Composite Kernel Association Test (CKAT) for SNP-set joint assessment of genotype and genotype-by-treatment interaction in Pharmacogenetics studies | Zendy

Hong Zhang | Zendy; Ni Zhao | Zendy; Devan V. Mehrotra | Zendy; Judong Shen | Zendy

Open Access

Composite Kernel Association Test (CKAT) for SNP-set joint assessment of genotype and genotype-by-treatment interaction in Pharmacogenetics studies

Author(s) -

Hong Zhang,

Ni Zhao,

Devan V. Mehrotra,

Judong Shen

Publication year - 2020

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btaa125

Subject(s) - type i and type ii errors , computer science , genome wide association study , pharmacogenetics , genetic association , kernel (algebra) , single nucleotide polymorphism , snp , statistical power , sample size determination , association test , computational biology , data mining , genotype , statistics , mathematics , biology , genetics , gene , combinatorics

It is of substantial interest to discover novel genetic markers that influence drug response in order to develop personalized treatment strategies that maximize therapeutic efficacy and safety. To help enable such discoveries, we focus on testing the association between the cumulative effect of multiple single nucleotide polymorphisms (SNPs) in a particular genomic region and a drug response of interest. However, the currently existing methods are either computational inefficient or not able to control type I error and provide decent power for whole exome or genome analysis in Pharmacogenetics (PGx) studies with small sample sizes.

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