ArticleProceedings of the National Academy of Sciences of the United States of America2024
Controlling for polygenic genetic confounding in epidemiologic association studies.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Polygenic risk scores in human genetics for study design discovery and translation.Human genetics · 2026Review
- Observational epidemiological studies can mitigate genetic confounding with a genetic relatedness matrix.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- The association of objectively and subjectively measured modifiable lifestyle factors with internalizing problems: the role of genetic confounding and shared method variance bias.Social psychiatry and psychiatric epidemiology · 2026Article
- Lessons in adjusting for genetic confounding in population research on education and health.SSM - population health · 2025Article
- Controlling for polygenic genetic confounding in epidemiologic association studies.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
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7 authors.
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Abstract
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.
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