General Kernel Machine Methods for Multi‐Omics Integration and Genome‐Wide Association Testing With Related Individuals

• Published in: Genetic epidemiology Vol. 49; no. 1; pp. e22610 - n/a
• Main Authors: Little, Amarise, Zhao, Ni, Mikhaylova, Anna, Zhang, Angela, Ling, Wodan, Thibord, Florian, Johnson, Andrew D., Raffield, Laura M., Curran, Joanne E., Blangero, John, O'Connell, Jeffrey R., Xu, Huichun, Rotter, Jerome I., Rich, Stephen S., Rice, Kenneth M., Chen, Ming‐Huei, Reiner, Alexander, Kooperberg, Charles, Vu, Thao, Hou, Lifang, Fornage, Myriam, Loos, Ruth J.F., Kenny, Eimear, Mathias, Rasika, Becker, Lewis, Smith, Albert V., Boerwinkle, Eric, Yu, Bing, Thornton, Timothy, Wu, Michael C.
• Format: Journal Article
• Language: English
• Published: United States 01.01.2025
• Subjects: Algorithms; association testing; composite kernel; general linear regression; Genome-Wide Association Study - methods; Genomics - methods; Genotype; Humans; integrative analysis; kernel machine regression; Machine Learning; Models, Genetic; Multiomics; multi‐omics; Phenotype; Polymorphism, Single Nucleotide; Quantitative Trait Loci; Regression Analysis; variance component test; variance component test; multi‐omics; association testing; general linear regression; kernel machine regression; integrative analysis; composite kernel
• ISSN: 0741-0395; 1098-2272; 1098-2272
• DOI: 10.1002/gepi.22610

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.