On Genetic Correlation Estimation With Summary Statistics From Genome-Wide Association Studies

Cross-trait polygenic risk score (PRS) method has gained popularity for assessing genetic correlation of complex traits using summary statistics from biobank-scale genome-wide association studies (GWAS). However, empirical evidence has shown a common bias phenomenon that highly significant cross-trait PRS can only account fora very small amount of genetic variance (R-2 can be < 1%) in independent testing GWAS. The aim of this paper is to investigate and address the bias phenomenon of cross-trait PRS in numerous GWAS applications. We show that the estimated genetic correlation can be asymptotically biased toward zero. A consistent cross-trait PRS estimator is then proposed to correct such asymptotic bias. 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. We analyze GWAS summary statistics of reaction time and brain structural magnetic resonance imaging-based features measured in the Pediatric Imaging, Neurocognition, and Genetics study. We find that the raw cross-trait PRS estimators heavily underestimate the genetic similarity between cognitive function and human brain structures (mean R-2 = 1.32%), whereas the bias-corrected estimators uncover the moderate degree of genetic overlap between these closely related heritable traits (mean R-2 = 22.42%). Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Automated summary

The study investigates and addresses the bias phenomenon of cross-trait PRS in numerous GWAS applications and proposes a consistent estimator to correct the bias. Results show that the bias-corrected estimators uncover a moderate degree of genetic overlap between cognitive function and human brain structures.