Calculation of posterior probabilities and Bayes factors for Hardy-Weinberg tests at X-chromosomal variants.
Function HWPosterior
calculates posterior probabilities and Bayes
factors for tests for Hardy-Weinberg equilibrium of autosomal and X-chromosomal
variants.
HWPosterior(X, verbose = TRUE, prior.af = c(0.5,0.5), prior.gf = c(0.333,0.333,0.333), x.linked = FALSE, precision = 0.05)
X |
A vector of genotype counts. The order c(A,B,AA,AB,BB) is assumed. Differently ordered vectors can be supplied but then elements must be labeled by their genotype |
verbose |
prints results if |
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 |
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.
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.
Xavi Puig xavier.puig@upc.edu and Jan Graffelman jan.graffelman@upc.edu
Puig, X., Ginebra, J. and Graffelman, J. (2017) A Bayesian test for Hardy-Weinberg equilibrium of bi-allelic X-chromosomal markers. To appear in Heredity.
# # An X-chromosomal example # x <- c(A=43,B=13,AA=26,AB=19,BB=3) out <- HWPosterior(x,verbose=TRUE,x.linked=TRUE) # # An autosomal example # data(JPTsnps) post.prob <- HWPosterior(JPTsnps[1,],x.linked=FALSE)
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