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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...

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Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
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Design and interpretation of eQTL-GWAS colocalisation studies: Lessons from a large-scale evaluation.

Guillermo Reales1, Jeffrey M Pullin2, Ichcha Manipur1

  • 1Cambridge Institute of Therapeutic Immunology & Infectious Disease (CITIID), Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, University of Cambridge, Cambridge, United Kingdom.

Plos Genetics
|May 8, 2026
PubMed
Summary
This summary is machine-generated.

Closing the "colocalisation gap" requires diverse cell types and large, high-granularity expression quantitative trait loci (eQTL) studies. Integrating genetic association studies (GWAS) with eQTL data is key for identifying disease-related genes.

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Area of Science:

  • Genetics and Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Genome-Wide Association Studies (GWAS) identify genetic variants associated with diseases, but pinpointing causal genes remains challenging.
  • Colocalisation analysis integrates GWAS with molecular quantitative trait loci (QTL) data to infer shared genetic architecture and identify candidate effector genes.
  • A significant portion of GWAS loci remain unexplained, termed the 'colocalisation gap', hindering the identification of disease-associated genes.

Purpose of the Study:

  • To systematically characterize two large-scale eQTL colocalisation studies.
  • To describe the determinants of the 'colocalisation gap'.
  • To inform the selection and design of future eQTL studies to close this gap.

Main Methods:

  • Analysis of over 1.3 million colocalisation tests from Open Targets Genetics (OTG).
  • Performance and analysis of colocalisations from 14 immune-mediated disease (IMD) GWAS and 12 diverse immune cell eQTL studies.
  • Utilisation of simulations, sensitivity analyses, and enhancer-promoter capture data.

Main Results:

  • 50% of GWAS peaks in OTG and 34% in IMDs colocalised, with higher likelihood near genes, having common lead variants, and in disease-relevant tissues.
  • Lower granularity eQTL studies yielded more colocalisations, especially for lower-frequency variants, while higher resolution studies showed higher colocalisation probability per eQTL.
  • Over 50% of colocalisations were cell-type specific, and 47% of GWAS peaks colocalised with multiple genes, suggesting coregulation.

Conclusions:

  • Closing the colocalisation gap necessitates diverse cellular contexts and large, high-granularity eQTL studies.
  • Cell-specific eQTLs are crucial, as many colocalisations occur in only one cell type.
  • Triangulation of observational data is vital for gene prioritisation, complementing experimental perturbation for causality assessment.