Detecting gene–gene interactions from GWAS using diffusion kernel principal components
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Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
Bmc Bioinformatics
Abstract
Genes and gene products do not function in isolation but as components of complex
networks of macromolecules through physical or biochemical interactions. Dependencies
of gene mutations on genetic background (i.e., epistasis) are believed to play a role
in understanding molecular underpinnings of complex diseases such as inflammatory
bowel disease (IBD). However, the process of identifying such interactions is complex
due to for instance the curse of high dimensionality, dependencies in the data and
non-linearity. Here, we propose a novel approach for robust and computationally
efficient epistasis detection. We do so by first reducing dimensionality, per gene via diffusion
kernel principal components (kpc). Subsequently, kpc gene summaries are used
for downstream analysis including the construction of a gene-based epistasis network.
We show that our approach is not only able to recover known IBD associated genes
but also additional genes of interest linked to this difficult gastrointestinal disease.
Description
Keywords
Inflammatory bowel disease, Diffusion kernel principal components, Bivariate synergy, Spike and slab priors, Gene epistasis network
Citation
Walakira, A., Ocira, J., Duroux, D., Fouladi, R., Moškon, M., Rozman, D., & Van Steen, K. (2022). Detecting gene–gene interactions from GWAS using diffusion kernel principal components. Bmc Bioinformatics, 23(1), 1-18. https://doi.org/10.1186/s12859-022-04580-7