Impute Genotypic Data
Impute missing genotypic data in advance intercross lines (AIL).
genoImpute(gdat, gmap, step, prd = NULL, gr = 2, pos = NULL,
method = c("Haldane", "Kosambi"), na.str = "NA", msg = FALSE)gdat |
Genotype data. Should be a matrix or a data frame, with each row representing an observation and each column a marker locus. The column names should be marker names. Genotypes can be 1, 2 and 3, or "AA", "AB" and "BB". Optional if an object |
gmap |
A genetic map. Should be data frame (snp, chr, dist,...), where "snp" is the SNP (marker) name, "chr" is the chromosome where the "snp" is, and "dist" is the genetic distance in centi-Morgan (cM) from the left of the chromosome. |
step |
Optional. If specified, it is the maximum distance (in cM) between two adjacent loci for which the probabilities are calculated. The distance corresponds to the "cumulative" recombination rate at |
prd |
An object from |
gr |
The generation under consideration. |
pos |
Data frame (chr, dist, snp, ...). If given, |
method |
Whether "Haldane" or "Kosambi" mapping function should be used. |
na.str |
String for missing values. |
msg |
A logical variable. If TRUE, certain information will be printed out during calculation. |
The missing genotypic value is randomly assigned with a probability conditional on the genotypes of the flanking SNPs (makers).
An object, prd, from genoProb alone can be used for the purpose of imputation. Then, the output (especially the putative loci) will be determined by prd. Optionally, it can be used together with gdat so that missing values in gdat will be imputed if possible, depending on whether loci in the columns of gdat can be identified in the third dimension of prd; this won't change the original genotypic data. See examples.
A matrix with the number of rows being the same as gdat and with the number of columns depending on the SNP set in both gdat and gmap and the step length.
Currently only suitable for advanced intercross lines.
data(miscEx) # briefly look at genotype data sum(is.na(gdatF8)) gdatF8[1:5,1:5] ## Not run: # run 'genoProb' gdtmp<- gdatF8 gdtmp<- replace(gdtmp,is.na(gdtmp),0) prDat<- genoProb(gdat=gdtmp, gmap=gmapF8, gr=8, method="Haldane", msg=TRUE) # imputation based on 'genoProb' object tmp<- genoImpute(prd=prDat) sum(is.na(tmp)) tmp[1:5,1:5] # imputation based on both genotype data and 'genoProb' object tmp<- genoImpute(gdatF8, prd=prDat) sum(is.na(tmp)) tmp[1:5,1:5] # imputation based on genotype data tmp<- genoImpute(gdatF8, gmap=gmapF8, gr=8, na.str=NA) sum(is.na(tmp)) tmp[1:5, 1:5] # set "msg=TRUE" for more information tmp<- genoImpute(gdatF8, gmap=gmapF8, gr=8, na.str=NA, msg=TRUE) sum(is.na(tmp)) tmp[1:5, 1:5] ## End(Not run)
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