Abstract
Genome-wide association studies (GWAS) suggest that the genetic architecture of complex diseases consists of unexpectedly numerous variants with small effect sizes. However, the polygenic architectures of many diseases have not been well characterized due to lack of simple and fast methods for unbiased estimation of the underlying proportion of disease-associated variants and their effect-size distribution. Applying empirical Bayes estimation of semi-parametric hierarchical mixture models to GWAS summary statistics, we confirmed that schizophrenia was extremely polygenic [~40% of independent genome-wide SNPs are risk variants, most within odds ratio (OR = 1.03)], whereas rheumatoid arthritis was less polygenic (~4 to 8% risk variants, significant portion reaching OR = 1.05 to 1.1). For rheumatoid arthritis, stratified estimations revealed that expression quantitative loci in blood explained large genetic variance, and low- and high-frequency derived alleles were prone to be risk and protective, respectively, suggesting a predominance of deleterious-risk and advantageous-protective mutations. Despite genetic correlation, effect-size distributions for schizophrenia and bipolar disorder differed across allele frequency. These analyses distinguished disease polygenic architectures and provided clues for etiological differences in complex diseases.
Highlights
Genome-wide association studies (GWAS) have identified numerous susceptibility variants for complex diseases (Welter et al, 2014)
We have developed a simple and fast method for unbiased estimation of the proportion of disease-associated variants and the effect-size distribution based on the empirical Bayes estimation of Semi-parametric Hierarchical Mixture Model (SP-HMM)
As we hypothesized in the introduction, we observed that the SP-HMM provided new insights in evaluating polygenic models of complex diseases: The SPHMM can effectively distinguish various polygenic architectures, including the degree of polygenicity and distributions of genotype log-odds ratio, across diseases, and can provide various perspectives of the polygenic architecture based on important variant categories such as derived allele frequency (DAF) and expression quantitative trait loci (eQTL)
Summary
Genome-wide association studies (GWAS) have identified numerous susceptibility variants for complex diseases (Welter et al, 2014). Quantitative evaluation of the polygenic architecture, in particular, the estimation of the proportion of disease-associated SNPs and their effect-size distribution, is essential to further determine the source of observed heritability (Wray et al, 2007; Park et al, 2010, 2011; Stahl et al, 2012; Agarwala et al, 2013; Chatterjee et al, 2013; Ripke et al, 2013). This method, is to evaluate effect sizes only for those SNPs with relatively large effects, not all the diseaseassociated SNPs, requiring adjustment for the winner’s curse (selection bias in using top significant SNPs) in the effect-size estimation
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