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“Magical Thinking in Individuals With High Polygenic Risk for Schizophrenia but No Non-affective Psychoses—a General Population Study”, Saarinen et al 2022

“Magical thinking in individuals with high polygenic risk for schizophrenia but no non-affective psychoses—a general population study”⁠, Aino Saarinen, Leo-Pekka Lyytikäinen, Jarmo Hietala, Henrik Dobewall, Veikka Lavonius, Olli Raitakari et al (2022-05-03; ⁠, ):

A strong genetic background for psychoses is well-established. Most individuals with a high genetic risk for schizophrenia⁠, however, do not develop the disorder.

We investigated whether individuals, who have a high genetic risk for schizophrenia but no non-affective psychotic disorders, are predisposed to develop milder forms of deviant thinking in terms of magical thinking⁠.

Participants came from the population-based Young Finns Study (n = 1,292). The polygenic risk score for schizophrenia (PRS) was calculated on the basis of the most recent genome-wide association study (GWAS). Psychiatric diagnoses over the lifespan were collected up to 2017 from the registry of hospital care. Magical thinking was evaluated with the Spiritual Acceptance Scale (eg. beliefs in telepathy⁠, miracles⁠, mystical events, or sixth sense) of the Temperament and Character Inventory in 1997, 2001, and 2012 (participants were 20–50-year-olds).

We found that, among those who did not develop non-affective psychotic disorders, high PRS predicted higher magical thinking in adulthood (p = 0.001). Further, PRS predicted different developmental courses: a low PRS predicted a steady decrease in magical thinking from age 20 to 50 years, while in individuals with high PRS the decrease in magical thinking ceased in middle age so that their level of magical thinking remained higher than expected for that age. These findings remained when controlling for sex, childhood family environment, and adulthood socioeconomic factors.

In conclusion, if high PRS does not lead to a non-affective psychotic disorder, it predicts milder forms of deviant thinking such as elevated magical thinking in adulthood, especially in middle age. The finding enhances our understanding of different outcomes of high genetic psychosis risk.

…In Models 1, we found that high weighted PRS (b = 0.077, p = 0.001, see Table 2) and high unweighted PRS (b = 0.082, p = 0.001, see Table 3) had a positive main effect on magical thinking (ie. high weighted and unweighted PRS predicted higher curve of magical thinking in adulthood). The statistically-significant main effects of age and age-squared indicated that the curve of magical thinking over age was curvilinear.

“Interaction Testing and Polygenic Risk Scoring to Estimate the Association of Common Genetic Variants With Treatment Resistance in Schizophrenia”, Pardiñas et al 2022

2022-pardinas.pdf: “Interaction Testing and Polygenic Risk Scoring to Estimate the Association of Common Genetic Variants With Treatment Resistance in Schizophrenia”⁠, Antonio F. Pardiñas, Sophie E. Smart, Isabella R. Willcocks, Peter A. Holmans, Charlotte A. Dennison et al (2022-01-12; ; similar):

Question: Can common genetic variants be used to differentiate between treatment-resistant schizophrenia (TRS) and other forms of this disorder?

Findings: Data from this genome-wide association study including 85 490 participants were used to estimate genome-wide single-nucleotide variation effect size differences between individuals with and without TRS, which were compatible with a polygenic model of treatment resistance. Results were used to generate a polygenic risk score, which was statistically-significantly associated with TRS status in independent incidence and prevalence samples.

Meaning: Findings of this study based on common genetic variants indicate that TRS is heritable with a modest but statistically-significant single-nucleotide variation-based heritability.

Importance: About 20% to 30% of people with schizophrenia have psychotic symptoms that do not respond adequately to first-line antipsychotic treatment. This clinical presentation, chronic and highly disabling, is known as treatment-resistant schizophrenia (TRS). The causes of treatment resistance and their relationships with causes underlying schizophrenia are largely unknown. Adequately powered genetic studies of TRS are scarce because of the difficulty in collecting data from well-characterized TRS cohorts.

Objective: To examine the genetic architecture of TRS through the reassessment of genetic data from schizophrenia studies and its validation in carefully ascertained clinical samples.

Design, Setting, & Participants: 2 case-control genome-wide association studies (GWASs) of schizophrenia were performed in which the case samples were defined as individuals with TRS (n = 10 501) and individuals with non-TRS (n = 20 325). The differences in effect-sizes for allelic associations were then determined between both studies, the reasoning being such differences reflect treatment resistance instead of schizophrenia. Genotype data were retrieved from the CLOZUK and Psychiatric Genomics Consortium (PGC) schizophrenia studies. The output was validated using polygenic risk score (PRS) profiling of 2 independent schizophrenia cohorts with TRS and non-TRS: a prevalence sample with 817 individuals (Cardiff Cognition in Schizophrenia [CardiffCOGS]) and an incidence sample with 563 individuals (Genetics Workstream of the Schizophrenia Treatment Resistance and Therapeutic Advances [STRATA-G]).

Main Outcomes & Measures: GWAS of treatment resistance in schizophrenia. The results of the GWAS were compared with complex polygenic traits through a genetic correlation approach and were used for PRS analysis on the independent validation cohorts using the same TRS definition.

Results: The study included a total of 85 490 participants (48 635 [56.9%] male) in its GWAS stage and 1,380 participants (859 [62.2%] male) in its PRS validation stage. Treatment resistance in schizophrenia emerged as a polygenic trait with detectable heritability (1% to 4%), and several traits related to intelligence and cognition were found to be genetically correlated with it (genetic correlation, 0.41–0.69). PRS analysis in the CardiffCOGS prevalence sample showed a positive association between TRS and a history of taking clozapine (R2 = 2.03%; p = 0.001), which was replicated in the STRATA-G incidence sample (R2 = 1.09%; p = 0.04).

Conclusions & Relevance: In this GWAS, common genetic variants were differentially associated with TRS, and these associations may have been obscured through the amalgamation of large GWAS samples in previous studies of broadly defined schizophrenia. Findings of this study suggest the validity of meta-analytic approaches for studies on patient outcomes, including treatment resistance.

“Rare Schizophrenia Risk Variant Burden Is Conserved in Diverse Human Populations”, Liu et al 2022

“Rare schizophrenia risk variant burden is conserved in diverse human populations”⁠, Dongjing Liu, Dara Meyer, Brian Fennessy, Claudia Feng, Esther Cheng, Jessica S. Johnson, You Jeong Park et al (2022-01-03; ; similar):

Schizophrenia is a chronic mental illness that is amongst the most debilitating conditions encountered in medical practice. A recent landmark schizophrenia study of the protein-coding regions of the genome identified a causal role for ten genes and a concentration of rare variant signals in evolutionarily constrained genes1. This study—and most other large-scale human genetic studies—was mainly composed of individuals of European ancestry, and the generalizability of the findings in non-European populations is unclear. To address this gap in knowledge, we designed a custom sequencing panel based on current knowledge of the genetic architecture of schizophrenia and applied it to a new cohort of 22,135 individuals of diverse ancestries. Replicating earlier work, cases carried a significantly higher burden of rare protein-truncating variants among constrained genes (OR = 1.48, p-value = 5.4 x 10−6). In meta-analyses with existing schizophrenia datasets totaling up to 35,828 cases and 107,877 controls, this excess burden was largely consistent across five continental populations. Two genes (SRRM2 and AKAP11) were newly implicated as schizophrenia risk genes, and one gene (PCLO) was identified as a shared risk gene for schizophrenia and autism. Overall, our results lend robust support to the rare allelic spectrum of the genetic architecture of schizophrenia being conserved across diverse human populations.

“Association of Schizophrenia Spectrum Disorders and Violence Perpetration in Adults and Adolescents from 15 Countries: A Systematic Review and Meta-Analysis”, Whiting et al 2021

2021-whiting.pdf: “Association of Schizophrenia Spectrum Disorders and Violence Perpetration in Adults and Adolescents from 15 Countries: A Systematic Review and Meta-Analysis”⁠, Daniel Whiting, Gautam Gulati, John R. Geddes, Seena Fazel (2021-12-22; ; similar):

Question: What are the absolute and relative risks of perpetrating violence toward others in individuals with schizophrenia spectrum disorders compared with the general population?

Findings: In this systematic review of 24 studies, the absolute risk of perpetrating violence in a subgroup of register-based studies was less than 1 in 20 in women with schizophrenia spectrum disorders and less than 1 in 4 in men with schizophrenia spectrum disorders over a 35-year period. The elevated relative risk for all violence-perpetration outcomes was higher for women with schizophrenia spectrum disorders than for men with schizophrenia spectrum disorders, but with substantial heterogeneity in the findings.

Meaning: Violence perpetration outcomes may be an important target for prevention and to reduce stigma in people with schizophrenia spectrum disorders.


Importance: Violence perpetration outcomes in individuals with schizophrenia spectrum disorders contribute to morbidity and mortality at a population level, disrupt care, and lead to stigma.

Objective: To conduct a systematic review and meta-analysis of the risk of perpetrating interpersonal violence in individuals with schizophrenia spectrum disorders compared with general population control individuals.

Data Sources: Multiple databases were searched for studies in any language from January 1970 to March 2021 using the terms violen✱ or homicid✱ and psychosis or psychoses or psychotic or schizophren✱ or schizoaffective or delusional and terms for mental disorders. Bibliographies of included articles were hand searched.

Study Selection: The study included case-control and cohort studies that allowed risks of interpersonal violence perpetration and/​or violent criminality in individuals with schizophrenia spectrum disorders to be compared with a general population group without these disorders.

Data Extraction and Synthesis: The study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines and the Meta-analyses of Observational Studies in Epidemiology (MOOSE) proposal. 2 reviewers extracted data. Quality was assessed using the Newcastle-Ottawa Quality Assessment Scale. Data were pooled using a random-effects model⁠.

Main Outcomes & Measures: The main outcome was violence to others obtained either through official records, self-report and/​or collateral-report, or medical file review and included any physical assault, robbery, sexual offenses, illegal threats or intimidation, and arson.

Results: The meta-analysis included 24 studies of violence perpetration outcomes in 15 countries over 4 decades (n = 51 309 individuals with schizophrenia spectrum disorders; reported mean age of 21 to 54 years at follow-up; of those studies that reported outcomes separately by sex, there were 19 976 male individuals and 14 275 female individuals). There was an increase in risk of violence perpetration in men with schizophrenia and other psychoses (pooled odds ratio [OR], 4.5; 95% CI⁠, 3.6–5.6) with substantial heterogeneity (I2 = 85%; 95% CI, 77–91). The risk was also elevated in women (pooled OR, 10.2; 95% CI, 7.1–14.6), with substantial heterogeneity (I2 = 66%; 95% CI, 31–83). Odds of perpetrating sexual offenses (OR, 5.1; 95% CI, 3.8–6.8) and homicide (OR, 17.7; 95% CI, 13.9–22.6) were also investigated. 3 studies found increased relative risks of arson but data were not pooled for this analysis owing to heterogeneity of outcomes. Absolute risks of violence perpetration in register-based studies were less than 1 in 20 in women with schizophrenia spectrum disorders and less than 1 in 4 in men over a 35-year period.

Conclusions & Relevance: This systematic review and meta-analysis found that the risk of perpetrating violent outcomes was increased in individuals with schizophrenia spectrum disorders compared with community control individuals, which has been confirmed in new population-based longitudinal studies and sibling comparison designs.

“High-impact Rare Genetic Variants in Severe Schizophrenia”, Zoghbi et al 2021

“High-impact rare genetic variants in severe schizophrenia”⁠, Anthony W. Zoghbi, Ryan S. Dhindsa, Terry E. Goldberg, Aydan Mehralizade, Joshua E. Motelow, Xinchen Wang et al (2021-12-21; ):

In this study, we found that selecting individuals with extremely severe forms of schizophrenia led to a substantially improved ability to detect disease-associated rare variants. The high prevalence of rare variant risk factors in individuals with severe, extremely treatment-resistant schizophrenia suggests future clinical opportunities for risk prediction, prognostic stratification, and genetic counseling. These findings have implications for the design of future genetic studies in schizophrenia and highlight a strategy to reduce phenotypic heterogeneity and improve gene discovery efforts in other neuropsychiatric disorders.


Extreme phenotype sequencing has led to the identification of high-impact rare genetic variants for many complex disorders but has not been applied to studies of severe schizophrenia.

We sequenced 112 individuals with severe, extremely treatment-resistant schizophrenia, 218 individuals with typical schizophrenia, and 4,929 controls. We compared the burden of rare, damaging missense and loss-of-function variants between severe, extremely treatment-resistant schizophrenia, typical schizophrenia, and controls across mutation intolerant genes.

Individuals with severe, extremely treatment-resistant schizophrenia had a high burden of rare loss-of-function (odds ratio⁠, 1.91; 95% CI, 1.39 to 2.63; p = 7.8 × 10−5) and damaging missense variants in intolerant genes (odds ratio, 2.90; 95% CI, 2.02 to 4.15; p = 3.2 × 10−9). A total of 48.2% of individuals with severe, extremely treatment-resistant schizophrenia carried at least one rare, damaging missense or loss-of-function variant in intolerant genes compared to 29.8% of typical schizophrenia individuals (odds ratio, 2.18; 95% CI, 1.33 to 3.60; p = 1.6 × 10−3) and 25.4% of controls (odds ratio, 2.74; 95% CI, 1.85 to 4.06; p = 2.9 × 10−7). Restricting to genes previously associated with schizophrenia risk strengthened the enrichment with 8.9% of individuals with severe, extremely treatment-resistant schizophrenia carrying a damaging missense or loss-of-function variant compared to 2.3% of typical schizophrenia (odds ratio, 5.48; 95% CI, 1.52 to 19.74; p = 0.02) and 1.6% of controls (odds ratio, 5.82; 95% CI, 3.00 to 11.28; p = 2.6 × 10−8).

These results demonstrate the power of extreme phenotype case selection in psychiatric genetics and an approach to augment schizophrenia gene discovery efforts.

[Keywords: schizophrenia, genomics, rare variants, treatment-resistant schizophrenia]

“Comparing Copy Number Variations in a Danish Case Cohort of Individuals With Psychiatric Disorders”, Sánchez et al 2021

2021-sanchez.pdf: “Comparing Copy Number Variations in a Danish Case Cohort of Individuals With Psychiatric Disorders”⁠, Xabier Calle Sánchez, Dorte Helenius, Jonas Bybjerg-Grauholm, Carsten Pedersen, David M. Hougaard, Anders D. Børglum et al (2021-11-24; ; similar):

Question: What are the population-based prevalence and risk of psychiatric disorders associated with pathogenic copy number variations (CNVs) and how do they compare?

Findings: In a cohort study including 86 189 individuals, increased CNV-associated risk of autism⁠, attention-deficit hyperactivity disorder⁠, schizophrenia, and major depressive disorder⁠, as well as bipolar disorder in men for deletion at 1q21.1, was observed. Population-based penetrance estimates were generally lower than those from prior studies; time-dependent analyses identified variegated disease trajectories across genomic loci, whereas deletions and duplications within each locus had similar trajectory patterns.

Meaning: The findings of this study suggest that population-based analysis substantially revises prevalence and penetrance estimates for pathogenic CNVs; precision health care needs to be tailored to the specific CNV, and to the age and gender of the affected individual.


Importance: Although the association between several recurrent genomic copy number variants (CNVs) and mental disorders has been studied for more than a decade, unbiased, population-based estimates of the prevalence, disease risks and trajectories, fertility, and mortality to contrast chromosomal abnormalities and advance precision health care are lacking.

Objective: To generate unbiased, population-based estimates of prevalence, disease risks and trajectories, fertility, and mortality of CNVs implicated in neuropsychiatric disorders.

Design, Setting, & Participants: In a population-based case-cohort study, using the Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH) 2012 database, individuals born between May 1, 1981, and December 31, 2005, and followed up until December 31, 2012, were analyzed. All individuals (n = 57 377) with attention-deficit/​hyperactivity disorder (ADHD), major depressive disorder (MDD), schizophrenia (SCZ), autism spectrum disorder (ASD), or bipolar disorder (BPD) were included, as well as 30 000 individuals randomly drawn from the database. Data analysis was conducted from 2017–07–01 to 2021–09–07.

Exposures: Copy number variants at 6 genomic loci (1q21.1, 15q11.2, 15q13.3, 16p11.2, 17p12, and 17q12).

Main Outcomes & Measures: Population-unbiased hazard ratio (HR) and survival estimates of CNV associations with the 5 ascertained psychiatric disorders, epilepsy⁠, intellectual disability, selected somatic disorders, fertility, and mortality.

Results: Participants’ age ranged from 1 to 32 years (mean, 12.0 [IQR, 6.9] years) during follow-up, and 38 662 were male (52.3%). Copy number variants broadly associated with an increased risk of autism spectrum disorder and ADHD, whereas risk estimates of SCZ for most CNVs were lower than previously reported. Comparison with previous studies suggests that the lower risk estimates are associated with a higher CNV prevalence in the general population than in control samples of most case-control studies. statistically-significant risk of major depressive disorder (HR, 5.8; 95% CI, 1.5–22.2) and sex-specific risk of bipolar disorder (HR, 17; 95% CI, 1.5–189.3, in men only) were noted for the 1q21.1 deletion. Although CNVs at 1q21.1 and 15q13.3 were associated with increased risk across most diagnoses, the 17p12 deletion consistently conferred less risk of psychiatric disorders (HR 0.4–0.8), although none of the estimates differed statistically-significantly from the general population. Trajectory analyses noted that, although diagnostic risk profiles differed across loci, they were similar for deletions and duplications within each locus. Sex-stratified analyses suggest that pathogenicity of many CNVs may be modulated by sex.

Conclusions & Relevance: The findings of this study suggest that the iPSYCH population case cohort reveals broad disease risk for some studied CNVs and narrower risk for others, in addition to sex differential liability. This finding on genomic risk variants at the level of a population may be important for health care planning and clinical decision-making, and thus the advancement of precision health care.

“Novel Disease Associations With Schizophrenia Genetic Risk Revealed in ~400,000 UK Biobank Participants”, Zhang et al 2021

2021-zhang.pdf: “Novel disease associations with schizophrenia genetic risk revealed in ~400,000 UK Biobank participants”⁠, Ruyue Zhang, Arvid Sjölander, Alexander Ploner, Donghao Lu, Cynthia M. Bulik, Sarah E. Bergen (2021-11-19; ; similar):

Schizophrenia is a serious mental disorder with considerable somatic and psychiatric morbidity. It is unclear whether comorbid health conditions predominantly arise due to shared genetic risk or consequent to having schizophrenia.

To explore the contribution of genetic risk for schizophrenia, we analysed the effect of schizophrenia polygenic risk scores (PRS) on a broad range of health problems in 406 929 individuals with no schizophrenia diagnosis from the UK Biobank⁠. Diagnoses were derived from linked health data including primary care, hospital inpatient records, and registers with information on cancer and deaths. Schizophrenia PRS were generated and tested for associations with general health conditions, 16 ICD10 main chapters, and 603 diseases using linear and logistic regressions.

Higher schizophrenia PRS was statistically-significantly associated with poorer overall health ratings, more hospital inpatient diagnoses, and more unique illnesses. It was also statistically-significantly positively associated with 4 ICD10 chapters: mental disorders; respiratory diseases; digestive diseases; and pregnancy, childbirth and the puerperium⁠, but negatively associated with musculoskeletal disorders. 31 specific phenotypes were statistically-significantly associated with schizophrenia PRS, and the 19 novel findings include several musculoskeletal diseases, respiratory diseases, digestive diseases, varicose veins⁠, pituitary hyperfunction, and other peripheral nerve disorders. These findings extend knowledge of the pleiotropic effect of genetic risk for schizophrenia and offer insight into how some conditions often comorbid with schizophrenia arise.

Additional studies incorporating the genetic basis of hormone regulation and involvement of immune mechanisms in the pathophysiology of schizophrenia may further elucidate the biological mechanisms underlying schizophrenia and its comorbid conditions.

“A General Framework for Identifying Rare Variant Combinations in Complex Disorders”, Pounraja & Girirajan 2021

“A general framework for identifying rare variant combinations in complex disorders”⁠, Vijay Kumar Pounraja, Santhosh Girirajan (2021-10-01; ; similar):

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 a priori 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.

“Adolescent Cannabis Use and Adult Psychoticism: A Longitudinal Co-twin Control Analysis Using Data from Two Cohorts”, Schaefer et al 2021

2021-schaefer.pdf: “Adolescent cannabis use and adult psychoticism: A longitudinal co-twin control analysis using data from two cohorts”⁠, Jonathan D. Schaefer, Seon-Kyeong Jang, Scott Vrieze, William G. Iacono, Matt McGue, Sylia Wilson (2021-09-23; ; similar):

Epidemiological studies have repeatedly shown that individuals who use cannabis are more likely to develop psychotic disorders than individuals who do not. It has been suggested that these associations represent a causal effect of cannabis use on psychosis, and that psychosis risk may be particularly elevated when use occurs in adolescence or in the context of genetic vulnerability. This study, however, does not support these hypotheses, suggesting instead that observed associations are more likely due to confounding by common vulnerability factors.


Observational studies have repeatedly linked cannabis use and increased risk of psychosis. We sought to clarify whether this association reflects a causal effect of cannabis exposure or residual confounding.

We analyzed data from 2 cohorts of twins who completed repeated, prospective measures of cannabis use (n = 1544) and cannabis use disorder symptoms (n = 1458) in adolescence and a dimensional measure of psychosis-proneness (the Personality Inventory for DSM-5 Psychoticism scale) in adulthood. Twins also provided molecular genetic data, which were used to estimate polygenic risk of schizophrenia.

Both cumulative adolescent cannabis use and use disorder were associated with higher Psychoticism scores in adulthood. However, we found no evidence of an effect of cannabis on Psychoticism or any of its facets in co-twin control models that compared the greater-cannabis-using twin to the lesser-using co-twin. We also observed no evidence of a differential effect of cannabis on Psychoticism by polygenic risk of schizophrenia.

Although cannabis use and disorder are consistently associated with increased risk of psychosis, the present results suggest this association is likely attributable to familial confounds rather than a causal effect of cannabis exposure. Efforts to reduce the prevalence and burden of psychotic illnesses thus may benefit from greater focus on other therapeutic targets.

…One powerful approach that can be helpful in testing for residual confounding involves comparing both monozygotic (MZ) and dizygotic (DZ) twins who differ in their cannabis exposure. Termed “discordant twin” or “co-twin control” analyses, this approach allows for examination of the effects of cannabis use while simultaneously controlling for all measured and unmeasured genetic and environmental factors shared between twins (McGue et al 2010). If cannabis is a causal contributor to long-term psychotic illness, both MZ and DZ twins who use more cannabis in adolescence than their co-twins should be more likely to experience psychosis. If this “twin difference” is not observed, it suggests the association between cannabis and psychosis is likely driven by confounding familial factors.

To date, only 2 studies have tested links between cannabis and psychotic symptoms using co-twin comparisons. Both reported that these associations were largely attributable to shared familial factors, but also that they observed evidence consistent with a small, independent, and potentially causal effect (Karcher et al 2019⁠; Nesvåg et al 2017). However, these studies are also characterized by a shared set of limitations, which constrains the implications of their findings.

One limitation is that both studies used data from cross-sectional surveys of adult twins, which precluded tests focusing specifically on cannabis use occurring during the sensitive period of adolescence. A second limitation is that both studies used single, lifetime assessments of cannabis use and use disorder, which are subject to the many well-documented sources of bias that reduce the accuracy of retrospective measures (eg. normal forgetting, revisionist recall). Methodological research suggests that this reduction in accuracy may be particularly problematic in a twin study context, as exposure measurement error tends to bias within-twin-pair estimates more dramatically than corresponding unpaired associations (Frisell et al 2012). Finally, the relatively coarse, binary measures of cannabis exposure used by these studies (including current use [yes/​no], frequent use [>100×/​not], and lifetime use disorder) are characterized by reduced variability relative to more continuous measures of cannabis use, and thus a reduced power to detect effects. Co-twin control studies of cannabis and psychosis that employ repeated, dimensional measures of cannabis use over time are thus needed to address these concerns and establish more accurate estimates of cannabis’s true causal effects.

The present study aimed to address these needs by examining associations between adolescent cannabis exposure and psychosis in a twin sample that combines data from 2 longitudinal cohort studies at the Minnesota Center for Twin and Family Research (MCTFR). In contrast to the few previous co-twin control studies, twins in these cohorts were assessed repeatedly using gold-standard, self-report and interview measures of cannabis use administered prospectively throughout adolescence. Using these measures, we created a continuous index measuring cumulative cannabis use prior to and during adolescence (“adolescent cannabis use index”) and a binary variable indicating presence or absence of a diagnosable cannabis use disorder (ie. abuse or dependence) prior to and during adolescence.

Does Adolescent Cannabis Exposure Predict Greater Adult Psychoticism Independent of Shared Environmental and Genetic Factors, Consistent With a Causal Effect? Results from co-twin control analyses are also presented in Table 4. Co-twin control models capitalize on twin differences to examine effects of cannabis exposure accounting for familial liability. In contrast to our individual-level analyses, these models indicated predominantly statistically-significant between-pair effects (estimates ranging from 0.14 to 0.20 for cannabis use index and from 0.43 to 0.59 for cannabis use disorder), suggesting an effect of preexisting, shared familial liability. They also indicated consistently small, nonsignificant within-pair effects (estimates ranging from −0.01 to 0.01 for cannabis use index and from −0.04 to 0.06 for cannabis use disorder), suggesting no effect of cannabis exposure (for full model results, see Supplemental Table 6).

Do We Find Evidence Suggesting a Potential Causal Effect of Cannabis on Psychoticism in Genetically Vulnerable Individuals? Although we observed no statistically-significant within-pair associations suggesting a causal effect of cannabis exposure on psychoticism in the full analytic sample, this does not rule out the possibility that cannabis may increase psychoticism in subsets of particularly vulnerable individuals. Consequently, we next conducted analyses examining this possibility using one of the most obvious indicators of potential vulnerability: polygenic risk of schizophrenia. Results from these analyses are presented in Table 5. Our first set of models showed that, consistent with our expectations, twins with higher schizophrenia polygenic risk scores tended to score higher on our measure of adult psychoticism as well as its facet scales. Higher polygenic risk of schizophrenia was also associated with higher scores on our adolescent cannabis use index (β [95% CI] = 0.08 [0.02, 0.14], p = 0.014), and higher likelihood of meeting criteria for an adolescent cannabis use disorder (OR [95% CI] = 1.53 [1.11, 2.20], p = 0.010). Our second set of models indicated that schizophrenia polygenic risk and each measure of cannabis exposure both generally made incremental contributions to the prediction of scores on the adult psychoticism scale and its facets. Our third set of models tested the hypothesis that cannabis and polygenic risk interact such that individuals with higher levels of genetic risk are more affected by adolescent cannabis exposure. Interactions between the cannabis use index and polygenic risk in these models were all nonsignificant (βs [95% CIs] ranging from −0.04 [−0.10, 0.02] to 0.04 [−0.02, 0.10], all ps ≥ 0.213). Similarly, all interactions between cannabis use disorder and polygenic risk in corresponding models were also nonsignificant (βs [95% CIs] ranging from −0.04 [−0.20, 0.12] to 0.12 [−0.04, 0.28], all ps ≥ 0.141), except in the model predicting Perceptual Dysregulation (β [95% CI] = 0.17 [0.01, 0.34], p = 0.038). Nevertheless, because this single statistically-significant result would not survive correction for multiple testing, we conclude that results suggest little to no moderation of the effects of cannabis on Psychoticism by polygenic risk of schizophrenia overall.

