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 (provided for subjects of European, Asian, Hispanic and African ancestries) 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 deduced using bootstrap aggregating and random variable selection.

1. HIBAG can be used by researchers with published parameter estimates (provided for subjects of European, Asian, Hispanic and African ancestries) 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.

github: https://github.com/zhengxwen/HIBAG

R Bioconductor: http://www.bioconductor.org/packages/devel/bioc/html/HIBAG.html

The website (Prof. Bruce S. Weir):

http://www.biostat.washington.edu/~bsweir/HIBAG/

R Examples

• Allele Frequency Net Database (AFND): http://www.allelefrequencies.net
• IMGT/HLA Database: http://www.ebi.ac.uk/imgt/hla
• HLA Nomenclature:
• GlaxoSmithKline (GSK): http://www.gsk.com