons and have diverse molecular functions that include the formation, structure, and function of the synapse. The associations of NMDA receptor subunit GRIN2A and AMPA receptor subunitGRIA3 provide support for the dysfunction of the glutamatergic system as a mechanistic hypothesis in the pathogenesis of schizophrenia. We find statistically-significant evidence for an overlap of rare variant risk between schizophrenia, autism spectrum disorders (ASD), and severe neurodevelopmental disorders (DD/ID), supporting a neurodevelopmental etiology for schizophrenia. We show that protein-truncating variants in GRIN2A, TRIO, and CACNA1G confer risk for schizophrenia whereas specific missense mutations in these genes confer risk for DD/ID. Nevertheless, few of the strongly associated schizophrenia genes appear to confer risk for DD/ID. We demonstrate that genes prioritized from common variant analyses of schizophrenia are enriched in rare variant risk, suggesting that common and rare genetic risk factors at least partially converge on the same underlying pathogenic biological processes. Even after excluding statistically-significantly associated genes, schizophrenia cases still carry a substantial excess of URVs, implying that more schizophrenia risk genes await discovery using this approach.
Schizophrenia is a psychiatric disorder whose pathophysiology is largely unknown. It has a heritability of 60–80%, much of which is attributable to common risk alleles, suggesting genome-wide association studies can inform our understanding of aetiology1. Here, in 69,369 people with schizophrenia and 236,642 controls, we report common variant associations at 270 distinct loci. Using fine-mapping and functional genomic data, we prioritise 19 genes based on protein-coding or UTR variation, and 130 genes in total as likely to explain these associations. Fine-mapped candidates were enriched for genes associated with rare disruptive coding variants in people with schizophrenia, including the glutamate receptor subunit GRIN2A and transcription factor SP4, and were also enriched for genes implicated by such variants in autism and developmental disorder. Associations were concentrated in genes expressed in CNS neurons, both excitatory and inhibitory, but not other tissues or cell types, and implicated fundamental processes related to neuronal function, particularly synaptic organisation, differentiation and transmission. We identify biological processes of pathophysiological relevance to schizophrenia, show convergence of common and rare variant associations in schizophrenia and neurodevelopmental disorders, and provide a rich resource of priority genes and variants to advance mechanistic studies.
Schizophrenia is a psychiatric disorder whose pathophysiology is largely unknown. It has a heritability of 60–80%, much of which is attributable to common risk alleles, suggesting genome-wide association studies can inform our understanding of aetiology. Here, in 69,369 people with schizophrenia and 236,642 controls, we report common variant associations at 270 distinct loci. Using fine-mapping and functional genomic data, we prioritise 19 genes based on protein-coding or UTR variation, and 130 genes in total as likely to explain these associations. Fine-mapped candidates were enriched for genes associated with rare disruptive coding variants in people with schizophrenia, including the glutamate receptor subunit GRIN2A and transcription factor SP4, and were also enriched for genes implicated by such variants in autism and developmental disorder. Associations were concentrated in genes expressed in CNS neurons, both excitatory and inhibitory, but not other tissues or cell types, and implicated fundamental processes related to neuronal function, particularly synaptic organisation, differentiation and transmission. We identify biological processes of pathophysiological relevance to schizophrenia, show convergence of common and rare variant associations in schizophrenia and neurodevelopmental disorders, and provide a rich resource of priority genes and variants to advance mechanistic studies.
Exome association studies to date have generally been underpowered to systematically evaluate the phenotypic impact of very rare coding variants. We leveraged extensive haplotype sharing between 49,960 exome-sequenced UK Biobank participants and the remainder of the cohort (total N~500K) to impute exome-wide variants at high accuracy (R2>0.5) down to minor allele frequency (MAF) ~0.00005. Association and fine-mapping analyses of 54 quantitative traits identified 1,189 statistically-significant associations (P<5 x 10-8) involving 675 distinct rare protein-altering variants (MAF<0.01) that passed stringent filters for likely causality; 600 ofthe 675 variants (89%) were not present in the NHGRI-EBIGWAS Catalog. We replicated the effect directions of 28 of 28 height-associated variants genotyped in previous exome array studies, including missense variants in newly-associated collagen genes COL16A1 and COL11A2. Across all traits, 49% of associations (578/1,189) occurred in genes with two or more hits; follow-up analyses of these genes identified long allelic series containing up to 45 distinct likely-causal variants within the same gene (on average exhibiting 93%-concordant effect directions). In particular, 24 rare coding variants in IFRD2 independently associated with reticulocyte indices, suggesting an important role of IFRD2 in red blood cell development, and 11 rare coding variants in NPR2 (a gene previously implicated in Mendelian skeletal disorders) exhibited intermediate-to-strong effects on height (0.18-1.09 s.d.). Our results demonstrate the utility of within-cohort imputation in population-scale GWAS cohorts, provide a catalog of likely-causal, large-effect coding variant associations, and foreshadow the insights that will be revealed as genetic biobank studies continue to grow.
