Hierarchical Bayesian method for analyzing high polymorphic HLA genome sequence

2017 Fiscal Year Final Research Report

• Project/Area Number: 15H02775
• Research Category: Grant-in-Aid for Scientific Research (B)
• Allocation Type: Single-year Grants
• Section: 一般
• Research Field: Life / Health / Medical informatics
• Research Institution: The University of Tokyo
• Principal Investigator: Imoto Seiya 東京大学, 医科学研究所, 教授 (10345027)
• Co-Investigator(Kenkyū-buntansha): 水野 晋一 九州大学, 先端医療イノベーションセンター, 特任准教授 (40569430)
• Project Period (FY): 2015-04-01 – 2018-03-31
• Keywords: ベイズモデル / HLA遺伝子型 / がんゲノム / マルコフ連鎖モンテカルロ / 免疫ゲノム

Outline of Final Research Achievements

Although 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. We 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 6-digit 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 and showed its high accuracy, 98.8% for WES data and 98.5% for WGS data at 4-digit resolution. We applied ALPHLARD to 2834 WGS data of PCAWG and showed somatic mutation landscape of HLA genes.

Free Research Field

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