Analysis Of Human Leukocyte Antigen (HLA) Immunogenetic Data For Hematopoietic Stem Cell Transplantation And Disease Association

Abstract

The Major Histocompatibility Complex (MHC) of chromosome 6 is the most polymorphic region of the human genome, and is also under very strong selection pressure, resulting in genetic divergence of immune gene variants between human populations. The human leukocyte antigen (HLA) genes located in the MHC region play a central role in the immune system as HLA proteins distinguish self from non-self through antigenic peptide presentation to T-cells. Hematopoietic stem cell transplantation (HSCT) is a curative therapy for many patients with hematologic diseases, but successful transplant requires a high degree of HLA matching between donor and recipient. Unfortunately, HLA-matched donors are not available for all patients. HLA diversity is vast as millions of unique HLA genotypes have been observed worldwide, many of which have high privacy to specific human populations. In response to this HLA-matching challenge, large registries of unrelated donors have been constructed worldwide to provide HLA-matched HSCT to patients. Even with large registries, minority and admixed race/ethnic groups in the United States have lower likelihood than European-Americans of finding an HLA match. Legacy high-throughput HLA typing methods give high levels of typing ambiguity at recruitment, resulting in a lack of initial confirmation that a suitable match exists. Current population genetics techniques fall short in addressing the unique challenges of stem cell registry analytics, resulting in a difficult search process for some patients. This thesis describes new techniques developed to analyze immunogenetics data with direct operational application in the registry setting. Advancement in computational techniques in population genetics to better handle HLA typing ambiguity has improved calculation of HLA haplotype frequencies, prediction of allele-level HLA typing for subjects with typing ambiguity in registry matching algorithms, and projection of HLA match likelihoods as registries expand. These advances have had direct operational impact for National Marrow Donor Program (NMDP) through more rapid identification of suitably-matched donors and optimized allocation of resources in order to serve more patients, especially in underserved minority groups. These computational techniques have also enabled more detailed evaluation of immunogenetic associations with disease, which may lead to new avenues for treatment for cancer and autoimmune diseases.