“Independent Contribution of Polygenic Risk for Schizophrenia and Cannabis Use in Predicting Psychotic-like Experiences in Young Adulthood: Testing Gene × Environment Moderation and Mediation”, Elkrief et al 2021

2021-elkrief.pdf: “Independent contribution of polygenic risk for schizophrenia and cannabis use in predicting psychotic-like experiences in young adulthood: testing gene × environment moderation and mediation”⁠, Laurent Elkrief, Bochao Lin, Mattia Marchi, Mohammad H. Afzali, Tobias Banaschewski, Arun L. W. Bokde et al (2021-09-23; ; similar):

Background: It has not yet been determined if the commonly reported cannabis-psychosis association is limited to individuals with pre-existing genetic risk for psychotic disorders.

Methods: We examined whether the relationship between polygenic risk score for schizophrenia (PRS-Sz) and psychotic-like experiences (PLEs), as measured by the Community Assessment of Psychic Experiences-42 (CAPE-42) questionnaire, is mediated or moderated by lifetime cannabis use at 16 years of age in 1740 of the individuals of the European IMAGEN cohort. Secondary analysis examined the relationships between lifetime cannabis use, PRS-Sz and the various sub-scales of the CAPE-42. Sensitivity analyses including covariates, including a PRS for cannabis use, were conducted and results were replicated using data from 1223 individuals in the Dutch Utrecht cannabis cohort.

Results: PRS-Sz statistically-significantly predicted cannabis use (p = 0.027) and PLE (p = 0.004) in the IMAGEN cohort. In the full model, considering PRS-Sz and covariates, cannabis use was also statistically-significantly associated with PLE in IMAGEN (p = 0.007). Results remained consistent in the Utrecht cohort and through sensitivity analyses. Nevertheless, there was no evidence of a mediation or moderation effects.

Conclusions: These results suggest that cannabis use remains a risk factor for PLEs, over and above genetic vulnerability for schizophrenia. This research does not support the notion that the cannabis-psychosis link is limited to individuals who are genetically predisposed to psychosis and suggests a need for research focusing on cannabis-related processes in psychosis that cannot be explained by genetic vulnerability.

“The Prevalence of Mental Disorders among Homeless People in High-income Countries: An Updated Systematic Review and Meta-regression Analysis”, Gutwinski et al 2021

“The prevalence of mental disorders among homeless people in high-income countries: An updated systematic review and meta-regression analysis”⁠, Stefan Gutwinski, Stefanie Schreiter, Karl Deutscher, Seena Fazel, Caitlin Moyer, Caitlin Moyer, Caitlin Moyer et al (2021-08-02; similar):

Background: Homelessness continues to be a pressing public health concern in many countries, and mental disorders in homeless persons contribute to their high rates of morbidity and mortality. Many primary studies have estimated prevalence rates for mental disorders in homeless individuals. We conducted a systematic review and meta-analysis of studies on the prevalence of any mental disorder and major psychiatric diagnoses in clearly defined homeless populations in any high-income country.

Methods and findings: We systematically searched for observational studies that estimated prevalence rates of mental disorders in samples of homeless individuals, using MEDLINE⁠, Embase⁠, PsycInfo⁠, and Google Scholar⁠. We updated a previous systematic review and meta-analysis conducted in 2007, and searched until 1 April 2021. Studies were included if they sampled exclusively homeless persons, diagnosed mental disorders by standardized criteria using validated methods, provided point or up to 12-month prevalence rates, and were conducted in high-income countries. We identified 39 publications with a total of 8,049 participants. Study quality was assessed using the JBI critical appraisal tool for prevalence studies and a risk of bias tool. Random effects meta-analyses of prevalence rates were conducted, and heterogeneity was assessed by meta-regression analyses. The mean prevalence of any current mental disorder was estimated at 76.2% (95% CI 64.0% to 86.6%). The most common diagnostic categories were alcohol use disorders, at 36.7% (95% CI 27.7% to 46.2%), and drug use disorders, at 21.7% (95% CI 13.1% to 31.7%), followed by schizophrenia spectrum disorders (12.4% [95% CI 9.5% to 15.7%]) and major depression (12.6% [95% CI 8.0% to 18.2%]). We found substantial heterogeneity in prevalence rates between studies, which was partially explained by sampling method, study location, and the sex distribution of participants. Limitations included lack of information on certain subpopulations (eg. women and immigrants) and unmet healthcare needs.

Conclusions: Public health and policy interventions to improve the health of homeless persons should consider the pattern and extent of psychiatric morbidity. Our findings suggest that the burden of psychiatric morbidity in homeless persons is substantial, and should lead to regular reviews of how healthcare services assess, treat, and follow up homeless people. The high burden of substance use disorders and schizophrenia spectrum disorders need particular attention in service development. This systematic review and meta-analysis has been registered with PROSPERO (CRD42018085216).

Trial registration: PROSPERO CRD42018085216⁠.

In an updated systematic review and meta analysis, Stefan Gutwinski, Stefanie Schreiter, and colleagues examine the prevalence of mental disorders among individuals who are homeless in high income countries.


Author summary: Why was this study done?:

  • Homelessness continues to affect a large number of people in high-income countries and is associated with an increased risk of mental disorders.

  • To guide service development, further research, and public policy, reliable estimates on the prevalence of mental disorders among homeless individuals are needed.

  • Many primary investigations into rates of mental disorders have been published since a previous comprehensive quantitative synthesis in 2008.

What did the researchers do and find?:

  • We performed a systematic database search, extracted data from primary reports, and assessed their risk of bias, resulting in a sample of 39 studies including information from over 8,000 homeless individuals in 11 countries.

  • We conducted random effects meta-analyses of 7 common diagnostic categories. Prevalence estimates were all increased in homeless individuals compared with those in the general population. Alcohol use disorders had the highest absolute rate, at 37%, with substantially elevated proportional excesses compared to the general population for schizophrenia spectrum disorders and drug use disorders as well.

  • There was substantial between-study variation in prevalence estimates, and meta-regression analyses found that sampling method, participant sex distribution, and study country explained some of the heterogeneity.

What do these findings mean?:

  • The high burden of substance use disorders and severe mental illness in homeless people represents an unique challenge to public health and policy.

  • Future research should prioritize quantification of unmet healthcare needs, and how they can be identified and effectively treated. Research on subgroups, including younger people and immigrant populations, is a priority for prevalence work.

“Genome-wide Association Meta-analysis of Childhood and Adolescent Internalizing Symptoms”, Jami et al 2021

“Genome-wide association meta-analysis of childhood and adolescent internalizing symptoms”⁠, Eshim S. Jami, Anke R. Hammerschlag, Hill F. Ip, Andrea G. Allegrini, Beben Benyamin, Richard Border et al (2021-07-31; similar):

Internalizing symptoms in childhood and adolescence are as heritable as adult depression and anxiety, yet little is known of their molecular basis. This genome-wide association meta-analysis of internalizing symptoms included repeated observations from 64,641 individuals, aged between 3 and 18. The N-weighted meta-analysis of overall internalizing symptoms (INToverall) detected no genome-wide statistically-significant hits and showed low SNP heritability (1.66%, 95% confidence intervals 0.84–2.48%, Neffective = 132,260). Stratified analyses indicated rater-based heterogeneity in genetic effects, with self-reported internalizing symptoms showing the highest heritability (5.63%, 95% confidence intervals 3.08–8.18%). Additive genetic effects on internalizing symptoms appeared stable over age, with overlapping estimates of SNP heritability from early-childhood to adolescence. Genetic correlations were observed with adult anxiety, depression, and the wellbeing spectrum (|rg|> 0.70), as well as with insomnia, loneliness, attention-deficit hyperactivity disorder, autism, and childhood aggression (range |rg| = 0.42–0.60), whereas there were no robust associations with schizophrenia, bipolar disorder, obsessive-compulsive disorder, or anorexia nervosa. The pattern of genetic correlations suggests that childhood and adolescent internalizing symptoms share substantial genetic vulnerabilities with adult internalizing disorders and other childhood psychiatric traits, which could partially explain both the persistence of internalizing symptoms over time and the high comorbidity amongst childhood psychiatric traits. Reducing phenotypic heterogeneity in childhood samples will be key in paving the way to future GWAS success.

“Cognitive Behavior Therapy for Depression From an Evolutionary Perspective”, Hollon et al 2021

“Cognitive Behavior Therapy for Depression From an Evolutionary Perspective”⁠, Steven D. Hollon, Paul W. Andrews, J. Anderson Thomson Jr. (2021-07-05; ; backlinks; similar):

Evolutionary medicine attempts to solve a problem with which traditional medicine has struggled historically; how do we distinguish between diseased states and “healthy” responses to disease states?

Fever and diarrhea represent classic examples of evolved adaptations that increase the likelihood of survival in response to the presence of pathogens in the body. Whereas, the severe mental disorders like psychotic mania or the schizophrenias may involve true “disease” states best treated pharmacologically, most non-psychotic “disorders” that revolve around negative affects like depression or anxiety are likely adaptations that evolved to serve a function that increased inclusive fitness in our ancestral past.

What this likely means is that the proximal mechanisms underlying the non-psychotic “disorders” are “species typical” and neither diseases nor disorders. Rather, they are coordinated “whole body” responses that prepare the individual to respond in a maximally functional fashion to the variety of different challenges that our ancestors faced.

A case can be made that depression evolved to facilitate a deliberate cognitive style (rumination) in response to complex (often social) problems. What this further suggests is that those interventions that best facilitate the functions that those adaptations evolved to serve (such as rumination) are likely to be preferred over those like medications that simply anesthetize the distress.

We consider the mechanisms that evolved to generate depression and the processes utilized in cognitive behavior therapy to facilitate those functions from an adaptationist evolutionary perspective.

  1. Introduction

  2. Why Do People Have Painful Feelings?

    It Is All About the Squids and the Sea Bass

  3. What Is the Evidence that Melancholia Is an Adaptation?

  4. What Is the Content of Rumination and What Is Its Function?

  5. What Is the Relationship Between Rumination and Spontaneous Remission?

  6. Why Do Depressed People Often Have Recurrences?

  7. Does CBT Disrupt Rumination or Make It More Efficient?

  8. Stigmatize Vs. Validate?

  9. Is It Better to Treat Depression With [antidepressant medications] ADM or CBT?

  10. Why Do Depressed People Often Have Inaccurate Beliefs?

  11. Summary And Conclusions

“Utility of Polygenic Embryo Screening for Disease Depends on the Selection Strategy”, Lencz et al 2021

“Utility of polygenic embryo screening for disease depends on the selection strategy”⁠, Todd Lencz, Daniel Backenroth, Einat Granot-Hershkovitz, Adam Green, Kyle Gettler, Judy H. Cho, Omer Weissbrod et al (2021-06-03; backlinks; similar):

Polygenic risk scores (PRSs) have been offered since 2019 to screen in vitro fertilization embryos for genetic liability to adult diseases, despite a lack of comprehensive modeling of expected outcomes. Here we predict, based on the liability threshold model⁠, the expected reduction in complex disease risk following polygenic embryo screening for a single disease. Our main finding is that a strong determinant of the potential utility of such screening is the selection strategy, a factor that has not been previously studied. Specifically, when only embryos with a very high PRS are excluded, the achieved risk reduction is minimal. In contrast, selecting the embryo with the lowest PRS can lead to substantial relative risk reductions, given a sufficient number of viable embryos. For example, a relative risk reduction of ≈50% for schizophrenia can be achieved by selecting the embryo with the lowest PRS out of five viable embryos. We systematically examine the impact of several factors on the utility of screening, including the variance explained by the PRS, the number of embryos, the disease prevalence, the parental PRSs, and the parental disease status. When quantifying the utility, we consider both relative and absolute risk reductions, as well as population-averaged and per-couple risk reductions. We also examine the risk of pleiotropic effects. Finally, we confirm our theoretical predictions by simulating “virtual” couples and offspring based on real genomes from schizophrenia and Crohn’s disease case-control studies. We discuss the assumptions and limitations of our model, as well as the potential emerging ethical concerns.

“Leroy’s Elusive Little People: A Systematic Review on Lilliputian Hallucinations”, Blom 2021

“Leroy’s elusive little people: A systematic review on lilliputian hallucinations”⁠, Jan Dirk Blom (2021-06; ; similar):

  • Lilliputian hallucinations are not as harmless as traditionally assumed.
  • Their etiology is diverse, with CNS pathology accounting for a third of the cases.
  • Therefore, in most cases auxiliary investigations are advisable.
  • Treatment is directed at the underlying cause.
  • A failure of size constancy may explain part of the underlying mechanism.

Lilliputian hallucinations concern hallucinated human, animal or fantasy entities of minute size. Having been famously described by the French psychiatrist Raoul Leroy in 1909, who wrote from personal experience, to date they are mentioned almost routinely in textbooks of psychiatry, albeit with little in-depth knowledge.

I therefore systematically reviewed 145 case reports and case series comprising 226 case descriptions, concluding that lilliputian hallucinations are visual (61%) or multimodal (39%) in nature. In 97% of the cases, they are perceived as grounded in the actual environment, thus indicating involvement of higher-level regions of the perceptual network subserving the fusion of sensory and hallucinatory content. Perceptual release and deafferentiation [“loss of peripheral afferent input, believed to lead under many circumstances to central hyperirritability or excitatory states”] are the most likely underlying mechanisms. Etiology is extremely diverse, with schizophrenia spectrum disorder, alcohol use disorder and loss of vision accounting for 50% of the cases and neurological disease for 36%. Recovery was obtained in 62% of the cases, whereas 18% of the cases ended in chronicity and 8% in death.

Recommendations are made for clinical practice and future research.

[Keywords: Alcohol hallucinosis, Charles Bonnet syndrome, entity experience, intoxication, multimodal hallucination, psychedelics, size constancy]

“Ultra-rare, Rare, and Common Genetic Variant Analysis Converge to Implicate Negative Selection and Neuronal Processes in the Aetiology of Schizophrenia”, Akingbuwa et al 2021

“Ultra-rare, rare, and common genetic variant analysis converge to implicate negative selection and neuronal processes in the aetiology of schizophrenia”⁠, Wonuola A. Akingbuwa, Anke R. Hammerschlag, Meike Bartels, Michel G. Nivard, Christel M. Middeldorp (2021-05-29; ; similar):

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.

“No Causal Associations between Childhood Family Income and Subsequent Psychiatric Disorders, Substance Misuse and Violent Crime Arrests: a Nationwide Finnish Study of >650 000 Individuals and Their Siblings”, Sariaslan et al 2021

“No causal associations between childhood family income and subsequent psychiatric disorders, substance misuse and violent crime arrests: a nationwide Finnish study of >650 000 individuals and their siblings”⁠, Amir Sariaslan, Janne Mikkonen, Mikko Aaltonen, Heikki Hiilamo, Pekka Martikainen, Seena Fazel (2021-05-29; ⁠, ; backlinks; similar):

  • The causal nature between childhood family income and subsequent risks for psychiatric disorders, substance misuse and violent crime remains unclear.
  • In this Finnish cohort study of 650 680 individuals, we initially found that increased family income was associated with lower risks of psychiatric disorders, substance misuse and arrest for a violent crime.
  • However, once we compared siblings who grew up in the same household but were exposed to varying income levels at specific ages, the associations were no longer present.
  • Associations between family income and subsequent psychiatric disorders, substance misuse and violent crime arrest were therefore explained by shared familial risks and were not consistent with a causal interpretation.

Background: Childhood family income has been shown to be associated with later psychiatric disorders, substance misuse and violent crime, but the consistency, strength and causal nature of these associations remain unclear.

Methods: We conducted a nationwide cohort and co-sibling study of 650 680 individuals (426 886 siblings) born in Finland between 1986 and 1996 to re-examine these associations by accounting for unmeasured confounders shared between siblings. The participants were followed up from their 15th birthday until they either migrated, died, met criteria for the outcome of interest or reached the end of the study period (31 December 2017 or 31 December 2018 for substance misuse). The associations were adjusted for sex, birth year and birth order, and expressed as adjusted hazard ratios (aHRs). The outcomes included a diagnosis of a severe mental illness (schizophrenia-spectrum disorders or bipolar disorder), depression and anxiety. Substance misuse (eg. medication prescription, hospitalization or death due to a substance use disorder or arrest for drug-related crime) and violent crime arrests were also examined. Stratified Cox regression models accounted for unmeasured confounders shared between differentially exposed siblings.

Results: For each $15,000 increase in family income at age 15 years, the risks of the outcomes were reduced by between 9% in severe mental illness (aHR = 0.91; 95% confidence interval: 0.90–0.92) and 23% in violent crime arrests (aHR = 0.77; 0.76–0.78). These associations were fully attenuated in the sibling-comparison models (aHR range: 0.99–1.00). Sensitivity analyses confirmed the latter findings.

Conclusions: Associations between childhood family income and subsequent risks for psychiatric disorders, substance misuse and violent crime arrest were not consistent with a causal interpretation.

[Keywords: socio-economic status⁠, family income, schizophrenia, bipolar disorder, depression, anxiety, substance-use disorders, violence, quasi-experimental research designs, public health] [See also “Childhood family income, adolescent violent criminality and substance misuse: quasi-experimental total population study”⁠, Sariaslan et al 2014; “Parental income and mental disorders in children and adolescents: prospective register-based study”⁠, Kinge et al 2021.]

“A Comparison of Ten Polygenic Score Methods for Psychiatric Disorders Applied across Multiple Cohorts”, Ni et al 2021

“A comparison of ten polygenic score methods for psychiatric disorders applied across multiple cohorts”⁠, Guiyan Ni, Jian Zeng, Joana A. Revez, Ying Wang, Zhili Zheng, Tian Ge, Restuadi Restuadi, Jacqueline Kiewa et al (2021-05-05; backlinks; similar):

Background: Polygenic scores (PGSs), which assess the genetic risk of individuals for a disease, are calculated as a weighted count of risk alleles identified in genome-wide association studies (GWASs). PGS methods differ in which DNA variants are included and the weights assigned to them; some require an independent tuning sample to help inform these choices. PGSs are evaluated in independent target cohorts with known disease status. Variability between target cohorts is observed in applications to real data sets, which could reflect a number of factors, eg. phenotype definition or technical factors.

Methods: The Psychiatric Genomics Consortium working groups for schizophrenia (SCZ) and major depressive disorder (MDD) bring together many independently collected case-control cohorts. We used these resources (31K SCZ cases, 41K controls; 248K MDD cases, 563K controls) in repeated application of leave-one-cohort-out meta-analyses, each used to calculate and evaluate PGS in the left-out (target) cohort. Ten PGS methods (the baseline PC+T method and nine methods that model genetic architecture more formally: SBLUP, LDpred2-Inf, LDpred-funct, LDpred2, Lassosum, PRS-CS, PRS-CS-auto, SBayesR, MegaPRS) are compared.

Results: Compared to PC+T, the other nine methods give higher prediction statistics, MegaPRS, LDPred2 and SBayesR significantly so, up to 9.2% variance in liability for SCZ across 30 target cohorts, an increase of 44%. For MDD across 26 target cohorts these statistics were 3.5% and 59%, respectively.

Conclusion: Although the methods that more formally model genetic architecture have similar performance, MegaPRS, LDpred2, and SBayesR rank highest in most comparison and are recommended in applications to psychiatric disorders.

“The Relationship between Cannabis and Schizophrenia: a Genetically Informed Perspective”, Johnson et al 2021

2021-johnson.pdf: “The relationship between cannabis and schizophrenia: a genetically informed perspective”⁠, Emma C. Johnson, Alexander S. Hatoum, Joseph D. Deak, Renato Polimanti, Robin M. Murray, Howard J. Edenberg et al (2021-05-05; ⁠, ; similar):

Background and Aims: While epidemiological studies support a role for heavy, high-potency cannabis use on first-episode psychosis, genetic models of causation suggest reverse causal effects of schizophrenia on cannabis use liability. We estimated the genetic relationship between cannabis use disorder (CUD) and schizophrenia (SCZ) and tested whether liability for CUD is causally associated with increased liability to SCZ while adjusting for tobacco smoking.

Design: This study used summary statistics from published genome-wide association studies (GWAS). We used genomic structural equation modeling (GSEM)⁠, latent causal variable (LCV) analysis⁠, and multivariable Mendelian randomization to examine genetic relationships between CUD, cannabis ever-use, ever-smoked tobacco regularly, nicotine dependence and SCZ, and to test for a causal relationship between liability to CUD and liability to SCZ.

Setting: Genome-wide association studies were published previously as part of international consortia.

Participants: Sample sizes of the GWAS summary statistics used in this study ranged from 161 405 to 357 806 individuals of European ancestry.

Measurements: Genome-wide summary statistics for CUD and SCZ were the primary measurements, while summary statistics for cannabis ever-use, ever-smoked tobacco regularly and nicotine dependence were included as additional variables in the genomic structural equation models and the multivariable Mendelian randomization analyses.

Findings: Genetic liability to CUD was statistically-significantly associated with SCZ [β = 0.29, 95% confidence interval (CI) = 0.11, 0.46, p = 0.001], even when accounting for cannabis ever-use, ever-smoked tobacco regularly and nicotine dependence as simultaneous predictors. We found mixed evidence of a causal relationship, with the latent causal variable analysis finding no evidence of causality (genetic causality proportion = −0.08, 95% CI = −0.40, 0.23, p = 0.87) but the multivariable Mendelian randomization analyses suggesting a statistically-significant, risk-increasing effect of CUD on liability to SCZ (β = 0.10, 95% CI = 0.02, 0.18, p = 0.02), accounting for the additional risk factors (cannabis ever-use, ever-smoked tobacco regularly and nicotine dependence).

Conclusions: Genetic liability for cannabis use disorder appears to be robustly associated with schizophrenia, above and beyond tobacco smoking and cannabis ever-use, with mixed evidence to support a causal relationship between cannabis use disorder and schizophrenia.

[Keywords: cannabis, genome-wide association study, genomic structural equation modeling, latent causal variable model, multivariable Mendelian randomization, schizophrenia, tobacco]

“Ditching Candidate Gene Association Studies: Lessons from Psychiatric Genetics”, Duarte et al 2021

“Ditching candidate gene association studies: lessons from psychiatric genetics”⁠, Rodrigo R. R. Duarte, Helena Brentani, Timothy R. Powell (2021-02-22; similar):

…Psychiatric genetics has largely moved away from historical candidate association studies, as most candidate genes failed to show associations in GWAS. In fact, a study that analyzed results from a schizophrenia GWAS showed that common variants in 25 historical candidate genes, including COMT⁠, BDNF and DISC1⁠, were no more associated with schizophrenia than control sets of non-candidate genes. Even for candidates that turned out to be associated with schizophrenia (eg. DRD2 and GRM3), their biological relevance remains unclear considering there are many other genes with a stronger association. Another study depicted a similar scenario for depression. However, despite the lack of success in candidate studies, a PubMed search for COMT revealed 269 research outputs published in 2020 alone (checked on January 20th, 2021). Many of these tested this gene for association with complex traits like pain, cognitive performance, behaviors, etc., even though the evidence of association between COMT and behavioral, psychiatric or neurological outcomes is weak, according to a phenome-wide association study (PheWAS) from the GWAS Atlas⁠, which analyzed 4,756 GWAS results (Figure 1).

Figure 1: The phenome-wide association plot of COMT, based on an analysis of 4,756 GWAS results, separated into 22 categories, shows that no psychiatric, behavioral or neurological traits are strongly associated with this gene. The Bonferroni p-value threshold is 1.05×10−5. Retrieved from the GWAS Atlas.

…considering the polygenic nature underlying complex human traits and the limitations of the candidate approach, we should be ditching association studies, especially if these are based solely on historical gene relevance. Psychiatrists, geneticists and neuroscientists must reconsider the cost-benefits of candidate studies when there is no prior robust evidence of trait association…

“Polygenic Burden Has Broader Impact on Health, Cognition, and Socioeconomic Outcomes Than Most Rare and High-risk Copy Number Variants”, Saarentaus et al 2021

“Polygenic burden has broader impact on health, cognition, and socioeconomic outcomes than most rare and high-risk copy number variants”⁠, Elmo Christian Saarentaus, Aki Samuli Havulinna, Nina Mars, Ari Ahola-Olli, Tuomo Tapio Johannes Kiiskinen et al (2021-02-01; ; similar):

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-associated CNVs 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 (DECIPHER CNVs, 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 CNVs without 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.

[Keywords: bipolar disorder, depression, genetics, predictive markers, schizophrenia]

Figure 3: Health impact of high-risk CNVs and PRSs in Finnish cohorts: A: Hazard ratios in a Cox regression model for mortality in unaffected carriers of high-risk CNVs and individuals at the PRS extremes in FINRISK (n = 22,210). ID gene deletions are not pictured as there were no deaths during follow-up for carriers of this type of CNV. B: Incidence rate ratio (IRR) of high-risk CNVs and PRS extremes in a Poisson regression model of the Charlson comorbidity index (CCI) in FINRISK individuals with no SNPD (n = 22,210). The incidence of one CCI unit was more than 3.5 higher in ID gene deletion carriers than in individuals with no high-risk CNV. C, D: Impact of CNVs and PRS outlier status on socioeconomic status and health. The odds of low SES and poor health were highest for individuals with low PRSIQ, and to a lesser extent for individuals at the lowest extreme of PRSEA (A). The odds of high SES and good health was lowest for individuals at the lowest extreme of PRSEA, and to a lesser extent for individuals at the lowest extreme of PRSIQ (B). Effects meta-analyzed using a random-effects assumption are denoted by triangles, otherwise, a fixed-effect assumption was made. The Bonferroni-adjusted p-value is denoted above the point estimate of each variant.

