A general kernel machine regression framework using principal component analysis for jointly testing main and interaction effects: Applications to human microbiome studies.

ABSTRACT: The effect of a treatment on a health or disease response can be modified by genetic or microbial variants. It is the matter of interaction effects between genetic or microbial variants and a treatment. To powerfully discover genetic or microbial biomarkers, it is crucial to incorporate such interaction effects in addition to the main effects. However, in the context of kernel machine regression analysis of its kind, existing methods cannot be utilized in a situation, where a kernel is available but its underlying real variants are unknown. To address such limitations, I introduce a general kernel machine regression framework using principal component analysis for jointly testing main and interaction effects. It begins with extracting principal components from an input kernel through the s

SUBMITTER: Koh H 

PROVIDER: S-EPMC11555437 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature