Population stratification correction using Bayesian shrinkage priors for genetic association studies.
Ann Hum Genet
; 87(6): 302-315, 2023 11.
Article
in En
| MEDLINE
| ID: mdl-37771252
ABSTRACT
INTRODUCTION:
Population stratification (PS) is a major source of confounding in population-based genetic association studies of quantitative traits. Principal component regression (PCR) and linear mixed model (LMM) are two commonly used approaches to account for PS in association studies. Previous studies have shown that LMM can be interpreted as including all principal components (PCs) as random-effect covariates. However, including all PCs in LMM may dilute the influence of relevant PCs in some scenarios, while including only a few preselected PCs in PCR may fail to fully capture the genetic diversity. MATERIALS ANDMETHODS:
To address these shortcomings, we introduce Bayestrat-a method to detect associated variants with PS correction under the Bayesian LASSO framework. To adjust for PS, Bayestrat accommodates a large number of PCs and utilizes appropriate shrinkage priors to shrink the effects of nonassociated PCs.RESULTS:
Simulation results show that Bayestrat consistently controls type I error rates and achieves higher power compared to its non-shrinkage counterparts, especially when the number of PCs included in the model is large. As a demonstration of the utility of Bayestrat, we apply it to the Multi-Ethnic Study of Atherosclerosis (MESA). Variants and genes associated with serum triglyceride or HDL cholesterol are identified in our analyses.DISCUSSION:
The automatic and self-selection features of Bayestrat make it particularly suited in situations with complex underlying PS scenarios, where it is unknown a priori which PCs are potential confounders, yet the number that needs to be considered could be large in order to fully account for PS.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Genome-Wide Association Study
/
Models, Genetic
Type of study:
Prognostic_studies
/
Risk_factors_studies
Limits:
Humans
Language:
En
Journal:
Ann Hum Genet
Year:
2023
Document type:
Article
Affiliation country:
United States