title: High-Accuracy HLA Type Inference from Whole-Genome Sequencing Data Using Population Reference Graphs.
abstract: Genetic variation at the Human Leucocyte Antigen (HLA) genes is associated with many autoimmune and infectious disease phenotypes, is an important element of the immunological distinction between self and non-self, and shapes immune epitope repertoires. Determining the allelic state of the HLA genes (HLA typing) as a by-product of standard whole-genome sequencing data would therefore be highly desirable and enable the immunogenetic characterization of samples in currently ongoing population sequencing projects. Extensive hyperpolymorphism and sequence similarity between the HLA genes, however, pose problems for accurate read mapping and make HLA type inference from whole-genome sequencing data a challenging problem. We describe how to address these challenges in a Population Reference Graph (PRG) framework. First, we construct a PRG for 46 (mostly HLA) genes and pseudogenes, their genomic context and their characterized sequence variants, integrating a database of over 10,000 known allele sequences. Second, we present a sequence-to-PRG paired-end read mapping algorithm that enables accurate read mapping for the HLA genes. Third, we infer the most likely pair of underlying alleles at G group resolution from the IMGT/HLA database at each locus, employing a simple likelihood framework. We show that HLA*PRG, our algorithm, outperforms existing methods by a wide margin. We evaluate HLA*PRG on six classical class I and class II HLA genes (HLA-A, -B, -C, -DQA1, -DQB1, -DRB1) and on a set of 14 samples (3 samples with 2 x 100bp, 11 samples with 2 x 250bp Illumina HiSeq data). Of 158 alleles tested, we correctly infer 157 alleles (99.4%). We also identify and re-type two erroneous alleles in the original validation data. We conclude that HLA*PRG for the first time achieves accuracies comparable to gold-standard reference methods from standard whole-genome sequencing data, though high computational demands (currently ~30-250 CPU hours per sample) remain a significant challenge to practical application.
authors: Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom.|NHGRI-NIH, Bethesda, MD, United States of America.
authors: Alexander T
authors: AT
authors: Dilthey
authors: Neurology Department, UCSF, San Francisco, United States of America.|Inserm unit 1064 ATIP-Avenir team 6, University of Nantes-Nantes University Hospitals, Nantes, France.
authors: Pierre-Antoine
authors: PA
authors: Gourraud
authors: Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom.
authors: Alexander J
authors: http://orcid.org/0000-0002-4502-2209
authors: AJ
authors: Mentzer
authors: Histogenetics, Ossining, United States of America.
authors: Nezih
authors: http://orcid.org/0000-0003-1408-7445
authors: N
authors: Cereb
authors: Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom.
authors: Zamin
authors: http://orcid.org/0000-0001-8466-7547
authors: Z
authors: Iqbal
authors: Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, United Kingdom.|Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom.
authors: Gil
authors: G
authors: McVean
mesh_terms: D000465:Algorithms; D002874:Chromosome Mapping / Q000379:methods*; D005828:Genetics, Population; D015894:Genome, Human / Q000235:genetics*; D000071020:Hemochromatosis Protein / Q000235:genetics*; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D012016:Reference Values
chemical_list: C103673:HFE protein, human; D000071020:Hemochromatosis Protein