# Decoding non-coding SNPs: systems genomics modelling dissects the heterogeneity of IBD

## TL;DR

This paper introduces a new systems genomics approach to understand how non-coding SNPs contribute to inflammatory bowel disease, revealing patient-specific mechanisms and pathways.

## Contribution

A novel systems genomics pipeline, iSNP, is introduced to model the cumulative impact of non-coding SNPs on gene regulatory networks and signaling pathways in IBD.

## Key findings

- Non-coding SNPs in IBD patients converge on shared signaling pathways but affect distinct downstream gene regulatory modules.
- SNP-propagated networks stratify IBD patients into clusters reflecting cell-type-specific gene dysregulation and treatment response.
- The iSNP approach bridges genotype-phenotype gaps in complex diseases like Crohn’s and ulcerative colitis.

## Abstract

Genome-wide association studies have identified numerous susceptibility loci in complex diseases, such as chronic immune-mediated inflammatory disorders (IMIDs), yet their impact on pathomechanisms remains poorly understood. Low effect sizes, polygenicity, and predominance within non-coding genomic regions remain major challenges to the functional interpretation of IMID-associated single-nucleotide polymorphisms (SNPs). To address this, we present a novel systems genomics approach which models the cumulative impact of non-coding SNPs on downstream cellular signalling and gene regulatory networks. Applying this to the prototypical chronic IMIDs of Crohn’s disease (CD) and ulcerative colitis (UC), both forms of inflammatory bowel disease (IBD), we individually analysed 2,636 patient genomes. Signals from non-coding SNPs were found to propagate towards well-established and novel CD- and UC-associated pathogenic pathways through the signalling and gene regulatory layers. The SNP-propagated gene regulatory networks stratified CD and UC patients into distinct clusters corresponding to cell type-specific gene dysregulation and potential therapeutic response. This approach bridges the gap between genotype and phenotype, laying the foundations for accelerating precision medicine in complex diseases.

The iSNP systems genomics pipeline models how non-coding SNPs cumulatively impact cellular signalling and gene regulatory networks, revealing patient-specific mechanisms linking disease-associated genotypes to disease-relevant pathways.

By propagating signals from non-coding SNPs associated with the prototypical complex disorder, inflammatory bowel disease (IBD), iSNP reveals how these genetic variants perturb signalling and gene regulatory networks to converge on both established and previously unrecognised regulators of the disease.A large patient cohort encompassing the two major IBD subtypes, ulcerative colitis and Crohn’s disease, shows minimal overlap in non-coding SNPs. However, patient-specific variants converge on many of the same signalling pathways, but subsequently perturb largely distinct downstream gene regulatory modules.SNP-propagated gene regulatory networks stratify IBD patients into clusters that reflect cell type-specific gene dysregulation and potential treatment response.

By propagating signals from non-coding SNPs associated with the prototypical complex disorder, inflammatory bowel disease (IBD), iSNP reveals how these genetic variants perturb signalling and gene regulatory networks to converge on both established and previously unrecognised regulators of the disease.

A large patient cohort encompassing the two major IBD subtypes, ulcerative colitis and Crohn’s disease, shows minimal overlap in non-coding SNPs. However, patient-specific variants converge on many of the same signalling pathways, but subsequently perturb largely distinct downstream gene regulatory modules.

SNP-propagated gene regulatory networks stratify IBD patients into clusters that reflect cell type-specific gene dysregulation and potential treatment response.

The iSNP systems genomics pipeline models how non-coding SNPs cumulatively impact cellular signalling and gene regulatory networks, revealing patient-specific mechanisms linking disease-associated genotypes to disease-relevant pathways.