MAF_filter: Remove low minor allele frequency positions from dataset

In AbbVie-ComputationalGenomics/POEMColoc: Implement POEM colocalization method

Description

Remove low minor allele frequency positions from dataset

Usage

MAF_filter(dataset, min_MAF)

Arguments

dataset	POEM_output format dataset Specifically this is a coloc format dataset with the following changes top_pos giving the position of the top p-value pos vector giving positions chr giving chromosome snp is mandatory MAF is mandatory imputation class that is either all or top Unlike coloc, length of pvalues or beta L is permitted to be zero
min_MAF	minimum minor allele frequency to use

Value

POEM_output format dataset

Examples

# example with p-values
dataset_full <- list(pos = c(1, 2, 3, 4, 5), MAF = c(0.14, 0.15, 0.25, 0.2, 0.4), N=1000, type ="quant", pvalues = c(2 * 10^-8, 4 * 10^-8, 2 * 10^-4, 0.6, 0.03), chr= "Z", imputation_class = "all", top_pos = 1, snp = as.character(1:5))
MAF_filter(dataset = dataset_full, min_MAF = 0.1)
MAF_filter(dataset = dataset_full, min_MAF = 0.2)
# example that should warn
dataset_full$MAF[2] <- 0.77
MAF_filter(dataset = dataset_full, min_MAF = 0.1)
# example with empty that should be TRUE
dataset_empty <- list(pos = integer(0), MAF = numeric(0), N=1000, type ="quant", pvalues = numeric(0), chr= "Z", imputation_class = "all", top_pos = 1, snp = character(0))
MAF_filter(dataset = dataset_empty, min_MAF = 0.1)

AbbVie-ComputationalGenomics/POEMColoc documentation built on May 20, 2020, 12:32 a.m.