Discovering genetic drivers in acute graft-versus-host disease after allogeneic hematopoietic stem cell transplantation

Abstract

Acute graft-versus-host disease (GVHD) is one of the major complications after allogeneic hematopoietic stem cell transplantation (allo-HCT) that cause non-relapse morbidity and mortality. Although the increasing matching rate of the human leukocyte antigen (HLA) genes between donor and recipient (DR) has significantly reduced the risk of GVHD, clinically significant GVHD remains as a transplantation challenge, even in HLA-identical transplants. Candidate gene studies and genome-wide association studies have revealed susceptible individual genes and gene pairs from DR pairs that are associated with acute GVHD; however, the roles of genetic disparities between donor and recipient remain to be understood. To identify genetic factors linked to acute GVHD, we investigated the classical HLA and non-HLA genes and conducted a genome-wide clinical outcome association study. Assessment of 4,646 antigen recognition domain (ARD)-matched unrelated donor allo-HCT cases showed that the frequency of mismatches outside the ARD in HLA genes is very low when the DR pairs are matched at ARD. Due to the low frequency of amino acid mismatches in the non-ARD region and their reportedly weak alloimmune reactions, we suggest that the non-ARD sequence mismatches within the ARD-matched DR pairs have limited influence on the development of acute GVHD, and may not be a primary factor. The genome-wide clinical outcome association study between DR pairs observed multiple autosomal minor histocompatibility antigens (MiHAs) restricted by HLA typing, though their association with acute GVHD outcome was not statistically significant. This result suggests that HLA mismatching outweighs other genetic mismatches as contributors to acute GVHD risk. In the cases of female donors to male recipients, we identified the significant association of the Y chromosome-specific peptides encoded by PCDH11Y, USP9Y, UTY, and NLGN4Y with the acute GVHD outcome. Additionally, we developed a machine learning-based genetic variant selection algorithm for ultra-high dimensional transplant genomic studies. The algorithm successfully selected a set of genes from over 1 M genetic variants, all of which have evidence to be linked to the transplant-related complications. This work offers evidence and guidance for further research in acute GVHD and allo-HCT and provides useful bioinformatics and data mining tools for transplant genomic studies.