Estimating Trans-Ancestry Genetic Correlation with Unbalanced Data Resources

• 成果类型: Article
• 署名作者: Zhao, Bingxin; Yang, Xiaochen; Zhu, Hongtu
• 署名单位: University of Pennsylvania; Purdue University System; Purdue University; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2024.2344703
• 发表日期: 2024
• 页码: 839-850
• 关键词: score regression; covariance; prediction; diseases; traits

摘要

The aim of this article is to propose a novel method for estimating trans-ancestry genetic correlations in genome-wide association studies (GWAS) using genetically predicted observations. These correlations describe how genetic architecture of complex traits varies among populations. Our new estimator corrects for biases arising from prediction errors in high-dimensional weak GWAS signals, while addressing the ethnic diversity inherent in GWAS data, such as linkage disequilibrium (LD) differences. A distinguishing feature of our approach is its flexibility regarding sample sizes: it necessitates a large GWAS sample only from one population, while the secondary population may have a much smaller cohort, even in the hundreds. This design directly addresses the existing imbalance in GWAS data resources, where datasets for European populations typically outnumber those of non-European ancestries. Through extensive simulations and real data analysis from the UK Biobank study encompassing 26 complex traits, we validate the reliability of our method. Our results illuminate the broader implications of transferring genetic findings across diverse populations. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.