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.
Highlights
It was Bateson [1] who first described epistasis as a biological process in which gene expression at one locus is suppressed by a gene at another locus
We propose a novel epistasis detection analysis workflow that (1) takes genome-wide association studies (GWAS) SNP data as input, (2) develops gene-level summaries via diffusion kernels on graphs, and (3) uses these summaries as new units in epistasis interaction modelling
Within gene networks differed by genes (Fig. 3). Genes such as PARK7, CD40, NUCKS1, MST1R and SLC22A4, known to be associated with Inflammatory Bowel Disease (IBD) (CD or UC) showed higherend densities (Fig. 3).The number of SNPs mapped to a respective gene varied from 140 SNPs for the HLA-K gene to 2 SNPs for genes such as ZNF7 and TRPT1
Summary
It was Bateson [1] who first described epistasis as a biological process in which gene expression at one locus is suppressed by a gene at another locus. We adopt the most commonly used reference to “epistasis”, as referring to any interaction between genes in which the contribution of one gene to a phenotype depends on genetic background. For more details about epistasis, what it means and does not mean, its analytic challenges and reproducibility concerns, we refer to [3] and more references in [4, 5]. Epistasis research has evolved into a more general theory and application framework for the analysis of interactions across and between omics strata.
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