CheckGeno: Check Genotype Matrix

Description

Check that the provided genotype matrix is in the correct format, and check for low call rate samples and SNPs.

Usage

CheckGeno(
  GenoM,
  quiet = FALSE,
  Plot = FALSE,
  Return = "GenoM",
  DumPrefix = c("F0", "M0")
)

Arguments

GenoM

the genotype matrix.

quiet

suppress messages.

Plot

display the plots of SnpStats.

Return

either 'GenoM' to return the cleaned-up genotype matrix, or 'excl' to return a list with excluded SNPs and individuals (see Value).

DumPrefix

length 2 vector, to check if these don't occur among genotyped individuals.

Value

If Return='excl' a list with, if any are found:

ExcludedSNPs

SNPs scored for <10 excluded when running sequoia

ExcludedSnps-mono

monomorphic (fixed) SNPs; automatically excluded when running sequoia. This includes nearly-fixed SNPs with MAF \(= 1/2N\). Column numbers are *after* removal of ExcludedSNPs, if any.

ExcludedIndiv

Individuals scored for <5 reliably included during pedigree reconstruction. Individual call rate is calculated after removal of 'Excluded SNPs'

Snps-LowCallRate

SNPs scored for 10 recommended to be filtered out

Indiv-LowCallRate

individuals scored for <50 recommended to be filtered out

When Return='excl' the return is invisible, i.e. a check is run and warnings or errors are always displayed, but nothing may be returned.

Thresholds

Appropriate call rate thresholds for SNPs and individuals depend on the total number of SNPs, distribution of call rates, genotyping errors, and the proportion of candidate parents that are SNPd (sibship clustering is more prone to false positives). Note that filtering first on SNP call rate tends to keep more individuals in.

Examples

Run this code

# NOT RUN {
data(Ped_HSg5)
GenoM <- SimGeno(Ped_HSg5, nSnp=400, CallRate = runif(400, 0.2, 0.8))
# quick alternative:
GenoM.checked <- CheckGeno(GenoM)

# user supervised alternative:
Excl <- CheckGeno(GenoM, Return = "excl")
GenoM.orig <- GenoM   # make a 'backup' copy
if ("ExcludedSnps" %in% names(Excl))
  GenoM <- GenoM[, -Excl[["ExcludedSnps"]]]
if ("ExcludedInd" %in% names(Excl))
  GenoM <- GenoM[!rownames(GenoM) %in% Excl[["ExcludedInd"]], ]
if ("ExcludedIndiv" %in% names(Excl))
  GenoM <- GenoM[!rownames(GenoM) %in% Excl[["ExcludedIndiv"]], ]

# warning about  SNPs scored for <50% of individuals ?
SnpCallRate <- apply(GenoM, MARGIN=2,
                     FUN = function(x) sum(x!=-9)) / nrow(GenoM)
hist(SnpCallRate, breaks=50, col="grey")
GenoM <- GenoM[, SnpCallRate > 0.6]

# to be on the safe side, filter out low call rate individuals
IndivCallRate <- apply(GenoM, MARGIN=1,
                       FUN = function(x) sum(x!=-9)) / ncol(GenoM)
hist(IndivCallRate, breaks=50, col="grey")
GoodSamples <- rownames(GenoM)[ IndivCallRate > 0.8]
# }
# NOT RUN {
# }