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

Victoria L. Arthur, Zhengbang Li, Rui Cao, William S. Oetting, Ajay K Israni, Pamala A. Jacobson, Marylyn D. Ritchie, Weihua Guan, Jinbo Chen

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number745773
JournalFrontiers in Genetics
Volume12
DOIs
StatePublished - Oct 13 2021

Bibliographical note

Funding Information:
This work was supported by the National Institutes of Health. Grant numbers R01-ES016626, R21-ES020811, 5U19-AI070119, and 5U01-AI-58013.

Publisher Copyright:
© Copyright © 2021 Arthur, Li, Cao, Oetting, Israni, Jacobson, Ritchie, Guan and Chen.

Keywords

  • genetic matching scores
  • joint testing
  • multi-marker testing
  • paired genetic data
  • transplant genetics

PubMed: MeSH publication types

  • Journal Article
  • Review

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