Distance-based signal identification

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.

Value

The function lists QTLs and meta-information.

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) { # \dontrun{
  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)
} # }