Composite Kernel Association Test (CKAT) for SNP-Set Joint Assessment of Genotype and Genotype-By-Treatment Interaction in Pharmacogenetics Studies

• Activity Number: 176 - Statistical Genetics III – Predictive Modeling, GxE Interaction, and Causal Inference
• Type: Contributed
• Date/Time: Tuesday, August 4, 2020 : 10:00 AM to 2:00 PM
• Sponsor: Section on Statistics in Genomics and Genetics
• Author(s): Judong Shen* and Hong Zhang and Ni Zhao and Devan Mehrotra
• Companies: Merck & Co., Inc. and Merck & Co., Inc. and Johns Hopkins University, Bloomberg School of Public Health and Merck
• Keywords: CKAT; Personalized medicine; Pharmacogenetics; Multi-kernel testing

Abstract

It is of substantial interest to discover novel genetic markers that influence drug response in order to develop personalized treatment strategies. To help enable such discoveries, we focus on testing the association between the cumulative effect of multiple single nucleotide polymorphisms (SNPs) in a genomic region and a drug response of interest. We propose the Composite Kernel Association Test (CKAT), a flexible and robust kernel machine based approach to jointly test the genetic main effect and SNP-treatment interaction effect for SNP-sets in Pharmacogenetics (PGx) assessments embedded within randomized clinical trials. An analytic procedure is developed to accurately calculate the P-value so that computationally extensive procedures (e.g., permutation) can be avoided. We evaluate CKAT through extensive simulation studies and application to the gene-level association test of Clostridium difficile infection recurrence reduction in patients treated with bezlotoxumab. The results demonstrate that the proposed CKAT controls type I error well, is efficient for whole exome/genome study analysis and provides better power performance than existing methods across multiple scenarios.