“From Genotype to Phenotype: Polygenic Prediction of Complex Human Traits”, Raben et al 2021

“From Genotype to Phenotype: polygenic prediction of complex human traits”⁠, Timothy G. Raben, Louis Lello, Erik Widen, Stephen D. H. Hsu (2021-01-14; similar):

Decoding the genome confers the capability to predict characteristics of the organism(phenotype) from DNA (genotype). We describe the present status and future prospects of genomic prediction of complex traits in humans. Some highly heritable complex phenotypes such as height and other quantitative traits can already be predicted with reasonable accuracy from DNA alone. For many diseases, including important common conditions such as coronary artery disease, breast cancer, type I and II diabetes, individuals with outlier polygenic scores (eg. top few percent) have been shown to have 5 or even 10× higher risk than average. Several psychiatric conditions such as schizophrenia and autism also fall into this category. We discuss related topics such as the genetic architecture of complex traits, sibling validation of polygenic scores, and applications to adult health, in vitro fertilization (embryo selection), and genetic engineering.

“Antidepressant Response in Major Depressive Disorder: A Genome-wide Association Study”, Pain et al 2020

“Antidepressant Response in Major Depressive Disorder: A Genome-wide Association Study”⁠, Oliver Pain, Karen Hodgson, Vassily Trubetskoy, Stephan Ripke, Victoria S. Marshe, Mark J. Adams, Enda M. Byrne et al (2020-12-15; similar):

Importance: Antidepressants are a first line treatment for depression. However, only a third of individuals remit after the first treatment. Genetic variation likely regulates antidepressant response, yet the success of previous genome-wide association studies has been limited by sample size.

Objective: Gain insight into underlying biology of antidepressant response, characterize SNP-based heritability and genetic overlap with related outcomes, and evaluate out-of-sample prediction using polygenic scores.

Design: Genome-wide meta-analysis of antidepressant response measures, Remission and Percentage Improvement in depression scores.

Setting: Multiple international recruitment sites, including clinical trial and open label studies.

Participants: Diagnosed with Major Depressive Disorder and assessed for depressive symptoms before and after prescription of an antidepressant medication.

Main Outcome(s) and Measure(s): Antidepressant response measured as Remission and Percentage Improvement.

Results: Genome-wide analysis of Remission (nremit = 1,852, nnon-remit = 3,299) and Percentage Improvement (n = 5,218) identified no genome-wide statistically-significant variants. The heritability from common variants was statistically-significantly different from zero for Remission (h2 = 0.132, SE = 0.056), but not Percentage Improvement (h2 = −0.018, SE = 0.032). Polygenic score analysis showed better antidepressant response was associated with lower genetic risk for schizophrenia, and higher genetic propensity for educational attainment. Polygenic scores for antidepressant response demonstrated weak but statistically-significant evidence of out-of-sample prediction across cohorts, though results varied in external cohorts.

Conclusions and Relevance: This study demonstrates antidepressant response is influenced by common genetic variation, has a genetic overlap with schizophrenia and educational attainment, and provides an useful resource for future research. Larger sample sizes are required to attain the potential of genetics for understanding and predicting antidepressant response.

Question: What is the genetic architecture of antidepressant response, and how is it associated with other traits?

Findings: This genome-wide association study of antidepressant response finds Remission SNP-based heritability was statistically-significantly different from zero for Remission (h2 = 0.132, SE = 0.056), but not Percentage Improvement (h2 = −0.018, SE = 0.032). Polygenic score analysis showed better antidepressant response was associated with lower genetic risk for schizophrenia, and higher genetic propensity for educational attainment.

Meaning: This study demonstrates antidepressant response is influenced by common genetic variation, has a genetic overlap with schizophrenia and educational attainment, and provides an useful resource for future research.

“Ten Years of Enhancing Neuro-imaging Genetics through Meta-analysis: An Overview from the ENIGMA Genetics Working Group”, Medland et al 2020

“Ten years of enhancing neuro-imaging genetics through meta-analysis: An overview from the ENIGMA Genetics Working Group”⁠, Sarah E. Medland, Katrina L. Grasby, Neda Jahanshad, Jodie N. Painter, Lucía Colodro‐Conde, Janita Bralten et al (2020-12-10; ⁠, ; similar):

Here we review the motivation for creating the enhancing neuroimaging genetics through meta-analysis (ENIGMA) Consortium and the genetic analyses undertaken by the consortium so far. We discuss the methodological challenges, findings, and future directions of the genetics working group. A major goal of the working group is tackling the reproducibility crisis affecting “candidate gene” and genome-wide association analyses in neuroimaging. To address this, we developed harmonized analytic methods, and support their use in coordinated analyses across sites worldwide, which also makes it possible to understand heterogeneity in results across sites. These efforts have resulted in the identification of hundreds of common genomic loci robustly associated with brain structure. We have found both pleiotropic and specific genetic effects associated with brain structures, as well as genetic correlations with psychiatric and neurological diseases.

Why Did We Build The Enigma Consortium? The consortium was formed in 2009, largely in response to the growing evidence of a lack of reproducibility dubbed “the replication crisis” in imaging genetics. At this time, the first major works of the Psychiatric Genomics Consortium were being presented at conferences (Neale et al 2010⁠; The Schizophrenia Psychiatric Genome-Wide Association Study [GWAS] Consortium, 2011a⁠, 2011b), and we had observed the improvement in statistical power and increase in reproducibility that could be achieved through large-scale meta-analysis. In late 2009, we were beginning to see a series of GWAS publications using phenotypes derived from magnetic resonance imaging (MRI) attempting to answer complex and important questions in psychiatry and neurology. At that time, it was common to see GWAS papers reporting not only main effect analyses but also interactions with diagnosis or putative risk variables in sample sizes of less than 1,000 people.

…In response to these issues, Thompson and Martin sent an email to neuro-imaging groups around the world asking for interest in being part of a collaborative meta-analysis consortium focusing on imaging genetics. The key points in this email were that, although every group would understandably want to publish its own paper reporting their own findings, (a) the power calculations do not change just because the phenotype acquisition is expensive, (b) it was likely that the individual studies would not be large enough to find statistically-significant genetic effects, and (c) even if they did, it would still be necessary to replicate these findings in independent samples. From these beginnings, the ENIGMA consortium now involves more than 2,000 scientists from over 400 institutions in more than 40 countries.

“Use of Genetically Informed Methods to Clarify the Nature of the Association Between Cannabis Use and Risk for Schizophrenia”, Gillespie & Kendler 2020

2020-gillespie.pdf: “Use of Genetically Informed Methods to Clarify the Nature of the Association Between Cannabis Use and Risk for Schizophrenia”⁠, Nathan A. Gillespie, Kenneth S. Kendler (2020-11-04; ⁠, ; similar):

When evaluating efforts to reduce cannabis use as a means of preventing schizophrenia, the proportion of this association that is causal is critical. Given the high heritability of schizophrenia, we reviewed reports that relied on 4 genetic methods (Table) capable of addressing the nature of the cannabis-schizophrenia association. We evaluated 3 hypotheses: (1) it is entirely causal, (2) it is partly causal and partly confounded by genetic/​familial effects and/​or reverse causation, or (3) it is entirely noncausal. (We are unable to review the literature regarding short-term psychiatric effects of cannabis administration.)

Confounding here refers to genetic risks that increase the probability of both using/​misusing cannabis and schizophrenia, thereby explaining at least part of the association. In this example, reverse causality refers to a theoretical underlying mechanism in which schizophrenia liability or symptoms increase the risk of using cannabis. The first 2 methods are natural experiments, discordant relative design, and Mendelian randomization, that directly evaluate each hypothesis. The 2 other methods, linkage disequilibrium score regression (LDSR) and polygenic risks scores (PRSs), measure genetic associations, which, although less definitive, provide evidence of correlated genetic risks that undermine the plausibility of hypothesis 1.

…As summarized in the Table, when triangulating across 4 genetically informative methods, the findings, with considerable but not complete consistency, argue against hypothesis 1. Although the number of available studies is modest, there is relatively reliable evidence across multiple methods that the cannabis-schizophrenia association stems partly from shared familial/​genetic risk factors and/​or reverse causation. We also have good evidence against hypothesis 3, ie, familial/​genetic risk factors explaining all of the association. The 1 study that directly estimated the degree of familial confounding 4 suggests that it is substantial and accounts for more than half of the observed association. Results from LDSR and PRS methods suggest more modest degrees of confounding while raising the possibility of reverse causation. A prudent conclusion is that the observed cannabis-schizophrenia association in the general population may arise from some potential causal effect of cannabis on the risk of schizophrenia, while an appreciable proportion of the association is not causal. When based on associations observed in the population (ie. without control for confounders), claims made about the changes in risk for schizophrenia stemming from changing levels of cannabis use are very likely to be exaggerated and potentially substantially so.

“Violence and Mental Disorders: a Structured Review of Associations by Individual Diagnoses, Risk Factors, and Risk Assessment”, Whiting et al 2020

2020-whiting.pdf: “Violence and mental disorders: a structured review of associations by individual diagnoses, risk factors, and risk assessment”⁠, Daniel Whiting, Paul Lichtenstein, Seena Fazel (2020-10-20; ; similar):

In this Review, we summarise evidence on the association between different mental disorders and violence, with emphasis on high quality designs and replicated findings. Relative risks are typically increased for all violent outcomes in most diagnosed psychiatric disorders compared with people without psychiatric disorders, with increased odds in the range of 2–4 after adjustment for familial and other sources of confounding. Absolute rates of violent crime over 5–10 years are typically below 5% in people with mental illness (excluding personality disorders, schizophrenia, and substance misuse), which increases to 6–10% in personality disorders and schizophrenia spectrum disorders, and to more than 10% in substance misuse. Past criminality and comorbid substance misuse are strongly predictive of future violence in many individual disorders. We reviewed national clinical practice guidelines, which vary in content and require updating to reflect the present epidemiological evidence. Standardised and clinically feasible approaches to the assessment and management of violence risk in general psychiatric settings need to be developed.

“Genetic Architecture of 11 Major Psychiatric Disorders at Biobehavioral, Functional Genomic, and Molecular Genetic Levels of Analysis”, Grotzinger et al 2020

“Genetic Architecture of 11 Major Psychiatric Disorders at Biobehavioral, Functional Genomic, and Molecular Genetic Levels of Analysis”⁠, Andrew D. Grotzinger, Travis T. Mallard, Wonuola A. Akingbuwa, Hill F. Ip, Mark J. Adams, Cathryn M. Lewis et al (2020-09-23; ):

We systematically interrogate the joint genetic architecture of 11 major psychiatric disorders at biobehavioral, functional genomic, and molecular genetic levels of analysis. We identify four broad factors (Neurodevelopmental, Compulsive, Psychotic, and Internalizing) that underlie genetic correlations among the disorders, and test whether these factors adequately explain their genetic correlations with biobehavioral traits. We introduce Stratified Genomic Structural Equation Modelling, which we use to identify gene sets and genomic regions that disproportionately contribute to pleiotropy, including protein-truncating variant intolerant genes expressed in excitatory and GABAergic brain cells that are enriched for pleiotropy between disorders with psychotic features. Multivariate association analyses detect a total of 152 (20 novel) independent loci which act on the four factors, and identify nine loci that act heterogeneously across disorders within a factor. Despite moderate to high genetic correlations across all 11 disorders, we find very little utility of, or evidence for, a single dimension of genetic risk across psychiatric disorders.

“Exome Sequencing Identifies Rare Coding Variants in 10 Genes Which Confer Substantial Risk for Schizophrenia”, Singh et al 2020

“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 et al (2020-09-18; ; similar):

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 subunit GRIA3 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.

Figure 6: The contributions of ultra-rare PTVs [protein-truncating variants] to schizophrenia risk. A: Genetic architecture of schizophrenia. statistically-significant genetic associations for schizophrenia from the most recent GWAS, CNV, and sequencing studies are displayed. The in-sample odds ratio is plotted against the minor allele frequency in the general population. The color of each dot corresponds to the source of the association, and the size of the dot to the odds ratio. The shaded area represented the LOESS-smoothed lines of the upper and lower bounds of the point estimates…Because schizophrenia as a trait is under strong selection38–40, we expect that URVs of large effect to be frequently de novo or of very recent origin and contribute to risk in only a fraction of diagnosed patients.

“Mapping Genomic Loci Prioritises Genes and Implicates Synaptic Biology in Schizophrenia”, Consortium et al 2020

“Mapping genomic loci prioritises genes and implicates synaptic biology in schizophrenia”⁠, Schizophrenia Working Group of the Psychiatric Genomics Consortium, Stephan Ripke, James T. R. Walters et al (2020-09-13; ; backlinks; similar):

Extended Data Figure 2: GWAS progress over time. The relationship of GWAS associations to sample-size is shown in this plot with selected SCZ GWAS meta-analyses of the past 11 years. The x-axis shows number of cases. The y-axis shows the number of independent loci discovered with at least one genome-wide statistically-significant index SNP in the discovery meta-analysis (eg. without replication data)…The slope of ~4 newly discovered loci per 1000 cases between 2013 and 2019 increased to a slope of ~6 with the latest sample-size increase.

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.

“Novel Ultra-Rare Exonic Variants Identified in a Founder Population Implicate Cadherins in Schizophrenia”, Lencz et al 2020

“Novel Ultra-Rare Exonic Variants Identified in a Founder Population Implicate Cadherins in Schizophrenia”⁠, Todd Lencz, Jin Yu, Raiyan Rashid Khan, Shai Carmi, Max Lam, Danny Ben-Avraham, Nir Barzilai, Susan Bressman et al (2020-09-11; ; similar):

Identification of rare genetic variants associated with schizophrenia has proven challenging due to multiple sources of heterogeneity, which may be reduced in founder populations. We examined ultra-rare exonic variants in 786 patients with schizophrenia and 463 healthy comparison subjects, all drawn from the Ashkenazi Jewish population. Cases had a higher frequency of novel missense or loss of function (MisLoF) variants compared to controls. Characterizing 141 “case-only” genes (in which ≥ 3 cases in our dataset had MisLoF variants with none found in controls), we identified cadherins as a novel gene set associated with schizophrenia, including a recurrent mutation in PCDHA3. Modeling the effects of purifying selection demonstrated that deleterious ultra-rare variants are greatly over-represented in the Ashkenazi population, resulting in enhanced power for rare variant association. Identification of cell adhesion genes in the cadherin/​protocadherin family helps specify the synaptic abnormalities central to the disorder, and suggests novel potential treatment strategies.

“The Polygenic Architecture of Schizophrenia—rethinking Pathogenesis and Nosology”, Smeland et al 2020

2020-smeland.pdf: “The polygenic architecture of schizophrenia—rethinking pathogenesis and nosology”⁠, Olav B. Smeland, Oleksandr Frei, Anders M. Dale, Ole A. Andreassen (2020-06-11; ; similar):

Schizophrenia is a severe psychiatric disorder with considerable morbidity and mortality. Although the past two decades have seen limited improvement in the treatment of schizophrenia, research into the genetic causes of this condition has made important advances that offer new insights into the aetiology of schizophrenia. This Review summarizes the evidence for a polygenic architecture of schizophrenia that involves a large number of risk alleles across the whole range of population frequencies. These genetic risk loci implicate biological processes related to neurodevelopment, neuronal excitability, synaptic function and the immune system in the pathogenesis of schizophrenia. Mathematical models also suggest a substantial overlap between schizophrenia and psychiatric, behavioural and cognitive traits, a situation that has implications for understanding its clinical epidemiology, psychiatric nosology and pathobiology. Looking ahead, further genetic discoveries are expected to lead to clinically relevant predictive approaches for identifying high-risk individuals, improved diagnostic accuracy, increased yield from drug development programmes and improved stratification strategies to address the heterogeneous disease course and treatment responses observed among affected patients.

Key points:

  • Schizophrenia is characterized by ‘positive’ psychotic symptoms (including hallucinations and delusions) and ‘negative’ symptoms (including blunted affect, apathy and social impairment); this disorder is associated with considerable morbidity and mortality.
  • In the past decade, important advances have been made in our understanding of the genetics of schizophrenia.
  • The polygenic architecture of schizophrenia is accounted for by thousands of common genetic variants with small effect sizes and a few rare variants with large effect sizes.
  • These genetic risk variants implicate dysregulation of biological processes linked to neurodevelopment, neuronal excitability, synaptic function and the immune system in schizophrenia.
  • Genetic risk factors associated with schizophrenia transcend diagnostic boundaries and form a continuum with normal psychosocial traits, which challenges current psychiatric nosology.
  • Although increasingly larger sample sizes will accelerate the discovery of genetic variants, novel statistical methodologies could also improve the efficiency of analyses, render discoveries clinically relevant and facilitate precision medicine approaches.

“Risk in Relatives, Heritability, SNP-Based Heritability, and Genetic Correlations in Psychiatric Disorders: A Review”, Baselmans et al 2020

2020-baselmans.pdf: “Risk in Relatives, Heritability, SNP-Based Heritability, and Genetic Correlations in Psychiatric Disorders: A Review”⁠, Bart M. L. Baselmans, Loïc Yengo, Wouter van Rheenen, Naomi R. Wray (2020-06-09; ; similar):

The genetic contribution to psychiatric disorders is observed through the increased rates of disorders in the relatives of those diagnosed with disorders. These increased rates are observed to be nonspecific; for example, children of those with schizophrenia have increased rates of schizophrenia but also a broad range of other psychiatric diagnoses. While many factors contribute to risk, epidemiological evidence suggests that the genetic contribution carries the highest risk burden. The patterns of inheritance are consistent with a polygenic architecture of many contributing risk loci. The genetic studies of the past decade have provided empirical evidence identifying thousands of DNA variants associated with psychiatric disorders. Here, we describe how these latest results are consistent with observations from epidemiology. We provide an R tool (CHARRGe) to calculate genetic parameters from epidemiological parameters and vice versa. We discuss how the single nucleotide polymorphism-based estimates of heritability and genetic correlation relate to those estimated from family records.

[Keywords: Family register data, Genetic correlation, GWAS, Heritability, Psychiatric genetics, Risk in relatives]

“Obscure and Unknown: Deliriants of the Edgewood Arsenal Human Experiments”, space_crustacean 2020

“Obscure and Unknown: Deliriants of the Edgewood Arsenal Human Experiments”⁠, space_crustacean (2020-06-08; ; backlinks; similar):

The Edgewood Arsenal Human Experiments were a series of classified studies conducted by the U.S. Army at the Edgewood Arsenal in Maryland between 1955 and 1975. A wide variety of chemical weapon applications and protections were tested, including the use of psychoactive agents. Unlike MKUltra, which was tested on many people without their consent, the Edgewood Arsenal Experiments were tested on volunteer army personnel. They were not however, prepared for the horrors they would be exposed to. The psychoactive compounds tested in these experiments include a variety of familiar chemicals like LSD, PCP, and various synthetic cannabinoids. Of particular interest however, are anticholinergics that were tested, better known as deliriants, a class of chemicals notorious for inducing hallucinations through a psychosis-like state, where the user has difficulty distinguishing hallucinations from reality.

Some of the deliriants that were tested will be detailed below. Almost all of the information on them comes from a series of summary reports on the experiments published by the National Academy of Medicine from 1982–1985, along with a detailed Memoir by one of the lead scientists behind the project Dr. James S. Ketchum. All of them except BZ were invented for the purpose of being studied and are only known by a codename “EA” (For Edgewood Arsenal) followed by 4 numbers. We will specifically look at BZ, EA-3443⁠, EA-3580⁠, EA-3834⁠, and EA-3167⁠. The effects of these extremely potent drugs are remarkable and terrifying.

…EA-3167 was most notable for its extreme duration, unlike that of any other psychoactive known of any class. Incapacitating effects could last anywhere from 5–10 days, which could sometimes present as a full 3 day long peak of vivid hallucinations, along with drawn out confusion, amnesia, and inhibition of speech and cognition4. Some subjects would not fully recover for almost 20 days4. After two weeks the symptoms experienced by subjects included:

“included increased irritability; mild impairment of memory, judgment, or abstraction; mental sluggishness with occasional confusion; nervousness; and tenseness.”1

Even 6 months later, a few of the subjects exposed to higher doses demonstrated:

“significant increases in the scores on the hypochondriasis, depression, hysteria, psychasthenia, schizophrenia, and mania scales”

Subjects would often have to be exposed to drawn out and extreme cumulative doses of physostigmine (which can be toxic itself at high doses) to stave off lasting delirium4.

…The potency of EA-3167 was in the range of other deliriants studied when given intramuscularly, 4.1 μg/​kg (254 μg in an average person)1. The power of this chemical is astounding-around a quarter of a milligram is enough to induce a 10 day marathon of incapacitated delirium, with at least 3 days of full blown delirious hallucinations. That such a compound can exist and that it is even possible to affect the human mind in that way is utterly terrifying.

“Conditional GWAS Analysis to Identify Disorder-specific SNPs for Psychiatric Disorders”, Byrne et al 2020

2020-bryne.pdf: “Conditional GWAS analysis to identify disorder-specific SNPs for psychiatric disorders”⁠, Enda M. Byrne, Zhihong Zhu, Ting Qi, Nathan G. Skene, Julien Bryois, Antonio F. Pardinas, Eli Stahl, Jordan W. Smoller et al (2020-05-12; ; similar):

Substantial genetic liability is shared across psychiatric disorders but less is known about risk variants that are specific to a given disorder. We used multi-trait conditional and joint analysis (mtCOJO) to adjust GWAS summary statistics of one disorder for the effects of genetically correlated traits to identify putative disorder-specific SNP associations. We applied mtCOJO to summary statistics for five psychiatric disorders from the Psychiatric Genomics Consortium—schizophrenia (SCZ), bipolar disorder (BIP), major depression (MD), attention-deficit hyperactivity disorder (ADHD) and autism (AUT). Most genome-wide statistically-significant variants for these disorders had evidence of pleiotropy (ie. impact on multiple psychiatric disorders) and hence have reduced mtCOJO conditional effect sizes. However, subsets of genome-wide statistically-significant variants had larger conditional effect sizes consistent with disorder-specific effects: 15 of 130 genome-wide statistically-significant variants for schizophrenia, 5 of 40 for major depression, 3 of 11 for ADHD and 1 of 2 for autism. We show that decreased expression of VPS29 in the brain may increase risk to SCZ only and increased expression of CSE1L is associated with SCZ and MD, but not with BIP. Likewise, decreased expression of PCDHA7 in the brain is linked to increased risk of MD but decreased risk of SCZ and BIP.

“Disentangling Selection on Genetically Correlated Polygenic Traits Using Whole-genome Genealogies”, Stern et al 2020

“Disentangling selection on genetically correlated polygenic traits using whole-genome genealogies”⁠, Aaron J. Stern, Leo Speidel, Noah A. Zaitlen, Rasmus Nielsen (2020-05-08; similar):

We present a full-likelihood method to estimate and quantify polygenic adaptation from contemporary DNA sequence data. The method combines population genetic DNA sequence data and GWAS summary statistics from up to thousands of nucleotide sites in a joint likelihood function to estimate the strength of transient directional selection acting on a polygenic trait. Through population genetic simulations of polygenic trait architectures and GWAS, we show that the method substantially improves power over current methods. We examine the robustness of the method under uncorrected GWAS stratification, uncertainty and ascertainment bias in the GWAS estimates of SNP effects, uncertainty in the identification of causal SNPs, allelic heterogeneity, negative selection, and low GWAS sample size. The method can quantify selection acting on correlated traits, fully controlling for pleiotropy even among traits with strong genetic correlation (|rg| = 80%; c.f. schizophrenia and bipolar disorder) while retaining high power to attribute selection to the causal trait. We apply the method to study 56 human polygenic traits for signs of recent adaptation. We find signals of directional selection on pigmentation (tanning, sunburn, hair, P = 5.5e-15, 1.1e-11, 2.2e-6, respectively), life history traits (age at first birth, EduYears, P = 2.5e-4, 2.6e-4, respectively), glycated hemoglobin (HbA1c, P = 1.2e-3), bone mineral density (P = 1.1e-3), and neuroticism (P = 5.5e-3). We also conduct joint testing of 137 pairs of genetically correlated traits. We find evidence of widespread correlated response acting on these traits (2.6× enrichment over the null expectation, P = 1.5e-7). We find that for several traits previously reported as adaptive, such as educational attainment and hair color, a significant proportion of the signal of selection on these traits can be attributed to correlated response, vs direct selection (P = 2.9e-6, 1.7e-4, respectively). Lastly, our joint test uncovers antagonistic selection that has acted to increase type 2 diabetes (T2D) risk and decrease HbA1c (P = 1.5e-5).

“Need to Account for Familial Confounding in Systematic Review and Meta-analysis of Prenatal Tobacco Smoke Exposure and Schizophrenia”, Quinn et al 2020

2020-quinn.pdf: “Need to Account for Familial Confounding in Systematic Review and Meta-analysis of Prenatal Tobacco Smoke Exposure and Schizophrenia”⁠, Patrick D. Quinn, Sandra M. Meier, Brian M. D’Onofrio (2020-04-02; ; similar):

In the context of continued uncertainty regarding the long-term mental health effects of prenatal exposure to maternal smoking during pregnancy, we read with great interest the recently published meta-analysis of smoking and schizophrenia by Hunter and colleagues. Although the meta-analysis found that “exposure to prenatal smoke increased the risk of schizophrenia by 29%” (p. 3), the authors noted that “familial confounding may explain some of the observed association” (p. 8). We agree with the importance of this alternative hypothesis. In fact, we were surprised that the review did not consider the results of sibling comparison studies that have directly addressed it, particularly given that the review had the opportunity to do so using data from articles included in the meta-analysis.

…The two studies of interest represented over 60% of the weighted sample. They yielded covariate-adjusted hazard ratios of 1.33 (95% confidence interval [CI], 1.23–1.45) and 1.13 (95% CI, 1.05–1.23). However, their sibling comparison results, which were excluded from the review, were weaker and not statistically-significant (hazard ratios, 1.21 [95% CI, 0.96–1.52] and 1.09 [0.84–1.42], respectively). These results suggest that familial confounding, rather than a true casual effect, explains much of the observed associations. The weaker associations from the sibling comparisons may thus dampen enthusiasm regarding a potentially meaningful role of exposure to maternal smoking during pregnancy in offspring schizophrenia. An approximately 10%–20% relative difference in rates of what is a rare outcome would suggest that modifying maternal smoking would have only a limited impact on the incidence of offspring schizophrenia.

