Bayesian LASSO for population stratification correction in rare haplotype association studies

Liu Zilu, Turkmen Asuman Seda and Lin Shili ()
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Liu Zilu: Department of Statistics, The Ohio State University, Columbus, OH 43210, USA
Turkmen Asuman Seda: Department of Statistics, The Ohio State University, Columbus, OH 43210, USA
Lin Shili: Department of Statistics, The Ohio State University, Columbus, OH 43210, USA

Statistical Applications in Genetics and Molecular Biology, 2024, vol. 23, issue 1, 12

Abstract: Population stratification (PS) is one major source of confounding in both single nucleotide polymorphism (SNP) and haplotype association studies. To address PS, principal component regression (PCR) and linear mixed model (LMM) are the current standards for SNP associations, which are also commonly borrowed for haplotype studies. However, the underfitting and overfitting problems introduced by PCR and LMM, respectively, have yet to be addressed. Furthermore, there have been only a few theoretical approaches proposed to address PS specifically for haplotypes. In this paper, we propose a new method under the Bayesian LASSO framework, QBLstrat, to account for PS in identifying rare and common haplotypes associated with a continuous trait of interest. QBLstrat utilizes a large number of principal components (PCs) with appropriate priors to sufficiently correct for PS, while shrinking the estimates of unassociated haplotypes and PCs. We compare the performance of QBLstrat with the Bayesian counterparts of PCR and LMM and a current method, haplo.stats. Extensive simulation studies and real data analyses show that QBLstrat is superior in controlling false positives while maintaining competitive power for identifying true positives under PS.

Keywords: Bayesian LASSO; haplotype; genetic association study; population stratification; spurious association (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1515/sagmb-2022-0034

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