Next Generation ABO Genetics and Genomics

Abstract

Accurately cross matching units of blood based on blood type is essential for successful transfusion therapy. ABO is the most clinically relevant blood group in transfusion therapy due to the presence of naturally occurring ABO antibodies. Failure to correctly match ABO blood type can cause fatal transfusion reactions even in transfusion naïve individuals. The ABO gene commonly encodes two different forms of a glycosyltransferase which adds A or B sugars (N-acetylgalactosamine for A or α-D-galactose for B) to the H-antigen substrate. Single nucleotide variants (SNVs) and insertion-deletions (indels) in the ABO gene affect function at the molecular level by altering the specificity and efficiency of the enzyme for specific sugars (leading to the A1, A2, and B blood types) or by knocking out gene function to generate the O blood type. Thus, variation in A, B, or O serological phenotype is the result of genetic variation in the coding portion of the ABO gene. Currently, approaches to genotype ABO are limited because ABO is a complex locus with a large number of functional haplotypes that can lead to the A, B, or O phenotype. In addition to many other factors, ABO blood type plays a role in determining an individual’s risk for multiple common complex diseases including the number one and number two causes of death in the United States: cardiovascular disease and cancer. However, the influence of specific ABO types and ABO subtype variants, such as the A1 and A2 haplotype/subtypes, on common complex disease risk has not yet been fully explored. In this dissertation, I directly explore the many forms of variation in the ABO gene in diverse human populations using multiple next-generation human genome sequencing datasets, while simultaneously addressing the limitations of both traditional serological methods and existing genotyping methods designed to determine ABO blood type from variation found in the ABO gene. I then discuss strategies and limitations of developing an automated approach to call high resolution phased ABO blood types from NGS data. The methods and analyses outlined in this dissertation can be used to generate higher resolution blood type and subtype calls leveraging the variation and phenotypes within large scale NGS populations based to explore the relationships between rare and common ABO variants, ABO haplotypes, and subtypes with common complex disease relatedphenotypes (i.e., cardiovascular disease, cancer, and type 2 diabetes). My hope is that the NGS tools developed in this thesis will be used to create a more comprehensive understanding of common complex disease etiology in the future.