Controlling for polygenic genetic confounding in epidemiologic association studies

• 成果类型: Article
• 署名作者: Zhao, Zijie; Yang, Xiaoyu; Dorn, Stephen; Miao, Jiacheng; Barcellos, Silvia H.; Fletcher, Jason M.; Lu, Qiongshi
• 署名单位: University of Wisconsin System; University of Wisconsin Madison; University of Southern California; University of Southern California; University of Wisconsin System; University of Wisconsin Madison; University of Wisconsin System; University of Wisconsin Madison
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-12539
• DOI: 10.1073/pnas.2408715121
• 发表日期: 2024-10-29
• 关键词: genome-wide association; educational-attainment; parental education; unified framework; score regression; bipolar disorder; HEALTH; RISK; diseases; heritability

摘要

Epidemiologic associations estimated from observational data are often confounded by genetics due to pervasive pleiotropy among complex traits. Many studies either neglect genetic confounding altogether or rely on adjusting for polygenic scores (PGS) in regression analysis. In this study, we unveil that the commonly employed PGS approach is inadequate for removing genetic confounding due to measurement error and model misspecification. To tackle this challenge, we introduce PENGUIN, a principled framework for polygenic genetic confounding control based on variance component estimation. In addition, we present extensions of this approach that can estimate genetically unconfounded associations using GWAS summary statistics alone as input and between multiple generations of study samples. Through simulations, we demonstrate superior statistical properties of PENGUIN compared to the existing approaches. Applying our method to multiple population cohorts, we reveal and remove substantial genetic confounding in the associations of educational attainment with various complex traits and between parental and offspring education. Our results show that PENGUIN is an effective solution for genetic confounding control in observational data analysis with broad applications in future epidemiologic association studies.