mirage: MIxture model based Rare variant Analysis on GEnes

This function implements rare variant test with MIRAGE model for variant set only without gene level information

mirage_vs(
  data,
  n1,
  n2,
  gamma = 3,
  sigma = 2,
  eta.init = 0.1,
  max.iter = 10000,
  tol = 1e-05,
  verbose = TRUE
)

Arguments

data

variant count data, a 4 column data frame for 1) locus ID 2) variant count in cases, 3) variant count in control and 4) variant category index for a variant. The 1st column is optional.

n1

sample size in cases.

n2

sample size in controls.

gamma

a list of category specific hyper prior shape parameter in Beta distribution for effect size, or a numeric value if all category share the same effect size.

sigma

a list of category specific hyper prior scale parameter in Beta distribution for effect size, or a numeric value if all category share the same effect size.

eta.init

initial value for prior on proportion of risk variants in a variant set.

max.iter

maximum number of iterations enforcing EM algorithm to stop

tol

threshold of parameter estimate difference to determine the convergence of EM algorithm

Value

full.info

Bayes factor of individual variant

eta.est

Estimate for proportion of risk variants in a variant group

eta.pvalue

Significant test for eta = 0

post.prob

posterior probability of variants

Examples

# see example at https://xinhe-lab.github.io/mirage/articles/mwe.html