Bayesian model selection for the study of Hardy-Weinberg proportions and homogeneity of gender allele frequencies

Standard statistical tests for Hardy-Weinberg equilibrium assume the equality of allele frequencies in the sexes, whereas tests for the equality of allele frequencies in the sexes assume Hardy-Weinberg equilibrium. This produces a circularity in the testing of genetic variants, which has recently be...

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Bibliographic Details
Published inHeredity Vol. 123; no. 5; pp. 549 - 564
Main Authors Puig, Xavier, Ginebra, Josep, Graffelman, Jan
Format Journal Article Publication
LanguageEnglish
Published England Springer Nature B.V 01.11.2019
Springer International Publishing
Subjects
Akaike's information criterion
Alleles
Animals
Bayesian analysis
Computer simulation
Dirichlet prior
Dirichlet problem
Estadística matemàtica
Female
Gene Frequency
Genetic diversity
Genetic variance
Genomes
Genotype
Genotypes
Hardy-Weinberg equilibrium
Humans
Inbreeding coefficient
Male
Marginal likelihood
Matemàtiques i estadística
Mathematical models
Mathematical statistics
Models, Genetic
Multinomial model
Population genetics
Statistical analysis
Statistical tests
Àrees temàtiques de la UPC
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Summary:Standard statistical tests for Hardy-Weinberg equilibrium assume the equality of allele frequencies in the sexes, whereas tests for the equality of allele frequencies in the sexes assume Hardy-Weinberg equilibrium. This produces a circularity in the testing of genetic variants, which has recently been resolved with new frequentist likelihood and exact procedures. In this paper, we tackle the same problem by posing it as a Bayesian model comparison problem. We formulate an exhaustive set of ten alternative scenarios for biallelic genetic variants. Using Dirichlet and Beta priors for genotype and allele frequencies, we derive marginal likelihoods for all scenarios, and select the most likely scenario using the posterior probabilities that each of these scenarios is the one in place. Different from the usual frequentist testing approach, the Bayesian approach allows one to compare any number of models, and not just two at a time, and the models compared do not have to be nested. We illustrate our Bayesian approach with genetic data from the 1,000 genomes project and through a simulation study.
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ISSN:0018-067X
1365-2540
DOI:10.1038/s41437-019-0232-0