Improved heritability partitioning and enrichment analyses using summary statistics with graphREML

Abstract

Heritability enrichment analysis using data from Genome-Wide Association Studies (GWAS) is often used to understand the functional basis of genetic architecture. Stratified LD score regression (S-LDSC) is a widely used method-of-moments estimator for heritability enrichment, but S-LDSC has low statistical power compared with likelihood-based approaches. We introduce graphREML, a precise and powerful likelihood-based heritability partition and enrichment analysis method. graphREML operates on GWAS summary statistics and linkage disequilibrium graphical models (LDGMs), whose sparsity makes likelihood calculations tractable. We validate our method using extensive simulations and in analyses of a wide range of real traits. On average across traits, graphREML produces enrichment estimates that are concordant with S-LDSC, indicating that both methods are unbiased; however, graphREML identifies 2.5 times more significant trait-annotation enrichments, demonstrating greater power compared to the moment-based S-LDSC approach. graphREML can also more flexibly model the relationship between the annotations of a SNP and its heritability, producing well-calibrated estimates of per-SNP heritability.