Genetic Architecture of 11 Major Psychiatric Disorders at Biobehavioral, Functional Genomic, and Molecular Genetic Levels of Analysis

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.

Download Full-text

Genetic heterogeneity in self-reported depressive symptoms identified through genetic analyses of the PHQ-9

Psychological Medicine ◽

10.1017/s0033291719002526 ◽

2019 ◽

Vol 50 (14) ◽

pp. 2385-2396 ◽

Cited By ~ 6

Author(s):

Jackson G. Thorp ◽

Andries T. Marees ◽

Jue-Sheng Ong ◽

Jiyuan An ◽

Stuart MacGregor ◽

...

Keyword(s):

Depressive Symptoms ◽

Genetic Heterogeneity ◽

Structural Equation ◽

Large Scale ◽

Clinical Symptoms ◽

Factor Model ◽

Genetic Correlations ◽

Genetic Covariance ◽

Association Analyses ◽

Genome Wide

AbstractBackgroundDepression is a clinically heterogeneous disorder. Previous large-scale genetic studies of depression have explored genetic risk factors of depression case–control status or aggregated sums of depressive symptoms, ignoring possible clinical or genetic heterogeneity.MethodsWe analyse data from 148 752 subjects of white British ancestry in the UK Biobank who completed nine items of a self-rated measure of current depressive symptoms: the Patient Health Questionnaire (PHQ-9). Genome-Wide Association analyses were conducted for nine symptoms and two composite measures. LD Score Regression was used to calculate SNP-based heritability (h2SNP) and genetic correlations (rg) across symptoms and to investigate genetic correlations with 25 external phenotypes. Genomic structural equation modelling was used to test the genetic factor structure across the nine symptoms.ResultsWe identified nine genome-wide significant genomic loci (8 novel), with no overlap in loci across symptoms. h2SNP ranged from 6% (concentration problems) to 9% (appetite changes). Genetic correlations ranged from 0.54 to 0.96 (all p < 1.39 × 10−3) with 30 of 36 correlations being significantly smaller than one. A two-factor model provided the best fit to the genetic covariance matrix, with factors representing ‘psychological’ and ‘somatic’ symptoms. The genetic correlations with external phenotypes showed large variation across the nine symptoms.ConclusionsPatterns of SNP associations and genetic correlations differ across the nine symptoms, suggesting that current depressive symptoms are genetically heterogeneous. Our study highlights the value of symptom-level analyses in understanding the genetic architecture of a psychiatric trait. Future studies should investigate whether genetic heterogeneity is recapitulated in clinical symptoms of major depression.

Download Full-text

A polygenic p factor for major psychiatric disorders

10.1101/287987 ◽

2018 ◽

Author(s):

Saskia Selzam ◽

Jonathan R. I. Coleman ◽

Avshalom Caspi ◽

Terrie E. Moffitt ◽

Robert Plomin

Keyword(s):

Psychiatric Disorders ◽

Genetic Architecture ◽

Genetic Correlations ◽

Genetic Research ◽

Strong Support ◽

Principal Component ◽

Complex Trait ◽

Genome Wide ◽

P Factor ◽

First Time

AbstractIt has recently been proposed that a single dimension, called the p factor, can capture a person’s liability to mental disorder. Relevant to the p hypothesis, recent genetic research has found surprisingly high genetic correlations between pairs of psychiatric disorders. Here, for the first time we compare genetic correlations from different methods and examine their support for a genetic p factor. We tested the hypothesis of a genetic p factor by using principal component analysis on matrices of genetic correlations between major psychiatric disorders estimated by three methods – family study, Genome-wide Complex Trait Analysis, and Linkage-Disequilibrium Score Regression – and on a matrix of polygenic score correlations constructed for each individual in a UK-representative sample of 7,026 unrelated individuals. All disorders loaded on a first unrotated principal component, which accounted for 57%, 43%, 34% and 19% of the variance respectively for each method. Our results showed that all four methods provided strong support for a genetic p factor that represents the pinnacle of the hierarchical genetic architecture of psychopathology.

Download Full-text

Genomic SEM Provides Insights into the Multivariate Genetic Architecture of Complex Traits

10.1101/305029 ◽

2018 ◽

Cited By ~ 20

Author(s):

Andrew D. Grotzinger ◽

Mijke Rhemtulla ◽

Ronald de Vlaming ◽

Stuart J. Ritchie ◽

Travis T. Mallard ◽

...

