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Related Experiment Video

Updated: Oct 6, 2025

Deciphering High-Resolution 3D Chromatin Organization via Capture Hi-C
09:32

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Fine mapping with epigenetic information and 3D structure.

Gisela Orozco1,2

  • 1Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, AV Hill Building, Oxford Road, Manchester, M13 9LJ, UK. gisela.orozco@manchester.ac.uk.

Seminars in Immunopathology
|January 13, 2022
PubMed
Summary
This summary is machine-generated.

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Genome-wide association studies (GWAS) identify genetic risks for complex diseases. New genomics and epigenomics tools help interpret these findings for personalized medicine and drug discovery.

Area of Science:

  • Genomics
  • Epigenomics
  • Translational Medicine

Background:

  • Thousands of genome-wide association studies (GWAS) have identified numerous genetic variants linked to complex traits and diseases like autoimmune conditions since 2005.
  • While GWAS data holds potential for personalized medicine and novel drug targets, clinical translation is hindered by challenges in functional interpretation and identifying causal variants/genes.

Purpose of the Study:

  • To explore how recent advances in genomics and epigenomics can overcome limitations in the clinical translation of GWAS findings.
  • To highlight the role of regulatory element and chromatin interaction mapping in fine-mapping GWAS loci and understanding disease mechanisms.

Main Methods:

  • Utilizing a wide array of genomics tools.
  • Employing epigenomics techniques to map regulatory elements.
Keywords:
Autoimmune diseaseChromatin conformationEpigeneticsFine mappingFunctional genomicsGenome-wide association studies

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  • Characterizing chromatin interactions.

    • Main Results:

    • Advances in genomics and epigenomics are facilitating the functional interpretation of GWAS risk variants.
    • These tools enable fine-mapping of GWAS loci, improving the identification of causal variants and genes.
    • Enhanced understanding of biological mechanisms underlying complex diseases.

    Conclusions:

    • Genomics and epigenomics tools are crucial for unlocking the clinical potential of GWAS data.
    • These approaches advance the identification of disease-causing genes and biological pathways.
    • Overcoming interpretation challenges paves the way for improved patient care and therapeutic strategies.