burdenTest: Compute weighted burden test
In bhattacharya-a-bt/isoTWAS: Isoform-level transcriptome-wide association studies

burdenTest

R Documentation

Compute weighted burden test

Description

The function takes in a gene expression model in MOSTWAS form and GWAS summary statistics and carries out the weighted burden Z-test for a trait

Usage

burdenTest(
  mod,
  ld,
  gene,
  sumStats,
  chr,
  pos,
  a1,
  a2,
  a1_mod = "ALT",
  a2_mod = "REF",
  snpName = "SNP",
  Z = NULL,
  beta = NULL,
  se = NULL,
  featureName = "Feature",
  R2cutoff = 0.01,
  alpha = 2.5e-06,
  nperms = 1000,
  usePos = F
)

Arguments

`mod`	data.frame, model for a given isoform
`ld`	matrix, ld reference matrix
`gene`	character, gene name
`sumStats`	data frame, GWAS summary statistics
`chr`	character, colnames in sumStats that keeps the chromosome
`pos`	character, colnames in sumStats that keeps the position
`a1`	character, colnames in sumStats that keeps the ALT allele
`a2`	character, colnames in sumStats that keeps the REF allele
`a1_mod`	character, colnames in model that keeps the ALT allele
`a2_mod`	character, colnames in model that keeps the REF allele
`snpName`	character, colnames in sumStats that keeps the SNP id
`Z`	character, colnames in sumStats that keeps the Z score
`beta`	character, colnames in sumStats that keeps the effect size
`se`	character, colnames in sumStats that keeps the standard error
`featureName`	character, colname in model that keeps the feature name
`R2cutoff`	numeric, predictive R2 cutoff
`alpha`	numeric, P-value threshold for permutation testing
`nperms`	numeric, number of permutations
`usePos`	logical, use SNP positions vs. SNP ids