Abstract

Emerging evidence suggests that donor/recipient matching in non-HLA (human leukocyte antigen) regions of the genome may impact transplant outcomes and recognizing these matching effects may increase the power of transplant genetics studies. Most available matching scores account for either single-nucleotide polymorphism (SNP) matching only or sum these SNP matching scores across multiple gene-coding regions, which makes it challenging to interpret the association findings. We propose a multi-marker Joint Score Test (JST) to jointly test for association between recipient genotype SNP effects and a gene-based matching score with transplant outcomes. This method utilizes Eigen decomposition as a dimension reduction technique to potentially increase statistical power by decreasing the degrees of freedom for the test. In addition, JST allows for the matching effect and the recipient genotype effect to follow different biological mechanisms, which is not the case for other multi-marker methods. Extensive simulation studies show that JST is competitive when compared with existing methods, such as the sequence kernel association test (SKAT), especially under scenarios where associated SNPs are in low linkage disequilibrium with non-associated SNPs or in gene regions containing a large number of SNPs. Applying the method to paired donor/recipient genetic data from kidney transplant studies yields various gene regions that are potentially associated with incidence of acute rejection after transplant.

Highlights

  • Transplant matching usually focuses on non-genetic factors related to the donor, the recipient, or the graft itself, such as recipient age, donor sex, or organ size

  • When recipient genotype single nucleotide polymorphism (SNP) were associated, we considered scenarios in which 5, 15, or 25% of the SNPs in the gene region were truly associated with outcome, and this group of SNPs was either in high linkage disequilibrium (LD) or low LD

  • We propose a multi-marker test statistic designed for use with paired genetic data in transplantation

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Summary

Introduction

Transplant matching usually focuses on non-genetic factors related to the donor, the recipient, or the graft itself, such as recipient age, donor sex, or organ size. More recent transplant genetic studies have identified gene regions outside of the HLA region that may act as genetic modifiers for transplant outcomes (Almoguera et al, 2014; Yang and Sarwal, 2017; Steers et al, 2019; Farouk et al, 2020; Marin et al, 2020; Reindl-Schwaighofer et al, 2020). Previous work on single nucleotide polymorphism (SNP) matching in transplant found that utilizing D/R matching scores in association analyses led to discovery of SNPs potentially associated with acute rejection after liver transplant Joint testing of these scores with the recipient genotype suggested that the scores were measuring some aspect other than the combination of the recipient and donor genotype, since there were cases where the score was associated with transplant outcome, but the recipient and donor genotypes were not (Arthur et al, 2020). In order to improve power, increase interpretability and reproducibility of association signals, and facilitate follow-up functional studies, it is of interest to extend these single SNP methods to a multi-marker framework

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