Aging is characterized by degeneration in cellular and organismal functions leading to increased disease susceptibility and death. Although our understanding of aging biology in model systems has increased dramatically, large-scale sequencing studies to understand human aging are now just beginning. We applied exome sequencing and association analyses (ExWAS) to identify age-related variants on58,470 participants of the DiscovEHR cohort. Linear Mixed Model regression analyses of age at last encounter revealed variants in genes known to be linked with clonal hematopoiesis of indeterminate potential, which are associated with myelodysplastic syndromes, as top signals in our analysis, suggestive of age-related somatic mutation accumulation in hematopoietic cells despite patients lacking clinical diagnoses. In addition to APOE, we identified rare DISP2 rs183775254 (p = 7.40×10−10) and ZYG11A rs74227999 (p = 2.50×10−08) variants that were negatively associated with age in either both sexes combined and females, respectively, which were replicated with directional consistency in two independent cohorts. Epigenetic mapping showed these variants are located within cell-type-specific enhancers, suggestive of important transcriptional regulatory functions. To discover variants associated with extreme age, we performed exome-sequencing on persons of Ashkenazi Jewish descent ascertained for extensive lifespans. Case-Control analyses in 525 Ashkenazi Jews cases (Males ≥ 92 years, Females ≥ 95years) were compared to 482 controls. Our results showed variants in APOE (rs429358, rs6857), and TMTC2 (rs7976168) passed Bonferroni-adjusted p-value, as well as several nominally-associated population-specific variants. Collectively, our Age-ExWAS, the largest performed to date, confirmed and identified previously unreported candidate variants associated with human age.
Purpose: Carrier status associates strongly with genetic ancestry, yet current carrier screening guidelines recommend testing for a limited set of conditions based on a patient’s self-reported ethnicity. Ethnicity, which can reflect both genetic ancestry and cultural factors (eg., religion), may be imperfectly known or communicated by patients. We sought to quantitatively assess the efficacy and equity with which ethnicity-based carrier screening captures recessive disease risk.
Methods: For 93,419 individuals undergoing a 96-gene expanded carrier screen (ECS), correspondence was assessed among carrier status, self-reported ethnicity, and a dual-component genetic ancestry (eg., 75% African/25% European) calculated from sequencing data.
Results: Self-reported ethnicity was an imperfect indicator of genetic ancestry, with 9% of individuals having >50% genetic ancestry from a lineage inconsistent with self-reported ethnicity. Limitations of self-reported ethnicity led to missed carriers in at-risk populations: for 10 ECS conditions, patients with intermediate genetic ancestry backgrounds—who did not self-report the associated ethnicity—had statistically-significantly elevated carrier risk. Finally, for 7 of the 16 conditions included in current screening guidelines, most carriers were not from the population the guideline aimed to serve.
Conclusion: Substantial and disproportionate risk for recessive disease is not detected when carrier screening is based on ethnicity, leading to inequitable reproductive care.
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):
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: There is considerable interest in whether genetic data can be used to improve standard cardiovascular disease risk calculators, as the latter are routinely used in clinical practice to manage preventative treatment.
Methods: This research has been conducted using the UK Biobank (UKB) resource. We developed our ownpolygenic risk score (PRS) for coronary artery disease (CAD), using novel and established methods to combine published genome-wide association study (GWAS) data with data from 114,196 UK Biobank individuals, also leveraging a large resource of other GWAS datasets along with functional information, to aid in the identification of causal variants, and thence define weights for > 8M genetic variants. We utilised a further 60,000 UKB individualsto develop an integrated risk tool (IRT) that combined ourPRS with established risk tools (either the American Heart Association/American College of Cardiology’s pooled cohort equations (PCE) or the UK’sQRISK3) which was then tested in an additional, independent, set of212,563 UKB individuals. We evaluated prediction performance in individuals of European ancestry, both as a whole and stratified by age and sex.
Findings: The novel CADPRS showed superior predictive power for CAD events, compared to other published PRSs. As an individual risk factor, it has similar predictive power to each of systolic blood pressure, HDL cholesterol, and LDL cholesterol, but is more predictivethan total cholesterol and smoking history. Our novel CADPRS is largely uncorrelated with PCE, QRISK3, and family history, and, whencombined with PCE into an integrated risk tool, had superiorpredictive accuracy. In individuals reclassified as high risk, CAD event rates were markedly and statistically-significantly higher compared to those reclassified as low risk. Overall, 9.7% of incident CAD cases were misclassified as low risk by PCE and correctlyclassified as high risk by the IRT, in contrast to 3.7% misclassifiedby the IRT and correctly classified by PCE. The overall netreclassification improvement for the IRT was 5.7% (95%CI 4.4–7.0), but when individuals were stratified into four age-by-sex subgroups the improvement was larger for all subgroups (range 7.7%-17.3%), with best performance in younger middle-aged men aged 40–54yo (17.3%, 95% CI 13.0–21.5). Broadly similar results were found using a different risk tool (QRISK3), and also for cardiovascular disease events defined more broadly.
Interpretation: An integrated risk tool that includes polygenic risk outperforms current, clinical risk stratification tools, and offers greater opportunity for early interventions. Given the plummeting costs of genetic tests, future iterations of CAD risk tools would be enhanced with the addition of a person’s polygenic risk.
