On genetic correlation estimation with summary statistics from genome-wide association studies.

Genome-wide association studies (GWAS) have been widely used to examine the association between single nucleotide polymorphisms (SNPs) and complex traits, where both the sample size n and the number of SNPs p can be very large. Recently, cross-trait polygenic risk score (PRS) method has gained extremely popular for assessing genetic correlation of complex traits based on GWAS summary statistics (e.g., SNP effect size). However, empirical evidence has shown a common bias phenomenon that even highly significant cross-trait PRS can only account for a very small amount of genetic variance (R^2 often <1%). The aim of this paper is to develop a novel and powerful method to address the bias phenomenon of cross-trait PRS. We theoretically show that the estimated genetic correlation is asymptotically biased towards zero when complex traits are highly polygenic/omnigenic. When all p SNPs are used to construct PRS, we show that the asymptotic bias of PRS estimator is independent of the unknown number of causal SNPs m. We propose a consistent PRS estimator to correct such asymptotic bias. We also develop a novel estimator of genetic correlation which is solely based on two sets of GWAS summary statistics. In addition, we investigate whether or not SNP screening by GWAS p-values can lead to improved estimation and show the effect of overlapping samples among GWAS. Our results may help demystify and tackle the puzzling "missing genetic overlap" phenomenon of cross-trait PRS for dissecting the genetic similarity of closely related heritable traits. We illustrate the finite sample performance of our bias-corrected PRS estimator by using both numerical experiments and the UK Biobank data, in which we assess the genetic correlation between brain white matter tracts and neuropsychiatric disorders.