Bayesian statistical methods for genetic association studies

Key Points p -values are commonly used as summaries of evidence for association between a genetic variant and phenotype, but they have an important limitation in that they are unable to quantify how confident one should be that a given SNP is truly associated with a phenotype. Bayesian methods provi...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Nature reviews. Genetics Jg. 10; H. 10; S. 681 - 690
Hauptverfasser: Stephens, Matthew, Balding, David J.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 01.10.2009
Nature Publishing Group
Schlagworte:
Agriculture
Animal Genetics and Genomics
Bayes Theorem
Bayesian analysis
Biological and medical sciences
Biomedical and Life Sciences
Biomedicine
Cancer Research
Classical genetics, quantitative genetics, hybrids
Fundamental and applied biological sciences. Psychology
Gene Function
Genetics
Genetics of eukaryotes. Biological and molecular evolution
Genome-Wide Association Study
Genomes
Genotype & phenotype
Human Genetics
Humans
Hypotheses
Methods, theories and miscellaneous
Polymorphism, Single Nucleotide - genetics
Population genetics
Probability
review-article
Single nucleotide polymorphisms
Software
Statistical methods
United States
United Kingdom
United Kingdom > UK
Statistical method
Review
Single nucleotide polymorphism
ISSN:1471-0056, 1471-0064, 1471-0064
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Key Points p -values are commonly used as summaries of evidence for association between a genetic variant and phenotype, but they have an important limitation in that they are unable to quantify how confident one should be that a given SNP is truly associated with a phenotype. Bayesian methods provide an alternative approach to assessing associations. We show that Bayesian analyses are not too difficult and can be rewarding — for example, unlike p -values, a Bayesian probability of association is comparable across SNPs and across studies. For a Bayesian analysis of single-SNP association in a case–control study, we discuss genetic models that can form an alternative to the null hypothesis of no association, in addition to effect-size distributions for the parameters of these models. An alternative Bayesian analysis derives a posterior distribution for effect size, without reference to a null hypothesis. We give an example of a multi-SNP Bayesian analysis for fine-scale mapping and discuss Bayesian approaches to multiple testing and meta-analysis. Broad guidelines are suggested for editors and reviewers of Bayesian analyses. Bayesian analyses are increasingly being used in genetics, particularly in the context of genome-wide association studies. This article provides a guide to using Bayesian analyses for assessing single-SNP associations and highlights the advantages of these methods compared with standard frequentist analyses. 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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ObjectType-Article-2
ObjectType-Feature-1
content type line 23
ObjectType-Review-3
ISSN:1471-0056
1471-0064
1471-0064
DOI:10.1038/nrg2615