# High-definition likelihood inference of genetic colocalization reveals protein biomarkers for human complex diseases

## TL;DR

This paper introduces a new method for genetic colocalization analysis that improves the identification of protein biomarkers for human diseases.

## Contribution

The novel HDL-C method provides more accurate inference of genetic colocalization compared to existing tools.

## Key findings

- HDL-C outperforms COLOC, SuSiE, and SharePro in detecting genetic colocalization.
- The method identified 40 validated drug-protein–disease combinations and 62 potential drug re-purposing opportunities.
- 63 novel protein–disease pairs were discovered as potential therapeutic targets.

## Abstract

Genetic colocalization analysis is essential for understanding the shared genetic basis between phenotypic traits. Such an analysis is particularly useful for identifying plasma proteins with potential as therapeutic targets or clinical biomarkers. Improvements to existing tools are needed for more accurate inference of potentially causal biomarkers.

We develop high-definition likelihood for colocalization inference (HDL-C), a method for genetic colocalization analysis. Based on simulations and observed rediscovery rates in real data analyses, we demonstrate that the HDL-C approach outperforms state-of-the-art methods, COLOC, SuSiE, and SharePro, in detecting genetic colocalization, thus enabling a more complete understanding of genetic connections at specific loci. Analyses of the top 50 protein–disease pairs identified by HDL-C in the male and female cohorts of the UK Biobank uncovered 40 previously validated drug-protein–disease combinations with approved drugs matching the phenotypes and 62 combinations with potential drug re-purposing opportunities. Additionally, we identified 63 novel protein–disease pairs that suggest promising candidates for future therapeutic interventions.

This research establishes a robust framework for detecting genetic colocalization signals, enabling the prioritization of disease-relevant protein targets and informing therapeutic development strategies.