A Convergent Study of Genetic Variants Associated With Crohn’s Disease: Evidence From GWAS, Gene Expression, Methylation, eQTL and TWAS

Abstract

Crohn’s Disease (CD) is one of the predominant forms of inflammatory bowel disease (IBD). A combination of genetic and non-genetic risk factors have been reported to contribute to the development of CD. Many high-throughput omics studies have been conducted to identify disease associated risk variants that might contribute to CD, such as genome-wide association studies (GWAS) and next generation sequencing studies. A pressing need remains to prioritize and characterize candidate genes that underlie the etiology of CD. In this study, we collected a comprehensive multi-dimensional data from GWAS, gene expression, and methylation studies and generated transcriptome-wide association study (TWAS) data to further interpret the GWAS association results. We applied our previously developed method called mega-analysis of Odds Ratio (MegaOR) to prioritize CD candidate genes (CDgenes). As a result, we identified consensus sets of CDgenes (62–235 genes) based on the evidence matrix. We demonstrated that these CDgenes were significantly more frequently interact with each other than randomly expected. Functional annotation of these genes highlighted critical immune-related processes such as immune response, MHC class II receptor activity, and immunological disorders. In particular, the constitutive photomorphogenesis 9 (COP9) signalosome related genes were found to be significantly enriched in CDgenes, implying a potential role of COP9 signalosome involved in the pathogenesis of CD. Finally, we found some of the CDgenes shared biological functions with known drug targets of CD, such as the regulation of inflammatory response and the leukocyte adhesion to vascular endothelial cell. In summary, we identified highly confident CDgenes from multi-dimensional evidence, providing insights for the understanding of CD etiology.

Reaching the End-Game for GWAS: Machine Learning Approaches for the Prioritization of Complex Disease Loci

“…This lack is contrasted by studies solely integrating wide-ranging omics data to calculate GWAS prioritization scores (Ayalew et al, 2012;Ciesielski et al, 2014) -and identifies potential for collaboration with ML to improve data integration methods. To date ML studies highlight the benefits of multi-omic integration, but few directly investigate that need (Merelli et al, 2013;Dai et al, 2019).…”

“…Furthermore, the cataloging of eQTLs mapped to non-coding RNA provides a better insight into how non-coding RNA affects gene expression (Branco et al, 2018), increasing the strength of regulatory information at the disposal of ML models. The growing integration of related biological features suggest this will provide clearer insight for models to be able to pinpoint the most likely disease causing genes in a locus (Branco et al, 2018;Dai et al, 2019).…”

Diverse types of genomic evidence converge on alcohol use disorder risk genes

“…We tested whether AUDgenes had significantly more interactions than the random expectation using two curated human PPI networks: influence graph,33 and a combined dataset of Human Protein Reference Database (HPRD) and Search Tool for the Retrieval of Interacting Genes/Proteins (Merging Affected Genes into Integrated networks) 34. These reference networks were described in a previous study 21. As shown in figure 3, for each set of AUDgenes from evidence-only matrix, we resampled 10 000 random gene sets in the same set sizes.…”

“…At the stable status, genes in S tended to have the optimised cOR. Further details can be found in our previous works 20 21…”

“…With this assumption, we recently developed an unsupervised machine learning approach named mega-analysis of OR (MegaOR), aiming at identifying groups of genes that collectively lead to the maximum load of evidence across multi-omics data 20. We have successfully implemented the method in studying the genetics of schizophrenia20 and Crohn’s Disease 21. In this study, we attempted to use MegaOR to identify AUD-associated genes.…”