Keyword(s):

Complex Traits ◽

Structural Equation ◽

Genetic Architecture ◽

Genetic Correlations ◽

Covariance Structure ◽

Equation Modeling ◽

Joint Analysis ◽

Summary Statistics ◽

Individual Traits ◽

Polygenic Scores

AbstractMethods for using GWAS to estimate genetic correlations between pairwise combinations of traits have produced “atlases” of genetic architecture. Genetic atlases reveal pervasive pleiotropy, and genome-wide significant loci are often shared across different phenotypes. We introduce genomic structural equation modeling (Genomic SEM), a multivariate method for analyzing the joint genetic architectures of complex traits. Using formal methods for modeling covariance structure, Genomic SEM synthesizes genetic correlations and SNP-heritabilities inferred from GWAS summary statistics of individual traits from samples with varying and unknown degrees of overlap. Genomic SEM can be used to identify variants with effects on general dimensions of cross-trait liability, boost power for discovery, and calculate more predictive polygenic scores. Finally, Genomic SEM can be used to identify loci that cause divergence between traits, aiding the search for what uniquely differentiates highly correlated phenotypes. We demonstrate several applications of Genomic SEM, including a joint analysis of GWAS summary statistics from five genetically correlated psychiatric traits. We identify 27 independent SNPs not previously identified in the univariate GWASs, 5 of which have been reported in other published GWASs of the included traits. Polygenic scores derived from Genomic SEM consistently outperform polygenic scores derived from GWASs of the individual traits. Genomic SEM is flexible, open ended, and allows for continuous innovations in how multivariate genetic architecture is modeled.

Download Full-text

Contrasting regional architectures of schizophrenia and other complex diseases using fast variance components analysis

10.1101/016527 ◽

2015 ◽

Cited By ~ 6

Author(s):

Po-Ru Loh ◽

Gaurav Bhatia ◽

Alexander Gusev ◽

Hilary K Finucane ◽

Brendan K Bulik-Sullivan ◽

...

Keyword(s):

Variance Components ◽

Genetic Architecture ◽

Complex Disease ◽

Genetic Correlations ◽

Complex Diseases ◽

Variance Components Analysis ◽

Trait Variance ◽

Significant Enrichment ◽

Components Analysis ◽

Genomic Regions

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 significant genetic correlations (ranging from 0.18 to 0.85) among several pairs of GERA diseases; genetic correlations were on average 1.3x 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.

Download Full-text

Genetic architecture of human plasma lipidome and its link to cardiovascular disease

Nature Communications ◽

10.1038/s41467-019-11954-8 ◽

2019 ◽

Vol 10 (1) ◽

Cited By ~ 25

Author(s):

Rubina Tabassum ◽

◽

Joel T. Rämö ◽

Pietari Ripatti ◽

Jukka T. Koskela ◽

...

Keyword(s):

Genetic Architecture ◽

Genetic Regulation ◽

Genetic Correlations ◽

Cvd Risk ◽

Association Analyses ◽

False Discovery ◽

Medium Length ◽

Genome Wide ◽

Lipid Species ◽

Polyunsaturated Lipids

Abstract Understanding genetic architecture of plasma lipidome could provide better insights into lipid metabolism and its link to cardiovascular diseases (CVDs). Here, we perform genome-wide association analyses of 141 lipid species (n = 2,181 individuals), followed by phenome-wide scans with 25 CVD related phenotypes (n = 511,700 individuals). We identify 35 lipid-species-associated loci (P <5 ×10−8), 10 of which associate with CVD risk including five new loci-COL5A1, GLTPD2, SPTLC3, MBOAT7 and GALNT16 (false discovery rate<0.05). We identify loci for lipid species that are shown to predict CVD e.g., SPTLC3 for CER(d18:1/24:1). We show that lipoprotein lipase (LPL) may more efficiently hydrolyze medium length triacylglycerides (TAGs) than others. Polyunsaturated lipids have highest heritability and genetic correlations, suggesting considerable genetic regulation at fatty acids levels. We find low genetic correlations between traditional lipids and lipid species. Our results show that lipidomic profiles capture information beyond traditional lipids and identify genetic variants modifying lipid levels and risk of CVD.

Download Full-text

The genetic basis of natural antibody titers of young healthy pigs and relationships with disease resilience

BMC Genomics ◽

10.1186/s12864-020-06994-0 ◽

2020 ◽

Vol 21 (1) ◽

Author(s):

Yulu Chen ◽

◽

Laura E. Tibbs Cortes ◽

Carolyn Ashley ◽

Austin M. Putz ◽

...

