bevimed_polytomous {BeviMed} | R Documentation |
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)), ... )
y |
Logical vector of case ( |
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 |
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 |
... |
Other arguments to pass to |
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.