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Bayesian model comparison in genetic association analysis: linear mixed modeling and SNP set testing.

Xiaoquan Wen1

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This study introduces a flexible Bayesian linear regression model for genetic association studies. It offers advantages in hypothesis testing and model comparison for single nucleotide polymorphism (SNP) set analysis.

Keywords:
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Area of Science:

  • Genetics
  • Statistical Genetics
  • Bayesian Statistics

Background:

  • Bayesian linear regression models are crucial for genetic association studies.
  • Linear mixed effect models and parametric models are used for Single Nucleotide Polymorphism (SNP) set analysis.
  • Hypothesis testing and model comparison are key challenges in genetic data analysis.

Purpose of the Study:

  • To develop a flexible Bayesian linear regression model for genetic association studies.
  • To derive analytic approximate Bayes factors for hypothesis testing and model comparison.
  • To demonstrate the advantages of Bayesian approaches in genetic association analyses.

Main Methods:

  • Formulation of a Bayesian linear regression model linked to linear mixed effect and parametric SNP set models.
  • Derivation of analytic approximate Bayes factors.
  • Application of Bayesian model averaging and hierarchical modeling.
  • Validation using real and simulated genetic data.

Main Results:

  • Established connections between derived Bayes factors and frequentist statistics (e.g., Wald, variance component score).
  • Demonstrated distinct advantages and flexibilities of the proposed Bayesian methods.
  • Successfully applied methods to single SNP association testing, multi-locus fine-mapping, and SNP set association testing.

Conclusions:

  • The proposed Bayesian framework provides a flexible and advantageous approach for genetic association studies.
  • Analytic approximate Bayes factors offer a powerful tool for hypothesis testing and model comparison in genetics.
  • Bayesian model averaging and hierarchical modeling enhance the utility of these methods for complex genetic analyses.