Keyword(s):

Genetic Correlations ◽

Parenteral Antibiotic ◽

Association Analyses ◽

Breeding Populations ◽

Young Pigs ◽

Genome Wide ◽

Antibody Titers ◽

Polymicrobial Disease ◽

Genomic Regions ◽

Indicator Traits

Abstract Background Disease resilience is the ability to maintain performance under pathogen exposure but is difficult to select for because breeding populations are raised under high health. Selection for resilience requires a trait that is heritable, easy to measure on healthy animals, and genetically correlated with resilience. Natural antibodies (NAb) are important parts of the innate immune system and are found to be heritable and associated with disease susceptibility in dairy cattle and poultry. Our objective was to investigate NAb and total IgG in blood of healthy, young pigs as potential indicator traits for disease resilience. Results Data were from Yorkshire x Landrace pigs, with IgG and IgM NAb (four antigens) and total IgG measured by ELISA in blood plasma collected ~ 1 week after weaning, prior to their exposure to a natural polymicrobial challenge. Heritability estimates were lower for IgG NAb (0.12 to 0.24, + 0.05) and for total IgG (0.19 + 0.05) than for IgM NAb (0.33 to 0.53, + 0.07) but maternal effects were larger for IgG NAb (0.41 to 0.52, + 0.03) and for total IgG (0.19 + 0.05) than for IgM NAb (0.00 to 0.10, + 0.04). Phenotypically, IgM NAb titers were moderately correlated with each other (average 0.60), as were IgG NAb titers (average 0.42), but correlations between IgM and IgG NAb titers were weak (average 0.09). Phenotypic correlations of total IgG were moderate with NAb IgG (average 0.46) but weak with NAb IgM (average 0.01). Estimates of genetic correlations among NAb showed similar patterns but with small SE, with estimates averaging 0.76 among IgG NAb, 0.63 among IgM NAb, 0.17 between IgG and IgM NAb, 0.64 between total IgG and IgG NAb, and 0.13 between total IgG and IgM NAb. Phenotypically, pigs that survived had slightly higher levels of NAb and total IgG than pigs that died. Genetically, higher levels of NAb tended to be associated with greater disease resilience based on lower mortality and fewer parenteral antibiotic treatments. Genome-wide association analyses for NAb titers identified several genomic regions, with several candidate genes for immune response. Conclusions Levels of NAb in blood of healthy young piglets are heritable and potential genetic indicators of resilience to polymicrobial disease.

Download Full-text

Genetic overlap and causal associations between smoking behaviours and psychiatric traits and disorders in adolescents and adults

10.1101/2020.02.07.20021089 ◽

2020 ◽

Author(s):

Wikus Barkhuizen ◽

Frank Dudbridge ◽

Angelica Ronald

Keyword(s):

Bipolar Disorder ◽

Psychiatric Disorders ◽

Structural Equation ◽

Negative Symptoms ◽

Genetic Correlations ◽

Genetic Influences ◽

Psychotic Experiences ◽

Genetic Associations ◽

Genetic Overlap ◽

Smoking Behaviours

AbstractBackgroundEpidemiological research shows that smoking is associated with psychiatric disorders and psychotic experiences, even after controlling for confounds such as cannabis use and sleep problems. We investigated degree of genetic overlap and tested for causal associations between smoking and psychiatric traits and disorders using genetic data. We tested whether genetic associations existed beyond genetic influences shared with confounding variables.MethodsGenetic correlations were estimated with LD-score regression between smoking behaviours (N=262,990-632,802) and psychiatric disorders (schizophrenia, bipolar disorder and depression; N=41,653-173,005), psychotic experiences in adolescents (N=6,297-10,098) and adults (N=116,787-117,794) and adult schizotypy (N=3,967-4,057). Genomic Structural Equation Modelling was performed to explore the associations while accounting for genetic influences of confounders (cannabis and alcohol use, risk-taking and insomnia). Causal associations were tested using Generalized Summary-based Mendelian Randomization (GSMR).ResultsSignificant genetic correlations were found between smoking and psychiatric disorders (rg = .10 - .38) and adult PE (rg = .33 - .40). After accounting for covarying genetic influences, genetic associations between most smoking phenotypes and schizophrenia and depression remained but not between smoking behaviours and bipolar disorder or most psychotic experiences. GSMR results supported a causal role of smoking initiation on psychiatric disorders and adolescent cognitive and negative psychotic experiences.ConclusionsPleiotropy between smoking behaviours and schizophrenia and depression exists beyond the common genetic basis of known confounders. Smoking also appears to be causally associated with psychiatric disorders and with cognitive PEs and negative symptoms during adolescence. Exploration of the biological links underlying smoking and psychiatric illness would be well-justified.