“Genome-wide Association Study of Creativity Reveals Genetic Overlap With Psychiatric Disorders, Risk Tolerance, and Risky Behaviors”, Li et al 2020

2020-li.pdf: “Genome-wide Association Study of Creativity Reveals Genetic Overlap With Psychiatric Disorders, Risk Tolerance, and Risky Behaviors”⁠, Huijuan Li, Chuyi Zhang, Xin Cai, Lu Wang, Fang Luo, Yina Ma, Ming Li, Xiao Xiao (2020-03-05; ; similar):

Creativity represents one of the most important and partially heritable human characteristics, yet little is known about its genetic basis. Epidemiological studies reveal associations between creativity and psychiatric disorders as well as multiple personality and behavioral traits.

To test whether creativity and these disorders or traits share genetic basis, we performed genome-wide association studies (GWAS) followed by polygenic risk score (PRS) analyses. Two cohorts of Han Chinese subjects (4,834 individuals in total) aged 18–45 were recruited for creativity measurement using typical performance test. After exclusion of the outliers with statistically-significantly deviated creativity scores and low-quality genotyping results, 4,664 participants were proceeded for GWAS. We conducted PRS analyses using both the classical pruning and thresholding (P+T) method and the LDpred method. The extent of polygenic risk was estimated through linear regression adjusting for the top 3 genotyping principal components. R2 was used as a measurement of the explained variance.

PRS analyses demonstrated statistically-significantly positive genetic overlap, respectively, between creativity with schizophrenia (P+T method: R2(max) ~ 0.196%, p = 0.00245; LDpred method: R2(max) ~ 0.226%, p = 0.00114), depression (P+T method: R2(max) ~ 0.178%, p = 0.00389; LDpred method: R2(max) ~ 0.093%, p = 0.03675), general risk tolerance (P+T method: R2(max) ~ 0.177%, p = 0.00399; LDpred method: R2(max) ~ 0.305%, p = 0.00016), and risky behaviors (P+T method: R2(max) ~ 0.187%, p = 0.00307; LDpred method: R2(max) ~ 0.155%, p = 0.00715).

Our results suggest that human creativity is probably a polygenic trait affected by numerous variations with tiny effects. Genetic variations that predispose to psychiatric disorders and risky behaviors may underlie part of the genetic basis of creativity, confirming the epidemiological associations between creativity and these traits.

“Genetics of Schizophrenia in the South African Xhosa”, Gulsuner et al 2020

2020-gulsuner.pdf: “Genetics of schizophrenia in the South African Xhosa”⁠, S. Gulsuner, D. J. Stein, E. S. Susser, G. Sibeko, A. Pretorius, T. Walsh, L. Majara, M. M. Mndini, S. G. Mqulwana et al (2020-01-31; ; similar):

Africa, the ancestral home of all modern humans, is the most informative continent for understanding the human genome and its contribution to complex disease. To better understand the genetics of schizophrenia, we studied the illness in the Xhosa population of South Africa, recruiting 909 cases and 917 age-matched, gender-matched, and residence-matched controls. Individuals with schizophrenia were statistically-significantly more likely than controls to harbor private, severely damaging mutations in genes that are critical to synaptic function, including neural circuitry mediated by the neurotransmitters glutamine, γ-aminobutyric acid, and dopamine⁠. Schizophrenia is genetically highly heterogeneous, involving severe ultra-rare mutations in genes that are critical to synaptic plasticity. The depth of genetic variation in Africa revealed this relationship with a moderate sample size and informed our understanding of the genetics of schizophrenia worldwide.

“Genomic Relationships, Novel Loci, and Pleiotropic Mechanisms across Eight Psychiatric Disorders”, Consortium 2019

2019-pgc.pdf: “Genomic Relationships, Novel Loci, and Pleiotropic Mechanisms across Eight Psychiatric Disorders”⁠, Cross-Disorder Group of the Psychiatric Genomics Consortium (2019-12-12; ; similar):

  • Three groups of highly genetically-related disorders among 8 psychiatric disorders
  • Identified 109 pleiotropic loci affecting more than one disorder
  • Pleiotropic genes show heightened expression beginning in 2nd prenatal trimester
  • Pleiotropic genes play prominent roles in neurodevelopmental processes

Genetic influences on psychiatric disorders transcend diagnostic boundaries, suggesting substantial pleiotropy of contributing loci. However, the nature and mechanisms of these pleiotropic effects remain unclear. We performed analyses of 232,964 cases and 494,162 controls from genome-wide studies of anorexia nervosa, attention-deficit/​hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depression, obsessive-compulsive disorder, schizophrenia, and Tourette syndrome. Genetic correlation analyses revealed a meaningful structure within the eight disorders, identifying three groups of inter-related disorders. Meta-analysis across these eight disorders detected 109 loci associated with at least two psychiatric disorders, including 23 loci with pleiotropic effects on four or more disorders and 11 loci with antagonistic effects on multiple disorders. The pleiotropic loci are located within genes that show heightened expression in the brain throughout the lifespan, beginning prenatally in the second trimester, and play prominent roles in neurodevelopmental processes. These findings have important implications for psychiatric nosology, drug development, and risk prediction.

“Genome-wide Association Studies in Schizophrenia: Recent Advances, Challenges and Future Perspective”, Dennison et al 2019

2019-dennison.pdf: “Genome-wide association studies in schizophrenia: Recent advances, challenges and future perspective”⁠, Charlotte A. Dennison, Sophie E. Legge, Antonio F. Pardiñas, James T. R. Walters (2019-11-25; )

“Genome-wide Association Study Identifies 49 Common Genetic Variants Associated With Handedness”, Partida et al 2019

“Genome-wide association study identifies 49 common genetic variants associated with handedness”⁠, Gabriel Cuellar Partida, Joyce Y. Tung, Nicholas Eriksson, Eva Albrecht, Fazil Aliev, Ole A. Andreassen et al (2019-11-07; similar):

Handedness, a consistent asymmetry in skill or use of the hands, has been studied extensively because of its relationship with language and the over-representation of left-handers in some neurodevelopmental disorders. Using data from the UK Biobank⁠, 23andMe and 32 studies from the International Handedness Consortium, we conducted the world’s largest genome-wide association study of handedness (1,534,836 right-handed, 194,198 (11.0%) left-handed and 37,637 (2.1%) ambidextrous individuals). We found 42 genetic loci associated with left-handedness and seven associated with ambidexterity at genome-wide levels of significance (p < 5×10−8). Tissue enrichment analysis implicated the central nervous system and brain tissues including the hippocampus and cerebrum in the etiology of left-handedness. Pathways including regulation of microtubules, neurogenesis, axonogenesis and hippocampus morphology were also highlighted. We found suggestive positive genetic correlations between being left-handed and some neuropsychiatric traits including schizophrenia and bipolar disorder. SNP heritability analyses indicated that additive genetic effects of genotyped variants explained 5.9% (95% CI = 5.8%–6.0%) of the underlying liability of being left-handed, while the narrow sense heritability was estimated at 12% (95% CI = 7.2%–17.7%). Further, we show that genetic correlation between left-handedness and ambidexterity is low (rg = 0.26; 95% CI = 0.08 –0.43) implying that these traits are largely influenced by different genetic mechanisms. In conclusion, our findings suggest that handedness, like many other complex traits is highly polygenic, and that the genetic variants that predispose to left-handedness may underlie part of the association with some psychiatric disorders that has been observed in multiple observational studies.

“Stanford Professor Who Changed America With Just One Study Was Also a Liar”, Cahalan 2019

“Stanford professor who changed America with just one study was also a liar”⁠, Susannah Cahalan (2019-11-02; ⁠, ⁠, ; backlinks; similar):

[Summary of investigation into David Rosenhan: like the Robbers Cave or Stanford Prison Experiment, his famous fake-insane patients experiment cannot be verified and many troubling anomalies have come to light. Cahalan is unable to find almost all of the supposed participants, Rosenhan hid his own participation & his own medical records show he fabricated details of his case, he throw out participant data that didn’t match his narrative, reported numbers are inconsistent, Rosenhan abandoned a lucrative book deal about it and avoided further psychiatric research, and showed some character traits of a fabulist eager to please.]

“Childhood Adoption and Mental Health in Adulthood: The Role of Gene-Environment Correlations and Interactions in the UK Biobank”, Lehto et al 2019

“Childhood Adoption and Mental Health in Adulthood: The Role of Gene-Environment Correlations and Interactions in the UK Biobank”⁠, Kelli Lehto, Sara Hägg, Donghao Lu, Robert Karlsson, Nancy L. Pedersen, Miriam A. Mosing (2019-10-31; ; similar):

Background: Being adopted early in life, an indicator of exposure to early-life adversity, has been consistently associated with poor mental health outcomes in adulthood. Such associations have largely been attributed to stressful environments, eg. exposure to trauma, abuse, or neglect. However, mental health is substantially heritable, and genetic influences may contribute to the exposure to childhood adversity, resulting in potential genetic confounding of such associations.

Methods: Here, we explored associations between childhood adoption and mental health-related outcomes in midlife in 243,797 UK Biobank participants (n adopted = 3151). We used linkage disequilibrium score regression and polygenic risk scores for depressive symptoms, schizophrenia, neuroticism, and subjective well-being to address potential genetic confounding (gene-environment correlations) and gene-environment interactions. As outcomes, we explored depressive symptoms, bipolar disorder, neuroticism, loneliness, and mental health-related socioeconomic and psychosocial measures in adoptees compared with non-adopted participants.

Results: Adoptees were slightly worse off on almost all mental, socioeconomic, and psychosocial measures. Each standard deviation increase in polygenic risk for depressive symptoms, schizophrenia, and neuroticism was associated with 6%, 5%, and 6% increase in the odds of being adopted, respectively. Statistically-significant genetic correlations between adoption status and depressive symptoms, major depression, and schizophrenia were observed. No evidence for gene-environment interaction between genetic risk and adoption on mental health was found.

Conclusions: The association between childhood adoption and mental health cannot fully be attributed to stressful environments but is partly explained by differences in genetic risk between adoptees and those who have not been adopted (ie. gene-environment correlation).

[Keywords: childhood adversity, depressive symptoms, gene-environment interplay, neuroticism, polygenic risk scores, schizophrenia]

“Penetrance and Pleiotropy of Polygenic Risk Scores for Schizophrenia in 106,160 Patients across Four Healthcare Systems”, Zheutlin et al 2019

“Penetrance and pleiotropy of polygenic risk scores for schizophrenia in 106,160 patients across four healthcare systems”⁠, Amanda B. Zheutlin, Jessica Dennis, Richard Karlsson Linnér, Arden Moscati, Nicole Restrepo, Peter Straub et al (2019-03-23; ; similar):

Objective: Individuals at high risk for schizophrenia may benefit from early intervention but few validated risk predictors are available. Genetic profiling is one approach to risk stratification that has been extensively validated in research cohorts, but its utility in clinical settings remains largely unexplored. Moreover, the broad health consequences of a high genetic risk of schizophrenia are poorly understood, despite being relevant to treatment decisions.

Method: We used electronic health records for 106,160 patients from four healthcare systems to evaluate the penetrance and pleiotropy of genetic risk for schizophrenia. Polygenic risk scores (PRSs) for schizophrenia were calculated from summary statistics and tested for association with 1359 disease categories, including schizophrenia and psychosis, in phenome-wide association studies. Effects were combined through meta-analysis across sites.

Results: PRSs were robustly associated with schizophrenia (odds ratio per standard deviation increase in PRS = 1.55 [95% confidence interval (CI), 1.4–1.7], p = 4.48 x 10−16) and patients in the highest risk decile of the PRS distribution had up to 4.6× increased odds of schizophrenia compared to those in the bottom decile (95% CI, 2.9–7.3, p = 1.37 x 10−10). PRSs were also positively associated with a range of other phenotypes, including anxiety, mood, substance use, neurological, and personality disorders, as well as suicidal behavior, memory loss, and urinary syndromes; they were inversely related to obesity.

Conclusions: We demonstrate that an available measure of genetic risk for schizophrenia is robustly associated with schizophrenia in healthcare settings and has pleiotropic effects on related psychiatric disorders as well as other medical syndromes. Our results provide an initial indication of the opportunities and limitations that may arise with the future application of PRS testing in healthcare systems.

“Genome Wide Meta-analysis Identifies Genomic Relationships, Novel Loci, and Pleiotropic Mechanisms across Eight Psychiatric Disorders”, Consortium et al 2019

“Genome wide meta-analysis identifies genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders”⁠, Cross-Disorder Group of the Psychiatric Genomics Consortium, Phil H. Lee, Verneri Anttila, Hyejung Won et al (2019-01-26; similar):

Genetic influences on psychiatric disorders transcend diagnostic boundaries, suggesting substantial pleiotropy of contributing loci. However, the nature and mechanisms of these pleiotropic effects remain unclear.

We performed a meta-analysis of 232,964 cases and 494,162 controls from genome-wide studies of anorexia nervosa, attention-deficit/​hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depression, obsessive-compulsive disorder, schizophrenia, and Tourette syndrome. Genetic correlation analyses revealed a meaningful structure within the eight disorders identifying three groups of inter-related disorders.

We detected 109 loci associated with at least two psychiatric disorders, including 23 loci with pleiotropic effects on four or more disorders and 11 loci with antagonistic effects on multiple disorders. The pleiotropic loci are located within genes that show heightened expression in the brain throughout the lifespan, beginning in the second trimester prenatally, and play prominent roles in a suite of neurodevelopmental processes.

These findings have important implications for psychiatric nosology, drug development, and risk prediction.

“Genome-wide Analysis Reveals Extensive Genetic Overlap between Schizophrenia, Bipolar Disorder, and Intelligence”, Smeland et al 2019

2019-smeland.pdf: “Genome-wide analysis reveals extensive genetic overlap between schizophrenia, bipolar disorder, and intelligence”⁠, Olav B. Smeland, Shahram Bahrami, Oleksandr Frei, Alexey Shadrin, Kevin O’Connell, Jeanne Savage, Kyoko Watanabe et al (2019-01-01; )

“Phenotypic Annotation: Using Polygenic Scores to Translate Discoveries From Genome-Wide Association Studies From the Top Down”, Belsky & Harden 2019

2019-belsky.pdf: “Phenotypic Annotation: Using Polygenic Scores to Translate Discoveries From Genome-Wide Association Studies From the Top Down”⁠, Daniel W. Belsky, K. Paige Harden (2019; ; similar):

Genome-wide association studies (GWASs) have identified specific genetic variants associated with complex human traits and behaviors, such as educational attainment, mental disorders, and personality. However, small effect sizes for individual variants, uncertainty regarding the biological function of discovered genotypes, and potential “outside-the-skin” environmental mechanisms leave a translational gulf between GWAS results and scientific understanding that will improve human health and well-being. We propose a set of social, behavioral, and brain-science research activities that map discovered genotypes to neural, developmental, and social mechanisms and call this research program phenotypic annotation. Phenotypic annotation involves (a) elaborating the nomological network surrounding discovered genotypes, (b) shifting focus from individual genes to whole genomes, and (c) testing how discovered genotypes affect life-span development. Phenotypic-annotation research is already advancing the understanding of GWAS discoveries for educational attainment and schizophrenia. We review examples and discuss methodological considerations for psychologists taking up the phenotypic-annotation approach.

“Common Polygenic Variations for Psychiatric Disorders and Cognition in Relation to Brain Morphology in the General Pediatric Population”, Alemany et al 2019

2019-alemany.pdf: “Common Polygenic Variations for Psychiatric Disorders and Cognition in Relation to Brain Morphology in the General Pediatric Population”⁠, Silvia Alemany, Philip R. Jansen, Ryan L. Muetzel, Natália Marques, Hanan El Marroun, Vincent W. V. Jaddoe et al (2019-01; ; similar):

Objective: This study examined the relation between polygenic scores (PGSs) for 5 major psychiatric disorders and 2 cognitive traits with brain magnetic resonance imaging morphologic measurements in a large population-based sample of children. In addition, this study tested for differences in brain morphology-mediated associations between PGSs for psychiatric disorders and PGSs for related behavioral phenotypes.

Method: Participants included 1,139 children from the Generation R Study assessed at 10 years of age with genotype and neuroimaging data available. PGSs were calculated for schizophrenia, bipolar disorder, major depression disorder, attention-deficit/​hyperactivity disorder (ADHD), autism spectrum disorder, intelligence, and educational attainment using results from the most recent genome-wide association studies. Image processing was performed using FreeSurfer to extract cortical and subcortical brain volumes.

Results: Greater genetic susceptibility for ADHD was associated with smaller caudate volume (strongest prior = 0.01: β = −0.07, p = 0.006). In boys, mediation analysis estimates showed that 11% of the association between the PGS for ADHD and the PGS attention problems was mediated by differences in caudate volume (n = 535), whereas mediation was not statistically-significant in girls or the entire sample. PGSs for educational attainment and intelligence showed positive associations with total brain volume (strongest prior = 0.5: β = 0.14, p = 7.12 × 10−8; and β = 0.12, p = 6.87 × 10−7, respectively).

Conclusion: The present findings indicate that the neurobiological manifestation of polygenic susceptibility for ADHD, educational attainment, and intelligence involve early morphologic differences in caudate and total brain volumes in childhood. Furthermore, the genetic risk for ADHD might influence attention problems through the caudate nucleus in boys.

[Keywords: polygenic risk score, neuroimaging, ADHD, educational attainment, intelligence]

“Schizophrenia Risk Conferred by Protein-coding de Novo Mutations”, Howrigan et al 2018

“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 et al (2018-12-13; ; similar):

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 SCZ affected parent-offspring trios from Taiwan along with DNMs from 1,077 published SCZ trios to better understand the contribution of coding DNMs to SCZ risk. Among 2,772 SCZ affected probands, the increased burden of DNMs is modest. Gene set analyses show that the modest increase in risk from DNMs in SCZ 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 model expectation of 5.1 genes (permuted 95% CI = 1–10 genes, permuted p = 3e-5). Overall, DNMs explain only a small fraction of SCZ 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.

“Using Genetics to Examine a General Liability to Childhood Psychopathology”, Riglin et al 2018

“Using genetics to examine a general liability to childhood psychopathology”⁠, Lucy Riglin, Ajay K. Thapar, Beate Leppert, Joanna Martin, Alexander Richards, Richard Anney, George Davey Smith et al (2018-11-21; similar):

Background: Psychiatric disorders show phenotypic as well as genetic overlaps. Factor analyses of child and adult psychopathology have found that phenotypic overlaps largely can be explained by a latent general “p” factor that reflects general liability to psychopathology. We investigated whether shared genetic liability across disorders would be reflected in associations between multiple different psychiatric polygenic risk scores (PRS) and a ‘general psychopathology’ factor in childhood.

Methods: The sample was a UK, prospective, population-based cohort (ALSPAC), including data on psychopathology at age 7 (n = 8161) years. PRS were generated from large published genome-wide association studies.

Outcomes

The general psychopathology factor was associated with both schizophrenia PRS and attention-deficit/​hyperactivity disorder (ADHD) PRS, whereas there was no strong evidence of association with major depressive disorder and autism spectrum disorder PRS. Schizophrenia PRS was also associated with a specific “emotional” problems factor.

Interpretation

Our findings suggest that genetic liability to schizophrenia and ADHD may contribute to shared genetic risks across childhood psychiatric diagnoses at least partly via the ‘general psychopathology’ factor. However, the pattern of observations could not be explained by a general “p” factor on its own.

Funding

This work was supported by the Wellcome Trust (204895/​Z/​16/​Z).Introduction

“The Genetic Relationship between Female Reproductive Traits and Six Psychiatric Disorders”, Ni et al 2018

“The genetic relationship between female reproductive traits and six psychiatric disorders”⁠, Guiyan Ni, Azmeraw Amare, Xuan Zhou, Natalie Mills, Jacob Gratten, Sang Hong Lee (2018-10-03; similar):

Female reproductive behaviors have an important implication in evolutionary fitness and health of offspring. Previous studies have shown that age at first birth of women (AFB) is genetically associated with schizophrenia (SCZ). However, for most other psychiatric disorders and reproductive traits, the latent shared genetic architecture is largely unknown. Here we used the second wave of UK Biobank data (n = 220,685) to evaluate the association between five female reproductive traits and polygenic risk scores (PRS) projected from genome-wide association study summary statistics of six psychiatric disorders (n = 429,178). We found that the PRS of attention-deficit/​hyperactivity disorder (ADHD) were strongly associated with AFB (genetic correlation of −0.68 ± 0.03 with p-value = 1.86E-89), age at first sexual intercourse (AFS) (−0.56 ± 0.03 with p-value = 3.42E-60), number of live births (NLB) (0.36 ± 0.04 with p-value = 4.01E-17) and age at menopause (−0.27 ± 0.04 with p-value = 5.71E-13). There were also robustly statistically-significant associations between the PRS of eating disorder (ED) and AFB (genetic correlation of 0.35 ± 0.06), ED and AFS (0.19 0.06), Major depressive disorder (MDD) and AFB (−0.27 ± 0.07), MDD and AFS (− 0.27 ± 0.03) and SCZ and AFS (−0.10 ± 0.03). Our findings reveal the shared genetic architecture between the five reproductive traits in women and six psychiatric disorders, which have a potential implication that helps to improve reproductive health in women, hence better child outcomes. Our findings may also explain, at least in part, an evolutionary hypothesis that causal mutations underlying psychiatric disorders have positive effects on reproductive success.

“Analysis of Polygenic Score Usage and Performance across Diverse Human Populations”, Duncan et al 2018

“Analysis of Polygenic Score Usage and Performance across Diverse Human Populations”⁠, LE Duncan, H. Shen, B. Gelaye, KJ Ressler, MW Feldman, RE Peterson, BW Domingue (2018-08-22; ; backlinks; similar):

Studies of the relationship between genetic and phenotypic variation have historically been carried out on people of European ancestry. Efforts are underway to address this limitation, but until they succeed, the legacy of a Euro-centric bias in medical genetic studies will continue to hinder research, including the use of polygenic scores, which are individual-level metrics of genetic risk. Ongoing debate surrounds the generalizability of polygenic scores based on genome-wide association studies (GWAS) conducted in European ancestry samples, to non-European ancestry samples. We analyzed the first decade of polygenic scoring studies (2008-2017, inclusive), and found that 67% of studies included exclusively European ancestry participants and another 19% included only East Asian ancestry participants. Only 3.8% of studies were carried out on samples of African, Hispanic, or Indigenous peoples. We find that effect sizes for European ancestry-derived polygenic scores are only 36% as large in African ancestry samples, as in European ancestry samples (t=-10.056, df = 22, p = 5.5×10−10). Poorer performance was also observed in other non-European ancestry samples. Analysis of polygenic scores in the 1000Genomes samples revealed many strong correlations with global principal components, and relationships between height polygenic scores and height phenotypes that were highly variable depending on methodological choices in polygenic score construction. As polygenic score use increases in research, precision medicine, and direct-to-consumer testing, improved handling of linkage disequilibrium and variant frequencies (both of which currently reduce transferability of scores) across populations will improve polygenic score performance. These findings bolster the rationale for large-scale GWAS in diverse human populations.

Significance Statement

The modern genetics revolution enabled rough calculations of individuals’ genetic liability for many phenotypes, including height, weight, and schizophrenia. Increasingly, polygenic scores, which are individual-level metrics of genetic liability, are available via direct-to-consumer testing, and they are already widely used in research. The performance of these scores depends on the availability of very large genetic studies, and consequently it is problematic that people of European ancestry are vastly over-represented in these studies. We quantify the magnitude of this problem on the performance of polygenic scores in global samples and also show ancestry-related properties of polygenic scores. These findings set benchmarks for future progress, and they demonstrate the need for large-scale genetic studies in diverse human populations.

Classification

Biological Sciences—Genetics

“Genetics & the Geography of Health, Behavior, and Attainment”, Belsky et al 2018

“Genetics & the Geography of Health, Behavior, and Attainment”⁠, Daniel W. Belsky, Avshalom Caspi, Louise Arseneault, David L. Corcoran, Benjamin W. Domingue, Kathleen Mullan Harris et al (2018-07-25; similar):

People’s life chances can be predicted by their neighborhoods. This observation is driving efforts to improve lives by changing neighborhoods. Some neighborhood effects may be causal, supporting neighborhood-level interventions. Other neighborhood effects may reflect selection of families with different characteristics into different neighborhoods, supporting interventions that target families/​individuals directly. To test how selection affects different neighborhood-linked problems, we linked neighborhood data with genetic, health, and social-outcome data for >7,000 European-descent UK and US young people in the E-Risk and Add Health Studies. We tested selection/​concentration of genetic risks for obesity, schizophrenia, teen-pregnancy, and poor educational outcomes in high-risk neighborhoods, including genetic analysis of neighborhood mobility. Findings argue against genetic selection/​concentration as an explanation for neighborhood gradients in obesity and mental-health problems, suggesting neighborhoods may be causal. In contrast, modest genetic selection/​concentration was evident for teen-pregnancy and poor educational outcomes, suggesting neighborhood effects for these outcomes should be interpreted with care.

“Schizophrenia Risk and Reproductive Success: A Mendelian Randomization Study”, Lawn et al 2018

“Schizophrenia risk and reproductive success: A Mendelian randomization study”⁠, Rebecca B. Lawn, Hannah M. Sallis, Amy E. Taylor, Robyn E. Wootton, George Davey Smith, Neil M. Davies et al (2018-06-28; similar):

Schizophrenia is a debilitating and heritable mental disorder associated with lower reproductive success. However, the prevalence of schizophrenia is stable over populations and time, resulting in an evolutionary puzzle: how is schizophrenia maintained in the population given its apparent fitness costs? One possibility is that increased genetic liability for schizophrenia, in the absence of the disorder itself, may confer some reproductive advantage. We assessed the correlation and causal effect of genetic liability for schizophrenia with number of children and age at first birth using data from the Psychiatric Genomics Consortium and UK Biobank. Linkage disequilibrium score regression showed little evidence of genetic correlation between genetic liability for schizophrenia and number of children (rg = 0.002, p = 0.84) or age at first birth (rg = −0.007, p = 0.45). Mendelian randomization indicated no robust evidence of a causal effect of genetic liability for schizophrenia on number of children (mean difference: 0.003 increase in number of children per doubling in the natural log odds ratio of schizophrenia risk, 95% CI: −0.003 to 0.009, p = 0.39) or age at first birth (−0.004 years lower age at first birth, 95% CI: −0.043 to 0.034, p = 0.82). These results suggest that increased genetic liability for schizophrenia does not confer a reproductive advantage.

“An Evolutionary Compass for Elucidating Selection Mechanisms Shaping Complex Traits”, Uricchio et al 2018

“An evolutionary compass for elucidating selection mechanisms shaping complex traits”⁠, Lawrence H. Uricchio, Hugo C. Kitano, Alexander Gusev, Noah A. Zaitlen (2018-06-07; ; similar):

Polygenic selection is likely to target some human traits, but the specific evolutionary mechanisms driving complex trait variation are largely unknown.

