Bivariate causal mixture model quantifies polygenic overlap between complex traits beyond genetic correlation

Frei, Oleksandr; Holland, Dominic; Smeland, Olav B.; Shadrin, Alexey; Fan, Chun Chieh; Maeland, Steffen; O’Connell, Kevin; Wang, Yunpeng; Djurovic, Srdjan; Thompson, Wesley K.; Andreassen, Ole A.; Dale, Anders M.

doi:10.1038/s41467-019-10310-0

Cited by 263 publications

(294 citation statements)

References 58 publications

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“…Recent large-scale genome-wide association studies (GWAS) have identified a large number of single-nucleotide polymorphisms (SNP) associated with psychiatric disorders such as schizophrenia (SCZ) 1 , bipolar disorder (BD) 2 , major depression (MD) 3 , attention deficit hyperactivity disorder (ADHD) 4 , autism spectrum disorders (ASD) 5 , post-traumatic stress disorder (PTSD) 6 , and anxiety (ANX) 7 . In addition to substantial polygenicity, previous findings have documented genetic overlap between disorders [8][9][10][11] , even in the absence of genetic correlations as recently demonstrated for schizophrenia and educational attainment 12,13 . Adding to the complexity, psychiatric disorders also overlap with multiple complex traits, such as BMI 14 and cardio-metabolic diseases 15 .…”

Section: Introductionmentioning

confidence: 85%

Phenotypically independent profiles relevant to mental health are genetically correlated

Roelfs

Alnæs

Frei

et al. 2020

Preprint

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Background: Genome-wide association studies (GWAS) and family-based studies have revealed partly overlapping genetic architectures between various psychiatric disorders. Given clinical overlap between disorders our knowledge of the genetic architectures underlying specific symptom profiles is limited. Here, we aimed to derive distinct profiles of mental health in healthy individuals and to study how these genetically relate to each other and to common psychiatric disorders. Methods: We decomposed self-report mental health questionnaires from 136,678 healthy individuals of the UK Biobank, excluding data from individuals with a diagnosed neurological or psychiatric disorder, into thirteen distinct mental health profiles using independent component analysis. Utilizing genotypes from 117,611 of those individuals with Caucasian ancestry, we performed GWAS for each mental health profile and assessed genetic correlations between these profiles, and between the profiles and common psychiatric disorders and cognitive traits. Results: We found that mental health profiles were genetically correlated with a wide range of psychiatric disorders and cognitive traits, with strongest effects typically observed between a given mental health profile and a disorder for which the profile is common (e.g. depression symptoms and major depressive disorder, psychosis and schizophrenia). Strikingly, although the profiles were phenotypically uncorrelated, many of them were genetically correlated with each other. Conclusions: This study provides evidence that statistically independent mental health profiles in healthy individuals partly share genetic underpinnings and show genetic overlaps with psychiatric disorders, suggesting that shared genetics across psychiatric disorders cannot be exclusively attributed to the overlapping symptomatology between the disorders.

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Section: Introductionmentioning

confidence: 85%

Phenotypically independent profiles relevant to mental health are genetically correlated

Roelfs

Alnæs

Frei

et al. 2020

Preprint

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“…Several available Bayesian approaches (Giambartolomei et al 2014;Pickrell et al 2016) explore whether two association signals in the same genomic region obtained from two different GWAS share a single causal variant or multiple causal variants. Frei and colleagues performed a similar analysis at the genome-wide level, estimating the proportion of phenotype-specific causal variants and shared variants between complex phenotypes using GWAS summary data, while controlling for shared participants (Frei et al 2019). The analysis demonstrates how the shared polygenic component may constitute a large fraction of the genetic architecture of one phenotype, while constituting a smaller fraction of the architecture of a phenotype with larger polygenicity.…”

Section: Comparison To Other Cross-trait Analytical Toolsmentioning

confidence: 99%

Discovery of shared genomic loci using the conditional false discovery rate approach

et al. 2019

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In recent years, genome-wide association study (GWAS) sample sizes have become larger, the statistical power has improved and thousands of trait-associated variants have been uncovered, offering new insights into the genetic etiology of complex human traits and disorders. However, a large fraction of the polygenic architecture underlying most complex phenotypes still remain undetected. We here review the conditional false discovery rate (condFDR) method, a modelfree strategy for analysis of GWAS summary data, which has improved yield of existing GWAS and provided novel findings of genetic overlap between a wide range of complex human phenotypes, including psychiatric, cardiovascular, and neurological disorders, as well as psychological and cognitive traits. The condFDR method was inspired by Empirical Bayes approaches and leverages auxiliary genetic information to improve statistical power for discovery of single-nucleotide polymorphisms (SNPs). The cross-trait condFDR strategy analyses separate GWAS data, and leverages overlapping SNP associations, i.e. cross-trait enrichment, to increase discovery of trait-associated SNPs. The extension of the condFDR approach to conjunctional FDR (conjFDR) identifies shared genomic loci between two phenotypes. The conjFDR approach allows for detection of shared genomic associations irrespective of the genetic correlation between the phenotypes, often revealing a mixture of antagonistic and agonistic directional effects among the shared loci. This review provides a methodological comparison between condFDR and other relevant cross-trait analytical tools and demonstrates how condFDR analysis may provide novel insights into the genetic relationship between complex phenotypes.

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“…Other studies have also shown a strong positive correlation between SZ genetic risk and EA, as previously mentioned [27,47,54,60,62,[67][68][69][70] albeit few studies found non-significant or discrepant results [53,55]. Using the cFDR statistical approach, 10 gene loci were associated with SZ and EA/high EA, with effects in opposite directions [69].…”

Section: Implication Of Cognition/ea On the Risk For Schizophrenia Anmentioning

confidence: 71%

Polygenic Risk Scores Shed Light on the Relationship between Schizophrenia and Cognitive Functioning: Review and Meta-Analysis

Mallet

Strat

Dubertret

et al. 2020

JCM

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Schizophrenia is a multifactorial disease associated with widespread cognitive impairment. Although cognitive deficits are one of the factors most strongly associated with functional impairment in schizophrenia (SZ), current treatment strategies hardly tackle these impairments. To develop more efficient treatment strategies in patients, a better understanding of their pathogenesis is needed. Recent progress in genetics, driven by large genome-wide association studies (GWAS) and the use of polygenic risk scores (PRS), has provided new insights about the genetic architecture of complex human traits, including cognition and SZ. Here, we review the recent findings examining the genetic links between SZ and cognitive functions in population-based samples as well as in participants with SZ. The performed meta-analysis showed a negative correlation between the polygenetic risk score of schizophrenia and global cognition (p < 0.001) when the samples rely on general and healthy participants, while no significant correlation was detected when the three studies devoted to schizophrenia patients were meta-analysed (p > 0.05). Our review and meta-analysis therefore argues against universal pleiotropy for schizophrenia alleles and cognition, since cognition in SZ patients would be underpinned by the same genetic factors than in the general population, and substantially independent of common variant liability to the disorder.

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Bivariate causal mixture model quantifies polygenic overlap between complex traits beyond genetic correlation

Cited by 263 publications

References 58 publications

Phenotypically independent profiles relevant to mental health are genetically correlated

Phenotypically independent profiles relevant to mental health are genetically correlated

Discovery of shared genomic loci using the conditional false discovery rate approach

Polygenic Risk Scores Shed Light on the Relationship between Schizophrenia and Cognitive Functioning: Review and Meta-Analysis

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