Related Experiment Video
Updated: Sep 20, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
Published on: June 26, 2013
Principal Component Analysis Reduces Collider Bias in Polygenic Score Effect Size Estimation
Nathaniel S Thomas1, Peter Barr2, Fazil Aliev3
1Department of Psychology, Virginia Commonwealth University, Box 842018, 23284-2018, Richmond, VA, United States. thomasns@vcu.edu.
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.
More Related Videos
Related Concept Videos
Polygenic Traits
Genome-wide Association Studies-GWAS
GWAS does not require the identification of the target gene involved in...
Bias in Epidemiological Studies
Pleiotropy
Behavioral Genetics and Its Designs
The primary methodologies used in behavior genetics include family studies, twin studies, and adoption studies, each providing unique...
Mechanistic Models: Compartment Models in Individual and Population Analysis

