June 1, 2021

Access full spectrum of polymorphisms in HLA class I & II genes, without imputation for disease association and evolutionary research.

Author(s): Ranade, S. and Harting, J. and Lee, W. and Eng, K. and Hepler, L. and Bowman, B. and Kooter, R. and Rebel, C. and Rozemuller, E. and Westering, N.

MHC class I and II genes are critically monitored by high-resolution sequencing for organ transplant decisions due to their role in GVHD. Their direct or linkage-based causal association, have increased their prominence as targets for drug sensitivity, autoimmune, cancer and infectious disease research. Monitoring HLA genes can however be tricky due to their highly polymorphic nature. Allele-level resolution is thus strongly preferred. However, most studies were historically focused on peptide binding domains of the HLA genes, due to technological challenges. As a result knowledge about the functional role of polymorphisms outside of exons 2 and 3 of HLA genes was rather limited. There are also relatively few full-length gene references currently available in the IMGT HLA database. This made it difficult to quickly adopt high-throughput reference-reliant methods for allele-level HLA sequencing. Increasing awareness regarding role of regulatory region polymorphisms of HLA genes in disease association1, nonetheless have brought about a revolution in full-length HLA gene sequencing. Researchers are now exploring ways to obtain complete information for HLA genes and integrate it with the current HLA database so it can be interpreted used by clinical researchers. We have explored advantages of SMRT Sequencing to obtain fully phased, allele-specific sequences of HLA class I and II genes for 96 samples using completely De novo consensus generation approach for imputation-free 4-field typing. With long read lengths (average >10 kb) and consensus accuracy exceeding 99.999% (Q50), a comprehensive snapshot of variants in exons, introns and UTRs could be obtained for spectrum of polymorphisms in phase across SNP-poor regions. Such information can provide invaluable insights in future causality association and population diversity research.

Organization: PacBio
Year: 2015

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