Most patients with rare diseases do not receive a molecular diagnosis and the aetiological variants and mediating genes for more than half such disorders remain to be discovered. We implemented whole-genome sequencing (WGS) in a national healthcare system to streamline diagnosis and to discover unknown aetiological variants, in the coding and non-coding regions of the genome. In a pilot study for the 100,000 Genomes Project, we generated WGS data for 13,037 participants, of whom 9,802 had a rare disease, and provided a genetic diagnosis to 1,138 of the 7,065 patients with detailed phenotypic data. We identified 95 Mendelian associations between genes and rare diseases, of which 11 have been discovered since 2015 and at least 79 are confirmed aetiological. Using WGS of UK Biobank1, we showed that rare alleles can explain the presence of some individuals in the tails of a quantitative red blood cell (RBC) trait. Finally, we reported 4 novel non-coding variants which cause disease through the disruption of transcription of ARPC1B, GATA1, LRBA and MPL. Our study demonstrates a synergy by using WGS for diagnosis and aetiological discovery in routine healthcare.
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):
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.
Genome-wide association studies often explore links between particular genes and phenotypes of interest. Known genetic variants, however, are responsible for only a small fraction of human lifespan variation evident from genetic twin studies. To account for the missing longevity variance, we hypothesized that the cumulative effect of deleterious variants may affect human longevity. Here, we report that the burden of rarest protein-truncating variants (PTVs) negatively impacts both human healthspan and lifespan in two large independent cohorts. Longer-living subjects have both fewer rarest PTVs and lessdamaging PTVs. In contrast, we show that the burden of frequent PTVsand rare non-PTVs is less deleterious, lacking association withlongevity. The combined effect of rare PTVs is similar to that of known variants associated with longer lifespan and accounts for 1 − 2 years of lifespan variability. We further find that somatic accumulation of PTVs accounts for a minute fraction of mortality and morbidity acceleration and hence provides little support for its causal role in aging. Thus, damaging mutations, germline and somatic, can only contribute to aging as a result of higher-order effects including interactions of multiple forms of damage.
“Schizophrenia risk conferred by protein-coding de novo mutations”, Daniel P. Howrigan, Samuel A. Rose, Kaitlin E. Samocha, Menachem Fromer, Felecia Cerrato, Wei J. Chen, Claire Churchhouse, Kimberly Chambert, Sharon D. Chandler, Mark J. Daly, Ashley Dumont, Giulio Genovese, Hai-Gwo Hwu, Nan Laird, Jack A. Kosmicki, Jennifer L. Moran, Cheryl Roe, Tarjinder Singh, Shi-Heng Wang, Stephen V. Faraone, Stephen J. Glatt, Steven A. McCarroll, Ming Tsuang, Benjamin M. Neale (2018-12-13; psychiatry / schizophrenia):
Protein-coding de novo mutations (DNMs) in the form of single nucleotide changes and short insertions/deletions are significant genetic risk factors for autism, intellectual disability, developmental delay, and epileptic encephalopathy. In contrast, the burden of DNMs has thus far only had a modest documented impact on schizophrenia (SCZ) risk. Here, we analyze whole-exome sequence from 1,695 SCZaffected parent-offspring trios from Taiwan along with DNMs from 1,077 published SCZ trios to better understand the contribution of coding DNMs toSCZ risk. Among 2,772 SCZ affected probands, the increased burden of DNMs is modest. Gene set analyses show that themodest increase in risk from DNMs inSCZ probands is concentrated in genes that are either highly brain expressed, under strong evolutionary constraint, and/or overlap with genes identified as DNM risk factors in other neurodevelopmental disorders. No single gene meets the criteria for genome-wide statistical-significance, but we identify 16 genes that are recurrently hit by a protein-truncating DNM, which is a 3.15× higher rate than mutation modelexpectation of 5.1 genes (permuted 95% CI = 1–10 genes, permuted p = 3e-5). Overall, DNMs explain only a small fraction ofSCZ risk, and this risk is polygenic in nature suggesting that coding variation across many different genes will be a risk factor for SCZ in the population.
There are thousands of rare human disorders caused by a single deleterious, protein-coding genetic variant 1. However, patients with the same genetic defect can have different clinical presentation 2–4, and some individuals carrying known disease-causing variants can appear unaffected 5. What explains these differences? Here, we show in a cohort of 6,987 children with heterogeneous severe neurodevelopmental disorders expected to be almost entirely monogenic that 7.7% of variance in risk is attributable to inherited common genetic variation. We replicated this genome wide common variant burden by showing that it is over-transmitted from parents to children in an independent sample of 728 trios from the same cohort. Our common variant signal is significantly positively correlated with genetic predisposition to fewer years of schooling, decreased intelligence, and risk of schizophrenia. We found that common variant risk was not significantly different between individuals with and without a known protein-coding diagnostic variant, suggesting that common variant risk is not confined to patients without a monogenic diagnosis. In addition, previously published common variant scores for autism, height, birth weight, and intracranial volume were all correlated with those traits within our cohort, suggesting that phenotypic expression in individuals with monogenic disorders is affected by the same variants as the general population. Our results demonstrate that common genetic variation affects both overall risk and clinical presentation in disorders typically considered to be monogenic.
