| Title: | General Kernel Machine Methods for Multi-Omics Integration and Genome-Wide Association Testing With Related Individuals |
| Journal: | Genetic Epidemiology |
| Published: | 15 Jan 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/39812506/ |
| DOI: | https://doi.org/10.1002/gepi.22610 |
Publication 14508
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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.</p>
11 Keywords
- Algorithms
- Computer Simulation
- Genome-Wide Association Study
- Genomics
- Genotype
- Humans
- Machine Learning
- Models, Genetic
- Multiomics
- Phenotype
- Polymorphism, Single Nucleotide
30 Authors
- Amarise Little
- Ni Zhao
- Anna Mikhaylova
- Angela Zhang
- Wodan Ling
- Florian Thibord
- Andrew D. Johnson
- Laura M. Raffield
- Joanne E. Curran
- John Blangero
- Jeffrey R. O'Connell
- Huichun Xu
- Jerome I. Rotter
- Stephen S. Rich
- Kenneth M. Rice
- Ming-Huei Chen
- Alexander Reiner
- Charles Kooperberg
- Thao Vu
- Lifang Hou
- Myriam Fornage
- Ruth J.F. Loos
- Eimear Kenny
- Rasika Mathias
- Lewis Becker
- Albert V. Smith
- Eric Boerwinkle
- Bing Yu
- Timothy Thornton
- Michael C. Wu
1 Application
| Application ID | Title |
|---|---|
| 25953 | Transcriptome-wide Association Study of Cardiometabolic Traits in the UK Biobank Cohort |
Enabling scientific discoveries that improve human health