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A general kernel machine regression framework using principal component analysis for jointly testing main and

Hyunwook Koh1

  • 1Department of Applied Mathematics and Statistics, The State University of New York, Korea, Incheon 21985, South Korea.

NAR Genomics and Bioinformatics
|November 13, 2024
PubMed
Summary
This summary is machine-generated.

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This study introduces a new kernel machine regression framework to identify genetic or microbial biomarkers by analyzing interaction effects between variants and treatments. The method robustly detects main and interaction effects, even when underlying genetic data is unknown, enhancing biomarker discovery.

Area of Science:

  • Genetics and Genomics
  • Microbiome Research
  • Statistical Bioinformatics

Background:

  • Treatment effects on health and disease can be influenced by genetic or microbial variants.
  • Identifying interaction effects between variants and treatments is crucial for discovering powerful biomarkers.
  • Existing kernel machine regression methods are limited when the underlying variants of a kernel are unknown.

Purpose of the Study:

  • To develop a general kernel machine regression framework capable of jointly testing main and interaction effects.
  • To address the limitation of existing methods by working with kernels without requiring knowledge of underlying variants.
  • To introduce an omnibus testing extension for multiple kernels, named OmniK, applicable to human microbiome studies.

Main Methods:

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  • Principal component analysis (PCA) is applied to extract principal components from an input kernel via singular value decomposition (SVD).
  • These principal components serve as surrogate variants to construct endogenous kernels for main effects, interaction effects, or both.
  • The framework enables robust detection of main and/or interaction effects using only the kernel as input.

Main Results:

  • The proposed framework successfully incorporates interaction effects alongside main effects in kernel machine regression.
  • It demonstrates robust detection of genetic or microbial biomarkers by analyzing interactions, even with unknown underlying variants.
  • The OmniK extension provides a powerful tool for omnibus testing across multiple kernels in complex studies like human microbiome analysis.

Conclusions:

  • The developed general kernel machine regression framework offers a flexible and powerful approach for biomarker discovery.
  • It overcomes limitations of existing methods by not requiring knowledge of the specific variants underlying a kernel.
  • The application to human microbiome studies highlights its utility in complex biological and health-related research.