The vast majority of human mutations have minor allele frequencies (MAF) under 1%, with the plurality observed only once (i.e., “singletons”). While Mendelian diseases are predominantly caused by rare alleles, their role in complex phenotypes remains largely unknown. We develop and rigorously validate an approach to jointly estimate the contribution of alleles with different frequencies, including singletons, to phenotypic variation. We apply our approach to transcriptional regulation, an intermediate between genetic variation and complex disease. Using whole genome DNA and RNA sequencing data from 360 European individuals, we find that singletons alone contribute ~23% of all cis-heritability across genes (dwarfing the contributions of other frequencies). We then integrate external estimates of global MAF from worldwide samples to improve our inference, and find that average cis-heritability is 15.3%. Strikingly, 50.9% of cis-heritability is contributed by globally rare variants (MAF<0.1%), implicatingpurifying selection as a pervasive force shaping the regulatory architecture of most human genes.
One Sentence Summary
The vast majority of variants so far discovered in humans are rare, and together they have a substantial impact on gene regulation.
Understanding the role of rare variants is important in elucidating the genetic basis of human diseases and complex traits. It is widely believed that negative selection can cause rare variants to have larger per-allele effect sizes than common variants. Here, we develop a method to estimate the minor allele frequency (MAF) dependence ofSNPeffect sizes. We use a model in which per-allele effect sizes have variance proportional to [p(1−p)]α, where p is the MAF and negative values of α imply larger effect sizes for rare variants. We estimate α by maximizing its profile likelihood in a linear mixed model framework using imputed genotypes, including rare variants (MAF >0.07%). We applied this method to 25 UK Biobank diseases and complex traits (n = 113,851). All traits produced negative α estimates with 20 significantly negative, implying larger rare variant effect sizes. The inferred best-fit distribution of true α values across traits had mean −0.38 (s.e. 0.02) and standard deviation 0.08 (s.e. 0.03), with statistically-significant heterogeneity across traits (p = 0.0014). Despite larger rare variant effect sizes, we show that for most traits analyzed, rare variants (MAF <1%) explain less than 10% of total SNP-heritability. Using evolutionary modeling and forward simulations, we validated the α model of MAF-dependent trait effects and estimated the level of coupling between fitness effects and trait effects. Based on this analysis an average genome-wide negative selection coefficient on the order of 10−4 or stronger is necessary to explain the α values that we inferred.
We used a case-control genome-wide association (GWA) design with cases consisting of 1238 individuals from the top 0.0003 (~170 mean IQ) of the population distribution of intelligence and 8172 unselected population-based controls. The single-nucleotide polymorphism heritability for the extreme IQ trait was 0.33 (0.02), which is the highest so far for a cognitive phenotype, and statistically-significant genome-wide genetic correlations of 0.78 were observed with educational attainment and 0.86 with population IQ. Three variants in locus ADAM12 achieved genome-wide statistical-significance, although they did not replicate with published GWA analyses of normal-range IQ or educational attainment. A genome-wide polygenic score constructed from the GWA results accounted for 1.6% of the variance of intelligence in the normal range in an unselected sample of 3414 individuals, which is comparable to the variance explained by GWA studies of intelligence with substantially larger sample sizes. The gene family plexins, members of which are mutated in several monogenic neurodevelopmental disorders, was statistically-significantly enriched for associations with high IQ. This study shows the utility of extreme trait selection for genetic study of intelligence and suggests that extremely high intelligence is continuous genetically with normal-range intelligence in the population.
“Quantifying the impact of rare and ultra-rare coding variation across the phenotypic spectrum”, Andrea Ganna, F. Kyle Satterstrom, Seyedeh M. Zekavat, Indraniel Das, Mitja I. Kurki, Claire Churchhouse, Jessica Alfoldi, Alicia R. Martin, Aki S. Havulinna, Andrea Byrnes, Wesley K. Thompson, Philip R. Nielsen, Konrad J. Karczewski, Elmo Saarentaus, Manuel A. Rivas, Namrata Gupta, Olli Pietiläinen, Connor A. Emdin, Francesco Lescai, Jonas Bybjerg-Grauholm, Jason Flannick, on behalf of GoT2D/T2D-GENES consortium, Josep Mercader, Miriam Udlerg, on behalf of SIGMA consortium, Helmsley IBD Exome Sequencing Project, FinMetSeq Consortium, iPSYCH-Broad Consortium, Markku Laakso, Veikko Salomaa, Christina Hultman, Samuli Ripatti, Eija Hämäläinen, Jukka S. Moilanen, Jarmo Körkkö, Outi Kuismin, Merete Nordentoft, David M. Hougaard, Ole Mors, Thomas Werge, Preben Bo Mortensen, Daniel MacArthur, Mark J. Daly, Patrick F. Sullivan, Adam E. Locke, Aarno Palotie, Anders D. Børglum, Sekar Kathiresan, Benjamin M. Neale (2017-06-09; psychiatry / schizophrenia):
Protein truncating variants (PTVs) are likely to modify gene function and have been linked to hundreds of Mendelian disorders1,2. However, the impact of PTVs on complex traits has been limited by the availablesample size of whole-exome sequencing studies (WES) 3. Here we assemble WES data from 100,304 individuals to quantify the impact ofrare PTVs on 13 quantitative traits and 10 diseases. We focus on thosePTVs that occur in PTV-intolerant (PI) genes, as these are more likelyto be pathogenic. Carriers of at least one PI-PTV were found to have an increased risk of autism, schizophrenia, bipolar disorder, intellectual disability and ADHD (p-value (p) range: 5×10−3−9×10−12). In controls, without these disorders, we found that this burden associated with increased risk of mental, behavioral and neurodevelopmental disorders as captured by electronic health record information. Furthermore, carriers of PI-PTVs tended to be shorter (p = 2×10−5), have fewer years of education (p = 2×10−4) and be younger (p = 2×10−7); the latter observation possibly reflecting reduced survival or study participation. While other gene-sets derived from in vivo experiments did not show any associations with PTV-burden, gene sets implicated inGWAS of cardiovascular-related traits and inflammatory bowel disease showed a significant PTV-burden with corresponding traits, mainly driven by established genes involved in familial forms of these disorders. We leveraged population health registries from 14,117 individuals to study the phenome-wide impact of PIPTVs and identified an increase inthe number of hospital visits among PI-PTV carriers. In conclusion, we provide the most thorough investigation to date of the impact of rare deleterious coding variants on complex traits, suggesting widespread pleiotropic risk.
