Family based HLA imputation and optimization of haplo-identical transplants
Zuriya Ansbacher-Feldman (1), Sapir Israeli (1), Martin Maiers (2 and, 3), Loren Gragert (2, 3, 4), Dianne De Santis (5), Moshe Israeli (6 and, 7), Yoram Louzoun (1) ((1) Department of Mathematics, Bar-Ilan University,, Ramat Gan, Israel, (2) Center for Blood

TL;DR
This paper introduces GRAMM, a family-based HLA imputation method that significantly improves haplotype phasing accuracy and recombination detection in family pedigrees, aiding haplo-identical transplant compatibility assessments.
Contribution
GRAMM is a novel graph-based algorithm that achieves near-perfect haplotype phasing accuracy in family data and low false positive recombination detection, outperforming existing methods.
Findings
GRAMM has almost no phasing errors with pedigree data.
High accuracy in allele imputation and recombination detection.
Estimated recombination rate in populations is 10-20% per family.
Abstract
Recently, haplo-identical transplantation with multiple HLA mismatches has become a viable option for system cell transplants. Haplotype sharing detection requires imputation of donor and recipient. We show that even in high-resolution typing when all alleles are known, there is a 15% error rate in haplotype phasing, and even more in low resolution typings. Similarly, in related donors, parents haplotypes should be imputed to determine what haplotype each child inherited. We propose GRAMM (GRaph bAsed FaMilly iMputation) to phase alleles in family pedigree HLA typing data, and in mother-cord blood unit pairs. We show that GRAMM has practically no phasing errors when pedigree data are available. We apply GRAMM to simulations with different typing resolutions as well as paired cord-mother typings, and show very high phasing accuracy, and improved alleles imputation accuracy. We use GRAMM…
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Taxonomy
TopicsRenal Transplantation Outcomes and Treatments · Cytomegalovirus and herpesvirus research · T-cell and B-cell Immunology
