A Multi-marker Test for Analyzing Paired Transplant Genetic Data
Evidence suggests that donor/recipient matching in some genetic regions may impact transplant outcomes. Most available matching scores account for either single-nucleotide polymorphism (SNP) matching only or matching across a long range of different gene regions, which makes it hard 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 outcome. This method utilizes Eigen decomposition as a dimension reduction technique to potentially increase statistical power by decreasing the degrees of freedom for the test. Additionally, we utilize a partially penalized Score test method, originally designed for use with high-dimensional data, to test for association between gene-based matching score and transplant outcome, with the potential for adjusting for a large number of recipient or donor SNP main effects. Extensive simulation studies show that JST is competitive when compared with existing methods, including SKAT and a partially penalized Score test. Testing for matching score effect is also shown to be powerful, both when only matching score is associated and when both matching score and SNP main effects are associated with outcome. Applying the methods to paired kidney transplant data yields various gene regions that are potentially associated with survival after transplant.
KeywordsStatistical Genetics, Statistical Methods, Transplant Genetics
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