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Contrasting genetic architectures of schizophrenia and other complex diseases using fast variance-components analysis.

ABSTRACT: Heritability analyses of genome-wide association study (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 architectures 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 1-Mb genomic regions harbor ?1 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) for several pairs of GERA diseases; genetic correlations were on average 1.3 tunes stronger than the correlations of overall disease liabilities. To accomplish these analyses, we developed a fast algorithm for multicomponent, multi-trait variance-components analysis that overcomes prior computational barriers that made such analyses intractable at this scale.

SUBMITTER: Loh PR

PROVIDER: S-EPMC4666835 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

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Contrasting genetic architectures of schizophrenia and other complex diseases using fast variance-components analysis.

Loh Po-Ru PR Bhatia Gaurav G Gusev Alexander A Finucane Hilary K HK Bulik-Sullivan Brendan K BK Pollack Samuela J SJ de Candia Teresa R TR Lee Sang Hong SH Wray Naomi R NR Kendler Kenneth S KS O'Donovan Michael C MC Neale Benjamin M BM Patterson Nick N Price Alkes L AL

Nature genetics 20151102 12

Heritability analyses of genome-wide association study (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 architectures 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 1-Mb genomic regions harbor ≥1 variant influ ...[more]

PMID: 26523775

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Project description:BackgroundThe genetic correlation between purebred and crossbred performance ([Formula: see text]) is an important parameter in pig and poultry breeding, because response to selection in crossbred performance depends on the value of [Formula: see text] when selection is based on purebred (PB) performance. The value of [Formula: see text] can be substantially lower than 1, which is partly due to differences in allele frequencies between parental lines when non-additive genetic effects are present. This relationship between [Formula: see text] and parental allele frequencies suggests that [Formula: see text] can be expressed as a function of genetic parameters for the trait in the parental lines. In this study, we derived expressions for [Formula: see text] based on genetic variances within, and the genetic covariance between parental lines. It is important to note that the variance components used in our expressions are not the components that are typically estimated in empirical data. The expressions were derived for a genetic model with additive and dominance effects (D), and additive and epistatic additive-by-additive effects (EAA). We validated our expressions using simulations of purebred parental lines and their crosses, where the parental lines were either selected or not. Finally, using these simulations, we investigated the value of [Formula: see text] for genetic models with both dominance and epistasis or with other types of epistasis, for which expressions could not be derived.ResultsOur simulations show that when non-additive effects are present, [Formula: see text] decreases with increasing differences in allele frequencies between the parental lines. Genetic models that involve dominance result in lower values of [Formula: see text] than genetic models that involve epistasis only. Using information of parental lines only, our expressions provide exact estimates of [Formula: see text] for models D and EAA, and accurate upper and lower bounds of [Formula: see text] for two other genetic models.ConclusionThis work lays the foundation to enable estimation of [Formula: see text] from information collected in PB parental lines only.

Dataset Information

Contrasting genetic architectures of schizophrenia and other complex diseases using fast variance-components analysis.

Publications

Contrasting genetic architectures of schizophrenia and other complex diseases using fast variance-components analysis.

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