HIBAG-package: HLA Genotype Imputation with Attribute Bagging

In HIBAG: HIBAG -- HLA Genotype Imputation with Attribute Bagging

Description

To impute HLA types from unphased SNP data using an attribute bagging method.

Details

Package:	HIBAG
Type:	Package
Version:	1.2.5
License:	GPL version 3

HIBAG is a state of the art software package for imputing HLA types using SNP data, and it uses the R statistical programming language. HIBAG is highly accurate, computationally tractable, and can be used by researchers with published parameter estimates instead of requiring access to large training sample datasets. It combines the concepts of attribute bagging, an ensemble classifier method, with haplotype inference for SNPs and HLA types. Attribute bagging is a technique which improves the accuracy and stability of classifier ensembles using bootstrap aggregating and random variable selection.

Features:
1) HIBAG can be used by researchers with published parameter estimates (http://www.biostat.washington.edu/~bsweir/HIBAG/) instead of requiring access to large training sample datasets.
2) A typical HIBAG parameter file contains only haplotype frequencies at different SNP subsets rather than individual training genotypes.
3) SNPs within the xMHC region (chromosome 6) are used for imputation.
4) HIBAG employs unphased genotypes of unrelated individuals as a training set.
5) HIBAG supports parallel computing with R.

Author(s)

Xiuwen Zheng [aut, cre] [e-mail], Bruce S. Weir [ctb, ths] [e-mail]

References

Zheng X, Shen J, Cox C, Wakefield J, Ehm M, Nelson M, Weir BS; HIBAG – HLA Genotype Imputation with Attribute Bagging; (Abstract 294, Platform/Oral Talk); Present at the 62nd Annual Meeting of the American Society of Human Genetics, November 9, 2012 in San Francisco, California.

Zheng X, Shen J, Cox C, Wakefield J, Ehm M, Nelson M, Weir BS; HIBAG – HLA Genotype Imputation with Attribute Bagging. The Pharmacogenomics Journal. doi: 10.1038/tpj.2013.18. http://dx.doi.org/10.1038/tpj.2013.18

Examples

# load HLA types and SNP genotypes
data(HLA_Type_Table, package="HIBAG")
data(HapMap_CEU_Geno, package="HIBAG")

head(HLA_Type_Table)
dim(HLA_Type_Table)  # 60 13

summary(HapMap_CEU_Geno)


######################################################################

# make a "hlaAlleleClass" object
hla.id <- "A"
hla <- hlaAllele(HLA_Type_Table$sample.id,
	H1 = HLA_Type_Table[, paste(hla.id, ".1", sep="")],
	H2 = HLA_Type_Table[, paste(hla.id, ".2", sep="")],
	locus=hla.id, assembly="hg19")

# divide HLA types randomly
set.seed(100)
hlatab <- hlaSplitAllele(hla, train.prop=0.5)
names(hlatab)
# "training"   "validation"
summary(hlatab$training)
summary(hlatab$validation)

# SNP predictors within the flanking region on each side
region <- 500   # kb
snpid <- hlaFlankingSNP(HapMap_CEU_Geno$snp.id, HapMap_CEU_Geno$snp.position,
	hla.id, region*1000, assembly="hg19")
length(snpid)  # 275

# training and validation genotypes
train.geno <- hlaGenoSubset(HapMap_CEU_Geno,
	snp.sel=match(snpid, HapMap_CEU_Geno$snp.id),
	samp.sel=match(hlatab$training$value$sample.id, HapMap_CEU_Geno$sample.id))
test.geno <- hlaGenoSubset(HapMap_CEU_Geno,
	samp.sel=match(hlatab$validation$value$sample.id, HapMap_CEU_Geno$sample.id))

# train a HIBAG model
set.seed(100)
# please use "nclassifier=100" when you use HIBAG for real data
model <- hlaAttrBagging(hlatab$training, train.geno, nclassifier=4,
	verbose.detail=TRUE)
summary(model)

# validation
pred <- predict(model, test.geno)
summary(pred)

# compare
(comp <- hlaCompareAllele(hlatab$validation, pred, allele.limit=model,
	call.threshold=0))
(comp <- hlaCompareAllele(hlatab$validation, pred, allele.limit=model,
	call.threshold=0.5))


# save the parameter file
mobj <- hlaModelToObj(model)
save(mobj, file="HIBAG_model.RData")
save(test.geno, file="testgeno.RData")
save(hlatab, file="HLASplit.RData")

# Clear Workspace
hlaClose(model)  # release all resources of model
rm(list = ls())


######################################################################

# NOW, load a HIBAG model from the parameter file
mobj <- get(load("HIBAG_model.RData"))
model <- hlaModelFromObj(mobj)

# validation
test.geno <- get(load("testgeno.RData"))
hlatab <- get(load("HLASplit.RData"))

pred <- predict(model, test.geno)
# compare
(comp <- hlaCompareAllele(hlatab$validation, pred, allele.limit=model,
	call.threshold=0.5))


#########################################################################
# import a PLINK BED file
#
bed.fn <- system.file("extdata", "HapMap_CEU.bed", package="HIBAG")
fam.fn <- system.file("extdata", "HapMap_CEU.fam", package="HIBAG")
bim.fn <- system.file("extdata", "HapMap_CEU.bim", package="HIBAG")
hapmap.ceu <- hlaBED2Geno(bed.fn, fam.fn, bim.fn, assembly="hg19")


#########################################################################
# predict
#
pred <- predict(model, hapmap.ceu, type="response")
head(pred$value)
#    sample.id allele1 allele2      prob
# 1    NA10859   01:01   03:01 0.9374860
# 2    NA11882   01:01   29:02 0.9999835
# ...


# delete the temporary files
unlink("HIBAG_model.RData", force=TRUE)
unlink("testgeno.RData", force=TRUE)
unlink("HLASplit.RData", force=TRUE)

HIBAG documentation built on May 2, 2019, 4:50 p.m.