Abstract
The power of genotype-phenotype association mapping studies increases significantly when the contributions of multiple variants in a focal genetic region are aggregated effectively. Currently, two categories of frequently used methods are used to aggregate variants. Transcriptome-wide association studies (TWAS) represent a category of emerging methods that utilize gene expressions to select genetic variants, before using a pretrained linear combination of selected variants for downstream association mapping. In contrast, kernel methods such as SKAT measure the genetic similarity in a focal region as modelled by various types of kernels to associate genotypic and phenotypic variance, allowing such methods to model nonlinear effects. Thus far, no thorough comparison has been made between these categories, and there are also no methods that integrate these two approaches. In this work we have developed a novel method called kTWAS that leverages TWAS-like feature selection followed by a SKAT-like kernel-based score test, to combine advantages from both approaches. We demonstrate the improved power of kTWAS against TWAS and multiple SKAT-based protocols through extensive simulations, and identify novel disease associated genes in WTCCC genotyping array data and MSSNG (Autism) sequence data. The source code for kTWAS and our simulations are available in our GitHub repository (https://github.com/theLongLab/kTWAS).
Competing Interest Statement
The authors have declared no competing interest.