kTWAS: integrating kernel machine with transcriptome-wide association studies improves statistical power and reveals novel genes

• Published in: Briefings in bioinformatics Vol. 22; no. 4
• Main Authors: Cao, Chen, Kwok, Devin, Edie, Shannon, Li, Qing, Ding, Bowei, Kossinna, Pathum, Campbell, Simone, Wu, Jingjing, Greenberg, Matthew, Long, Quan
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 20.07.2021; Oxford Publishing Limited (England)
• Subjects: Agglomeration; Autism; Computer Simulation; Feature selection; Gene mapping; Genes; Genetic Association Studies; Genetic Variation; Genome-Wide Association Study; Genotype; Genotypes; Genotyping; Humans; Kernel functions; Learning algorithms; Machine learning; Mapping; Models, Genetic; Phenotypes; Phenotypic variations; Software; Source code; Statistical power; Transcriptome; Transcriptomes; kernel methods; nonlinear genetic effects; transcriptome-wide association studies (TWAS); power analysis

Abstract The power of genotype–phenotype association mapping studies increases greatly when contributions from multiple variants in a focal region are meaningfully aggregated. Currently, there are two popular categories of variant aggregation methods. Transcriptome-wide association studies (TWAS) re...