Last updated: 2025-02-03

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Current Research on IBD

This section summarize the WES and GWAS studies on IBD, giving background information on the disease and current research on the diease.

Disease Background

General Background of IBD

  • IBD is Chronic inflammation influenced by genetics, environment, microbiota, and immunity.

  • Genetic Contribution

    • Crohn’s Disease (CD): 15% family history; twin studies show 50% concordance in monozygotic (MZ) twins vs. less than 10% in dizygotic (DZ) twins.

    • GWAS identified 163 loci; trans-ethnic studies identified an additional 38 loci.

  • Epigenetics

    • Genome-environment interactions affect disease progression.

    • Emerging research focuses on the role of epigenetics in IBD.

Genetic Associations in IBD

  • NOD2: First CD-associated gene (2001), with key variants R702W and G908R.

  • Autophagy Genes: ATG16L1, LRRK2, and IRGM predispose individuals to IBD.

  • IL-10 Receptor Mutations: IL10RA, IL10RB are linked to colitis.

  • IBD-Associated Loci: ~240 loci identified (as of 2022); 30 shared between CD and UC.

  • CD Predictive Loci: FOXO3, IGFBP1, and XACT as potential markers.

Large-Scale Exome Sequencing Identifies Novel Genes and Pathways in Crohn’s Disease

Sazonovs, Aleksejs et al.

Motivation

  • CD is a chronic inflammatory disorder with a strong genetic component.

  • GWAS has primarily focused on common variants, but rare coding variants remain under-explored.

Study Design

Findings

  • Single Variant Analysis: 94 out of 116 variants replicated in discovery datasets.

  • Burden Test: Missense vs. frameshift variants analyzed. #### Takeaways

  • Exome Sequencing Complements GWAS

    • Addresses gaps in genetic architecture (low-frequency and rare variants) that earlier CD GWAS meta-analyses could not observe.

  • Significance of Coding Variants:

    • Coding variants, although fewer than noncoding variants, are highly enriched for associations with both common and rare diseases.
    • Tend to have stronger effects due to natural selection, which often keeps their frequencies low.
    • Provide direct links to specific genes and pathogenic mechanisms, unlike noncoding variants.

  • Key Findings in CD Pathogenesis:

    • Novel genes such as PDLIM5, SDF2L1, HGFAC, PAF-R, and CCR7 emphasize the role of mesenchymal cells (MCs) in intestinal inflammation.

  • Therapeutic Implications:

    • Findings highlight the potential for therapeutic strategies targeting mesenchymal niche balance to address CD pathogenesis.
    • Genetic evidence for drug targets is valuable for driving drug development.

  • Future Directions:

    • Expanded sequencing efforts in ulcerative colitis and integrated analyses with larger GWAS studies are expected to identify more linked genes.

Genome-wide association study implicates immune activation of multiple integrin genes in inflammatory bowel disease

de Lange, K., Moutsianas, L., Lee, J. et al.

Motivation

  • Current treatments involve immunomodulators, but patients often experience side effects or treatment resistance.

  • GWAS and Immunochip studies have identified risk loci but have had limited therapeutic impact.

Study Design

Findings

Identified 25 new GWAS loci

  • 4 loci with significant variants
    • SLAMF8
    • RORC
    • PLCG2
    • NCF4
  • Another 4 loci within integrin gene
    • 3 loci showed larger than 90% probability have colocalization with eQTL
    • 1 loci showed intermediate evidendence

Takeaways

  • Integrins are not only important in cell trafficking but can also participate in cellular signaling.

  • Highlighted integrins as key therapeutic targets:

    • Monoclonal antibodies like vedolizumab and etrolizumab targeting integrins have shown efficacy in IBD.
    • Identified SMAD7, a modulator of \(TGF-\beta\) signaling, as a potential target for Crohn’s disease treatment.

  • Emphasized the importance of gut-selective therapies to minimize risks like progressive multifocal leukoencephalopathy (PML).

  • These discoveries have demonstrated that the effect sizes of GWAS associations do not necessarily reflect the importance or therapeutic relevance of their underlying biological pathways.

