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

• 成果类型: Article
• 署名作者: Zhao, Bingxin; Zhu, Hongtu
• 署名单位: University of North Carolina; University of North Carolina Chapel Hill
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2021.1906684
• 发表日期: 2022
• 页码: 1-11
• 关键词: polygenic risk scores; complex traits; regression; architecture; diseases

摘要

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.