Principal Component Analysis Reduces Collider Bias in Polygenic Score Effect Size Estimation.
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AuthorThomas, Nathaniel S
Kuo, Sally I-Chun
Dick, Danielle M
Edenberg, Howard J
Salvatore, Jessica E
Journal titleBehavior genetics
Publication Begin page268
Publication End page280
MetadataShow full item record
AbstractIn this study, we test principal component analysis (PCA) of measured confounders as a method to reduce collider bias in polygenic association models. We present results from simulations and application of the method in the Collaborative Study of the Genetics of Alcoholism (COGA) sample with a polygenic score for alcohol problems, DSM-5 alcohol use disorder as the target phenotype, and two collider variables: tobacco use and educational attainment. Simulation results suggest that assumptions regarding the correlation structure and availability of measured confounders are complementary, such that meeting one assumption relaxes the other. Application of the method in COGA shows that PC covariates reduce collider bias when tobacco use is used as the collider variable. Application of this method may improve PRS effect size estimation in some cases by reducing the effect of collider bias, making efficient use of data resources that are available in many studies.
CitationThomas NS, Barr P, Aliev F, Stephenson M, Kuo SI, Chan G, Dick DM, Edenberg HJ, Hesselbrock V, Kamarajan C, Kuperman S, Salvatore JE. Principal Component Analysis Reduces Collider Bias in Polygenic Score Effect Size Estimation. Behav Genet. 2022 Sep;52(4-5):268-280. doi: 10.1007/s10519-022-10104-z. Epub 2022 Jun 8. PMID: 35674916.
The following license files are associated with this item:
- Creative Commons
Except where otherwise noted, this item's license is described as © 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
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