Method

Prioritizing disease-mediating genes leveraging trans-gene regulation

Motivation

  • Complex Genetic Architecture of Diseases:
    • Complex traits are highly polygenic, involving numerous genes with varying effect sizes.
    • The omnigenic model posits that all expressed genes in disease-relevant tissues contribute to disease risk, but not all genes have equal importance.
  • Importance of Disease-Mediating Genes:
    • Disease-associated genes can be categorized into:
      • Disease-mediating genes: Directly affect the disease.
      • Peripheral genes: Indirectly influence the disease through trans-regulatory networks.
    • Identifying disease-mediating genes is critical for uncovering pathways and mechanisms relevant to treatment development.
  • Limitations of Current Gene Prioritization Methods:
    • GWAS:
      • Identifies important disease-mediating genes but may miss many due to lack of power in detecting low-frequency or rare variants with large effects.
    • Rare Variant Methods:
      • Gene-based approaches, like burden tests in WES, improve detection but are limited by current sample sizes.
    • Graph and Canonical Correlation Models:
      • Recent approaches predict candidate genes or link expressions to variants but fail to explicitly differentiate disease-mediating from peripheral genes or reveal trans-regulatory networks.
  • Challenges with Cis-eQTLs in Disease Gene Discovery:
    • Disease genes often lack strong cis-QTL signals due to stronger selective constraints.
    • Cis-eQTL limitations necessitate focusing on trans-QTLs, as their target genes share similar selective constraints with disease genes.
  • Significance of Trans-Regulation:
    • Trans-regulatory networks explain a large portion (~70%) of disease heritability.
    • Understanding trans-regulation provides insights into collaborative gene contributions to disease risk and highlights the importance of completing trans-gene regulatory networks for understanding complex trait genetics.

COTA

  • COTA Model: Integrates trans-regulatory effects to identify disease-mediating genes.

  • GBAT: Predicts gene expression using machine learning models.

  • DACT: Estimates gene effects from burden tests.

Findings

  • COTA enhances GWAS interpretability by revealing trans-regulatory networks.

  • New gene discoveries provide insights into disease mechanisms.

  • Potential for targeted therapy development based on genetic findings.

Code

Data

Whole Exon Sequencing

Trans Effect

GWAS

  1. Numerized item 1

    1. Numerized item 1.1

    2. Numerized item 1.2

  2. Numerized item 2

3. Insert figures

Way 1

Way 2

knitr::include_graphics(c("assets/logo.png"), error = FALSE)
Figure: Logo.

Figure: Logo.

4. Insert tables

  • By Markdown syntax
AAA 123
bbb
  • ccc: 123
  • ddd: 123
  • eee: 123
  • fff: 123
  • ggg: 123
  • By R script
res = data.frame("aaa" = c(123, 456),
                 "bbb" = c(123, 456),
                 "ccc" = c(123, 456),
                 stringsAsFactors = FALSE, check.names = FALSE)
knitr::kable(res)
aaa bbb ccc
123 123 123
456 456 456
  • By reading a file
res = read.table('./data/file_res.txt', header = TRUE, sep = "\t", stringsAsFactors = TRUE)
knitr::kable(res)
aaa bbb ccc
123 123 123
456 456 456

Another week

1. Analysis 1

Analysis

Figures, tables

Observations

2. Analysis 2


R version 4.2.2 (2022-10-31)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Ventura 13.3.1

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2-arm64/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] workflowr_1.7.1

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.12      highr_0.10       compiler_4.2.2   pillar_1.10.1   
 [5] bslib_0.5.0      later_1.3.1      git2r_0.33.0     jquerylib_0.1.4 
 [9] tools_4.2.2      getPass_0.2-4    digest_0.6.31    jsonlite_1.8.8  
[13] evaluate_0.21    lifecycle_1.0.4  tibble_3.2.1     pkgconfig_2.0.3 
[17] rlang_1.1.3      cli_3.6.2        rstudioapi_0.14  yaml_2.3.7      
[21] xfun_0.39        fastmap_1.1.1    httr_1.4.7       stringr_1.5.1   
[25] knitr_1.43       fs_1.6.2         vctrs_0.6.5      sass_0.4.6      
[29] rprojroot_2.0.4  glue_1.7.0       R6_2.5.1         processx_3.8.1  
[33] rmarkdown_2.22   callr_3.7.3      magrittr_2.0.3   whisker_0.4.1   
[37] ps_1.7.5         promises_1.2.0.1 htmltools_0.5.5  httpuv_1.6.11   
[41] stringi_1.8.4    cachem_1.0.8