We developed an evolutionary compass for detecting selection and mutational bias that uses polarized GWAS summary statistics from a single population. We found evidence for selection and mutational bias acting on variation in five traits (BMI⁠, schizophrenia, Crohn’s disease, educational attainment, and height). We then used model-based analyses to show that these signals can be explained by stabilizing selection with shifts in the fitness-phenotype relationship.

We additionally provide evidence that selection has acted on Neanderthal alleles for height, schizophrenia, and depression, and discuss potential sources of confounding.

Our results provide a flexible and powerful framework for evolutionary analysis of complex phenotypes in humans and other organisms, and provide insights into the evolutionary mechanisms driving variation in human polygenic traits.

“Common Genetic Variants Contribute to Risk of Rare Severe Neurodevelopmental Disorders”, Niemi et al 2018

“Common genetic variants contribute to risk of rare severe neurodevelopmental disorders”⁠, Mari E. K. Niemi, Hilary C. Martin, Daniel L. Rice, Giuseppe Gallone, Scott Gordon, Martin Kelemen, Kerrie McAloney et al (2018-05-04; ; similar):

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.

“Evolutionary Perspectives on Genetic and Environmental Risk Factors for Psychiatric Disorders”, Keller 2018

2018-keller.pdf: “Evolutionary Perspectives on Genetic and Environmental Risk Factors for Psychiatric Disorders”⁠, Matthew C. Keller (2018-05; ; backlinks; similar):

Evolutionary medicine uses evolutionary theory to help elucidate why humans are vulnerable to disease and disorders. I discuss two different types of evolutionary explanations that have been used to help understand human psychiatric disorders.

First, a consistent finding is that psychiatric disorders are moderately to highly heritable, and many, such as schizophrenia, are also highly disabling and appear to decrease Darwinian fitness. Models used in evolutionary genetics to understand why genetic variation exists in fitness-related traits can be used to understand why risk alleles for psychiatric disorders persist in the population. The usual explanation for species-typical adaptations—natural selection—is less useful for understanding individual differences in genetic risk to disorders. Rather, two other types of models, mutation-selection-drift and balancing selection, offer frameworks for understanding why genetic variation in risk to psychiatric (and other) disorders exists, and each makes predictions that are now testable using whole-genome data.

Second, species-typical capacities to mount reactions to negative events are likely to have been crafted by natural selection to minimize fitness loss. The pain reaction to tissue damage is almost certainly such an example, but it has been argued that the capacity to experience depressive symptoms such as sadness, anhedonia, crying, and fatigue in the face of adverse life situations may have been crafted by natural selection as well. I review the rationale and strength of evidence for this hypothesis.

Evolutionary hypotheses of psychiatric disorders are important not only for offering explanations for why psychiatric disorders exist, but also for generating new, testable hypotheses and understanding how best to design studies and analyze data.

[Keywords: evolution, psychiatric disorders, genetics, schizophrenia, depression],/​p>

“Genetic Variants Associated With Subjective Well-being, Depressive Symptoms, and Neuroticism Identified through Genome-wide Analyses”, Okbay et al 2018

2016-okbay.pdf: “Genetic variants associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses”⁠, Aysu Okbay, Bart M. L. Baselmans, Jan-Emmanuel De Neve, Patrick Turley, Michel G. Nivard, Mark Alan Fontana et al (2018-04-26; ⁠, ; similar):

Major depressive disorder (MDD) [vs subjective well-being] is a common illness accompanied by considerable morbidity, mortality, costs, and heightened risk of suicide.

We conducted a genome-wide association meta-analysis based in 135,458 cases and 344,901 controls and identified 44 independent and statistically-significant loci.

The genetic findings were associated with clinical features of major depression and implicated brain regions exhibiting anatomical differences in cases. Targets of antidepressant medications and genes involved in gene splicing were enriched for smaller association signal. We found important relationships of genetic risk for major depression with educational attainment, body mass, and schizophrenia: lower educational attainment and higher body mass were putatively causal, whereas major depression and schizophrenia reflected a partly shared biological etiology.

All humans carry lesser or greater numbers of genetic risk factors for major depression. These findings help refine the basis of major depression and imply that a continuous measure of risk underlies the clinical phenotype.

“Genome-wide Association Analyses of Chronotype in 697,828 Individuals Provides New Insights into Circadian Rhythms in Humans and Links to Disease”, Jones et al 2018

“Genome-wide association analyses of chronotype in 697,828 individuals provides new insights into circadian rhythms in humans and links to disease”⁠, Samuel E. Jones, Jacqueline M. Lane, Andrew R. Wood, Vincent T. van Hees, Jessica Tyrrell, Robin N. Beaumont et al (2018-04-19; similar):

Using data from 697,828 research participants from 23andMe and UK Biobank, we identified 351 loci associated with being a morning person, a behavioural indicator of a person’s underlying circadian rhythm. These loci were validated in 85,760 individuals with activity-monitor derived measures of sleep timing: the mean sleep timing of the 5% of individuals carrying the most “morningness” alleles was 25.1 minutes (95% CI: 22.5, 27.6) earlier than the 5% carrying the fewest. The loci were enriched for genes involved in circadian rhythm and insulin pathways, and those expressed in the retina, hindbrain, hypothalamus, and pituitary (all FDRw<1%). We provide some evidence that being a morning person was causally associated with reduced risk of schizophrenia (OR: 0.89; 95% CI: 0.82, 0.96), depression (OR: 0.94; 95% CI: 0.91, 0.98) and a lower age at last childbirth in women (β: -046 years; 95% CI: -0.067, -0.025), but was not associated with BMI (β: -4.6×10−4; 95% CI: -0.044, 0.043) or type 2 diabetes (OR: 1.00; 95% CI: 0.91, 1.1). This study offers new insights into the biology of circadian rhythms and disease links in humans.

“GWAS in 446,118 European Adults Identifies 78 Genetic Loci for Self-reported Habitual Sleep Duration Supported by Accelerometer-derived Estimates”, Dashti et al 2018

“GWAS in 446,118 European adults identifies 78 genetic loci for self-reported habitual sleep duration supported by accelerometer-derived estimates”⁠, Hassan S. Dashti, Samuel E. Jones, Andrew R. Wood, Jacqueline M. Lane, Vincent T. van Hees, Heming Wang et al (2018-04-19; similar):

Sleep is an essential homeostatically-regulated state of decreased activity and alertness conserved across animal species, and both short and long sleep duration associate with chronic disease and all-cause mortality1,2. Defining genetic contributions to sleep duration could point to regulatory mechanisms and clarify causal disease relationships. Through genome-wide association analyses in 446,118 participants of European ancestry from the UK Biobank, we discover 78 loci for self-reported sleep duration that further impact accelerometer-derived measures of sleep duration, daytime inactivity duration, sleep efficiency and number of sleep bouts in a subgroup (n = 85,499) with up to 7-day accelerometry. Associations are enriched for genes expressed in several brain regions, and for pathways including striatum and subpallium development, mechanosensory response, dopamine binding, synaptic neurotransmission, catecholamine production, synaptic plasticity, and unsaturated fatty acid metabolism. Genetic correlation analysis indicates shared biological links between sleep duration and psychiatric, cognitive, anthropometric and metabolic traits and Mendelian randomization highlights a causal link of longer sleep with schizophrenia.

“Improving Genetic Prediction by Leveraging Genetic Correlations among Human Diseases and Traits”, Maier et al 2018

“Improving genetic prediction by leveraging genetic correlations among human diseases and traits”⁠, Robert M. Maier, Zhihong Zhu, Sang Hong Lee, Maciej Trzaskowski, Douglas M. Ruderfer, Eli A. Stahl, Stephan Ripke et al (2018-03-07; ⁠, ; backlinks; similar):

Genomic prediction has the potential to contribute to precision medicine. However, to date, the utility of such predictors is limited due to low accuracy for most traits. Here theory and simulation study are used to demonstrate that widespread pleiotropy among phenotypes can be utilised to improve genomic risk prediction. We show how a genetic predictor can be created as a weighted index that combines published genome-wide association study (GWAS) summary statistics across many different traits. We apply this framework to predict risk of schizophrenia and bipolar disorder in the Psychiatric Genomics consortium data, finding substantial heterogeneity in prediction accuracy increases across cohorts. For six additional phenotypes in the UK Biobank data, we find increases in prediction accuracy ranging from 0.7% for height to 47% for type 2 diabetes⁠, when using a multi-trait predictor that combines published summary statistics from multiple traits, as compared to a predictor based only on one trait.

“Genome-wide Association Analysis of Lifetime Cannabis Use (N=184,765) Identifies New Risk Loci, Genetic Overlap With Mental Health, and a Causal Influence of Schizophrenia on Cannabis Use”, Pasman et al 2018

“Genome-wide association analysis of lifetime cannabis use (N=184,765) identifies new risk loci, genetic overlap with mental health, and a causal influence of schizophrenia on cannabis use”⁠, Joëlle A. Pasman, Karin J. H. Verweij, Zachary Gerring, Sven Stringer, Sandra Sanchez-Roige, Jorien L. Treur et al (2018-01-08; ; similar):

Cannabis use is a heritable trait [1] that has been associated with adverse mental health outcomes. To identify risk variants and improve our knowledge of the genetic etiology of cannabis use, we performed the largest genome-wide association study (GWAS) meta-analysis for lifetime cannabis use (n = 184,765) to date. We identified 4 independent loci containing genome-wide statistically-significant SNP associations. Gene-based tests revealed 29 genome-wide statistically-significant genes located in these 4 loci and 8 additional regions. All SNPs combined explained 10% of the variance in lifetime cannabis use. The most statistically-significantly associated gene, CADM2, has previously been associated with substance use and risk-taking phenotypes [2–4]. We used S-PrediXcan to explore gene expression levels and found 11 unique eGenes. LD-score regression uncovered genetic correlations with smoking, alcohol use and mental health outcomes, including schizophrenia and bipolar disorder. Mendelian randomisation analysis provided evidence for a causal positive influence of schizophrenia risk on lifetime cannabis use.

“Association Between Population Density and Genetic Risk for Schizophrenia”, Colodro-Conde et al 2018

2018-colodroconde.pdf: “Association Between Population Density and Genetic Risk for Schizophrenia”⁠, Lucía Colodro-Conde, Baptiste Couvy-Duchesne, John B. Whitfield, Fabian Streit, Scott Gordon, Kathryn E. Kemper et al (2018-01-01; )

“Genome-wide Association Meta-analysis in 269,867 Individuals Identifies New Genetic and Functional Links to Intelligence”, Savage et al 2018

“Genome-wide association meta-analysis in 269,867 individuals identifies new genetic and functional links to intelligence”⁠, Jeanne E. Savage, Philip R. Jansen, Sven Stringer, Kyoko Watanabe, Julien Bryois, Christiaan A. de Leeuw et al (2018; ⁠, ; backlinks; similar):

Intelligence is highly heritable1 and a major determinant of human health and well-being2. Recent genome-wide meta-analyses have identified 24 genomic loci linked to variation in intelligence3–7, but much about its genetic underpinnings remains to be discovered.

Here, we present a large-scale genetic association study of intelligence (n = 269,867), identifying 205 associated genomic loci (190 new) and 1,016 genes (939 new) via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping, and gene-based association analysis. We find enrichment of genetic effects in conserved and coding regions and associations with 146 nonsynonymous exonic variants. Associated genes are strongly expressed in the brain, specifically in striatal medium spiny neurons and hippocampal pyramidal neurons. Gene set analyses implicate pathways related to nervous system development and synaptic structure.

We confirm previous strong genetic correlations with multiple health-related outcomes, and Mendelian randomization analysis results suggest protective effects of intelligence for Alzheimer’s disease and ADHD and bidirectional causation with pleiotropic effects for schizophrenia. These results are a major step forward in understanding the neurobiology of cognitive function as well as genetically related neurological and psychiatric disorders.

“Common Schizophrenia Alleles Are Enriched in Mutation-intolerant Genes and in Regions under Strong Background Selection”, Pardiñas et al 2018

2018-pardinas.pdf: “Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection”⁠, Antonio F. Pardiñas, Peter Holmans, Andrew J. Pocklington, Valentina Escott-Price, Stephan Ripke, Noa Carrera et al (2018; ; backlinks; similar):

Schizophrenia is a debilitating psychiatric condition often associated with poor quality of life and decreased life expectancy. Lack of progress in improving treatment outcomes has been attributed to limited knowledge of the underlying biology, although large-scale genomic studies have begun to provide insights.

We report a new genome-wide association study of schizophrenia (11,260 cases and 24,542 controls), and through meta-analysis with existing data we identify 50 novel associated loci and 145 loci in total. Through integrating genomic fine-mapping with brain expression and chromosome conformation data, we identify candidate causal genes within 33 loci. We also show for the first time that the common variant association signal is highly enriched among genes that are under strong selective pressures.

These findings provide new insights into the biology and genetic architecture of schizophrenia, highlight the importance of mutation-intolerant genes and suggest a mechanism by which common risk variants persist in the population.

“Effects of Latent Toxoplasmosis on Olfactory Functions of Men and Women”, Flegr et al 2017

“Effects of latent Toxoplasmosis on olfactory functions of men and women”⁠, Jaroslav Flegr, Manfred Milinski, Šárka Kaňková, Martin Hůla, Jana Hlaváčová, Kateřina Sýkorová (2017-12-10; ⁠, ; similar):

The prevalence of Toxoplasmosis is higher in schizophrenics than in the general population. It has been suggested that certain symptoms of schizophrenia, including changes in olfactory functions, are in fact symptoms of Toxoplasmosis that can be easily detected in schizophrenics only due to the increased prevalence of Toxoplasmosis in this population. Schizophrenics have impaired identification of odors and lower sensitivity of odor detection. Here we searched for differences in olfactory functions between 62 infected and 61 non infected non-schizophrenic subjects. The infected men scored better in the standard odor-identification test. The infected women rated all smells as more intensive while the infected men rated nearly all smells as less intensive. Infected women rated the pleasantness of the smell of undiluted cat urine as higher than the non-infected women and the opposite was true for the men (the opposite direction shifts in men and women were described earlier for highly diluted cat urine). Toxoplasmosis had no effect on the rated pleasantness of the smell of other stimuli. Our results suggest that latent Toxoplasmosis is associated with changes in the olfactory functions in humans; however, the observed changes differ from those observed in schizophrenics.

Key findings

Infected men but not women show better odor identification ability than the non-infected controls.

The infected women rated all smells as more and men as less intensive than the controls.

The infected women rated smell of cat urine as more and men as less pleasurable than the controls.

Toxoplasmosis had no effect on the rated pleasantness of the smell of other stimuli.

We found no new evidence for the Toxoplasmosis hypothesis of schizophrenia.

“Polygenic Prediction of the Phenome, across Ancestry, in Emerging Adulthood”, Docherty et al 2017

“Polygenic prediction of the phenome, across ancestry, in emerging adulthood”⁠, Anna R. Docherty, Arden Moscati, Danielle Dick, Jeanne E. Savage, Jessica E. Salvatore, Megan Cooke, Fazil Aliev et al (2017-11-27; ⁠, ; backlinks; similar):

Background: Identifying genetic relationships between complex traits in emerging adulthood can provide useful etiological insights into risk for psychopathology. College-age individuals are under-represented in genomic analyses thus far, and the majority of work has focused on the clinical disorder or cognitive abilities rather than normal-range behavioral outcomes.

Methods: This study examined a sample of emerging adults 18–22 years of age (n = 5947) to construct an atlas of polygenic risk for 33 traits predicting relevant phenotypic outcomes. 28 hypotheses were tested based on the previous literature on samples of European ancestry, and the availability of rich assessment data allowed for polygenic predictions across 55 psychological and medical phenotypes.

Results: Polygenic risk for schizophrenia (SZ) in emerging adults predicted anxiety, depression, nicotine use, trauma, and family history of psychological disorders. Polygenic risk for neuroticism predicted anxiety, depression, phobia, panic, neuroticism, and was correlated with polygenic risk for cardiovascular disease.

Conclusions: These results demonstrate the extensive impact of genetic risk for SZ, neuroticism, and major depression on a range of health outcomes in early adulthood. Minimal cross-ancestry replication of these phenomic patterns of polygenic influence underscores the need for more genome-wide association studies of non-European populations.

“Common Risk Variants Identified in Autism Spectrum Disorder”, Grove et al 2017

“Common risk variants identified in autism spectrum disorder”⁠, Jakob Grove, Stephan Ripke, Thomas D. Als, Manuel Mattheisen, Raymond Walters, Hyejung Won, Jonatan Pallesen et al (2017-11-25; similar):

Autism spectrum disorder (ASD) is a highly heritable and heterogeneous group of neurodevelopmental phenotypes diagnosed in more than 1% of children. Common genetic variants contribute substantially to ASD susceptibility, but to date no individual variants have been robustly associated with ASD.

With a marked sample size increase from an unique Danish population resource, we report a genome-wide association meta-analysis of 18,381 ASD cases and 27,969 controls that identifies five genome-wide statistically-significant loci. Leveraging GWAS results from three phenotypes with significantly overlapping genetic architectures (schizophrenia, major depression, and educational attainment), seven additional loci shared with other traits are identified at equally strict significance levels.

Dissecting the polygenic architecture we find both quantitative and qualitative polygenic heterogeneity across ASD subtypes, in contrast to what is typically seen in other complex disorders. These results highlight biological insights, particularly relating to neuronal function and corticogenesis and establish that GWAS performed at scale will be much more productive in the near term in ASD, just as it has been in a broad range of important psychiatric and diverse medical phenotypes.

“The Molecular Genetics of Participation in the Avon Longitudinal Study of Parents and Children”, Taylor et al 2017

“The molecular genetics of participation in the Avon Longitudinal Study of Parents and Children”⁠, Amy E. Taylor, Hannah J. Jones, Hannah Sallis, Jack Euesden, Evie Stergiakouli, Neil M. Davies, Stanley Zammit et al (2017-10-20; similar):

Background: It is often assumed that selection (including participation and dropout) does not represent an important source of bias in genetic studies. However, there is little evidence to date on the effect of genetic factors on participation.

Methods: Using data on mothers (n = 7,486) and children (n = 7,508) from the Avon Longitudinal Study of Parents and Children, we (1) examined the association of polygenic risk scores for a range of socio-demographic, lifestyle characteristics and health conditions related to continued participation, (2) investigated whether associations of polygenic scores with body mass index (BMI; derived from self-reported weight and height) and self-reported smoking differed in the largest sample with genetic data and a sub-sample who participated in a recent follow-up and (3) determined the proportion of variation in participation explained by common genetic variants using genome-wide data.

Results: We found evidence that polygenic scores for higher education, agreeableness and openness were associated with higher participation and polygenic scores for smoking initiation, higher BMI, neuroticism, schizophrenia, ADHD and depression were associated with lower participation. Associations between the polygenic score for education and self-reported smoking differed between the largest sample with genetic data (OR for ever smoking per SD increase in polygenic score:0.85, 95% CI:0.81,0.89) and sub-sample (OR:0.95, 95% CI:0.88,1.02). In genome-wide analysis, single nucleotide polymorphism based heritability explained 17–31% of variability in participation.

Conclusion: Genetic association studies, including Mendelian randomization, can be biased by selection, including loss to follow-up. Genetic risk for dropout should be considered in all analyses of studies with selective participation.

“Genome-wide Association Study of Social Relationship Satisfaction: Significant Loci and Correlations With Psychiatric Conditions”, Warrier et al 2017

“Genome-wide association study of social relationship satisfaction: significant loci and correlations with psychiatric conditions”⁠, Varun Warrier, the 23andMe Research Team, Thomas Bourgeron, Simon Baron-Cohen (2017-10-05; ⁠, ⁠, ; similar):

Dissatisfaction in social relationships is reported widely across many psychiatric conditions. We investigated the genetic architecture of family relationship satisfaction and friendship satisfaction in the UK Biobank. We leveraged the high genetic correlation between the two phenotypes (rg = 0.87±0.03; p < 2.2×10−16) to conduct multi-trait analysis of Genome Wide Association Study (GWAS) (Neffective family = 164,112; Neffective friendship = 158,116). We identified two genome-wide statistically-significant associations for both the phenotypes: rs1483617 on chromosome 3 and rs2189373 on chromosome 6, a region previously implicated in schizophrenia. eQTL and chromosome conformation capture in neural tissues prioritizes several genes including NLGN1. Gene-based association studies identified several significant genes, with highest expression in brain tissues. Genetic correlation analysis identified significant negative correlations for multiple psychiatric conditions including highly significant negative correlation with cross-psychiatric disorder GWAS, underscoring the central role of social relationship dissatisfaction in psychiatric diagnosis. The two phenotypes were enriched for genes that are loss of function intolerant. Both phenotypes had modest, significant additive SNP heritability of ~6%. Our results underscore the central role of social relationship satisfaction in mental health and identify genes and tissues associated with it.

“Different Worlds”, Alexander 2017

“Different Worlds”⁠, Scott Alexander (2017-10-02; ; backlinks; similar):

[Psychiatrist muses about individual differences: how do people perceive & experience such extremely different ‘worlds’, such that some lurch from drama to trauma while others experience few problems, a large fraction of Americans are Young Earth Creationists while he knows none personally, some constantly experience ‘sexism’ and ‘racism’ while others never experience it, some psychiatrists get patients who melt down regularly while others (like him) never do, and so on? (See also: the Dodo Bird Verdict⁠/​therapist-specific effects, heritability, reactive gene-environment interaction, typical mind fallacy⁠, cognitive biases, ‘everything is correlated’⁠, the Metallic Laws⁠.)]

People self-select into bubbles along all sorts of axes. Some of these bubbles are obvious and easy to explain, like rich people mostly meeting other rich people at the country club. Others are more mysterious, like how some non-programmer ends up with mostly programmer friends. Still others are horrible and completely outside comprehension, like someone who tries very hard to avoid abusers but ends up in multiple abusive relationships anyway. Even for two people living in the same country, city, and neighborhood, they can have a “society” made up of very different types of people. People vary widely on the way they perceive social interaction. A paranoid schizophrenic will view every interaction as hostile; a Williams Syndrome kid will view every interaction as friendly. In between, there will be a whole range of healthy people without any psychiatric disorder who tend toward one side or the other. Only the most blatant data can be interpreted absent the priors that these dispositions provide; everything else will only get processed through preexisting assumptions about how people tend to act. Since things like racism rarely take the form of someone going up to you and saying “Hello, I am a racist and because of your skin color I plan to discriminate against you in the following ways…”, they’ll end up as ambiguous stimuli that everyone will interpret differently. Finally, some people have personalities or styles of social interaction that unconsciously compel a certain response from their listeners. Call these “niceness fields” or “meanness fields” or whatever: some people are the sort who—if they became psychotherapists—would have patients who constantly suffered dramatic emotional meltdowns, and others’ patients would calmly discuss their problems.

The old question goes: are people basically good or basically evil? Different philosophers give different answers. But so do different random people I know who aren’t thinking philosophically at all. Some people describe a world of backstabbing Machiavellians, where everybody’s a shallow social climber who will kick down anyone it takes to get to the top. Other people describe a world where everyone is basically on the same page, trying to be nice to everyone else but getting stuck in communication difficulties and honest disagreements over values.

I think both groups are right. Some people experience worlds of basically-good people who treat them nicely. Other people experience worlds of awful hypocritical backstabbers. This can be true even if they live in the same area as each other, work the same job as each other, et cetera.

To return to a common theme: nothing makes sense except in light of inter-individual variation. Variation in people’s internal experience⁠. Variation in people’s basic beliefs and assumptions⁠. Variation in level of abstract thought⁠. And to all of this I would add a variation in our experience of other people.

“Linkage Disequilibrium-dependent Architecture of Human Complex Traits Shows Action of Negative Selection”, Gazal et al 2017

2017-gazal.pdf: “Linkage disequilibrium-dependent architecture of human complex traits shows action of negative selection”⁠, Steven Gazal, Hilary K. Finucane, Nicholas A. Furlotte, Po-Ru Loh, Pier Francesco Palamara, Xuanyao Liu et al (2017-09-11; ⁠, ; backlinks; similar):

Recent work has hinted at the linkage disequilibrium (LD)-dependent architecture of human complex traits, where SNPs with low levels of LD (LLD) have larger per-SNP heritability. Here we analyzed summary statistics from 56 complex traits (average n = 101,401) by extending stratified LD score regression to continuous annotations. We determined that SNPs with low LLD have statistically-significantly larger per-SNP heritability and that roughly half of this effect can be explained by functional annotations negatively correlated with LLD, such as DNase I hypersensitivity sites (DHSs). The remaining signal is largely driven by our finding that more recent common variants tend to have lower LLD and to explain more heritability (p = 2.38 × 10−104); the youngest 20% of common SNPs explain 3.9× more heritability than the oldest 20%, consistent with the action of negative selection. We also inferred jointly statistically-significant effects of other LD-related annotations and confirmed via forward simulations that they jointly predict deleterious effects.

“GWAS Meta-analysis (N=279,930) Identifies New Genes and Functional Links to Intelligence”, Savage et al 2017

“GWAS meta-analysis (N=279,930) identifies new genes and functional links to intelligence”⁠, Jeanne E. Savage, Philip R. Jansen, Sven Stringer, Kyoko Watanabe, Julien Bryois, Christiaan A. de Leeuw et al (2017-09-06; backlinks; similar):

Intelligence is highly heritable1 and a major determinant of human health and well-being2. Recent genome-wide meta-analyses have identified 24 genomic loci linked to intelligence3–7, but much about its genetic underpinnings remains to be discovered.

Here, we present the largest genetic association study of intelligence to date (n = 279,930), identifying 206 genomic loci (191 novel) and implicating 1,041 genes (963 novel) via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping, and gene-based association analysis. We find enrichment of genetic effects in conserved and coding regions and identify 89 nonsynonymous exonic variants. Associated genes are strongly expressed in the brain and specifically in striatal medium spiny neurons and cortical and hippocampal pyramidal neurons. Gene-set analyses implicate pathways related to neurogenesis, neuron differentiation and synaptic structure.

We confirm previous strong genetic correlations with several neuropsychiatric disorders, and Mendelian Randomization results suggest protective effects of intelligence for Alzheimer’s dementia and ADHD, and bidirectional causation with strong pleiotropy for schizophrenia. These results are a major step forward in understanding the neurobiology of intelligence as well as genetically associated neuropsychiatric traits.

