gap (version 1.6)

qtlFinder: Distance-based signal identification

Description

Distance-based signal identification

Usage

qtlFinder(
  d,
  Chromosome = "Chromosome",
  Position = "Position",
  MarkerName = "MarkerName",
  Allele1 = "Allele1",
  Allele2 = "Allele2",
  EAF = "Freq1",
  Effect = "Effect",
  StdErr = "StdErr",
  log10P = "log10P",
  N = "N",
  radius = 1e+06,
  collapse.hla = TRUE,
  build = "hg19"
)

Value

The function lists QTLs and meta-information.

Arguments

d

input data.

Chromosome

chromosome.

Position

position.

MarkerName

RSid or SNPid.

Allele1

effect allele.

Allele2

other allele.

EAF

effect allele frequency.

Effect

b.

StdErr

SE.

log10P

-log(P).

N

sample size.

radius

a flanking distance.

collapse.hla

a flag to collapse signals in the HLA region.

build

genome build to define the HLA region.

Details

This function implements an iterative merging algorithm to identify signals. The setup follows output from METAL. When collapse.hla=TRUE, a single most significant signal in the HLA region is chosen. The Immunogenomics paper gives hg19/GRCh37: chr6:28477797-33448354 (6p22.1-21.3), hg38/GRCh38: chr6:28510020-33480577.

Examples

if (FALSE) {
  f <- "ZPI_dr.p.gz"
  varlist=c("Chromosome","Position","MarkerName","Allele1","Allele2",
            "Freq1","FreqSE","MinFreq","MaxFreq",
            "Effect","StdErr","log10P","Direction",
            "HetISq","HetChiSq","HetDf","logHetP","N")
  d <- read.table(f,col.names=varlist,check.names=FALSE)
  qtlFinder(d)
}