Helmsley IBD Exome Sequencing Project: Dermot McGovern, Judy H Cho, Ann Pulver, Vincent Plagnol, Tony Segal, Gil Atzmon, Dan Turner, Ben Glaser, Inga Peter, Ramnik Xavier, Harry Sokol, Rinse Weersma, Andre Franke, John Rioux, Tariq Ahmad, Martti Färkkilä, Kimmo Kontula.
FinMetSeq Consortium: Haley J Abel, Michael Boehnke, Lei Chen, Charleston WK Chiang, Colby C Chiang, Susan K Dutcher, Nelson B Freimer, Robert S Fulton, Liron Ganel, Ira M Hall, Anne U Jackson, Krishna L Kanchi, Chul Joo Kang, Daniel C Koboldt, Hannele Laivuori, David E Larson, Karyn Meltz Steinberg, Joanne Nelson, Thomas J Nicholas, Arto Pietilä, Matti Pirinen, Vasily Ramensky, Debashree Ray, Chiara Sabatti, Laura J Scott, Susan Service, Laurel Stell, Nathan O Stitziel, Heather M Stringham, Ryan Welch, Richard K Wilson, Pranav Yajnik.
iPSYCH-Broad Consortium: Marianne G Pedersen, Marie Bækvad-Hansen, Christine S Hansen.
Pedigree-based analyses of intelligence have reported that genetic differences account for 50–80% of the phenotypic variation. For personality traits these effects are smaller, with 34–48% of the variance being explained by genetic differences. However, molecular genetic studies using unrelated individuals typically report a heritability estimate of around 30% for intelligence and between 0% and 15% for personality variables. Pedigree-based estimates and molecular genetic estimates may differ because current genotyping platforms are poor at tagging causal variants, variants with low minor allele frequency, copy number variants, and structural variants. Using ~20 000 individuals in the Generation Scotland family cohort genotyped for ~700 000 single nucleotide polymorphisms (SNPs), we exploit the high levels of linkage disequilibrium (LD) found in members of the same family to quantify the total effect of genetic variants that are not tagged in GWASs of unrelated individuals. In our models, genetic variants in low LDwith genotyped SNPs explain over half of the genetic variance in intelligence, education, and neuroticism. By capturing these additional genetic effects our models closely approximate the heritability estimates from twin studies for intelligence and education, but not for neuroticism and extraversion. We then replicated our finding using imputed molecular genetic data from unrelated individuals to show that ~50% of differences in intelligence, and ~40% of the differences in education, can be explained by genetic effects when a larger number of rare SNPs are included. From an evolutionary genetic perspective, a substantial contribution of rare genetic variants to individual differences in intelligence and education is consistent with mutation-selection balance.