“GWAS Meta-Analysis of Neuroticism (N=449,484) Identifies Novel Genetic Loci and Pathways”, Nagel et al 2017

“GWAS Meta-Analysis of Neuroticism (N=449,484) Identifies Novel Genetic Loci and Pathways”⁠, Mats Nagel, Philip R. Jansen, Sven Stringer, Kyoko Watanabe, Christiaan A. de Leeuw, Julien Bryois, Jeanne E. Savage et al (2017-09-05; similar):

Neuroticism is an important risk factor for psychiatric traits including depression1, anxiety2,3, and schizophrenia4–6. Previous genome-wide association studies7–12 (GWAS) reported 16 genomic loci10–12. Here we report the largest neuroticism GWAS meta-analysis to date (n = 449,484), and identify 136 independent genome-wide statistically-significant loci (124 novel), implicating 599 genes. Extensive functional follow-up analyses show enrichment in several brain regions and involvement of specific cell-types, including dopaminergic neuroblasts (P = 3×10−8), medium spiny neurons (P = 4×10−8) and serotonergic neurons (P = 1×10−7). Gene-set analyses implicate three specific pathways: neurogenesis (P = 4.4×10−9), behavioural response to cocaine processes (P = 1.84×10−7), and axon part (p = 5.26×10−8). We show that Neuroticism’s genetic signal partly originates in two genetically distinguishable subclusters13 (depressed affect and worry, the former being genetically strongly related to depression, rg = 0.84), suggesting distinct causal mechanisms for subtypes of individuals. These results vastly enhance our neurobiological understanding of neuroticism, and provide specific leads for functional follow-up experiments.

“Heritability of Schizophrenia and Schizophrenia Spectrum Based on the Nationwide Danish Twin Register”, Hilker et al 2017

2017-hilker.pdf: “Heritability of Schizophrenia and Schizophrenia Spectrum Based on the Nationwide Danish Twin Register”⁠, Rikke Hilker, Dorte Helenius, Birgitte Fagerlund, Axel Skytthe, Kaare Christensen, Thomas M. Werge, Merete Nordentoft et al (2017-08-30; ⁠, ; similar):

Background: Twin studies have provided evidence that both genetic and environmental factors contribute to schizophrenia (SZ) risk. Heritability estimates of SZ in twin samples have varied methodologically. This study provides updated heritability estimates based on nationwide twin data and an improved statistical methodology.

Methods: Combining 2 nationwide registers, the Danish Twin Register and the Danish Psychiatric Research Register, we identified a sample of twins born between 1951 and 2000 (n = 31,524 twin pairs). Twins were followed until June 1, 2011. Liability threshold models adjusting for censoring with inverse probability weighting were used to estimate proband-wise concordance rates and heritability of the diagnoses of SZ and SZ spectrum disorders.

Results: The proband-wise concordance rate of SZ is 33% in monozygotic twins and 7% in dizygotic twins. We estimated the heritability of SZ to be 79%. When expanding illness outcome to include SZ spectrum disorders, the heritability estimate was almost similar (73%).

Conclusions: The key strength of this study is the application of a novel statistical method accounting for censoring in the follow-up period to a nationwide twin sample. The estimated 79% heritability of SZ is congruent with previous reports and indicates a substantial genetic risk. The high genetic risk also applies to a broader phenotype of SZ spectrum disorders. The low concordance rate of 33% in monozygotic twins demonstrates that illness vulnerability is not solely indicated by genetic factors.

[Keywords: censoring, concordance, heritability, register, schizophrenia, twin study]

“Genome-wide Analysis of Risk-taking Behaviour and Cross-disorder Genetic Correlations in 116 255 Individuals from the UK Biobank Cohort”, Strawbridge et al 2017

“Genome-wide analysis of risk-taking behaviour and cross-disorder genetic correlations in 116 255 individuals from the UK Biobank cohort”⁠, Rona J. Strawbridge, Joey Ward, Breda Cullen, Elizabeth M. Tunbridge, Sarah Hartz, Laura Bierut, Amy Horton et al (2017-08-16; similar):

Risk-taking behaviour is a key component of several psychiatric disorders and could influence lifestyle choices such as smoking, alcohol use and diet. Risk-taking behaviour therefore fits within a Research Domain Criteria (RDoC) approach, whereby elucidation of the genetic determinants of this trait has the potential to improve our understanding across different psychiatric disorders. Here we report a genome wide association study in 116 255 UK Biobank participants who responded yes/​no to the question “would you consider yourself a risk-taker?” Risk-takers (compared to controls) were more likely to be men, smokers and have a history of mental illness. Genetic loci associated with risk-taking behaviour were identified on chromosomes 3 (rs13084531) and 6 (rs9379971). The effects of both lead SNPs were comparable between men and women. The chromosome 3 locus highlights CADM2, previously implicated in cognitive and executive functions⁠, but the chromosome 6 locus is challenging to interpret due to the complexity of the HLA region. Risk-taking behaviour shared significant genetic risk with schizophrenia, bipolar disorder, attention deficit hyperactivity disorder and post-traumatic stress disorder⁠, as well as with smoking and total obesity. Despite being based on only a single question, this study furthers our understanding of the biology of risk-taking behaviour, a trait which has a major impact on a range of common physical and mental health disorders.

“Evidence for Evolutionary Shifts in the Fitness Landscape of Human Complex Traits”, Uricchio et al 2017

“Evidence for evolutionary shifts in the fitness landscape of human complex traits”⁠, Lawrence H. Uricchio, Hugo C. Kitano, Alexander Gusev, Noah A. Zaitlen (2017-08-08; ; similar):

Selection alters human genetic variation, but the evolutionary mechanisms shaping complex traits and the extent of selection’s impact on polygenic trait evolution remain largely unknown. Here, we develop a novel polygenic selection inference method (Polygenic Ancestral Selection Test Encompassing Linkage, or PASTEL) relying on GWAS summary data from a single population. We use model-based simulations of complex traits that incorporate human demography, stabilizing selection, and polygenic adaptation to show how shifts in the fitness landscape generate distinct signals in GWAS summary data. Our test retains power for relatively ancient selection events and controls for potential confounding from linkage disequilibrium. We apply PASTEL to nine complex traits, and find evidence for selection acting on five of them (height, BMI, schizophrenia, Crohn’s disease, and educational attainment). This study provides evidence that selection modulates the relationship between frequency and effect size of trait-altering alleles for a wide range of traits, and provides a flexible framework for future investigations of selection on complex traits using GWAS data.

“Genomic Dissection of Bipolar Disorder and Schizophrenia including 28 Subphenotypes”, Ruderfer et al 2017

“Genomic dissection of bipolar disorder and schizophrenia including 28 subphenotypes”⁠, Douglas M. Ruderfer, Stephan Ripke, Andrew McQuillin, James Boocock, Eli A. Stahl, Jennifer M. Whitehead Pavlides et al (2017-08-08; similar):

Schizophrenia (SCZ) and bipolar disorder (BD) are highly heritable disorders that share a significant proportion of common risk variation. Understanding the genetic factors underlying the specific symptoms of these disorders will be crucial for improving diagnosis, intervention and treatment. In case-control data consisting of 53,555 cases (20,129 BD, 33,426 SCZ) and 54,065 controls, we identified 114 genome-wide statistically-significant loci (GWS) when comparing all cases to controls, of which 41 represented novel findings. Two genome-wide statistically-significant loci were identified when comparing SCZ to BD and a third was found when directly incorporating functional information. Regional joint association identified a genomic region of overlapping association in BD and SCZ with disease-independent causal variants indicating a fourth region contributing to differences between these disorders. Regional SNP-heritability analyses demonstrated that the estimated heritability of BD based on the SCZ GWS regions was significantly higher than that based on the average genomic region (91 regions, p = 1.2×10−6) while the inverse was not significant (19 regions, p = 0.89). Using our BD and SCZ GWAS we calculated polygenic risk scores and identified several statistically-significant correlations with: (1) SCZ subphenotypes: negative symptoms (SCZ, p = 3.6×10−6) and manic symptoms (BD, p = 2×10−5), (2) BD subphenotypes: psychotic features (SCZ p = 1.2×10−10, BD p = 5.3×10−5) and age of onset (SCZ p = 7.9×10−4). Finally, we show that psychotic features in BD has significant SNP-heritability (h2SNP = 0.15, SE = 0.06), and a statistically-significant genetic correlation with SCZ (rg = 0.34) in addition there is a significant sign test result between SCZ GWAS and a GWAS of BD cases contrasting those with and without psychotic features (p = 0.0038, one-side binomial test). For the first time, we have identified specific loci pointing to a potential role of 4 genes (DARS2, ARFGEF2, DCAKD and GATAD2A) that distinguish between BD and SCZ, providing an opportunity to understand the biology contributing to clinical differences of these disorders. Our results provide the best evidence so far of genomic components distinguishing between BD and SCZ that contribute directly to specific symptom dimensions.

“Genome-wide Association Study Identifies 30 Loci Associated With Bipolar Disorder”, Stahl et al 2017

“Genome-wide association study identifies 30 Loci Associated with Bipolar Disorder”⁠, Eli A. Stahl, Andreas J. Forstner, Andrew McQuillin, Stephan Ripke, Vassily Trubetskoy, Manuel Mattheisen et al (2017-08-08; similar):

Bipolar disorder is a highly heritable psychiatric disorder that features episodes of mania and depression. We performed the largest genome-wide association study to date, including 20,352 cases and 31,358 controls of European descent, with follow-up analysis of 881 sentinel variants at loci with p < 1×10−4 in an independent sample of 9,412 cases and 137,760 controls. In the combined analysis, 30 loci achieved genome-wide statistical-significance including 20 novel loci. These statistically-significant loci contain genes encoding ion channels and neurotransmitter transporters (CACNA1C, GRIN2A, SCN2A, SLC4A1), synaptic components (RIMS1, ANK3), immune and energy metabolism components, and multiple potential therapeutic targets for mood stabilizer drugs. Bipolar disorder type I (depressive and manic episodes; ~73% of our cases) is strongly genetically correlated with schizophrenia whereas type II (depressive and hypomanic episodes; ~17% of our cases) correlated more with major depression. Furthermore, bipolar disorder has a positive genetic correlation with educational attainment yet has no statistically-significant genetic correlation with intelligence. These findings address key clinical questions and provide potential new biological mechanisms for bipolar disorder.

“Genome-wide Association Analyses Identify 44 Risk Variants and Refine the Genetic Architecture of Major Depressive Disorder”, Wray et al 2017

“Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depressive disorder”⁠, Naomi R. Wray, Stephan Ripke, Manuel Mattheisen, Maciej Trzaskowski, Enda M. Byrne, Abdel Abdellaoui et al (2017-07-24; similar):

Major depressive disorder (MDD) is a notably complex illness with a lifetime prevalence of 14%.1 It is often chronic or recurrent and is thus accompanied by considerable morbidity, excess mortality, substantial costs, and heightened risk of suicide.2–7 MDD is a major cause of disability worldwide.8 We conducted a genome-wide association (GWA) meta-analysis in 130,664 MDD cases and 330,470 controls, and identified 44 independent loci that met criteria for statistical-significance. We present extensive analyses of these results which provide new insights into the nature of MDD. The genetic findings were associated with clinical features of MDD, and implicated prefrontal and anterior cingulate cortex in the pathophysiology of MDD (regions exhibiting anatomical differences between MDD cases and controls). Genes that are targets of antidepressant medications were strongly enriched for MDD association signals (p = 8.5×10−10), suggesting the relevance of these findings for improved pharmacotherapy of MDD. Sets of genes involved in gene splicing and in creating isoforms were also enriched for smaller MDD GWA p-values, and these gene sets have also been implicated in schizophrenia and autism. Genetic risk for MDD was correlated with that for many adult and childhood onset psychiatric disorders. Our analyses suggested important relations of genetic risk for MDD with educational attainment, body mass, and schizophrenia: the genetic basis of lower educational attainment and higher body mass were putatively causal for MDD whereas MDD and schizophrenia reflected a partly shared biological etiology. All humans carry lesser or greater numbers of genetic risk factors for MDD, and a continuous measure of risk underlies the observed clinical phenotype. MDD is not a distinct entity that neatly demarcates normalcy from pathology but rather an useful clinical construct associated with a range of adverse outcomes and the end result of a complex process of intertwined genetic and environmental effects. These findings help refine and define the fundamental basis of MDD.

“Quantifying the Impact of Rare and Ultra-rare Coding Variation across the Phenotypic Spectrum”, Ganna et al 2017

“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 et al (2017-06-09; ; similar):

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 available sample size of whole-exome sequencing studies (WES) 3. Here we assemble WES data from 100,304 individuals to quantify the impact of rare PTVs on 13 quantitative traits and 10 diseases. We focus on those PTVs that occur in PTV-intolerant (PI) genes, as these are more likely to 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 in GWAS 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 in the 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.

“Estimating Degree of Polygenicity, Causal Effect Size Variance, and Confounding Bias in GWAS Summary Statistics”, Holland et al 2017

“Estimating degree of polygenicity, causal effect size variance, and confounding bias in GWAS summary statistics”⁠, Dominic Holland, Chun-Chieh Fan, Oleksandr Frei, Alexey A. Shadrin, Olav B. Smeland, V. S. Sundar, Enhancing Neuro Imaging Genetics through Meta Analysis Consortium et al (2017-05-24; similar):

Of signal interest in the genetics of traits are estimating the proportion, π1, of causally associated single nucleotide polymorphisms (SNPs), and their effect size variance, σ[^2^~β~]{.supsub}, which are components of the mean heritabilities captured by the causal SNP.

Here we present the first model, using detailed linkage disequilibrium structure, to estimate these quantities from genome-wide association studies (GWAS) summary statistics, assuming a Gaussian distribution of SNP effect sizes, β. We apply the model to three diverse phenotypes—schizophrenia, putamen volume, and educational attainment—and validate it with extensive simulations. We find that schizophrenia is highly polygenic, with ~5 × 104 causal SNPs distributed with small effect size variance, σ[^2^~β~]{.supsub} = 3.5 × 10−5 (in units where the phenotype variance is normalized to 1), requiring a GWAS study with more than 0.5 million samples in each arm for full discovery. In contrast, putamen volume involves only ≃ 3 × 102 causal SNPs, but with σ[^2^~β~]{.supsub} = 1.2 × 10−3, indicating a much larger proportion of the causal SNPs that are strongly associated. Educational attainment has similar polygenicity to schizophrenia, but with effects that are substantially weaker, σ[^2^~β~]{.supsub} = 5 × 10−6, leading to much lower heritability.

Thus the model is able to describe the broad genetic architecture of phenotypes where both polygenicity and effect size variance range over several orders of magnitude, shows why only small proportions of heritability have been explained for discovered SNPs, and provides a roadmap for future GWAS discoveries.

“Genome-wide Analysis of 113,968 Individuals in UK Biobank Identifies 4 Loci Associated With Mood Instability”, Ward et al 2017

“Genome-wide analysis of 113,968 individuals in UK Biobank identifies 4 loci associated with mood instability”⁠, Joey Ward, Rona J. Strawbridge, Nicholas Graham, Mark E. S. Bailey, Amy Freguson, Donald M. Lyall, Breda Cullen et al (2017-03-17; similar):

Mood instability is a core clinical feature of affective disorders, particularly major depressive disorder (MDD) and bipolar disorder (BD). It may be an useful construct in line with the Research Domain Criteria (RDoC) approach, which proposes studying dimensional psychopathological traits that cut across diagnostic categories as a more effective strategy for identifying the underlying biology of psychiatric disorders.

Here we report a genome-wide association study (GWAS) of mood instability in a very large study of 53,525 cases and 60,443 controls from the UK Biobank cohort, the only such GWAS reported to date. We identified four independent loci (on chromosomes 8, 9, 14 and 18) statistically-significantly associated with mood instability, with a common SNP-based heritability estimate for mood instability of ~8%. We also found a strong genetic correlation between mood instability and MDD (0.60, SE = 0.07, p = 8.95×10−17), a small but statistically statistically-significant genetic correlation with schizophrenia (0.11, SE = 0.04, p = 0.01), but no genetic correlation with BD.

Several candidate genes harbouring variants in linkage disequilibrium with the associated loci may have a role in the pathophysiology of mood disorders, including the DCC netrin 1 receptor (DCC), eukaryotic initiation factor 2B (EIF2B2), placental growth factor (PGF) and protein tyrosine phosphatase, receptor type D (PTPRD) genes. Strengths of this study include the large sample size; however, our measure of mood instability may be limited by the use of a single self-reported question.

Overall, this work suggests a polygenic basis for mood instability and opens up the field for the further biological investigation of this important cross-diagnostic psychopathological trait.

“Widespread Signatures of Positive Selection in Common Risk Alleles Associated to Autism Spectrum Disorder”, Polimanti & Gelernter 2017

“Widespread signatures of positive selection in common risk alleles associated to autism spectrum disorder”⁠, Renato Polimanti, Joel Gelernter (2017-02-07; similar):

The human brain is the outcome of innumerable evolutionary processes; the systems genetics of psychiatric disorders could bear their signatures. On this basis, we analyzed five psychiatric disorders, attention deficit hyperactivity disorder, autism spectrum disorder (ASD), bipolar disorder, major depressive disorder, and schizophrenia (SCZ), using GWAS summary statistics from the Psychiatric Genomics Consortium. Machine learning-derived scores were used to investigate two natural-selection scenarios: complete selection (loci where a selected allele reached fixation) and incomplete selection (loci where a selected allele has not yet reached fixation). ASD GWAS results positively correlated with incomplete-selection (p = 3.53×10−4). Variants with ASD GWAS p < 0.1 were shown to have a 19%-increased probability to be in the top-5% for incomplete-selection score (OR = 1.19, 95%CI = 1.11–1.8, p = 9.56×10−7). Investigating the effect directions of minor alleles, we observed an enrichment for positive associations in SNPs with ASD GWAS p < 0.1 and top-5% incomplete-selection score (permutation p < 10−4). Considering the set of these ASD-positive-associated variants, we observed gene-expression enrichments for brain and pituitary tissues (p = 2.3×10−5 and p = 3×10−5, respectively) and 53 gene ontology (GO) enrichments, such as nervous system development (GO:0007399, p = 7.57×10−12), synapse organization (GO:0050808, p = 8.29×10−7), and axon guidance (GO:0007411, p = 1.81×10−7). Previous genetic studies demonstrated that ASD positively correlates with childhood intelligence, college completion, and years of schooling. Accordingly, we hypothesize that certain ASD risk alleles were under positive selection during human evolution due to their involvement in neurogenesis and cognitive ability.

Author summary:

Predisposition to psychiatric disorders is due to the contribution of many genes involved in numerous molecular mechanisms. Since brain evolution has played a pivotal role in determining the success of the human species, the molecular pathways involved with the onset of mental illnesses are likely to be informative as we seek an understanding of the mechanisms involved in the evolution of human brain. Accordingly, we tested whether the genetics of psychiatric disorders is enriched for signatures of positive selection. We observed a strong finding related to the genetics of autism spectrum disorders (ASD): common risk alleles are enriched for genomic signatures of incomplete selection (loci where a selected allele has not yet reached fixation). The genes where these alleles map tend to be expressed in brain and pituitary tissues, to be involved in molecular mechanisms related to nervous system development, and surprisingly, to be associated with increased cognitive ability. Previous studies identified signatures of purifying selection in genes affected by ASD rare alleles. Accordingly, at least two different evolutionary mechanisms appear to be present in relation to ASD genetics: (1) rare disruptive alleles eliminated by purifying selection; (2) common alleles selected for their beneficial effects on cognitive skills. This scenario would explain ASD prevalence, which is higher than that expected for a trait under purifying selection, as the evolutionary cost of polygenic adaptation related to cognitive ability.

“No Evidence That Schizophrenia Candidate Genes Are More Associated With Schizophrenia Than Non-candidate Genes”, Johnson et al 2017

“No Evidence That Schizophrenia Candidate Genes Are More Associated With Schizophrenia Than Non-candidate Genes”⁠, Emma C. Johnson, Richard Border, Whitney E. Melroy-Greif, Christiaan A. de Leeuw, Marissa A. Ehringer et al (2017; backlinks; similar):

Background: A recent analysis of 25 historical candidate gene polymorphisms for schizophrenia in the largest genome-wide association study conducted to date suggested that these commonly studied variants were no more associated with the disorder than would be expected by chance. However, the same study identified other variants within those candidate genes that demonstrated genome-wide statistically-significant associations with schizophrenia. As such, it is possible that variants within historic schizophrenia candidate genes are associated with schizophrenia at levels above those expected by chance, even if the most-studied specific polymorphisms are not.

Methods: The present study used association statistics from the largest schizophrenia genome-wide association study conducted to date as input to a gene set analysis to investigate whether variants within schizophrenia candidate genes are enriched for association with schizophrenia.

Results: As a group, variants in the most-studied candidate genes were no more associated with schizophrenia than were variants in control sets of non-candidate genes. While a small subset of candidate genes did appear to be statistically-significantly associated with schizophrenia, these genes were not particularly noteworthy given the large number of more strongly associated non-candidate genes.

Conclusions: The history of schizophrenia research should serve as a cautionary tale to candidate gene investigators examining other phenotypes: our findings indicate that the most investigated candidate gene hypotheses of schizophrenia are not well supported by genome-wide association studies, and it is likely that this will be the case for other complex traits as well.

“Genome-Wide Association Study Reveals First Locus for Anorexia Nervosa and Metabolic Correlations”, Duncan et al 2016

“Genome-Wide Association Study Reveals First Locus for Anorexia Nervosa and Metabolic Correlations”⁠, E. L. Duncan, L. M. Thornton, A. Hinney, M. J. Daly, P. F. Sullivan, E. Zeggini, G. Breen, C. M. Bulik et al (2016-11-25; similar):

Anorexia nervosa (AN) is a serious eating disorder characterized by restriction of energy intake relative to requirements, resulting in abnormally low body weight. It has a lifetime prevalence of ~1%, disproportionately affects females1,2, and has no well replicated evidence of effective pharmacological or psychological treatments despite high morbidity and mortality2. Twin studies support a genetic basis for the observed aggregation of AN in families3, with heritability estimates of 48%–74%4. Although initial genome-wide association studies (GWASs) were underpowered5,6, evidence suggested that signals for AN would be detected with increased power5. We present a GWAS of 3,495 AN cases and 10,982 controls with one genome-wide statistically-significant locus (index variant rs4622308, p = 4.3×10−9) in a region (chr12:56,372,585–56,482,185) which includes six genes. The SNP-chip heritability of AN from these data is 0.20 (SE = 0.02), suggesting that a substantial fraction of the twin-based heritability stems from common genetic variation. Using these GWAS results, we also find significant positive genetic correlations with schizophrenia, neuroticism, educational attainment, and HDL cholesterol, and significant negative genetic correlations with body mass, insulin, glucose, and lipid phenotypes. Our results support the reconceptualization of AN as a disorder with both psychiatric and metabolic components.

“Polygenic Transmission Disequilibrium Confirms That Common and Rare Variation Act Additively to Create Risk for Autism Spectrum Disorders”, Weiner et al 2016

“Polygenic transmission disequilibrium confirms that common and rare variation act additively to create risk for autism spectrum disorders”⁠, Daniel J. Weiner, Emilie M. Wigdor, Stephan Ripke, Raymond K. Walters, Jack A. Kosmicki, Jakob Grove et al (2016-11-23; ; similar):

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 that polygenic 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.

“Partitioning Heritability Analysis Reveals a Shared Genetic Basis of Brain Anatomy and Schizophrenia”, Lee et al 2016

2016-lee.pdf: “Partitioning heritability analysis reveals a shared genetic basis of brain anatomy and schizophrenia”⁠, P. H. Lee, J. T. Baker, A. J. Holmes, N. Jahanshad, T. Ge, J-Y. Jung, Y. Cruz, D. S. Manoach, D. P. Hibar et al (2016-10-11; ; similar):

Schizophrenia is a devastating neurodevelopmental disorder with a complex genetic etiology. Widespread cortical gray matter loss has been observed in patients and prodromal samples. However, it remains unresolved whether schizophrenia-associated cortical structure variations arise due to disease etiology or secondary to the illness.

Here we address this question using a partitioning-based heritability analysis of genome-wide single-nucleotide polymorphism (SNP) and neuroimaging data from 1750 healthy individuals.

We find that schizophrenia-associated genetic variants explain a statistically-significantly enriched proportion of trait heritability in 8 brain phenotypes (false discovery rate = 10%). In particular, intracranial volume and left superior frontal gyrus thickness exhibit statistically-significant and robust associations with schizophrenia genetic risk under varying SNP selection conditions. Cross-disorder comparison suggests that the neuro-genetic architecture of schizophrenia-associated brain regions is, at least in part, shared with other psychiatric disorders.

Our study highlights key neuroanatomical correlates of schizophrenia genetic risk in the general population. These may provide fundamental insights into the complex pathophysiology of the illness, and a potential link to neurocognitive deficits shaping the disorder.

“Genetic Overlap between Schizophrenia and Developmental Psychopathology: a Longitudinal Approach Applied to Common Childhood Disorders between Age 7 and 15 Years”, Nivard et al 2016

“Genetic overlap between schizophrenia and developmental psychopathology: a longitudinal approach applied to common childhood disorders between age 7 and 15 years”⁠, Michel G. Nivard, Suzanne H. Gage, Jouke J. Hottenga, Catherina E. M. van Beijsterveldt, Abdel Abdellaoui et al (2016-05-11; ; similar):

Various non-psychotic psychiatric disorders in childhood and adolescence can precede the onset of schizophrenia, but the nature of this relationship remains unclear. We investigated to what extent the association between schizophrenia and psychiatric disorders in childhood is explained by shared genetic risk factors.

Polygenic risk scores (PRS), reflecting an individual’s genetic risk for schizophrenia, were constructed for participants in two birth cohorts (2,588 children from the Netherlands Twin Register (NTR) and 6,127 from the Avon Longitudinal Study of Parents And Children (ALSPAC)). The associations between schizophrenia PRS and measures of anxiety, depression, attention deficit hyperactivity disorder (ADHD), and oppositional defiant disorder/​conduct disorder (ODD/​CD) were estimated at age 7, 10, 12–13 and 15 years in the two cohorts. Results were then meta-analyzed, and age-effects and differences in the associations between disorders and PRS were formally tested in a meta-regression.

The schizophrenia PRS was associated with childhood and adolescent psychopathology Where the association was weaker for ODD/​CD at age 7. The associations increased with age this increase was steepest for ADHD and ODD/​CD. The results are consistent with a common genetic etiology of schizophrenia and developmental psychopathology as well as with a stronger shared genetic etiology between schizophrenia and adolescent onset psychopathology.

A multivariate meta-analysis of multiple and repeated observations enabled to optimally use the longitudinal data across diagnoses in order to provide knowledge on how childhood disorders develop into severe adult psychiatric disorders.

