| Title: | High-definition likelihood inference of genetic colocalization reveals protein biomarkers for human complex diseases |
| Journal: | GigaScience |
| Published: | 21 Jan 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/41574934/ |
| DOI: | https://doi.org/10.1093/gigascience/giaf155 |
Publication 18585
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Abstract
BACKGROUND: 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.</p>
FINDINGS: 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.</p>
CONCLUSION: This research establishes a robust framework for detecting genetic colocalization signals, enabling the prioritization of disease-relevant protein targets and informing therapeutic development strategies.</p>
6 Keywords
- Biomarkers
- Computational Biology
- Female
- Genetic Predisposition to Disease
- Humans
- Male
6 Authors
- Yuying Li
- Ranran Zhai
- Zhijian Yang
- Ting Li
- Yudi Pawitan
- Xia Shen
Enabling scientific discoveries that improve human health