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Population stratification correction using Bayesian shrinkage priors for genetic association studies.

Zilu Liu1, Asuman S Turkmen1, Shili Lin1

  • 1Department of Statistics, The Ohio State University, Columbus, Ohio, USA.

Annals of Human Genetics
|September 29, 2023
PubMed
Summary

Bayestrat, a new Bayesian LASSO method, effectively corrects for population stratification in genetic association studies. It controls type I error rates and increases power, especially with many principal components (PCs).

Keywords:
Bayesian shrinkage priorsconfoundinggenetic associationpopulation stratificationspurious association

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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Population stratification (PS) is a significant confounder in genetic association studies.
  • Existing methods like Principal Component Regression (PCR) and Linear Mixed Models (LMM) have limitations in fully addressing PS.
  • LMMs may dilute relevant principal components (PCs), while PCR may not capture sufficient genetic diversity.

Purpose of the Study:

  • Introduce Bayestrat, a novel method for detecting associated variants with PS correction.
  • Address shortcomings of existing methods by accommodating numerous PCs and using shrinkage priors.
  • Provide a robust tool for genetic association studies with complex population structures.

Main Methods:

  • Bayestrat employs a Bayesian LASSO framework for PS correction.
  • It incorporates a large number of PCs.
  • Utilizes shrinkage priors to minimize the impact of non-associated PCs.

Main Results:

  • Bayestrat demonstrates consistent control of type I error rates.
  • Achieves higher statistical power compared to non-shrinkage methods, particularly with many PCs.
  • Successfully applied to the Multi-Ethnic Study of Atherosclerosis (MESA), identifying variants associated with lipid traits.

Conclusions:

  • Bayestrat is well-suited for complex PS scenarios where confounders are not known a priori.
  • Its automatic and self-selection features enhance its applicability.
  • Offers improved performance in genetic association studies requiring robust PS correction.