“Genetic Evidence for Natural Selection in Humans in the Contemporary United States”, Beauchamp 2016

“Genetic evidence for natural selection in humans in the contemporary United States”⁠, Jonathan Beauchamp (2016-05-05; similar):

Recent findings from molecular genetics now make it possible to test directly for natural selection by analyzing whether genetic variants associated with various phenotypes have been under selection. I leverage these findings to construct polygenic scores that use individuals’ genotypes to predict their body mass index, educational attainment (EA), glucose concentration, height, schizophrenia, total cholesterol, and (in females) age at menarche. I then examine associations between these scores and fitness to test whether natural selection has been occurring. My study sample includes individuals of European ancestry born between 1931 and 1953 in the Health and Retirement Study, a representative study of the US population.

My results imply that natural selection has been slowly favoring lower EA in both females and males, and are suggestive that natural selection may have favored a higher age at menarche in females. For EA, my estimates imply a rate of selection of about -1.5 months of education per generation (which pales in comparison with the increases in EA observed in contemporary times). Though they cannot be projected over more than one generation, my results provide additional evidence that humans are still evolving—albeit slowly, especially when compared to the rapid secular changes that have occurred over the past few generations due to cultural and environmental factors.

“Genome-wide Analyses of Empathy and Systemizing: Heritability and Correlates With Sex, Education, and Psychiatric Risk”, Warrier et al 2016

“Genome-wide analyses of empathy and systemizing: heritability and correlates with sex, education, and psychiatric risk”⁠, Varun Warrier, Roberto Toro, Bhismadev Chakrabarti, Nadia Litterman, David A. Hinds, Thomas Bourgeron et al (2016-04-29; ⁠, ; similar):

Empathy is the drive to identify the mental states of others and respond to these with an appropriate emotion. Systemizing is the drive to analyse or build lawful systems. Difficulties in empathy have been identified in different psychiatric conditions including autism and schizophrenia. In this study, we conducted genome-wide association studies of empathy and systemizing using the Empathy Quotient (EQ) (n = 46,861) and the Systemizing Quotient-Revised (SQ-R) (n = 51,564) in participants from 23andMe, Inc. We confirmed significant sex-differences in performance on both tasks, with a male advantage on the SQ-R and female advantage on the EQ. We found highly significant heritability explained by single nucleotide polymorphisms (SNPs) for both the traits (EQ: 0.11±0.014; p = 1.7 × 10−14 and SQ-R: 0.12±0.012; p = 1.2 × 10−20) and these were similar for males and females. However, genes with higher expression in the male brain appear to contribute to the male advantage for the SQ-R. Finally, we identified statistically-significant genetic correlations between high score for empathy and risk for schizophrenia (p = 2.5 × 10−5), and correlations between high score for systemizing and higher educational attainment (p = 5 × 10−4). These results shed light on the genetic contribution to individual differences in empathy and systemizing, two major cognitive functions of the human brain.

“Molecular Genetic Contributions to Self-rated Health”, Harris et al 2016

“Molecular genetic contributions to self-rated health”⁠, Sarah E. Harris, Saskia P. Hagenaars, Gail Davies, W. David Hill, David CM Liewald, Stuart J. Ritchie et al (2016-04-12; ⁠, ⁠, ; similar):

Background: Poorer self-rated health (SRH) predicts worse health outcomes, even when adjusted for objective measures of disease at time of rating. Twin studies indicate SRH has a heritability of up to 60% and that its genetic architecture may overlap with that of personality and cognition.

Methods: We carried out a genome-wide association study (GWAS) of SRH on 111 749 members of the UK Biobank sample. Univariate genome-wide complex trait analysis (GCTA)-GREML analyses were used to estimate the proportion of variance explained by all common autosomal SNPs for SRH. Linkage Disequilibrium (LD) score regression and polygenic risk scoring, two complementary methods, were used to investigate pleiotropy between SRH in UK Biobank and up to 21 health-related and personality and cognitive traits from published GWAS consortia.

Results: The GWAS identified 13 independent signals associated with SRH, including several in regions previously associated with diseases or disease-related traits. The strongest signal was on chromosome 2 (rs2360675, p = 1.77×10−10) close to KLF7, which has previously been associated with obesity and type 2 diabetes⁠. A second strong peak was identified on chromosome 6 in the major histocompatibility region (rs76380179, p = 6.15×10−10). The proportion of variance in SRH that was explained by all common genetic variants was 13%. Polygenic scores for the following traits and disorders were associated with SRH: cognitive ability, education, neuroticism, BMI, longevity, ADHD, major depressive disorder, schizophrenia, lung function, blood pressure, coronary artery disease, large vessel disease stroke, and type 2 diabetes.

Conclusion: Individual differences in how people respond to a single item on SRH are partly explained by their genetic propensity to many common psychiatric and physical disorders and psychological traits.

Key Messages

Genetic variants associated with common diseases and psychological traits are associated with self-rated health.

The SNP-based heritability of self-rated health is 0.13 (SE 0.006).

There is pleiotropy between self-rated health and psychiatric and physical diseases and psychological traits.

“Genome-wide Association Study of Cognitive Functions and Educational Attainment in UK Biobank (n = 112 151)”, Davies et al 2016

“Genome-wide association study of cognitive functions and educational attainment in UK Biobank (n = 112 151)”⁠, G. Davies, R. E. Marioni, D. C. Liewald, W. D. Hill, S. P. Hagenaars, S. E. Harris, S. J. Ritchie, M. Luciano et al (2016-04-05; ⁠, ⁠, ; backlinks; similar):

People’s differences in cognitive functions are partly heritable and are associated with important life outcomes. Previous genome-wide association (GWA) studies of cognitive functions have found evidence for polygenic effects yet, to date, there are few replicated genetic associations. Here we use data from the UK Biobank sample to investigate the genetic contributions to variation in tests of three cognitive functions and in educational attainment. GWA analyses were performed for verbal-numerical reasoning (n = 36 035), memory (n = 112 067), reaction time (n = 111 483) and for the attainment of a college or an university degree (n = 111 114). We report genome-wide statistically-significant single-nucleotide polymorphism (SNP)-based associations in 20 genomic regions, and statistically-significant gene-based findings in 46 regions. These include findings in the ATXN2, CYP2DG, APBA1 and CADM2 genes. We report replication of these hits in published GWA studies of cognitive function, educational attainment and childhood intelligence. There is also replication, in UK Biobank, of SNP hits reported previously in GWA studies of educational attainment and cognitive function. GCTA-GREML analyses, using common SNPs (minor allele frequency>0.01), indicated statistically-significant SNP-based heritabilities of 31% (s.e.m. = 1.8%) for verbal-numerical reasoning, 5% (s.e.m. = 0.6%) for memory, 11% (s.e.m. = 0.6%) for reaction time and 21% (s.e.m. = 0.6%) for educational attainment. Polygenic score analyses indicate that up to 5% of the variance in cognitive test scores can be predicted in an independent cohort. The genomic regions identified include several novel loci, some of which have been associated with intracranial volume, neurodegeneration, Alzheimer’s disease and schizophrenia.

“Genetic Contributions to Self-reported Tiredness”, Deary et al 2016

“Genetic contributions to self-reported tiredness”⁠, Vincent Deary, Saskia P. Hagenaars, Sarah E. Harris, W. David Hill, Gail Davies, David CM Liewald, International Consortium for Blood Pressure GWAS et al (2016-04-05; ; similar):

Self-reported tiredness and low energy, often called fatigue, is associated with poorer physical and mental health. Twin studies have indicated that this has a heritability between 6% and 50%. In the UK Biobank sample (n = 108 976) we carried out a genome-wide association study of responses to the question, “Over the last two weeks, how often have you felt tired or had little energy?” Univariate GCTA-GREML found that the proportion of variance explained by all common SNPs for this tiredness question was 8.4% (SE = 0.6%). GWAS identified one genome-wide statistically-significant hit (Affymetrix id 1:64178756_C_T; p = 1.36 x 10−11). LD score regression and polygenic profile analysis were used to test for pleiotropy between tiredness and up to 28 physical and mental health traits from GWAS consortia. Significant genetic correlations were identified between tiredness and BMI, HDL cholesterol, forced expiratory volume, grip strength, HbA1c, longevity, obesity, self-rated health, smoking status, triglycerides, type 2 diabetes⁠, waist-hip ratio, ADHD, bipolar disorder, major depressive disorder, neuroticism, schizophrenia, and verbal-numerical reasoning (absolute rg effect sizes between 0.11 and 0.78). Significant associations were identified between tiredness phenotypic scores and polygenic profile scores for BMI, HDL cholesterol, LDL cholesterol, coronary artery disease, HbA1c, height, obesity, smoking status, triglycerides, type 2 diabetes, and waist-hip ratio, childhood cognitive ability, neuroticism, bipolar disorder, major depressive disorder, and schizophrenia (standardised β’s between −0.016 and 0.03). These results suggest that tiredness is a partly-heritable, heterogeneous and complex phenomenon that is phenotypically and genetically associated with affective, cognitive, personality, and physiological processes.

“Hech, sirs! But I’m wabbit, I’m back frae the toon;

I ha’ena dune pechin’—jist let me sit doon.

From Glesca’

By William Dixon Cocker (1882-1970)

“Individuals With Pronounced Schizotypal Traits Are Particularly Successful in Tickling Themselves”, Lemaitre et al 2016

2016-lemaitre.pdf: “Individuals with pronounced schizotypal traits are particularly successful in tickling themselves”⁠, Anne-Laure Lemaitre, Marion Luyat, Gilles Lafargue (2016-04-01; ; similar):

Highlights:

  • We assessed tickling sensation in healthy subjects with pronounced schizotypal traits.
  • They were particularly successful in tickling themselves.
  • The ability to self-tickle was linked to feelings of control by outside forces.
  • Thus, the formation of odd beliefs may be related to sensory prediction deficits.

We assessed self-tickling sensations in a group of participants high in schizotypal traits (n = 27) and group of participants low in schizotypal traits (n = 27). The groups were formed by screening a pool of 397 students for extreme scores in the French version of the Schizotypal Personality Questionnaire. As observed in a previous study involving psychiatric people with auditory hallucinations and/​or passivity experiences our results showed that self-applied tactile stimulations are felt to be more ticklish by healthy individuals high in schizotypal traits. In contrast, there were no statistically-significant intergroup differences in the mean tickle rating in the externally-produced tickling condition. Furthermore, more successful self-tickling was associated with more frequent self-reports of unusual perceptual experiences (such as supernatural experiences) and passivity experiences in particular (such as a feeling of being under the control of an outside force or power).

[Keywords: schizotypy⁠, schizophrenia, agency, delusions, passivity experiences, ticklishness, efference copy, predictive sensorimotor process]

“Molecular Genetic Contributions to Social Deprivation and Household Income in UK Biobank (n = 112,151)”, Hill et al 2016

“Molecular genetic contributions to social deprivation and household income in UK Biobank (n = 112,151)”⁠, W. David Hill, Saskia P. Hagenaars, Riccardo E. Marioni, Sarah E. Harris, David C. M. Liewald, Gail Davies et al (2016-03-09; ⁠, ⁠, ⁠, ; backlinks; similar):

Individuals with lower socio-economic status (SES) are at increased risk of physical and mental illnesses and tend to die at an earlier age. Explanations for the association between SES and health typically focus on factors that are environmental in origin. However, common single nucleotide polymorphisms (SNPs) have been found collectively to explain around 18% (SE = 5%) of the phenotypic variance of an area-based social deprivation measure of SES. Molecular genetic studies have also shown that physical and psychiatric diseases are at least partly heritable. It is possible, therefore, that phenotypic associations between SES and health arise partly due to a shared genetic etiology.

We conducted a genome-wide association study (GWAS) on social deprivation and on household income using the 112,151 participants of UK Biobank. We find that common SNPs explain 21% (SE = 0.5%) of the variation in social deprivation and 11% (SE = 0.7%) in household income. 2 independent SNPs attained genome-wide statistical-significance for household income, rs187848990 on chromosome 2, and rs8100891 on chromosome 19. Genes in the regions of these SNPs have been associated with intellectual disabilities, schizophrenia, and synaptic plasticity. Extensive genetic correlations were found between both measures of socioeconomic status and illnesses, anthropometric variables, psychiatric disorders, and cognitive ability.

These findings show that some SNPs associated with SES are involved in the brain and central nervous system. The genetic associations with SES are probably mediated via other partly-heritable variables, including cognitive ability, education, personality, and health.

Genetic correlation between household income and health variables.

“Shared Genetic Aetiology between Cognitive Functions and Physical and Mental Health in UK Biobank (n = 112,151) and 24 GWAS Consortia”, Hagenaars et al 2016

“Shared genetic aetiology between cognitive functions and physical and mental health in UK Biobank (n = 112,151) and 24 GWAS consortia”⁠, S. P. Hagenaars, S. E. Harris, G. Davies, W. D. Hill, D. C. M. Liewald, S. J. Ritchie, R. E. Marioni et al (2016-01-26; ; backlinks; similar):

Causes of the well-documented association between low levels of cognitive functioning and many adverse neuropsychiatric outcomes, poorer physical health and earlier death remain unknown. We used linkage disequilibrium regression and polygenic profile scoring to test for shared genetic aetiology between cognitive functions and neuropsychiatric disorders and physical health. Using information provided by many published genome-wide association study consortia, we created polygenic profile scores for 24 vascular-metabolic, neuropsychiatric, physiological-anthropometric and cognitive traits in the participants of UK Biobank, a very large population-based sample (n = 112 151). Pleiotropy between cognitive and health traits was quantified by deriving genetic correlations using summary genome-wide association study statistics and to the method of linkage disequilibrium score regression⁠. Substantial and statistically-significant genetic correlations were observed between cognitive test scores in the UK Biobank sample and many of the mental and physical health-related traits and disorders assessed here. In addition, highly statistically-significant associations were observed between the cognitive test scores in the UK Biobank sample and many polygenic profile scores, including coronary artery disease, stroke, Alzheimer’s disease, schizophrenia, autism, major depressive disorder, body mass index, intracranial volume, infant head circumference and childhood cognitive ability. Where disease diagnosis was available for UK Biobank participants, we were able to show that these results were not confounded by those who had the relevant disease. These findings indicate that a substantial level of pleiotropy exists between cognitive abilities and many human mental and physical health disorders and traits and that it can be used to predict phenotypic variance across samples.

“Genome-wide Analysis of over 106 000 Individuals Identifies 9 Neuroticism-associated Loci”, Smith et al 2016

2016-smith.pdf: “Genome-wide analysis of over 106 000 individuals identifies 9 neuroticism-associated loci”⁠, D J. Smith, V. Escott-Price, G. Davies, M. E S. Bailey, L. Colodro-Conde, J. Ward, A. Vedernikov, R. Marioni et al (2016; ⁠, ; similar):

Neuroticism is a personality trait of fundamental importance for psychological well-being and public health. It is strongly associated with major depressive disorder (MDD) and several other psychiatric conditions. Although neuroticism is heritable, attempts to identify the alleles involved in previous studies have been limited by relatively small sample sizes. Here we report a combined meta-analysis of genome-wide association study (GWAS) of neuroticism that includes 91 370 participants from the UK Biobank cohort, 6659 participants from the Generation Scotland: Scottish Family Health Study (GS:SFHS) and 8687 participants from a QIMR (Queensland Institute of Medical Research) Berghofer Medical Research Institute (QIMR) cohort. All participants were assessed using the same neuroticism instrument, the Eysenck Personality Questionnaire-Revised (EPQ-R-S) Short Form’s Neuroticism scale. We found a single-nucleotide polymorphism-based heritability estimate for neuroticism of ~15% (s.e. = 0.7%). Meta-analysis identified nine novel loci associated with neuroticism. The strongest evidence for association was at a locus on chromosome 8 (P = 1.5 × 10−15) spanning 4 Mb and containing at least 36 genes. Other associated loci included interesting candidate genes on chromosome 1 (GRIK3 (glutamate receptor ionotropic kainate 3)), chromosome 4 (KLHL2 (Kelch-like protein 2)), chromosome 17 (CRHR1 (corticotropin-releasing hormone receptor 1) and MAPT (microtubule-associated protein Tau)) and on chromosome 18 (CELF4 (CUGBP elav-like family member 4)). We found no evidence for genetic differences in the common allelic architecture of neuroticism by sex. By comparing our findings with those of the Psychiatric Genetics Consortia, we identified a strong genetic correlation between neuroticism and MDD and a less strong but statistically-significant genetic correlation with schizophrenia, although not with bipolar disorder. Polygenic risk scores derived from the primary UK Biobank sample captured ~1% of the variance in neuroticism in the GS:SFHS and QIMR samples, although most of the genome-wide statistically-significant alleles identified within a UK Biobank-only GWAS of neuroticism were not independently replicated within these cohorts. The identification of nine novel neuroticism-associated loci will drive forward future work on the neurobiology of neuroticism and related phenotypes.

“Schizophrenia Risk Alleles and Neurodevelopmental Outcomes in Childhood: a Population-based Cohort Study”, Riglin et al 2016

2016-riglin.pdf: “Schizophrenia risk alleles and neurodevelopmental outcomes in childhood: a population-based cohort study”⁠, Lucy Riglin, Stephan Collishaw, Alexander Richards, Ajay K. Thapar, Barbara Maughan, Michael C. O’Donovansych et al (2016-01-01; )

“Genetic Markers of Human Evolution Are Enriched in Schizophrenia”, Srinivasan et al 2016

“Genetic Markers of Human Evolution Are Enriched in Schizophrenia”⁠, Saurabh Srinivasan, Francesco Bettella, Morten Mattingsdal, Yunpeng Wang, Aree Witoelar, Andrew J. Schork et al (2016; similar):

Background: Why schizophrenia has accompanied humans throughout our history despite its negative effect on fitness remains an evolutionary enigma. It is proposed that schizophrenia is a by-product of the complex evolution of the human brain and a compromise for humans’ language, creative thinking, and cognitive abilities.

Methods: We analyzed recent large genome-wide association studies of schizophrenia and a range of other human phenotypes (anthropometric measures, cardiovascular disease risk factors, immune-mediated diseases) using a statistical framework that draws on polygenic architecture and ancillary information on genetic variants. We used information from the evolutionary proxy measure called the Neanderthal selective sweep (NSS) score.

Results: Gene loci associated with schizophrenia are significantly (p = 7.30 × 10−9) more prevalent in genomic regions that are likely to have undergone recent positive selection in humans (ie. with a low NSS score). Variants in brain-related genes with a low NSS score confer significantly higher susceptibility than variants in other brain-related genes. The enrichment is strongest for schizophrenia, but we cannot rule out enrichment for other phenotypes. The false discovery rate conditional on the evolutionary proxy points to 27 candidate schizophrenia susceptibility loci, 12 of which are associated with schizophrenia and other psychiatric disorders or linked to brain development.

Conclusions: Our results suggest that there is a polygenic overlap between schizophrenia and NSS score, a marker of human evolution, which is in line with the hypothesis that the persistence of schizophrenia is related to the evolutionary process of becoming human.

“Haplotypes of Common SNPs Can Explain Missing Heritability of Complex Diseases”, Bhatia et al 2015

“Haplotypes of common SNPs can explain missing heritability of complex diseases”⁠, Gaurav Bhatia, Alexander Gusev, Po-Ru Loh, Bjarni J. Vilhjálmsson, Stephan Ripke, Schizophrenia Working Group of the Psychiatric Genomics Consortium et al (2015-07-12; similar):

While genome-wide statistically-significant associations generally explain only a small proportion of the narrow-sense heritability of complex disease (h2), recent work has shown that more heritability is explained by all genotyped SNPs (hg2). However, much of the heritability is still missing (hg2 < h2). For example, for schizophrenia, h2 is estimated at 0.7–0.8 but hg2 is estimated at ~0.3. Efforts at increasing coverage through accurately imputed variants have yielded only small increases in the heritability explained, and poorly imputed variants can lead to assay artifacts for case-control traits.

We propose to estimate the heritability explained by a set of haplotype variants (haploSNPs) constructed directly from the study sample (hhap2). Our method constructs a set of haplotypes from phased genotypes by extending shared haplotypes subject to the 4-gamete test. In a large schizophrenia data set (PGC2-SCZ), haploSNPs with MAF > 0.1% explained substantially more phenotypic variance (hhap2 = 0.64 (S.E. 0.084)) than genotyped SNPs alone (hg2 = 0.32 (S.E. 0.029)). These estimates were based on cross-cohort comparisons, ensuring that cohort-specific assay artifacts did not contribute to our estimates.

In a large multiple sclerosis data set (WTCCC2-MS), we observed an even larger difference between hhap2 and hg2, though data from other cohorts will be required to validate this result. Overall, our results suggest that haplotypes of common SNPs can explain a large fraction of missing heritability of complex disease, shedding light on genetic architecture and informing disease mapping strategies.

“Polygenic Risk Scores for Schizophrenia and Bipolar Disorder Predict Creativity”, Power et al 2015

2015-power.pdf: “Polygenic risk scores for schizophrenia and bipolar disorder predict creativity”⁠, Robert A. Power, Stacy Steinberg, Gyda Bjornsdottir, Cornelius A. Rietveld, Abdel Abdellaoui, Michel M. Nivard et al (2015-06-08; ; backlinks; similar):

We tested whether polygenic risk scores for schizophrenia and bipolar disorder would predict creativity. Higher scores were associated with artistic society membership or creative profession in both Icelandic (p = 5.2 × 10−6 and 3.8 × 10−6 for schizophrenia and bipolar disorder scores, respectively) and replication cohorts (p = 0.0021 and 0.00086). This could not be accounted for by increased relatedness between creative individuals and those with psychoses, indicating that creativity and psychosis share genetic roots.

“Contrasting Regional Architectures of Schizophrenia and Other Complex Diseases Using Fast Variance Components Analysis”, Loh et al 2015

“Contrasting regional architectures of schizophrenia and other complex diseases using fast variance components analysis”⁠, Po-Ru Loh, Gaurav Bhatia, Alexander Gusev, Hilary K. Finucane, Brendan K. Bulik-Sullivan, Samuela J. Pollack et al (2015-06-05; similar):

Heritability analyses of GWAS cohorts have yielded important insights into complex disease architecture, and increasing sample sizes hold the promise of further discoveries. Here, we analyze the genetic architecture of schizophrenia in 49,806 samples from the PGC, and nine complex diseases in 54,734 samples from the GERA cohort. For schizophrenia, we infer an overwhelmingly polygenic disease architecture in which ≥71% of 1Mb genomic regions harbor at least one variant influencing schizophrenia risk. We also observe significant enrichment of heritability in GC-rich regions and in higher-frequency SNPs for both schizophrenia and GERA diseases. In bivariate analyses, we observe statistically-significant genetic correlations (ranging from 0.18 to 0.85) among several pairs of GERA diseases; genetic correlations were on average 1.3× stronger than correlations of overall disease liabilities. To accomplish these analyses, we developed a fast algorithm for multi-component, multi-trait variance components analysis that overcomes prior computational barriers that made such analyses intractable at this scale.

“An Atlas of Genetic Correlations across Human Diseases and Traits”, Bulik-Sullivan et al 2015

“An Atlas of Genetic Correlations across Human Diseases and Traits”⁠, Brendan Bulik-Sullivan, Hilary K. Finucane, Verneri Anttila, Alexander Gusev, Felix R. Day, ReproGen Consortium et al (2015-04-06; similar):

Identifying genetic correlations between complex traits and diseases can provide useful etiological insights and help prioritize likely causal relationships. The major challenges preventing estimation of genetic correlation from genome-wide association study (GWAS) data with current methods are the lack of availability of individual genotype data and widespread sample overlap among meta-analyses. We circumvent these difficulties by introducing a technique for estimating genetic correlation that requires only GWAS summary statistics and is not biased by sample overlap. We use our method to estimate 300 genetic correlations among 25 traits, totaling more than 1.5 million unique phenotype measurements. Our results include genetic correlations between anorexia nervosa and schizophrenia, anorexia and obesity and associations between educational attainment and several diseases. These results highlight the power of genome-wide analyses, since there currently are no genome-wide statistically-significant SNPs for anorexia nervosa and only three for educational attainment.

“Does Population Density and Neighborhood Deprivation Predict Schizophrenia? A Nationwide Swedish Family-Based Study of 2.4 Million Individuals”, Sariaslan et al 2014b

2014-sariaslan-2.pdf: “Does Population Density and Neighborhood Deprivation Predict Schizophrenia? A Nationwide Swedish Family-Based Study of 2.4 Million Individuals”⁠, Amir Sariaslan, Henrik Larsson, Brian D’Onofrio, Niklas Långström, Seena Fazel, Paul Lichtenstein (2014-07-22; ⁠, ; backlinks; similar):

People living in densely populated and socially disorganized areas have higher rates of psychiatric morbidity, but the potential causal status of such factors is uncertain.

We used nationwide Swedish longitudinal registry data to identify all children born 1967–1989 (n = 2,361,585), including separate datasets for all cousins (n = 1,715,059) and siblings (n = 1,667,894). The nature of the associations between population density and neighborhood deprivation and individual risk for a schizophrenia diagnosis was investigated while adjusting for unobserved familial risk factors (through cousin and sibling comparisons) and then compared with similar associations for depression. We generated familial pedigree structures using the Multi-Generation Registry and identified study participants with schizophrenia and depression using the National Patient Registry. Fixed-effects logistic regression models were used to study within-family estimates.

Population density, measured as ln(population size/​km2), at age 15 predicted subsequent schizophrenia in the population (OR = 1.10; 95% CI: 1.09; 1.11). Unobserved familial risk factors shared by cousins within extended families attenuated the association (1.06; 1.03; 1.10), and the link disappeared entirely within nuclear families (1.02; 0.97; 1.08). Similar results were found for neighborhood deprivation as predictor and for depression as outcome. Sensitivity tests demonstrated that timing and accumulation effects of the exposures (mean scores across birth, ages 1–5, 6–10, and 11–15 years) did not alter the findings.

Excess risks of psychiatric morbidity, particularly schizophrenia, in densely populated and socioeconomically deprived Swedish neighborhoods appear, therefore, to result primarily from unobserved familial selection factors. Previous studies may have overemphasized the etiological importance of these environmental factors.

