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.

全基因组关联研究（Genome-wide association studies）已证实，多效性（pleiotropy）是一种普遍现象，有望通过利用该特性提升易感基因座（susceptibility loci）的检出效能。本研究提出了基于遗传力的效能优化方法（heritability informed power optimization, HIPO），用于借助汇总级关联统计量（summary-level association statistics）开展高效的多效性分析。该方法通过整合各性状的遗传力估计值、样本量差异及性状间的重叠情况，可求得跨性状关联系数的最优线性组合，以期最大化基础检验统计量的非中心参数（non-centrality parameter）。模拟研究结果表明，所提方法的I类错误率（type I error）控制准确，对人群分层具有鲁棒性，且可实现预期的全基因组关联信号富集。将该方法应用于三组遗传相关性状的公开数据：脂质性状（样本量N=188577）、精神疾病（病例组样本量Ncase=33332，对照组样本量Ncontrol=27888）及社会科学相关性状（单个性状样本量范围为161460至298420），与单性状分析结果相比，其全基因组显著基因座的数量分别提升了12%、200%及50%。在后续针对单性状开展的更大规模研究中，多数上述基因座均得到了重复验证的证据支持。HIPO有望通过降维（dimension reduction）的方式拓展至高维表型（high-dimensional phenotypes）分析，从而提升后续遗传关联检验的效能。