Colocalization Analysis

Context

Unveiling Shared Genetic Mechanisms Across Traits

Summary of lipid classes (blue, orange, green) that co-localize with coronary artery disease. Adapted from Figure 6, Cadby et al. (2022). Comprehensive genetic analysis of the human lipidome identifies novel loci controlling lipid homeostasis with links to coronary artery disease. Nat Commun 13, 3124.

Genetic variants associated with diseases are often found in non-coding regions of the genome, raising critical questions: How do these variants influence disease traits? Could they regulate gene expression (eQTL) or other quantitative traits (QTLs e.g. pQTL)? Colocalization analysis bridges this gap, providing a probabilistic framework to identify shared causal variants between genome-wide association studies (GWAS) and molecular data like gene expression (eQTL).

For example, colocalization can determine whether a genetic variant associated with coronary artery disease (CAD) risk also influences the expression of a nearby gene, identifying potential mechanisms driving disease. By revealing these shared pathways, colocalization analysis supports targeted drug discovery and deepens understanding of disease biology.

How We Can Help

Colocalization analysis is a powerful yet intricate process, demanding advanced statistical models, high-quality data, and computational efficiency. At Aganitha, we simplify this complexity, providing a seamless experience from study design to actionable insights. Our customizable colocalization pipelines are designed to address challenges like handling biobank-scale datasets, accounting for pleiotropy, and integrating multiple data types.

End-to-end colocalization pipeline

  • Integrate large-scale datasets (e.g., UK Biobank Pharma Proteomics Project) to enhance disease risk prediction.
  • Standardize datasets with quality controls like minor allele count thresholds and top loci detection to ensure reliable results.
  • Use Bayesian algorithms to confidently identify causal variants, reducing false positives and trait overlap.
  • Apply advanced frameworks like Coloc to evaluate shared genetic mechanisms across traits, providing robust causal inference.
  • Implement multi-locus and pleiotropy-aware models for sensitivity analysis to generate more reliable genetic insights.
  • Create intuitive visual outputs like heatmaps and credible set comparisons, supported by expert interpretation.
  • Utilize tools such as TWAS-Hub, Coloc-R, fastEnloc, and eQTpLot to tailor analysis pipelines to specific research needs.

Discover our offerings across the biopharma value chain

Our Solutions

Customizable platforms and solutions across the drug discovery value chain from target discovery to therapeutic development
Read More

Our Services

Offering services in computational sciences and technology to complement biopharma R&D

Read More

Case Studies

Helping clients accelerate drug discovery and development by building computational biology, computational chemistry, technology, and cloud solutions
Read More

Learn more about our MR Services

Contact Us
Contact Us