Leveraging large-scale biobanks for therapeutic target discovery

Large biobanks, including the Million Veteran Program (MVP), the UK Biobank, and FinnGen, provide genetic association results for more than 1 million individuals for hundreds of phenotypes. To select targets for pharmaceutical development, as well as to improve the understanding of existing targets, we harmonized these studies and performed two-sample Mendelian randomization (MR) on 2,003 phenotypes using genetic variants associated with gene expression (derived from GTEx and eQTLGen) and plasma protein levels (derived from ARIC, Fenland, and deCODE) as proxies of target modulation. We found 69,669 gene-trait pairs with evidence (p ≤ 1.6 × 10<sup>-9</sup>) for causal effects. From the selected gene-trait pairs, we observed 6,447 genes with strong causal evidence for at least one of 2,003 investigated traits. As expected, being identified as a gene-trait pair in our approach was significantly associated with higher odds of being an approved drug target and indication. We were able to rediscover 9% of approved drug targets in ChEMBL 34. Moreover, identified gene-traits were significantly associated with higher odds of being previously described as a gene-trait pair in OMIM, ClinVar, mouse knockout data, and rare variant burden studies. To enhance the translational potential of the resource, we developed a predictive ranking model trained using approved drug targets described in ChEMBL 34 as well as several different biological annotations. This model was able to accurately predict the odds of a particular significant MR result being developed into an approved drug and its clinical indication (precision-recall area under the receiver operating characteristic curve 0.79). We make our results publicly available in CIPHER.

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Publication Details

Journal
Human Genetics and Genomics Advances
Published
2025-12-10
DOI
https://doi.org/10.1016/j.xhgg.2025.100556
Primary Topic
Genetic Associations and Epidemiology
Type
article
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article

Leveraging large-scale biobanks for therapeutic target discovery

Hesam Dashti, Danielle Rasooly, Liam Gaziano, Luis E. Selva et al.
Human Genetics and Genomics Advances
Genetic Associations and Epidemiology
article

Leveraging large-scale biobanks for therapeutic target discovery

Hesam Dashti, Danielle Rasooly, Liam Gaziano, Luis E. Selva, Jennifer E. Huffman, Emanuele Di Angelantonio, J. Michael Gaziano, Gibran Hemani, Nicole Kosik, John C. Whittaker, Katherine P. Liao, Juan P. Casas, Mohd Anisul Karim, Claudia Giambartolomei, Alexandre C. Pereira, Lauren Costa, Fiona Hunter, Tom R. Gaunt, Andrew R. Leach, Gina M. Peloso, Sumitra Muralidhar, Brian R. Ferolito, Kelly Cho, Harris Ioannidis, Gian Gaetano Tartaglia, Gabriele Proietti, Barbara Zdrazil, Claudia Langenberg, Philip S. Tsao, Daniel J Golden, Maya Ghoussaini, Yakov A. Tsepilov, Ines Smit, Jennifer Moser, Tianxi Cai, Stacey B. Whitbourne, Andréa R. V. R. Horimoto, Scott M. Damrauer, Giorgio Bini, Adam S. Butterworth, Rachael Matty, Yi Liu, Kai Gravel-Pucillo
article en

Abstract

Large biobanks, including the Million Veteran Program (MVP), the UK Biobank, and FinnGen, provide genetic association results for more than 1 million individuals for hundreds of phenotypes. To select targets for pharmaceutical development, as well as to improve the understanding of existing targets, we harmonized these studies and performed two-sample Mendelian randomization (MR) on 2,003 phenotypes using genetic variants associated with gene expression (derived from GTEx and eQTLGen) and plasma protein levels (derived from ARIC, Fenland, and deCODE) as proxies of target modulation. We found 69,669 gene-trait pairs with evidence (p ≤ 1.6 × 10<sup>-9</sup>) for causal effects. From the selected gene-trait pairs, we observed 6,447 genes with strong causal evidence for at least one of 2,003 investigated traits. As expected, being identified as a gene-trait pair in our approach was significantly associated with higher odds of being an approved drug target and indication. We were able to rediscover 9% of approved drug targets in ChEMBL 34. Moreover, identified gene-traits were significantly associated with higher odds of being previously described as a gene-trait pair in OMIM, ClinVar, mouse knockout data, and rare variant burden studies. To enhance the translational potential of the resource, we developed a predictive ranking model trained using approved drug targets described in ChEMBL 34 as well as several different biological annotations. This model was able to accurately predict the odds of a particular significant MR result being developed into an approved drug and its clinical indication (precision-recall area under the receiver operating characteristic curve 0.79). We make our results publicly available in CIPHER.

Human Genetics and Genomics AdvancesVol. 7(1)
Institució Catalana de Recerca i Estudis Avançats (ES), Novartis (Switzerland) (CH), European Bioinformatics Institute (GB), Harvard University (US), Regeneron (United States) (US), VA Palo Alto Health Care System (US), University of Cambridge (GB), At Bristol (GB), VA Boston Healthcare System (US), VA Office of Research and Development (US), Italian Institute of Technology (IT), British Heart Foundation (GB), University of Bristol (GB), Open Targets (GB), Palo Alto Veterans Institute for Research (US), VA New England Healthcare System (US), Human Technopole (IT), Philadelphia VA Medical Center (US), MRC Biostatistics Unit (GB), Berlin Institute of Health at Charité - Universitätsmedizin Berlin (DE), Center for Genomic Science (IT), University of Pennsylvania (US), University of Genoa (IT)
Openalex Percentile: Top 59%
Genetic Associations and Epidemiology
DOIOpen Access PDFOpenAlex
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