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用于基因关联研究的贝叶斯统计方法。

Bayesian statistical methods for genetic association studies.

作者信息

Stephens Matthew, Balding David J

机构信息

Departments of Statistics and Human Genetics, University of Chicago, Chicago, IL 60637, USA.

出版信息

Nat Rev Genet. 2009 Oct;10(10):681-90. doi: 10.1038/nrg2615.

Abstract

Bayesian statistical methods have recently made great inroads into many areas of science, and this advance is now extending to the assessment of association between genetic variants and disease or other phenotypes. We review these methods, focusing on single-SNP tests in genome-wide association studies. We discuss the advantages of the Bayesian approach over classical (frequentist) approaches in this setting and provide a tutorial on basic analysis steps, including practical guidelines for appropriate prior specification. We demonstrate the use of Bayesian methods for fine mapping in candidate regions, discuss meta-analyses and provide guidance for refereeing manuscripts that contain Bayesian analyses.

摘要

贝叶斯统计方法最近在许多科学领域取得了重大进展,并且这一进展现在正扩展到对基因变异与疾病或其他表型之间关联的评估。我们回顾这些方法,重点关注全基因组关联研究中的单核苷酸多态性(SNP)测试。我们讨论了在这种情况下贝叶斯方法相对于经典(频率论)方法的优势,并提供了基本分析步骤的教程,包括适当先验设定的实用指南。我们展示了贝叶斯方法在候选区域精细定位中的应用,讨论了荟萃分析,并为评审包含贝叶斯分析的手稿提供指导。

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