qtlFinder: Distance-based signal identification

In gap: Genetic Analysis Package

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"
)

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.

Value

The function lists QTLs and meta-information.

Examples

## Not run: 
  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)

## End(Not run)

gap documentation built on Sept. 11, 2024, 5:36 p.m.