Heritability informed power optimization (HIPO) leads to enhanced detection of genetic associations across multiple traits

Genome-wide association studies have shown that pleiotropy is a common phenomenon that can potentially be exploited for enhanced detection of susceptibility loci. We propose heritability informed power optimization (HIPO) for conducting powerful pleiotropic analysis using summary-level association statistics. We find optimal linear combinations of association coefficients across traits that are expected to maximize non-centrality parameter for the underlying test statistics, taking into account estimates of heritability, sample size variations and overlaps across the traits. Simulation studies show that the proposed method has correct type I error, robust to population stratification and leads to desired genome-wide enrichment of association signals. Application of the proposed method to publicly available data for three groups of genetically related traits, lipids (N = 188,577), psychiatric diseases (Ncase = 33,332, Ncontrol = 27,888) and social science traits (N ranging between 161,460 to 298,420 across individual traits) increased the number of genome-wide significant loci by 12%, 200% and 50%, respectively, compared to those found by analysis of individual traits. Evidence of replication is present for many of these loci in subsequent larger studies for individual traits. HIPO can potentially be extended to high-dimensional phenotypes as a way of dimension reduction to maximize power for subsequent genetic association testing.Author summary: Pleiotropy is a common phenomenon in genetics that one genetic variant has effects on multiple traits. The shared genetic information across correlated traits can potentially be exploited for enhanced detection of susceptibility loci. Most existing multi-trait methods borrow information across phenotypes but not across SNPs, which can be inefficient for traits that have major overlap. We propose a method that borrows information both across traits and across SNPs to conduct powerful association analysis using summary-level data. Simulations show that the method has correct type-I error rate and substantial increase in power. Application to blood lipids, psychiatric diseases and social science traits identified plenty of new loci that cannot be detected by individual trait analysis. Our method can potentially be extended to high-dimensional phenotypes as a dimension reduction technique.