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.
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)),
...
)Logical vector of case (TRUE) control (FALSE) status.
Integer matrix of variant counts per individual, one row per individual and one column per variant.
Integer vector giving ploidy of samples.
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.
The prior probability of association.
Beta shape hyper-priors for prior on rate of case labels.
Character vector giving mode of inheritance for each model.
List of named lists of parameters to use in bevimed_m applications for specific models.
Other arguments to pass to bevimed_m.
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.