Bayesian LASSO for population stratification correction in rare haplotype association studies

Area of Science:

• Genetics
• Statistical genetics
• Bioinformatics

Background:

• Population stratification (PS) is a significant confounder in genetic association studies, impacting both single nucleotide polymorphism (SNP) and haplotype analyses.
• Current methods like principal component regression (PCR) and linear mixed models (LMM) for SNP association studies have limitations, including underfitting and overfitting, when applied to haplotype data.
• Few theoretical approaches specifically address PS in haplotype association studies.

Purpose of the Study:

• To introduce QBLstrat, a novel Bayesian LASSO-based method for accounting for population stratification in identifying rare and common haplotype associations with continuous traits.
• To address the underfitting and overfitting issues associated with existing PCR and LMM methods in the context of haplotype studies.

Main Methods:

• QBLstrat employs the Bayesian LASSO framework, incorporating a large number of principal components (PCs) with appropriate priors.
• The method effectively corrects for population stratification while simultaneously shrinking estimates for unassociated haplotypes and PCs.
• Performance is evaluated against Bayesian counterparts of PCR and LMM, as well as the haplo.stats method.

Main Results:

• Extensive simulation studies and real data analyses demonstrate QBLstrat's superior performance.
• QBLstrat effectively controls false positive rates in the presence of population stratification.
• The method maintains competitive statistical power for detecting true haplotype associations.

Conclusions:

• QBLstrat offers a robust solution for addressing population stratification in haplotype association studies.
• The proposed Bayesian LASSO approach provides a more accurate and reliable method for genetic analysis compared to existing standards.
• This method enhances the identification of genetic variants associated with complex traits by mitigating confounding effects.