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

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.

Data Ingestion and Preprocessing:

We standardize your datasets, applying quality controls like minor allele count thresholds, top loci detection, and conditional analyses to ensure reliable results.

Leveraging Available Datasets

We leverage available datasets such as the UK Biobank Pharma Proteomics Project (UKB-PPP) to integrate large-scale proteome data, improving disease risk prediction.

Fine-Mapping and Causal Variant Detection

Using Bayesian algorithms, we pinpoint causal variants with high confidence, resolving overlap between traits and reducing false positives.

Advanced Colocalization Analysis

Leveraging methods like the Coloc framework, we evaluate shared genetic mechanisms across traits, providing robust causal inference. Additionally, using multi-trait colocalization can help detect colocalization across vast numbers of traits simultaneously.

Sensitivity Analysis

Our approach includes sensitivity analysis beyond simple single-variant models, helping account for potential multi-locus effects and pleiotropy. This leads to more reliable insights and fewer false positives.

Visualization and Interpretation

From heatmaps to credible set comparisons, our visual outputs make complex results intuitive, paired with expert interpretation to guide your research decisions.

Customization

With various tools available, such as TWAS-Hub, Coloc-R, fastEnloc, and eQTpLot, we can evaluate performance and run analyses to determine the optimal pipeline for your needs. You can customize your colocalization pipeline based on parameters like allelic heterogeneity, fine mapping algorithm, prior probability, and posterior probability cut-off.

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