A Multi-Marker Test for Analyzing Paired Genetic Data in Transplantation

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.

Donor–Recipient Non-HLA Variants, Mismatches and Renal Allograft Outcomes: Evolving Paradigms

“…Hence, a thorough analysis of D-R variants implicated in renal allograft outcomes will require giving consideration to donor variants, recipient variants (including ancestry-specific variants) and integrating information from D-R genome interactions or "mismatches" (Figure 1). Two research groups have now proposed comprehensive and integrative genetic approaches (20,32) to simultaneously account for individual variants in recipients and also genome-genome interactions; however, such data are yet to be reported from actual patient cohorts.…”

An integration test based only on the maximum and minimum Z-scores for meta analysis

“…In modern medical field, one may encounter many summary values such as Z-scores( [1], [2])and p-values [3]. For example, Coronary artery disease and its major complication myocardial infarction are the leading causes of death worldwide.…”