2017-mcrae.pdf: “Prevalence and architecture of de novo mutations in developmental disorders”, Jeremy F. McRae, Stephen Clayton, Tomas W. Fitzgerald, Joanna Kaplanis, Elena Prigmore, Diana Rajan, Alejandro Sifrim, Stuart Aitken, Nadia Akawi, Mohsan Alvi, Kirsty Ambridge, Daniel M. Barrett, Tanya Bayzetinova, Philip Jones, Wendy D. Jones, Daniel King, Netravathi Krishnappa, Laura E. Mason, Tarjinder Singh, Adrian R. Tivey, Munaza Ahmed, Uruj Anjum, Hayley Archer, Ruth Armstrong, Jana Awada, M. Balasubramanian, Siddharth Banka, Diana Baralle, Angela Barnicoat, Paul Batstone, David Baty, Chris Bennett, Jonathan Berg, Birgitta Bernhard, A. Paul Bevan, Maria BitnerGlindzicz, Edward Blair, Moira Blyth, David Bohanna, Louise Bourdon, David Bourn, Lisa Bradley, Angela Brady, Simon Brent, Carole Brewer, Kate Brunstrom, David J. Bunyan, John Burn, Natalie Canham, Bruce Castle, Kate Chandler, Elena Chatzimichali, Deirdre Cilliers, Angus Clarke, Susan Clasper, Jill ClaytonSmith, Virginia Clowes, Andrea Coates, Trevor Cole, Irina Colgiu, Amanda Collins, Morag N. Collinson, Fiona Connell, Nicola Cooper, Helen Cox, Lara Cresswell, Gareth Cross, Yanick Crow, Mariella DAlessandro, Tabib Dabir, Rosemarie Davidson, Sally Davies, Dylan de Vries, John Dean, Charu Deshpande, Gemma Devlin, Abhijit Dixit, Angus Dobbie, Alan Donaldson, Dian Donnai, Deirdre Donnelly, Carina Donnelly, Angela Douglas, Sofia Douzgou, Alexis Duncan, Jacqueline Eason, Sian Ellard, Ian Ellis, Frances Elmslie, Karenza Evans, Sarah Everest, Tina Fendick, Richard Fisher, Frances Flinter, Nicola Foulds, Andrew Fry, Alan Fryer, Carol Gardiner, Lorraine Gaunt, Neeti Ghali, Richard Gibbons, Harinder Gill, Judith Goodship, David Goudie, Emma Gray, Andrew Green, Philip Greene, Lynn Greenhalgh, Susan Gribble, Rachel Harrison, Lucy Harrison, Victoria Harrison, Rose Hawkins, Liu He, Stephen Hellens, Alex Henderson, Sarah Hewitt, Lucy Hildyard, Emma Hobson, Simon Holden, Muriel Holder, Susan Holder, Georgina Hollingsworth, Tessa Homfray, Mervyn Humphreys, Jane Hurst, Ben Hutton, Stuart Ingram, Melita Irving, Lily Islam, Andrew Jackson, Joanna Jarvis, Lucy Jenkins, Diana Johnson, Elizabeth Jones, Dragana Josifova, Shelagh Joss, Beckie Kaemba, Sandra Kazembe, Rosemary Kelsell, Bronwyn Kerr, Helen Kingston, Usha Kini, Esther Kinning, Gail Kirby, Claire Kirk, Emma Kivuva, Alison Kraus, Dhavendra Kumar, V. K. Ajith Kumar, Katherine Lachlan, Wayne Lam, Anne Lampe, Caroline Langman, Melissa Lees, Derek Lim, Cheryl Longman, Gordon Lowther, Sally A. Lynch, Alex Magee, Eddy Maher, Alison Male, Sahar Mansour, Karen Marks, Katherine Martin, Una Maye, Emma McCann, Vivienne McConnell, Meriel McEntagart, Ruth McGowan, Kirsten McKay, Shane McKee, Dominic J. McMullan, Susan McNerlan, Catherine McWilliam, Sarju Mehta, Kay Metcalfe, Anna Middleton, Zosia Miedzybrodzka, Emma Miles, Shehla Mohammed, Tara Montgomery, David Moore, Sian Morgan, Jenny Morton, Hood Mugalaasi, Victoria Murday, Helen Murphy, Swati Naik, Andrea Nemeth, Louise Nevitt, Ruth NewburyEcob, Andrew Norman, Rosie OShea, Caroline Ogilvie, KaiRen Ong, SooMi Park, Michael J. Parker, Chirag Patel, Joan Paterson, Stewart Payne, Daniel Perrett, Julie Phipps, Daniela T. Pilz, Martin Pollard, Caroline Pottinger, Joanna Poulton, Norman Pratt, Katrina Prescott, Sue Price, Abigail Pridham, Annie Procter, Hellen Purnell, Oliver Quarrell, Nicola Ragge, Raheleh Rahbari, Josh Randall, Julia Rankin, Lucy Raymond, Debbie Rice, Leema Robert, Eileen Roberts, Jonathan Roberts, Paul Roberts, Gillian Roberts, Alison Ross, Elisabeth Rosser, Anand Saggar, Shalaka Samant, Julian Sampson, Richard Sandford, Ajoy Sarkar, Susann Schweiger, Richard Scott, Ingrid Scurr, Ann Selby, Anneke Seller, Cheryl Sequeira, Nora Shannon, Saba Sharif, Charles ShawSmith, Emma Shearing, Debbie Shears, Eamonn Sheridan, Ingrid Simonic, Roldan Singzon, Zara Skitt, Audrey Smith, Kath Smith, Sarah Smithson, Linda Sneddon, Miranda Splitt, Miranda Squires, Fiona Stewart, Helen Stewart, Volker Straub, Mohnish Suri, Vivienne Sutton, Ganesh Jawahar Swaminathan, Elizabeth Sweeney, Kate TattonBrown, Cat Taylor, Rohan Taylor, Mark Tein, I. Karen Temple, Jenny Thomson, Marc Tischkowitz, Susan Tomkins, Audrey Torokwa, Becky Treacy, Claire Turner, Peter Turnpenny, Carolyn Tysoe, Anthony Vandersteen, Vinod Varghese, Pradeep Vasudevan, Parthiban Vijayarangakannan, Julie Vogt, Emma Wakeling, Sarah Wallwark, Jonathon Waters, Astrid Weber, Diana Wellesley, Margo Whiteford, Sara Widaa, Sarah Wilcox, Emily Wilkinson, Denise Williams, Nicola Williams, Louise Wilson, Geoff Woods, Christopher Wragg, Michael Wright, Laura Yates, Michael Yau, Chris Nellker, Michael Parker, Helen V. Firth, Caroline F. Wright, David R. FitzPatrick, Jeffrey C. Barrett Matthew E. Hurles (2017-01-25; genetics / selection; backlinks):
The genomes of individuals with severe, undiagnosed developmental disorders are enriched in damaging de novo mutations (DNMs) in developmentally important genes. Here we have sequenced the exomes of 4,293 families containing individuals with developmental disorders, and meta-analysed these data with data from another 3,287 individuals with similar disorders. We show that the most important factors influencing the diagnostic yield of DNMs are the sex of the affected individual, the relatedness of their parents, whether close relatives are affected and the parental ages. We identified 94 genes enriched in damaging DNMs, including 14 that previously lacked compelling evidence of involvement in developmental disorders. We have also characterized the phenotypic diversity among these disorders. We estimate that 42% of our cohort carry pathogenic DNMs in coding sequences; approximatelyhalf of these DNMs disrupt gene function and the remainder result in altered protein function. We estimate that developmental disorders caused by DNMs have an average prevalence of 1 in 213 to 1 in 448 births, depending on parental age. Given current global demographics, this equates to almost 400,000 children born per year.
