Genetic correlation is a central parameter for understanding shared genetic architecture between complex traits. By using summary statistics from genome-wide association studies (GWAS), linkage disequilibrium score regression (LDSC) was developed for unbiased estimation of genetic correlations. Although easy to use, LDSC only partially utilizes LD information. By fully accounting for LD across the genome, we develop a high-definition likelihood (HDL) method to improve precision in genetic correlation estimation. Compared to LDSC, HDL reduces the variance of genetic correlation estimates by about 60%, equivalent to a 2.5-fold increase in sample size. We apply HDL and LDSC to estimate 435 genetic correlations among 30 behavioral and disease-related phenotypes measured in the UK Biobank (UKBB). In addition to 154 significant genetic correlations observed for both methods, HDL identified another 57 significant genetic correlations, compared to only another 2 significant genetic correlations identified by LDSC. HDL brings more power to genomic analyses and better reveals the underlying connections across human complex traits.

遗传相关性是理解复杂性状之间共享遗传结构的中心参数。通过使用全基因组关联研究（GWAS）的摘要统计数据，开发了连锁不平衡得分回归（LDSC）来进行遗传相关性的无偏估计。尽管易于使用，但LDSC仅部分利用LD信息。通过全面考虑整个基因组中的LD，我们开发了一种高清可能性（HDL）方法，以提高遗传相关估计的准确性。与LDSC相比，HDL将遗传相关估计的方差降低了约60％，相当于样本量增加了2.5倍。我们应用HDL和LDSC来估计在英国生物库（UKBB）中测量的30种行为和疾病相关表型中的435种遗传相关性。除了两种方法均观察到的154个显着遗传相关性之外，HDL还鉴定了另外57个显着遗传相关性，而LDSC仅鉴定了另外2个显着遗传相关性。HDL为基因组分析带来更大的力量，并更好地揭示了人类复杂性状之间的潜在联系。