PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
March 31, 2026Current Drug Targets0 citations

Systematic Identification and Characterization of Causal Risk GenesImplicated in Colorectal Cancer by Integrating GWAS, eQTL, and mQTLData

View Full Paper
SXShuai XuGGGuanglin GuoJGJie Gao

Key Points

  • The aim is to uncover the mechanisms by which genetic variations influence colorectal cancer susceptibility.
  • Developed an integrative framework to analyze GWAS, eQTL, and mQTL data.
  • Employed the SMR&HEIDI and TSMR methods for prioritizing causal risk genes.
  • Validated findings through gene expression data and transcription factor binding affinity analysis.
  • Identified 10 tissue-specific gene-SNP pairs and 3 blood eQTL-gene pairs.
  • Discovered 26 gene-CpG-SNP regulatory modules and 39 microbiota-associated gene-SNP pairs.
  • Highlighted potential regulatory influences linked to common genetic variations affecting CRC development.

Abstract

Introduction: Knowledge of the mechanisms through which common single-nucleotide polymorphisms (SNPs) modulate colorectal cancer (CRC) susceptibility is central to elucidating the molecular basis of this disease. Genome-wide association studies (GWAS) reveal noncoding SNPs influencing CRC susceptibility, yet their functional mechanisms, particularly through gene expression dysregulation, DNA methylation alterations, and interactions with gut microbiota, remain uncharacterized. Through integrative analysis, systematically exploring the effects of genetic variations on gene expression heterogeneity, DNA methylation, and gut microbiome is expected to yield potential biomarkers for early diagnosis and intervention of CRC. Methods: An integrative framework is developed to prioritize causal risk genes at CRC-associated GWAS loci, applying the SMR&HEIDI (Summary-data-based Mendelian randomization and heterogeneity in dependent instruments) and TSMR (Two-sample Mendelian Randomisation) methods. The findings were validated via gene expression and TF binding affinity. Results: 10 tissue-specific gene-SNP pairs, 3 blood eQTL-gene pairs, 26 gene-CpG-SNP regulatory modules, and 39 microbiota-associated gene-SNP pairs are identified. A few potential regulatory influences on CRC development associated with genes and variants, such as POU5F1B and rs10797801, were identified. Moreover, the genetic variants disrupted TF binding affinity while only a few promoted the binding of transcription factors (TFs). discussion: No Discussion Discussion: The data integration enabled us to prioritize genes according to different regulatory mechanisms, such as gene expression and DNA methylation, and bridge the gap between statistical associations and biological functionality. Conclusion: Multi-omics integration reveals some causal risk genes and variants implicated in CRC. These findings offer novel insight into the molecular mechanisms underlying CRC susceptibility and provide valuable clues for diagnosis and therapeutic intervention strategies.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Xu et al. (2026) studied this question.

synapsesocial.com/papers/69cb64f0e6a8c024954b8f71https://doi.org/10.2174/0113894501444884260130225445
Ask AI
Helpful
Bookmark
Share
View Full Paper