Autism spectrum disorder (ASD) risk is influenced by both common polygenic and de novo variation. The purpose of this analysis was to clarify the influence of common polygenic risk for ASDs and to identify subgroups of cases, including those with strong acting de novo variants, in which different types of polygenic risk are relevant. To do so, we extend the transmission disequilibrium approach to encompass polygenic risk scores, and introduce the polygenic transmission disequilibrium test. Using data from more than 6,400 children with ASDs and 15,000 of their family members, we show thatpolygenic risk for ASDs,schizophrenia, and greater educational attainment is over transmitted to children with ASDs in two independent samples, but not to their unaffected siblings. These findings hold independent of proband IQ. We find that common polygenic variation contributes additively to risk in ASD cases that carry a very strong acting de novo variant. Lastly, we find evidence that elements of polygenic risk are independent and differ in their relationship with proband phenotype. These results confirm that ASDs’ genetic influences are highly additive and suggest that they create risk through at least partially distinct etiologic pathways.
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):
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.
“The contribution of de novo coding mutations to autism spectrum disorder”, Iossifov, Ivan O'Roak, Brian J. Sanders, Stephan J. Ronemus, Michael Krumm, Niklas Levy, Dan Stessman, Holly A. Witherspoon, Kali T. Vives, Laura Patterson, Karynne E. Smith, Joshua D. Paeper, Bryan Nickerson, Deborah A. Dea, Jeanselle Dong, Shan Gonzalez, Luis E. Mandell, Jeffrey D. Mane, Shrikant M. Murtha, Michael T. Sullivan, Catherine A. Walker, Michael F. Waqar, Zainulabedin Wei, Liping Willsey, A. Jeremy Yamrom, Boris Lee, Yoon-ha Grabowska, Ewa Dalkic, Ertugrul Wang, Zihua Marks, Steven Andrews, Peter Leotta, Anthony Kendall, Jude Hakker, Inessa Rosenbaum, Julie Ma, Beicong Rodgers, Linda Troge, Jennifer Narzisi, Giuseppe Yoon, Seungtai Schatz, Michael C. Ye, Kenny McCombie, W. Richard Shendure, Jay Eichler, Evan E. State, Matthew W. Wigler, Michael (2014; psychiatry / schizophrenia):
Whole exome sequencing has proven to be a powerful tool for understanding the genetic architecture of human disease. Here we apply it to more than 2,500 simplex families, each having a child with an autistic spectrum disorder. By comparing affected to unaffected siblings, we show that 13% of de novo missense mutations and 43% of de novo likely gene-disrupting (LGD) mutations contribute to 12% and 9% of diagnoses, respectively. Including copy number variants, coding de novo mutations contribute to about 30% of all simplex and 45% of female diagnoses. Almost all LGD mutations occur opposite wild-typealleles. LGD targets in affected females significantly overlap the targets in males of lower intelligence quotient (IQ), but neither overlaps significantly with targets in males of higher IQ. We estimate that LGD mutation in about 400 genes can contribute to the joint class of affected females and males of lower IQ, with an overlapping and similar number of genes vulnerable to contributory missense mutation. LGD targets in the joint class overlap with published targets for intellectual disability and schizophrenia, and are enriched for chromatin modifiers, FMRP-associated genes and embryonically expressed genes. Most of the significance for the latter comes from affected females.
Study Objectives: Earlier work described a mutation in DEC2 alsoknown as BHLHE41 (basic helix-loophelix family member e41) as causal in a family of short sleepers, who needed just 6 h sleep per night. We evaluated whether there were other variants of this gene in two well-phenotyped cohorts.
Design: Sequencing of the BHLHE41 gene, electroencephalographic data, and delta power analysis and functional studies using cell-based luciferase.
