Complex traits can share a substantial proportion of their polygenic heritability. However, genome-wide polygenic correlations between pairs of traits can mask heterogeneity in their shared polygenic effects across loci. We propose a novel method (weighted maximum likelihood-regional polygenic correlation [RPC]) to evaluate polygenic correlation between two complex traits in small genomic regions using summary association statistics. Our method tests for evidence that the polygenic effect at a given region affects two traits concurrently. We show through simulations that our method is well calibrated, powerful, and more robust to misspecification of linkage disequilibrium than other methods under a polygenic model. As small genomic regions are more likely to harbor specific genetic effects, our method is ideal to identify heterogeneity in shared polygenic correlation across regions. We illustrate the usefulness of our method by addressing two questions related to cardiometabolic traits. First, we explored how RPC can inform on the strong epidemiological association between high-density lipoprotein cholesterol and coronary artery disease (CAD), suggesting a key role for triglycerides metabolism. Second, we investigated the potential role of PPARγ activators in the prevention of CAD. Our results provide a compelling argument that shared heritability between complex traits is highly heterogeneous across loci.
Keywords: 1000 Genomes Project; complex traits; genetic correlation; linkage disequilibrium; maximum likelihood; polygenic inheritance.
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