Bayesian LASSO for population stratification correction in rare haplotype association studies

Zilu Liu; Asuman Seda Turkmen; Shili Lin

doi:10.1515/sagmb-2022-0034

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

Corresponding author: Shili Lin, Department of Statistics, The Ohio State University, Columbus, OH 43210, USA, E-mail: shili@stat.osu.edu

Funding source: National Institutes of Health

Award Identifier / Grant number: R01GM114142

Funding source: National Center for Advancing Translational Sciences

Award Identifier / Grant number: Unassigned

Funding source: National Heart, Lung, and Blood Institute

Award Identifier / Grant number: Unassigned

Acknowledgment

The authors would like to acknowledge the Hoffman Family Center in Genetics and Epidemiology and the National Center for Advancing Translational Sciences (NCATS) for supporting the DHS study, and the National Heart, Lung, and Blood Institute (NHLBI) for the MESA data collection.

Research ethics: Not applicable.
Author contributions: The authors have accepted responsibility for theentire content of this manuscript and approved its submission.
Competing interests: The authors state no conflict of interest.
Research funding: Research on this project is supported in part by National Institutes of Health (NIH) R01GM114142.
Data availability: The raw data can be obtained on request from the corresponding author.

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Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/sagmb-2022-0034).

Received: 2022-07-19

Accepted: 2023-12-19

Published Online: 2024-01-19

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