Results: We identified new variants of the BHLHE41 gene in two cohorts who had either acute sleep deprivation (n = 200) or chronic partial sleep deprivation (n = 217). One variant, Y362H, at another location in the same exon occurred in one twin in a dizygotic twin pair and was associated with reduced sleep duration, less recovery sleep following sleep deprivation, and fewer performance lapses during sleep deprivation than the homozygous twin. Both twins had almost identical amounts of non rapid eye movement (NREM) sleep. This variantreduced the ability of BHLHE41 to suppress CLOCK/BMAL1 and NPAS2/BMAL1 transactivation in vitro. Another variant in the same exome had no effect on sleep or response to sleep deprivation and no effect on CLOCK/BMAL1 transactivation. Random mutagenesis identified a number ofother variants of BHLHE41 that affect its function.
Conclusions: There are a number of mutations of BHLHE41. Mutationsreduce total sleep while maintaining NREM sleep and provide resistance to the effects of sleep loss. Mutations that affect sleep also modify the normal inhibition of BHLHE41 of CLOCK/BMAL1 transactivation. Thus, clock mechanisms are likely involved in setting sleep length and the magnitude of sleep homeostasis.
Citation: Pellegrino R, Kavakli IH, Goel N, Cardinale CJ, Dinges DF, Kuna ST, Maislin G, Van Dongen HP, Tufik S, Hogenesch JB, Hakonarson H, Pack AI. A novel BHLHE41 variant is associated withshort sleep and resistance to sleep deprivation in humans. SLEEP 2014;37(8):1327-1336.
Study Objectives: To determine if the large and highly reproducible interindividual differences in rates of performance deficit accumulation during sleep deprivation, as determined by the number of lapses on a sustained reaction time test, the Psychomotor Vigilance Task (PVT), arise from a heritable trait.
Design: Prospective, observational cohort study.
Setting: Academic medical center.
Participants: There were 59 monozygotic (mean age 29.2 ± 6.8 [SD] yr; 15 male and 44 female pairs) and 41 dizygotic (mean age 26.6 ± 7.6 yr; 15 male and 26 female pairs) same-sex twin pairs with a normal polysomnogram.
Interventions: Thirty-eight hr of monitored, continuous sleep deprivation.
Measurements and Results: Patients performed the 10-min PVT every 2 hr during the sleep deprivation protocol. The primary outcome was change from baseline in square root transformed total lapses (response time ≥ 500 ms) per trial. Patient-specific linear rates of performance deficit accumulation were separated from circadian effects using multiple linear regression. Using the classic approach to assess heritability, the intraclass correlation coefficients for accumulating deficits resulted in a broad sense heritability (h2) estimate of 0.834. The mean within-pair and among-pair heritability estimates determined by analysis of variance-based methods was 0.715. When variance components of mixed-effect multilevel models were estimated by maximum likelihood estimation and used to determine the proportions of phenotypic variance explained by genetic and nongenetic factors, 51.1% (standard error = 8.4%, P < 0.0001) of twin variance was attributed to combined additive and dominance genetic effects.
Conclusion: Genetic factors explain a large fraction of interindividual variance among rates of performance deficit accumulations on PVT during sleep deprivation.
Sleep deprivation can impair human health and performance. Habitual total sleep time and homeostatic sleep response to sleep deprivation are quantitative traits in humans. Genetic loci for these traits have been identified in model organisms, but none of these potential animal models have a corresponding human genotype and phenotype.
We have identified a mutation in a transcriptional repressor (hDEC2-P385R) that is associated with a human short sleep phenotype. Activity profiles and sleep recordings of transgenic mice carrying this mutation showed increased vigilance time and less sleep time than control mice in a zeitgeber time-dependent and sleep deprivation-dependent manner.
These mice represent a model of human sleep homeostasis that provides an opportunity to probe the effect of sleep on human physical and mental health.
Background: The genetic basis of variation in human cognitive abilities is poorly understood. RIMS1 encodes a synapse active-zone protein with important roles in the maintenance of normal synaptic function: mice lacking this protein have greatly reduced learning ability and memory function.
Objective: An established paradigm examining the structural and functional effects of mutations in genes expressed in the eye and the brain was used to study a kindred with an inherited retinal dystrophy due to RIMS1 mutation.
Materials and Methods: Neuropsychological tests and high-resolution MRI brain scanning were undertaken in the kindred. In a population cohort, neuropsychological scores were associated with common variation in RIMS1. Additionally, RIMS1 was sequenced intop-scoring individuals. Evolution of RIMS1 was assessed, and its expression in developing human brain was studied.
Results: Affected individuals showed significantly enhanced cognitive abilities across a range of domains. Analysis suggests that factors other than RIMS1 mutation were unlikely to explain enhanced cognition. No association with common variation and verbal IQ was found in the population cohort, and no other mutations in RIMS1 weredetected in the highest scoring individuals from this cohort. RIMS1protein is expressed in developing human brain, but RIMS1 does not seem to have been subjected to accelerated evolution in man.
Conclusions: A possible role for RIMS1 in the enhancement of cognitive function at least in this kindred is suggested. Although further work is clearly required to explore these findings before a role for RIMS1 in human cognition can be formally accepted, the findings suggest that genetic mutation may enhance human cognition in some cases.