“Genetic Predisposition to Schizophrenia Associated With Increased Use of Cannabis”, Power et al 2014

“Genetic predisposition to schizophrenia associated with increased use of cannabis”⁠, R A. Power, K. J H. Verweij, M. Zuhair, G. W Montgomery, A. K Henders, A. C Heath, P. A F. Madden, S. E Medland et al (2014; ; similar):

Cannabis is the most commonly used illicit drug worldwide. With debate surrounding the legalization and control of use, investigating its health risks has become a pressing area of research. One established association is that between cannabis use and schizophrenia, a debilitating psychiatric disorder affecting ~1% of the population over their lifetime. Although considerable evidence implicates cannabis use as a component cause of schizophrenia, it remains unclear whether this is entirely due to cannabis directly raising risk of psychosis, or whether the same genes that increases psychosis risk may also increase risk of cannabis use. In a sample of 2082 healthy individuals, we show an association between an individual’s burden of schizophrenia risk alleles and use of cannabis. This was significant both for comparing those who have ever versus never used cannabis (p = 2.6 × 10−4), and for quantity of use within users (p = 3.0 × 10−3). Although directly predicting only a small amount of the variance in cannabis use, these findings suggest that part of the association between schizophrenia and cannabis is due to a shared genetic aetiology. This form of gene-environment correlation is an important consideration when calculating the impact of environmental risk factors, including cannabis use.

“The Contribution of de Novo Coding Mutations to Autism Spectrum Disorder”, Iossifov et al 2014

“The contribution of de novo coding mutations to autism spectrum disorder”⁠, Ivan Iossifov, Brian J. O’Roak, Stephan J. Sanders, Michael Ronemus, Niklas Krumm, Dan Levy, Holly A. Stessman et al (2014; ; similar):

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-type alleles. 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.

“Schizophrenia and Cortical Blindness: Protective Effects and Implications for Language”, Leivada & Boeckx 2014

“Schizophrenia and cortical blindness: protective effects and implications for language”⁠, Evelina Leivada, Cedric Boeckx (2014; similar):

The repeatedly noted absence of case-reports of individuals with schizophrenia and congenital/​early developed blindness has led several authors to argue that the latter can confer protective effects against the former. In this work, we present a number of relevant case-reports from different syndromes that show comorbidity of congenital and early blindness with schizophrenia. On the basis of these reports, we argue that a distinction between different types of blindness in terms of the origin of the visual deficit, cortical or peripheral, is crucial for understanding the observed patterns of comorbidity. We discuss the genetic underpinnings and the brain structures involved in schizophrenia and blindness, with insights from language processing, laying emphasis on the three structures that particularly stand out: the occipital cortex, the lateral geniculate nucleus (LGN), and the pulvinar. Last, we build on previous literature on the nature of the protective effects in order to offer novel insights into the nature of the protection mechanism from the perspective of the brain structures involved in each type of blindness.

“Biological Insights from 108 Schizophrenia-associated Genetic Loci”

“Biological insights from 108 schizophrenia-associated genetic loci”⁠, (2014; ; backlinks; similar):

Schizophrenia is a highly heritable disorder. Genetic risk is conferred by a large number of alleles, including common alleles of small effect that might be detected by genome-wide association studies. Here we report a multi-stage schizophrenia genome-wide association study of up to 36,989 cases and 113,075 controls. We identify 128 independent associations spanning 108 conservatively defined loci that meet genome-wide statistical-significance, 83 of which have not been previously reported. Associations were enriched among genes expressed in brain, providing biological plausibility for the findings. Many findings have the potential to provide entirely new insights into aetiology, but associations at DRD2 and several genes involved in glutamatergic neurotransmission highlight molecules of known and potential therapeutic relevance to schizophrenia, and are consistent with leading pathophysiological hypotheses. Independent of genes expressed in brain, associations were enriched among genes expressed in tissues that have important roles in immunity, providing support for the speculated link between the immune system and schizophrenia.

“Large-scale Genomics Unveils the Genetic Architecture of Psychiatric Disorders”, Gratten et al 2014

“Large-scale genomics unveils the genetic architecture of psychiatric disorders”⁠, Jacob Gratten, Naomi R. Wray, Matthew C. Keller, Peter M. Visscher (2014; similar):

Family study results are consistent with genetic effects making substantial contributions to risk of psychiatric disorders such as schizophrenia, yet robust identification of specific genetic variants that explain variation in population risk had been disappointing until the advent of technologies that assay the entire genome in large samples. We highlight recent progress that has led to a better understanding of the number of risk variants in the population and the interaction of allele frequency and effect size. The emerging genetic architecture implies a large number of contributing loci (that is, a high genome-wide mutational target) and suggests that genetic risk of psychiatric disorders involves the combined effects of many common variants of small effect, as well as rare and de novo variants of large effect. The capture of a substantial proportion of genetic risk facilitates new study designs to investigate the combined effects of genes and the environment.

“Molecular Genetic Evidence for Overlap between General Cognitive Ability and Risk for Schizophrenia: a Report from the Cognitive Genomics ConsorTium (COGENT)”, Lencz et al 2014

“Molecular genetic evidence for overlap between general cognitive ability and risk for schizophrenia: a report from the Cognitive Genomics consorTium (COGENT)”⁠, T Lencz, E. Knowles, G. Davies, S. Guha, D. C Liewald, J. M Starr, S. Djurovic, I. Melle, K. Sundet, A. Christoforou et al (2014; ; backlinks; similar):

It has long been recognized that generalized deficits in cognitive ability represent a core component of schizophrenia (SCZ), evident before full illness onset and independent of medication. The possibility of genetic overlap between risk for SCZ and cognitive phenotypes has been suggested by the presence of cognitive deficits in first-degree relatives of patients with SCZ; however, until recently, molecular genetic approaches to test this overlap have been lacking. Within the last few years, large-scale genome-wide association studies (GWAS) of SCZ have demonstrated that a substantial proportion of the heritability of the disorder is explained by a polygenic component consisting of many common single-nucleotide polymorphisms (SNPs) of extremely small effect. Similar results have been reported in GWAS of general cognitive ability. The primary aim of the present study is to provide the first molecular genetic test of the classic endophenotype hypothesis, which states that alleles associated with reduced cognitive ability should also serve to increase risk for SCZ. We tested the endophenotype hypothesis by applying polygenic SNP scores derived from a large-scale cognitive GWAS meta-analysis (~5000 individuals from nine nonclinical cohorts comprising the Cognitive Genomics consorTium (COGENT)) to four SCZ case-control cohorts. As predicted, cases had significantly lower cognitive polygenic scores compared to controls. In parallel, polygenic risk scores for SCZ were associated with lower general cognitive ability. In addition, using our large cognitive meta-analytic data set, we identified nominally statistically-significant cognitive associations for several SNPs that have previously been robustly associated with SCZ susceptibility. Results provide molecular confirmation of the genetic overlap between SCZ and general cognitive ability, and may provide additional insight into pathophysiology of the disorder.

“Polygenic Risk for Schizophrenia Is Associated With Cognitive Change Between Childhood and Old Age”, McIntosh et al 2013

2013-mcintosh.pdf: “Polygenic Risk for Schizophrenia Is Associated with Cognitive Change Between Childhood and Old Age”⁠, Andrew M. McIntosh, Alan Gow, Michelle Luciano, Gail Davies, David C. Liewald, Sarah E. Harris, Janie Corley et al (2013-05-15; ⁠, ; backlinks; similar):

Background: Genome-wide association studies (GWAS) have shown a polygenic component to the risk of schizophrenia. The disorder is associated with impairments in general cognitive ability that also have a substantial genetic contribution. No study has determined whether cognitive impairments can be attributed to schizophrenia’s polygenic architecture using data from GWAS.

Methods: Members of the Lothian Birth Cohort 1936 (LBC1936, n = 937) were assessed using the Moray House Test at age 11 and with the Moray House Test and a further cognitive battery at age 70. To create polygenic risk scores for schizophrenia, we obtained data from the latest GWAS of the Psychiatric GWAS Consortium on Schizophrenia. Schizophrenia polygenic risk profile scores were calculated using information from the Psychiatric GWAS Consortium on Schizophrenia GWAS.

Results: In LBC1936, polygenic risk for schizophrenia was negatively associated with IQ at age 70 but not at age 11. Greater polygenic risk for schizophrenia was associated with more relative decline in IQ between these ages. These findings were maintained when the results of LBC1936 were combined with that of the independent Lothian Birth Cohort 1921 (n = 517) in a meta-analysis.

Conclusions: Increased polygenic risk of schizophrenia is associated with lower cognitive ability at age 70 and greater relative decline in general cognitive ability between the ages of 11 and 70. Common genetic variants may underlie both cognitive aging and risk of schizophrenia.

[Keywords: Aging, cognition, dementia, schizophrenia]

“Fecundity of Patients With Schizophrenia, Autism, Bipolar Disorder, Depression, Anorexia Nervosa, or Substance Abuse vs Their Unaffected Siblings”, Power et al 2013

“Fecundity of Patients With Schizophrenia, Autism, Bipolar Disorder, Depression, Anorexia Nervosa, or Substance Abuse vs Their Unaffected Siblings”⁠, Robert A. Power, Simon Kyaga, Rudolf Uher, James H. MacCabe, Niklas Långström, Mikael Landen, Peter McGuffin et al (2013-01; backlinks; similar):

Context: It is unknown how genetic variants conferring liability to psychiatric disorders survive in the population despite strong negative selection. However, this is key to understanding their etiology and designing studies to identify risk variants.

Objectives: To examine the reproductive fitness of patients with schizophrenia and other psychiatric disorders vs their unaffected siblings and to evaluate the level of selection on causal genetic variants.

Design: We measured the fecundity of patients with schizophrenia, autism, bipolar disorder, depression, anorexia nervosa, or substance abuse and their unaffected siblings compared with the general population.

Setting: Population databases in Sweden, including the Multi-Generation Register and the Swedish Hospital Discharge Register.

Participants: In total, 2.3 million individuals among the 1950 to 1970 birth cohort in Sweden.

Main Outcome Measures: Fertility ratio (FR), reflecting the mean number of children compared with that of the general population, accounting for age, sex, family size, and affected status.

Results: Except for women with depression, affected patients had statistically-significantly fewer children (FR range for those with psychiatric disorder, 0.23–0.93; p < 10−10). This reduction was consistently greater among men than women, suggesting that male fitness was particularly sensitive. Although sisters of patients with schizophrenia and bipolar disorder had increased fecundity (FR range, 1.02–1.03; p < 0.01), this was too small on its own to counterbalance the reduced fitness of affected patients. Brothers of patients with schizophrenia and autism showed reduced fecundity (FR range, 0.94–0.97; p < 0.001). Siblings of patients with depression and substance abuse had statistically-significantly increased fecundity (FR range, 1.01–1.05; p < 10−10). In the case of depression, this more than compensated for the lower fecundity of affected individuals.

Conclusions: Our results suggest that strong selection exists against schizophrenia, autism, and anorexia nervosa and that these variants may be maintained by new mutations or an as-yet unknown mechanism. Bipolar disorder did not seem to be under strong negative selection. Vulnerability to depression, and perhaps substance abuse, may be preserved by balancing selection, suggesting the involvement of common genetic variants in ways that depend on other genes and on environment.

“Genetic Relationship between Five Psychiatric Disorders Estimated from Genome-wide SNPs”, Lee et al 2013

“Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs”⁠, S Hong Lee, Stephan Ripke, Benjamin M. Neale, Stephen V. Faraone, Shaun M. Purcell, Roy H. Perlis, Bryan J. Mowry et al (2013; ; similar):

Most psychiatric disorders are moderately to highly heritable. The degree to which genetic variation is unique to individual disorders or shared across disorders is unclear. To examine shared genetic etiology, we use genome-wide genotype data from the Psychiatric Genomics Consortium (PGC) for cases and controls in schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorders (ASD) and attention-deficit/​hyperactivity disorder (ADHD). We apply univariate and bivariate methods for the estimation of genetic variation within and covariation between disorders. SNPs explained 17–29% of the variance in liability. The genetic correlation calculated using common SNPs was high between schizophrenia and bipolar disorder (0.68 ± 0.04 s.e.), moderate between schizophrenia and major depressive disorder (0.43 ± 0.06 s.e.), bipolar disorder and major depressive disorder (0.47 ± 0.06 s.e.), and ADHD and major depressive disorder (0.32 ± 0.07 s.e.), low between schizophrenia and ASD (0.16 ± 0.06 s.e.) and non-significant for other pairs of disorders as well as between psychiatric disorders and the negative control of Crohn’s disease. This empirical evidence of shared genetic etiology for psychiatric disorders can inform nosology and encourages the investigation of common pathophysiologies for related disorders.

“Shared Polygenic Contribution between Childhood Attention-deficit Hyperactivity Disorder and Adult Schizophrenia”, Hamshere et al 2013

“Shared polygenic contribution between childhood attention-deficit hyperactivity disorder and adult schizophrenia”⁠, Marian L. Hamshere, Evangelia Stergiakouli, Kate Langley, Joanna Martin, Peter Holmans, Lindsey Kent et al (2013; backlinks; similar):

Background: There is recent evidence of some degree of shared genetic susceptibility between adult schizophrenia and childhood attention-deficit hyperactivity disorder (ADHD) for rare chromosomal variants.

Aims: To determine whether there is overlap between common alleles conferring risk of schizophrenia in adults with those that do so for ADHD in children.

Method: We used recently published Psychiatric Genome-wide Association Study (GWAS) Consortium (PGC) adult schizophrenia data to define alleles over-represented in people with schizophrenia and tested whether those alleles were more common in 727 children with ADHD than in 2067 controls.

Results: Schizophrenia risk alleles discriminated ADHD cases from controls (p = 1.04 × 10−4, R(2) = 0.45%); stronger discrimination was given by alleles that were risk alleles for both adult schizophrenia and adult bipolar disorder (also derived from a PGC data-set) (p = 9.98 × 10−6, R(2) = 0.59%).

Conclusions: This increasing evidence for a small, but significant, shared genetic susceptibility between adult schizophrenia and childhood ADHD highlights the importance of research work across traditional diagnostic boundaries.

“Identification of Risk Loci With Shared Effects on Five Major Psychiatric Disorders: a Genome-wide Analysis”

“Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis”⁠, (2013; ; backlinks; similar):

Background: Findings from family and twin studies suggest that genetic contributions to psychiatric disorders do not in all cases map to present diagnostic categories. We aimed to identify specific variants underlying genetic effects shared between the five disorders in the Psychiatric Genomics Consortium: autism spectrum disorder, attention deficit-hyperactivity disorder, bipolar disorder, major depressive disorder, and schizophrenia.

Methods: We analysed genome-wide single-nucleotide polymorphism (SNP) data for the five disorders in 33,332 cases and 27,888 controls of European ancestry. To characterise allelic effects on each disorder, we applied a multinomial logistic regression procedure with model selection to identify the best-fitting model of relations between genotype and phenotype. We examined cross-disorder effects of genome-wide statistically-significant loci previously identified for bipolar disorder and schizophrenia, and used polygenic risk-score analysis to examine such effects from a broader set of common variants. We undertook pathway analyses to establish the biological associations underlying genetic overlap for the five disorders. We used enrichment analysis of expression quantitative trait loci (eQTL) data to assess whether SNPs with cross-disorder association were enriched for regulatory SNPs in post-mortem brain-tissue samples.

Findings: SNPs at four loci surpassed the cutoff for genome-wide statistical-significance (p < 5×10−8) in the primary analysis: regions on chromosomes 3p21 and 10q24, and SNPs within two L-type voltage-gated calcium channel subunits, CACNA1C and CACNB2. Model selection analysis supported effects of these loci for several disorders. Loci previously associated with bipolar disorder or schizophrenia had variable diagnostic specificity. Polygenic risk scores showed cross-disorder associations, notably between adult-onset disorders. Pathway analysis supported a role for calcium channel signalling genes for all five disorders. Finally, SNPs with evidence of cross-disorder association were enriched for brain eQTL markers.

Interpretation: Our findings show that specific SNPs are associated with a range of psychiatric disorders of childhood onset or adult onset. In particular, variation in calcium-channel activity genes seems to have pleiotropic effects on psychopathology. These results provide evidence relevant to the goal of moving beyond descriptive syndromes in psychiatry, and towards a nosology informed by disease cause.

Funding: National Institute of Mental Health.

“Genome-wide Association Study of Clinical Dimensions of Schizophrenia: Polygenic Effect on Disorganized Symptoms”, Fanous et al 2012

“Genome-wide association study of clinical dimensions of schizophrenia: polygenic effect on disorganized symptoms”⁠, Ayman H. Fanous, Baiyu Zhou, Steven H. Aggen, Sarah E. Bergen, Richard L. Amdur, Jubao Duan, Alan R. Sanders et al (2012; backlinks; similar):

Objective: Multiple sources of evidence suggest that genetic factors influence variation in clinical features of schizophrenia. The authors present the first genome-wide association study (GWAS) of dimensional symptom scores among individuals with schizophrenia.

Method: Based on the Lifetime Dimensions of Psychosis Scale ratings of 2,454 case subjects of European ancestry from the Molecular Genetics of Schizophrenia (MGS) sample, three symptom factors (positive, negative/​disorganized, and mood) were identified with exploratory factor analysis⁠. Quantitative scores for each factor from a confirmatory factor analysis were analyzed for association with 696,491 single-nucleotide polymorphisms (SNPs) using linear regression, with correction for age, sex, clinical site, and ancestry. Polygenic score analysis was carried out to determine whether case and comparison subjects in 16 Psychiatric GWAS Consortium (PGC) schizophrenia samples (excluding MGS samples) differed in scores computed by weighting their genotypes by MGS association test results for each symptom factor.

Results: No genome-wide statistically-significant associations were observed between SNPs and factor scores. Most of the SNPs producing the strongest evidence for association were in or near genes involved in neurodevelopment, neuroprotection, or neurotransmission, including genes playing a role in Mendelian CNS diseases, but no statistically-significant effect was observed for any defined gene pathway. Finally, polygenic scores based on MGS GWAS results for the negative/​disorganized factor were significantly different between case and comparison subjects in the PGC data set; for MGS subjects, negative/​disorganized factor scores were correlated with polygenic scores generated using case-control GWAS results from the other PGC samples.

Conclusions: The polygenic signal that has been observed in cross-sample analyses of schizophrenia GWAS data sets could be in part related to genetic effects on negative and disorganized symptoms (ie. core features of chronic schizophrenia).

“Genome-wide Association Study Identifies Five New Schizophrenia Loci”, Consortium 2011

“Genome-wide association study identifies five new schizophrenia loci”⁠, The Schizophrenia Psychiatric Genome-Wide Association Study (GWAS) Consortium (2011-09-18; ; backlinks; similar):

We examined the role of common genetic variation in schizophrenia in a genome-wide association study of substantial size: a stage 1 discovery sample of 21,856 individuals of European ancestry and a stage 2 replication sample of 29,839 independent subjects. The combined stage 1 and 2 analysis yielded genome-wide statistically-significant associations with schizophrenia for seven loci, five of which are new (1p21.3, 2q32.3, 8p23.2, 8q21.3 and 10q24.32-q24.33) and two of which have been previously implicated (6p21.32-p22.1 and 18q21.2). The strongest new finding (p = 1.6 × 10−11) was with rs1625579 within an intron of a putative primary transcript for MIR137 (microRNA 137), a known regulator of neuronal development. Four other schizophrenia loci achieving genome-wide statistical-significance contain predicted targets of MIR137, suggesting MIR137-mediated dysregulation as a previously unknown etiologic mechanism in schizophrenia. In a joint analysis with a bipolar disorder sample (16,374 affected individuals and 14,044 controls), three loci reached genome-wide statistical-significance: CACNA1C (rs4765905, p = 7.0 × 10−9), ANK3 (rs10994359, p = 2.5 × 10−8) and the ITIH3-ITIH4 region (rs2239547, p = 7.8 × 10−9).

“Large-scale Genome-wide Association Analysis of Bipolar Disorder Identifies a New Susceptibility Locus near ODZ4”

“Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4”⁠, (2011; backlinks; similar):

We conducted a combined genome-wide association study (GWAS) of 7,481 individuals with bipolar disorder (cases) and 9,250 controls as part of the Psychiatric GWAS Consortium. Our replication study tested 34 SNPs in 4,496 independent cases with bipolar disorder and 42,422 independent controls and found that 18 of 34 SNPs had p < 0.05, with 31 of 34 SNPs having signals with the same direction of effect (p = 3.8 × 10−7). An analysis of all 11,974 bipolar disorder cases and 51,792 controls confirmed genome-wide statistically-significant evidence of association for CACNA1C and identified a new intronic variant in ODZ4. We identified a pathway comprised of subunits of calcium channels enriched in bipolar disorder association intervals. Finally, a combined GWAS analysis of schizophrenia and bipolar disorder yielded strong association evidence for SNPs in CACNA1C and in the region of NEK4-ITIH1-ITIH3-ITIH4. Our replication results imply that increasing sample sizes in bipolar disorder will confirm many additional loci.

“Premorbid IQ in Schizophrenia: A Meta-Analytic Review”, Woodberry et al 2008

“Premorbid IQ in Schizophrenia: A Meta-Analytic Review”⁠, Kristen A. Woodberry, Anthony J. Giuliano, Larry J. Seidman (2008-05-01; ; backlinks; similar):

Objective: Over the past three decades, there have been substantial changes in the diagnostic criteria for schizophrenia as well as changes in measurement of IQ. The last quantitative review of the literature on premorbid IQ in schizophrenia was published more than two decades ago. Since that time, there have been many published studies of data sets pertaining to this issue. The purpose of the present review was to provide an updated meta-analysis of premorbid IQ in individuals who later develop schizophrenia.

Method: The authors performed a systematic literature search, which yielded 18 studies that met criteria for the meta-analysis. Inclusion criteria were 1. premorbid psychometric measures of IQ in subjects who were later diagnosed with schizophrenia, schizoaffective disorder, or schizophreniform disorder, 2. similar comparison data, and 3. sufficient data for calculation of an effect size. The analogue to the analysis of variance method was used to model between-study variance due to key study-design features.

Results: Overall, schizophrenia samples demonstrated a reliable, medium-sized impairment in premorbid IQ. The heterogeneity of effect sizes was minimal and almost exclusively the result of one study. Methodological differences, such as diagnostic criteria, type of IQ measure, sample ascertainment, and age at premorbid testing, contributed minimally to the effect size variance. A cross-sectional analysis of all studies by age and a descriptive review of studies that used repeated measures of IQ in a single sample did not support the presence of a relative decline in IQ during the premorbid period in individuals with schizophrenia. However, all studies with pre-onset and post-onset testing within the same sample suggested that a [substantial] decline in the IQ of individuals with schizophrenia, relative to comparison subjects, was associated with the onset of frank psychosis.

Conclusions: Years before the onset of psychotic symptoms, individuals with schizophrenia, as a group, demonstrate mean IQ scores approximately one-half of a standard deviation below that of healthy comparison subjects.

“Nicotine As Therapy”, Powledge 2004

“Nicotine as Therapy”⁠, Tabitha M. Powledge (2004-11; ; backlinks):

[Discussion of possible therapeutic applications of nicotine: depression, schizophrenia, adult ADHD, through attention; pain relief; weight loss via motivation control and appetite; and nicotine analogues for targeting specific nicotinic receptors (and making patentable drugs).]

“An Economic Evaluation of Manic-depressive Illness--1991”, Wyatt & Henter 1995

“An economic evaluation of manic-depressive illness--1991”⁠, R J. Wyatt, I. Henter (1995; backlinks; similar):

In 1991, the costs for manic-depressive illness, which has a lifetime prevalence of 1.3% among adult Americans, totaled $108.8$45.01991 billion. Costs were broken down into their direct and indirect components. Direct costs totaling $16.9$7.01991 billion consist of expenditures for inpatient and outpatient care, which are treatment related, as well as non-treatment-related expenditures such as those for the criminal justice system used by individuals with manic-depressive illness. Indirect costs, which were $91.9$38.01991 billion, include the lost productivity of both wage-earners ($41.1$17.01991 billion) and homemakers ($7.3$3.01991 billion), individuals who are in institutions ($7.3$3.01991 billion) or who have committed suicide ($19.3$8.01991 billion), and caregivers who take care of manic-depressive family members ($14.5$6.01991 billion). The method for determining each expenditure is provided, and the implications of these staggering costs are discussed. These calculations rely heavily on methods and data bases that were developed for the accompanying paper on the costs of schizophrenia.

“On Being Sane in Insane Places: A Supplemental Report”, Lando 1976

1976-lando.pdf: “On being sane in insane places: A supplemental report”⁠, Harry A. Lando (1976; ; backlinks; similar):

Describes the author’s experiences as a pseudo-patient on the psychiatric ward of a large public hospital for 19 days. Hospital facilities were judged excellent, and therapy tended to be extensive. Close contact with both patients and staff was obtained. Despite this contact, however, not only was the author’s simulation not detected, but his behavior was seen as consistent with the admitting diagnosis of “chronic undifferentiated schizophrenia.” Even with this misattribution it is concluded that the present institution had many positive aspects and that the depersonalization of patients so strongly emphasized by D. Rosenhan (see record 1973-21600-001) did not exist in this setting. It is recommended that future research address positive characteristics of existing institutions and possibly emulate these in upgrading psychiatric care.

…I was the ninth pseudopatient in the Rosenhan study, and my data were not included in the original report.

“On Pseudoscience in Science, Logic in Remission, and Psychiatric Diagnosis: A Critique of Rosenhan's ‘On Being Sane in Insane Places’”, Spitzer 1975

“On Pseudoscience in Science, Logic in Remission, and Psychiatric Diagnosis: A Critique of Rosenhan's ‘On Being Sane in Insane Places’”⁠, Robert L. Spitzer (1975; ; backlinks; similar):

Rosenhan’s “On Being Sane in Insane Places” is pseudoscience presented as science. Just as his pseudopatients were diagnosed at discharge as “schizophrenia in remission”, so a careful examination of this study’s methods, results, and conclusion leads to a diagnosis of “logic in remission”. Rosenhan’s study proves that pseudopatients are not detected by psychiatrists as having simulated signs of mental illness. This rather unremarkable finding is not relevant to the real problems of the reliability and validity of psychiatric diagnosis and only serves to obscure them. A correct interpretation of these data contradicts the conclusions that were drawn. In the setting of a psychiatric hospital, psychiatrists seem remarkably able to distinguish the “sane” from the “insane”.

“An Experience in Submarine Psychiatry”, Serxner 1968

1968-serxner.pdf: “An Experience in Submarine Psychiatry”⁠, Jonathan L. Serxner (1968-07-01; ; backlinks; similar):

The psychiatric experience of a medical officer on two submerged Polaris submarine patrols, each lasting two months, is presented. One psychiatric emergency—an acute paranoid schizophrenic reaction—was managed, and some minor anxiety reactions and depressions were treated. The author suggests the nature of the submarine’s psychological atmosphere by means of a brief discussion of the submarine as a physical entity, the patrol cycle, and the procedures of personnel selection and training.

The Origin of Consciousness in the Breakdown of the Bicameral Mind

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Negative selection (natural selection)

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Bicameral mentality

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Background selection

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Miscellaneous