HWPosterior: Calculation of posterior probabilities and Bayes factors for...

In HardyWeinberg: Statistical Tests and Graphics for Hardy-Weinberg Equilibrium

Calculation of posterior probabilities and Bayes factors for Hardy-Weinberg tests at X-chromosomal variants.

Description

Function HWPosterior calculates posterior probabilities and Bayes factors for tests for Hardy-Weinberg equilibrium of autosomal and X-chromosomal variants.

Usage

HWPosterior(males, females, verbose = TRUE, prior.af = c(0.5,0.5), prior.gf =
            c(0.333,0.333,0.333), x.linked = FALSE, precision = 0.05) 

Arguments

males	A vector of male genotype counts. For autosomal variants, this should be a three-element named vector like (AA,AB,BB); for X-chromosomal variants it should be a two-element vector giving the counts of the hemizygous genotypes like (A,B).
females	A vector of female genotype counts. This should be a three-element named vector like (AA,AB,BB)
verbose	prints results if verbose = TRUE
prior.af	Beta prior parameters for male and female allele frequencies
prior.gf	Dirichlet prior parameters for female genotype frequencies
x.linked	logical indicating whether the variant is autosomal or X-chromosomal
precision	precision parameter for marginal likelihoods that require numeric integration

Details

For X-chromosomal variants, four possible models are considered, and the posterior probabilities and Bayes factors for each model are calculated.

For autosomal variants, ten possible scenarios are considered, and the posterior probabilities for all models are calculated.

In general, default Dirichlet priors are used for genotype frequencies, and beta prior are used for allele frequencies.

Value

For X-chromosomal variants, a matrix with posterior probabilities and Bayes factors will be produced. For autosomal variants, a vector of posterior probabilities is produced.

Author(s)

Xavi Puig [e-mail] and Jan Graffelman [e-mail]

References

Puig, X., Ginebra, J. and Graffelman, J. (2017) A Bayesian test for Hardy-Weinberg equilibrium of bi-allelic X-chromosomal markers. Heredity 119(4):226–236. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1038/hdy.2017.30")}.

Puig, X., Ginebra, J. and Graffelman, J. (2019) Bayesian model selection for the study of Hardy-Weinberg proportions and homogeneity of gender allele frequencies. Heredity 123(5), pp. 549-564. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1038/s41437-019-0232-0")}

See Also

HWChisq, HWExact, HWExactStats

Examples

#
# An X-chromosomal example
#
males   <- c(A=43,B=13)
females <- c(AA=26,AB=19,BB=3)
out     <- HWPosterior(males,females,verbose=TRUE,x.linked=TRUE)
#
# An autosomal example
#
data(JPTsnps)
males     <- JPTsnps[1,1:3]
females   <- JPTsnps[1,4:6]
post.prob <- HWPosterior(males,females,x.linked=FALSE)

HardyWeinberg documentation built on May 29, 2024, 6:17 a.m.