Faculty, Staff and Student Publications

Language

English

Publication Date

1-1-2025

Journal

Genetic Epidemiology

DOI

10.1002/gepi.22610

PMID

39812506

PMCID

PMC13170581

PubMedCentral® Posted Date

5-14-2026

PubMedCentral® Full Text Version

Author MSS

Abstract

Integrating multi-omics data may help researchers understand the genetic underpinnings of complex traits and diseases. However, the best ways to integrate multi-omics data and use them to address pressing scientific questions remain a challenge. One important and topical problem is how to assess the aggregate effect of multiple genomic data types (e.g. genotypes and gene expression levels) on a phenotype, particularly while accommodating routine issues, such as having related subjects' data in analyses. In this paper, we extend an existing composite kernel machine regression model to integrate two multi-omics data types, while accommodating for general correlation structures amongst outcomes. Due to the kernel machine regression framework, our methods allow for the integration of high-dimensional omics data with small, nonlinear, and interactive effects, and accommodation of general study designs. Here, we focus on scientific questions that aim to assess the association between a functional grouping (such as a gene or a pathway) and a quantitative trait of interest. We use a kernel machine regression to integrate the two multi-omics data types, as they may relate to the trait, and perform a global test of association. We demonstrate the advantage of this approach over single data type association tests via simulation. Finally, we apply this method to a large, multi-ethnic data set to investigate how predicted gene expression and rare genetic variation may be related to two platelet traits.

Keywords

Humans, Genome-Wide Association Study, Genomics, Phenotype, Models, Genetic, Genotype, Polymorphism, Single Nucleotide, Machine Learning, Quantitative Trait Loci, Regression Analysis, Algorithms, Multiomics

Published Open-Access

yes

Download

Included in

Public Health Commons

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.