Structure-informed clustering for population stratification in association studies.

ABSTRACT:

Background

Identifying variants associated with complex traits is a challenging task in genetic association studies due to linkage disequilibrium (LD) between genetic variants and population stratification, unrelated to the disease risk. Existing methods of population structure correction use principal component analysis or linear mixed models with a random effect when modeling associations between a trait of interest and genetic markers. However, due to stringent significance thresholds and latent interactions between the markers, these methods often fail to detect genuinely associated variants.

Results

To overcome this, we propose CluStrat, which corrects for complex arbitrarily structured populations while leveraging the linkage disequilibrium induced distances between ge

SUBMITTER: Bose A 

PROVIDER: S-EPMC10619291 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature