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Bayesian statistical methods for genetic association studies.

Matthew Stephens¹, David J Balding

¹Departments of Statistics and Human Genetics, University of Chicago, Chicago, IL 60637, USA. mstephens@uchicago.edu

Nature Reviews. Genetics

|September 19, 2009

Summary

This summary is machine-generated.

Bayesian statistics offer advantages for genetic association studies, enhancing the analysis of genetic variants and disease phenotypes. This review covers Bayesian methods for genome-wide association studies, prior specification, and meta-analyses.

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Area of Science:

Genetics and Bioinformatics
Statistical Genetics
Computational Biology

Background:

Bayesian statistical methods are increasingly adopted across scientific disciplines.
Application of Bayesian approaches is expanding to genetic association studies for disease and phenotype analysis.
Classical (frequentist) methods have limitations in analyzing genetic variant associations.

Purpose of the Study:

To review Bayesian statistical methods for assessing associations between genetic variants and phenotypes.
To highlight the advantages of Bayesian over frequentist approaches in genome-wide association studies (GWAS).
To provide practical guidance on Bayesian analysis steps, including prior specification.

Main Methods:

Focus on single-nucleotide polymorphism (SNP) testing within GWAS.
Tutorial on basic Bayesian analysis steps for genetic association.
Demonstration of Bayesian methods for fine mapping in candidate regions and meta-analyses.

Main Results:

Bayesian methods offer distinct advantages over frequentist approaches for genetic association analysis.
Practical guidelines are provided for prior specification in Bayesian genetic analyses.
The utility of Bayesian methods is shown for fine mapping and meta-analysis of genetic data.

Conclusions:

Bayesian statistical methods provide a powerful framework for genetic association studies.
The review offers practical insights for researchers and reviewers dealing with Bayesian analyses in genetics.
Adoption of Bayesian methods can enhance the interpretation and robustness of genetic association findings.