Journal article
Principal Component Analysis Reduces Collider Bias in Polygenic Score Effect Size Estimation
Behavior genetics, Vol.52(4-5), pp.268-280
06/08/2022
DOI: 10.1007/s10519-022-10104-z
PMCID: PMC10103419
PMID: 35674916
Abstract
In 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.
Details
- Title: Subtitle
- Principal Component Analysis Reduces Collider Bias in Polygenic Score Effect Size Estimation
- Creators
- Nathaniel S Thomas - Virginia Commonwealth UniversityPeter Barr - SUNY Downstate Health Sciences UniversityFazil Aliev - Virginia Commonwealth UniversityMallory Stephenson - Virginia Institute for Psychiatric and Behavioral Genetics, Richmond, Virginia, United States.Sally I-Chun Kuo - Rutgers, The State University of New JerseyGrace Chan - University of ConnecticutDanielle M Dick - Virginia Commonwealth UniversityHoward J Edenberg - Indiana UniversityVictor Hesselbrock - University of ConnecticutChella Kamarajan - SUNY Downstate Health Sciences UniversitySamuel Kuperman - University of IowaJessica E Salvatore - Rutgers, The State University of New Jersey
- Resource Type
- Journal article
- Publication Details
- Behavior genetics, Vol.52(4-5), pp.268-280
- DOI
- 10.1007/s10519-022-10104-z
- PMID
- 35674916
- PMCID
- PMC10103419
- NLM abbreviation
- Behav Genet
- ISSN
- 0001-8244
- eISSN
- 1573-3297
- Grant note
- U10AA008401 / NIAAA NIH HHS R01AA028064 / NIAAA NIH HHS K01AA024152 / NIAAA NIH HHS
- Language
- English
- Date published
- 06/08/2022
- Academic Unit
- Psychiatry
- Record Identifier
- 9984293757802771
Metrics
16 Record Views