bevimed_polytomous {BeviMed}

Model selection for multiple association models

Description

Apply bevimed to the no association model (gamma = 0) and multiple association models for different sets of variants, for instance, corresponding to different functional consequences.

Usage

bevimed_polytomous(
  y,
  G,
  ploidy = rep(2L, length(y)),
  variant_sets,
  prior_prob_association = rep(0.01/length(variant_sets), length(variant_sets)),
  tau0_shape = c(1, 1),
  moi = rep("dominant", length(variant_sets)),
  model_specific_args = vector(mode = "list", length = length(variant_sets)),
  ...
)

Arguments

y

Logical vector of case (TRUE) control (FALSE) status.

G

Integer matrix of variant counts per individual, one row per individual and one column per variant.

ploidy

Integer vector giving ploidy of samples.

variant_sets

List of integer vectors corresponding to sets of indices of G, each of which is to be considered in a model explaining the phenotype, y.

prior_prob_association

The prior probability of association.

tau0_shape

Beta shape hyper-priors for prior on rate of case labels.

moi

Character vector giving mode of inheritance for each model.

model_specific_args

List of named lists of parameters to use in bevimed_m applications for specific models.

...

Other arguments to pass to bevimed_m.

References

Greene et al., A Fast Association Test for Identifying Pathogenic Variants Involved in Rare Diseases, The American Journal of Human Genetics (2017), http://dx.doi.org/10.1016/j.ajhg.2017.05.015.

See Also

bevimed_m, bevimed