Localizing Components of Shared Transethnic Genetic Architecture of Complex Traits from GWAS Summary Data

Abstract

Despite strong transethnic genetic correlations reported in the literature for many complex traits, the non-transferability of polygenic risk scores across populations suggests the presence of population-specific components of genetic architecture. We propose an approach that models GWAS summary data for one trait in two populations to estimate genome-wide proportions of population-specific/shared causal SNPs. In simulations across various genetic architectures, we show that our approach yields approximately unbiased estimates with in-sample LD and slight upward-bias with out-of-sample LD. We analyze nine complex traits in individuals of East Asian and European ancestry, restricting to common SNPs (MAF > 5%), and find that most common causal SNPs are shared by both populations. Using the genome-wide estimates as priors in an empirical Bayes framework, we perform fine-mapping and observe that high-posterior SNPs (for both the population-specific and shared causal configurations) have highly correlated effects in East Asians and Europeans. In population-specific GWAS risk regions, we observe a 2.8× enrichment of shared high-posterior SNPs, suggesting that population-specific GWAS risk regions harbor shared causal SNPs that are undetected in the other GWASs due to differences in LD, allele frequencies, and/or sample size. Finally, we report enrichments of shared high-posterior SNPs in 53 tissue-specific functional categories and find evidence that SNP-heritability enrichments are driven largely by many low-effect common SNPs.

Keywords: GWAS; PRS; ancestry; complex traits; fine-mapping; linkage disequilibrium; polygenicity; transethnic.

Figure 1

Toy Examples to Illustrate How Population-Specific LD Patterns Affect GWAS Associations (A) SNPs 3 and 5 are causal in both East Asians and Europeans and have the same causal effect size of 0.1. However, due to different LD patterns in East Asians and Europeans, SNPs 2 and 4 are observed to be GWAS significant, respectively. (B) Different SNPs are causal in East Asians (SNPs 1 and 5) and Europeans (SNPs 2 and 4). However, due to population-specific LD, SNP 3 is observed to be GWAS significant in both populations. The stars in the rightmost plots represent the SNPs with true nonzero effects; the GWAS-significant SNP is highlighted in a darker color.

Figure 2

Genome-wide Estimates of the Numbers of Population-Specific/Shared Causal SNPs in Simulations The estimates are approximately unbiased when in-sample LD is used (top) and upward-biased when external reference LD is used (bottom). For both populations, we simulate such that the product of SNP-heritability and GWAS sample size is 500. Mean and standard errors were obtained from 25 independent simulations. Error bars represent ±1.96 of the standard error.

Figure 3

Distributions of the Numbers of Population-Specific/Shared Causal SNPs across 1,368 Regions that are Approximately Independent in Both EAS and EUR Each violin plot represents the distribution of the posterior expected number of population-specific (red/green) or shared (blue) causal SNPs per region; details on how the regions were defined can be found in the Material and Methods. For a single region, the posterior expected number of SNPs in a given causal configuration is estimated by summing, across all SNPs in the region, the per-SNP posterior probabilities of having that causal configuration. The dark lines mark the means of the distributions. The traits are sorted on the x-axis by the average number of shared high-posterior SNPs per region.

Figure 4

Distributions of the Numbers of Population-Specific/Shared Causal Variants at GWAS Risk Regions for Mean Corpuscular Hemoglobin (MCH) Each violin plot represents the distribution of the posterior expected number of population-specific (red/green) or shared (blue) causal SNPs at regions with significant associations (${\text{p}}_{\text{GWAS}}<5\times {10}^{-8}$) in EAS GWAS only, EUR GWAS only, both EAS and EUR, and neither GWAS. The dark lines mark the means of the distributions.

Figure 5

Marginal Regression Coefficients of High-Posterior SNPs for Nine Complex Traits Each plot corresponds to one of the three causal configurations of interest: EAS-specific (A), EUR-specific (B), and shared (C). Each point represents a SNP with posterior probability > 0.8 for a single trait. The x-axis and y-axis mark the estimated marginal effect sizes in EAS and EUR, respectively. The colors indicate whether the SNP is nominally significant (${\text{p}}_{\text{GWAS}}<5\times {10}^{-6}$) in both GWASs (purple), the EAS GWAS only (orange), the EUR GWAS only (green), or in neither GWAS (gray). The gray band marks the 95% confidence interval of the regression line.

Figure 6

Enrichments of Shared High-Posterior SNPs in 53 Tissue-Specific Functional Categories are Highly Correlated with SNP-Heritability Enrichments Each point is a trait-tissue pair; each tissue-specific functional category (SEG annotation) is a set of genes that are “specifically expressed” in one of 53 GTEx tissues. The x-axis is the estimated enrichment of shared high-posterior SNPs in the SEG annotation from PESCA. The y-axis is the meta-analyzed transethnic SNP-heritability explained by the SEG annotation, defined as the inverse-variance weighted average of the EAS and EUR SNP-heritability enrichments (estimated separately using stratified LD score regression). The points are colored by whether the trait has a statistically significant enrichment of shared high-posterior SNPs in the corresponding SEG annotation (FDR < 0.1). The gray band marks the 95% confidence interval of the regression line. Enrichment estimates and standard errors for each trait-tissue pair can be found in Figures S40–S44.