HIBAG -- an R Package for HLA Genotype Imputation with Attribute Bagging

In HIBAG: HLA Genotype Imputation with Attribute Bagging

Overview

The human leukocyte antigen (HLA) system, located in the major histocompatibility complex (MHC) on chromosome 6p21.3, is highly polymorphic. This region has been shown to be important in human disease, adverse drug reactions and organ transplantation [@Shiina09]. HLA genes play a role in the immune system and autoimmunity as they are central to the presentation of antigens for recognition by T cells. Since they have to provide defense against a great diversity of environmental microbes, HLA genes must be able to present a wide range of peptides. Evolutionary pressure at these loci have given rise to a great deal of functional diversity. For example, the HLA--B locus has 1,898 four-digit alleles listed in the April 2012 release of the IMGT-HLA Database [@Robinson13] (http://www.ebi.ac.uk/imgt/hla/).

Classical HLA genotyping methodologies have been predominantly developed for tissue typing purposes, with sequence based typing (SBT) approaches currently considered the gold standard. While there is widespread availability of vendors offering HLA genotyping services, the complexities involved in performing this to the standard required for diagnostic purposes make using a SBT approach time-consuming and cost-prohibitive for most research studies wishing to look in detail at the involvement of classical HLA genes in disease.

Here we introduce a new prediction method for HLA Imputation using attribute BAGging, HIBAG, that is highly accurate, computationally tractable, and can be used with published parameter estimates, eliminating the need to access large training samples [@Zheng13]. It relies on a training set with known HLA and SNP genotypes, and combines the concepts of attribute bagging with haplotype inference from unphased SNPs and HLA types. Attribute bagging is a technique for improving the accuracy and stability of classifier ensembles using bootstrap aggregating and random variable selection [@Breiman96; @Breiman01; @Bryll03]. In this case, individual classifiers are created which utilize a subset of SNPs to predict HLA types and haplotype frequencies estimated from a training data set of SNPs and HLA types. Each of the classifiers employs a variable selection algorithm with a random component to select a subset of the SNPs. HLA type predictions are determined by maximizing the average posterior probabilities from all classifiers.

Features

• HIBAG can be used by researchers with published parameter estimates (http://www.biostat.washington.edu/~bsweir/HIBAG/ or https://hibag.s3.amazonaws.com/hlares_index.html) instead of requiring access to large training sample datasets.
• A typical HIBAG parameter file contains only haplotype frequencies at different SNP subsets rather than individual training genotypes.
• SNPs within the xMHC region (chromosome 6) are used for imputation.
• HIBAG employs unphased genotypes of unrelated individuals as a training set.
• HIBAG supports parallel computing with R.

Examples

options(width=110)

library(HIBAG)

Pre-fit HIBAG Models for HLA Imputation

# load the published parameter estimates from European ancestry
#   e.g., filename <- "HumanOmniExpressExome-European-HLA4-hg19.RData"
#   here, we use example data in the package
filename <- system.file("extdata", "ModelList.RData", package="HIBAG")
model.list <- get(load(filename))

# HLA imputation at HLA-A
hla.id <- "A"
model <- hlaModelFromObj(model.list[[hla.id]])
summary(model)
plot(model)    # show the frequency of SNP marker in the model

# SNPs in the model
head(model$snp.id)

head(model$snp.position)

#########################################################################
# Import your PLINK BED file
#
yourgeno <- hlaBED2Geno(bed.fn=".bed", fam.fn=".fam", bim.fn=".bim")
summary(yourgeno)

# best-guess genotypes and all posterior probabilities
pred.guess <- hlaPredict(model, yourgeno, type="response+prob")
summary(pred.guess)

pred.guess$value
pred.guess$postprob

Build a HIBAG Model for HLA Genotype Imputation

library(HIBAG)

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

# a list of HLA genotypes
# e.g., train.HLA <- hlaAllele(sample.id, H1=c("01:02", "05:01", ...),
#                        H2=c("02:01", "03:01", ...), locus="A")
#     the HLA type of the first individual is 01:02/02:01,
#                 the second is 05:01/03:01, ...
hla.id <- "A"
train.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")

# selected SNPs:
#    the flanking region of 500kb on each side,
#    or an appropriate flanking size without sacrificing predictive accuracy
region <- 500   # kb
snpid <- hlaFlankingSNP(HapMap_CEU_Geno$snp.id,
    HapMap_CEU_Geno$snp.position, hla.id, region*1000, assembly="hg19")
length(snpid)

# training genotypes
#   import your PLINK BED file,
#   e.g., train.geno <- hlaBED2Geno(bed.fn=".bed", fam.fn=".fam", bim.fn=".bim")
#   or,
train.geno <- hlaGenoSubset(HapMap_CEU_Geno,
    snp.sel = match(snpid, HapMap_CEU_Geno$snp.id))
summary(train.geno)

# train a HIBAG model
set.seed(100)
model <- hlaAttrBagging(train.HLA, train.geno, nclassifier=100)

mobj <- get(load(system.file("extdata", "ModelList.RData", package="HIBAG")))
model <- hlaModelFromObj(mobj$A)

summary(model)

