4.7 Article

ALPHLARD: a Bayesian method for analyzing HLA genes from whole genome sequence data

期刊

BMC GENOMICS
卷 19, 期 -, 页码 -

出版社

BMC
DOI: 10.1186/s12864-018-5169-9

关键词

HLA genotyping; Next generation sequencing; Whole genome sequencing; Whole exome sequencing; Cancer immunogenomics; Bayesian hierarchical model; Markov chain Monte Carlo

资金

  1. Japan Society for the Promotion of Science [15H02775, 15H05912]
  2. Grants-in-Aid for Scientific Research [15H02775] Funding Source: KAKEN

向作者/读者索取更多资源

BackgroundAlthough human leukocyte antigen (HLA) genotyping based on amplicon, whole exome sequence (WES), and RNA sequence data has been achieved in recent years, accurate genotyping from whole genome sequence (WGS) data remains a challenge due to the low depth. Furthermore, there is no method to identify the sequences of unknown HLA types not registered in HLA databases.ResultsWe developed a Bayesian model, called ALPHLARD, that collects reads potentially generated from HLA genes and accurately determines a pair of HLA types for each of HLA-A, -B, -C, -DPA1, -DPB1, -DQA1, -DQB1, and -DRB1 genes at 3rd field resolution. Furthermore, ALPHLARD can detect rare germline variants not stored in HLA databases and call somatic mutations from paired normal and tumor sequence data. We illustrate the capability of ALPHLARD using 253 WES data and 25 WGS data from Illumina platforms. By comparing the results of HLA genotyping from SBT and amplicon sequencing methods, ALPHLARD achieved 98.8% for WES data and 98.5% for WGS data at 2nd field resolution. We also detected three somatic point mutations and one case of loss of heterozygosity in the HLA genes from the WGS data.ConclusionsALPHLARD showed good performance for HLA genotyping even from low-coverage data. It also has a potential to detect rare germline variants and somatic mutations in HLA genes. It would help to fill in the current gaps in HLA reference databases and unveil the immunological significance of somatic mutations identified in HLA genes.

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