Download Full-text

Localizing genomic regions contributing to the extremes of externalizing behavior: ADHD, aggressive and antisocial behaviors

10.1101/750091 ◽

2019 ◽

Author(s):

Mariana L. Rodríguez-López ◽

Hilleke Hulshoff Pol ◽

Barbara Franke ◽

Marieke Klein

Keyword(s):

Externalizing Behavior ◽

Meta Analysis ◽

Neurodevelopmental Disorder ◽

Expression Patterns ◽

Genetic Correlations ◽

Brain Regions ◽

Genetic Loci ◽

Association Analyses ◽

Meta Analyses ◽

Genomic Regions

AbstractAttention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder, which in some cases occurs comorbid with aggressive and antisocial behavior (AGG; ASB). The three externalizing behaviors are moderately to highly heritable and are genetically correlated. However, the genomic regions underlying this correlation are unknown. In this study, we aimed to localize genetic loci shared between ADHD, AGG, and ASB, using two complementary approaches.GWAS summary statistics for ADHD, AGG, and ASB were used for (1) cross-trait gene-based meta-analysis association analyses and (2) local genetic correlation analyses to identify shared genetic loci. Results of both complementary methods were combined to retrieve overlapping genes. Biological functionality of prioritized genes was assessed by exploring gene expression patterns in brain tissues and testing for gene-based association with (subcortical) brain regions.We confirmed previous findings that ADHD, AGG, and ASB were positively genetically correlated at a global level. We identified eleven significant genes in cross-trait gene-based meta-analyses, 31 loci shared between traits; 34 genes were identified when both approaches were combined.This study emphasizes the complex genetic architecture underlying global genetic correlations at the locus level. Converging evidence from these cross-trait analyses highlights novel candidate genes underlying biological mechanisms shared by ADHD, AGG, and ASB.

Download Full-text

Pleiotropy or linkage? Their relative contributions to the genetic correlation of quantitative traits and detection by multi-trait GWA studies

Genetics ◽

10.1093/genetics/iyab159 ◽

2021 ◽

Author(s):

Jobran Chebib ◽

Frédéric Guillaume

Keyword(s):

Genetic Correlation ◽

Mutation Rate ◽

Genetic Architecture ◽

Quantitative Traits ◽

Association Studies ◽

Genetic Correlations ◽

Association Analyses ◽

And Migration ◽

Gwa Studies ◽

Mutational Variance

Abstract Genetic correlations between traits may cause correlated responses to selection. Previous models described the conditions under which genetic correlations are expected to be maintained. Selection, mutation and migration are all proposed to affect genetic correlations, regardless of whether the underlying genetic architecture consists of pleiotropic or tightly linked loci affecting the traits. Here, we investigate the conditions under which pleiotropy and linkage have different effects on the genetic correlations between traits by explicitly modeling multiple genetic architectures to look at the effects of selection strength, degree of correlational selection, mutation rate, mutational variance, recombination rate, and migration rate. We show that at mutation-selection(-migration) balance, mutation rates differentially affect the equilibrium levels of genetic correlation when architectures are composed of pairs of physically linked loci compared to architectures of pleiotropic loci. Even when there is perfect linkage (no recombination within pairs of linked loci), a lower genetic correlation is maintained than with pleiotropy, with a lower mutation rate leading to a larger decrease. These results imply that the detection of causal loci in multi-trait association studies will be affected by the type of underlying architectures, whereby pleiotropic variants are more likely to be underlying multiple detected associations. We also confirm that tighter linkage between non-pleiotropic causal loci maintains higher genetic correlations at the traits and leads to a greater proportion of false positives in association analyses.

Download Full-text

Bayesian analysis of GWAS summary data reveals differential signatures of natural selection across human complex traits and functional genomic categories

10.1101/752527 ◽

2019 ◽

Cited By ~ 6

Author(s):

Jian Zeng ◽

Angli Xue ◽

Longda Jiang ◽

Luke R Lloyd-Jones ◽

Yang Wu ◽

...

Keyword(s):

Natural Selection ◽

Complex Traits ◽

Genetic Architecture ◽

Evolutionary Genetics ◽

Functional Genomic ◽

Individual Level ◽

Level Data ◽

Genomic Regions ◽

Evolutionary Simulations ◽

Transcriptional Start Sites

AbstractUnderstanding how natural selection has shaped the genetic architecture of complex traits and diseases is of importance in medical and evolutionary genetics. Bayesian methods have been developed using individual-level data to estimate multiple features of genetic architecture, including signatures of natural selection. Here, we present an enhanced method (SBayesS) that only requires GWAS summary statistics and incorporates functional genomic annotations. We analysed GWAS data with large sample sizes for 155 complex traits and detected pervasive signatures of negative selection with diverse estimates of SNP-based heritability and polygenicity. Projecting these estimates onto a map of genetic architecture obtained from evolutionary simulations revealed relatively strong natural selection on genetic variants associated with cardiorespiratory and cognitive traits and relatively small number of mutational targets for diseases. Averaging across traits, the joint distribution of SNP effect size and MAF varied across functional genomic regions (likely to be a consequence of natural selection), with enrichment in both the number of associated variants and the magnitude of effect sizes in regions such as transcriptional start sites, coding regions and 5’- and 3’-UTRs.

Download Full-text