Build and Predict in Parallel

library(HIBAG)
library(parallel)

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

# a list of HLA genotypes
# e.g., train.HLA <- hlaAllele(sample.id, H1=c("01:02", "05:01", ...),
#                        H2=c("02:01", "03:01", ...), locus="A")
#     the HLA type of the first individual is 01:02/02:01,
#                 the second is 05:01/03:01, ...
hla.id <- "A"
train.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")

# selected SNPs:
#    the flanking region of 500kb on each side,
#    or an appropriate flanking size without sacrificing predictive accuracy
region <- 500   # kb
snpid <- hlaFlankingSNP(HapMap_CEU_Geno$snp.id,
    HapMap_CEU_Geno$snp.position, hla.id, region*1000, assembly="hg19")
length(snpid)

# training genotypes
#   import your PLINK BED file,
#   e.g., train.geno <- hlaBED2Geno(bed.fn=".bed", fam.fn=".fam", bim.fn=".bim")
#   or,
train.geno <- hlaGenoSubset(HapMap_CEU_Geno,
    snp.sel = match(snpid, HapMap_CEU_Geno$snp.id))
summary(train.geno)


# Multithreading
cl <- 2    # 2 -- # of threads

# Building ...
set.seed(1000)
hlaParallelAttrBagging(cl, train.HLA, train.geno, nclassifier=100,
    auto.save="AutoSaveModel.RData")
model.obj <- get(load("AutoSaveModel.RData"))
model <- hlaModelFromObj(model.obj)
summary(model)

# best-guess genotypes and all posterior probabilities
pred.guess <- hlaPredict(model, yourgeno, type="response+prob", cl=cl)

Evaluate Overall Accuracy, Sensitivity, Specificity, etc

The function hlaReport() can be used to automatically generate a tex or HTML report when a validation dataset is available.

library(HIBAG)

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

# make a list of HLA types
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)
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)

# 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))
summary(train.geno)

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)
model <- hlaAttrBagging(hlatab$training, train.geno, nclassifier=100)

mobj <- get(load(system.file("extdata", "OutOfBag.RData", package="HIBAG")))
model <- hlaModelFromObj(mobj)

summary(model)

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

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

Evaluation in Figures

The distance matrix is calculated based on the haplotype similarity carrying HLA alleles:

# hierarchical cluster analysis
d <- hlaDistance(model)
p <- hclust(as.dist(d))
plot(p, xlab="HLA alleles")

# violin plot
hlaReportPlot(pred, model=model, fig="matching")

Matching proportion is a measure or proportion describing how the SNP profile matches the SNP haplotypes observed in the training set, i.e., the likelihood of SNP profile in a random-mating population consisting of training haplotypes. It is not directly related to confidence score, but a very low value of matching indicates that it is underrepresented in the training set.

hlaReportPlot(pred, hlatab$validation, fig="call.rate")
hlaReportPlot(pred, hlatab$validation, fig="call.threshold")

Report in Text

Output to plain text format:

# report overall accuracy, per-allele sensitivity, specificity, etc
hlaReport(comp, type="txt")

Report in Markdown

Output to a markdown file:

# report overall accuracy, per-allele sensitivity, specificity, etc
hlaReport(comp, type="markdown")

Report in LaTeX

Output to a tex file, and please add \usepackage{longtable} to your tex file:

# report overall accuracy, per-allele sensitivity, specificity, etc
hlaReport(comp, type="tex", header=FALSE)

Release HIBAG Models without Confidential Information

library(HIBAG)

# make a list of HLA types
hla.id <- "DQA1"
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")

# training genotypes
region <- 500   # kb
snpid <- hlaFlankingSNP(HapMap_CEU_Geno$snp.id, HapMap_CEU_Geno$snp.position,
    hla.id, region*1000, assembly="hg19")
train.geno <- hlaGenoSubset(HapMap_CEU_Geno,
    snp.sel = match(snpid, HapMap_CEU_Geno$snp.id),
    samp.sel = match(hla$value$sample.id, HapMap_CEU_Geno$sample.id))

set.seed(1000)
model <- hlaAttrBagging(hla, train.geno, nclassifier=100)
summary(model)

# remove unused SNPs and sample IDs from the model
mobj <- hlaPublish(model,
    platform = "Illumina 1M Duo",
    information = "Training set -- HapMap Phase II",
    warning = NULL,
    rm.unused.snp=TRUE, anonymize=TRUE)

save(mobj, file="Your_HIBAG_Model.RData")

Release a Collection of HIBAG Models

# assume the HIBAG models are stored in R objects: mobj.A, mobj.B, ...

ModelList <- list()
ModelList[["A"]] <- mobj.A
ModelList[["B"]] <- mobj.B
...

# save to an R data file
save(ModelList, file="HIBAG_Model_List.RData")

Resources

• Allele Frequency Net Database (AFND): http://www.allelefrequencies.net.
• IMGT/HLA Database: http://www.ebi.ac.uk/imgt/hla.
• HLA Nomenclature: G Codes and P Codes for reporting of ambiguous allele typings.

Session Info

sessionInfo()

References

HIBAG documentation built on March 24, 2021, 6 p.m.