The genetic study of proteins is a breakthrough i


image: By comparing the genetically inferred causal relationships of proteins on human disease with historical drug development programs, this study, for the first time, showed that protein-disease pairs with predicted genetic evidence of causation are more likely to be drugs approved for the same indications. To support open science, the working group created a graphical database, the EpiGraphDB Proteome PheWAS browser (www.epigraphdb.org/pqtl/), which makes the more than 220,000 pairs of protein-disease associations openly accessible to the public. The team also shared the analysis protocol with the public via GitHub (https://github.com/MRCIEU/epigraphdb-pqtl).
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Credit: Dr Jie Zheng

An innovative genetic study of protein levels in the blood, conducted by researchers in the MRC Integrative Epidemiology Unit (MRC-IEU) at the University of Bristol, demonstrated how genetic data can be used to support the prioritization of drug targets by identifying the causal effects of proteins on disease.

Working with pharmaceutical companies, Bristol researchers developed a comprehensive analytical pipeline using genetic prediction of protein levels to prioritize drug targets, and quantified the potential of this approach to reduce the rate of developmental failure. drugs.

Genetic studies of proteins are in their infancy. The aim of this research, published in Genetics of nature, was to establish whether the genetic prediction of the effects of protein targets could predict the success of drug trials. Dr Jie Zheng, Professor Tom Gaunt and his colleagues at the University of Bristol have worked with pharmaceutical companies to establish a multidisciplinary collaboration to answer this scientific question.

Using a set of genetic epidemiology approaches, including Mendelian randomization and genetic colocalization, the researchers constructed a causal network of 1002 plasma proteins in 225 human diseases. In doing so, they identified 111 putative causal effects of 65 proteins on 52 diseases, spanning a wide range of disease areas. The results of this study are accessible via EpiGraphDB: http://www.epigraphdb.org/pqtl/

Lead author Dr Zheng said their estimated effects of proteins on human disease could be used to predict the effects of drugs targeting these proteins.

“This pipeline of analysis could be used to validate both the efficacy and potential adverse effects of new drug targets, as well as provide evidence to reassign existing drugs to other indications.

“This study lays a solid methodological basis for future genetic studies of omics. The next step is to use the analytical protocol in the pipeline for validating early drug targets by the pharmaceutical collaborators of the study. We hope these results will support further drug development – increase the success rate of drug trials, reduce drug costs and benefit patients, ”said Dr. Zheng.

Tom Gaunt, Professor of Health and Biomedical Informatics, University of Bristol, and Member of the NIHR Bristol Biomedical Research Center, added: “Our study used publicly available data published by many researchers around the world (compiled by the MRC-IEU OpenGWAS database), and truly demonstrates the potential of sharing open data to enable new discoveries in health research. We have demonstrated that this reuse of existing data offers an effective approach to reduce drug development costs with expected benefits for health and society. ”

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Paper

“Mendelian randomization at the phenome scale mapping the influence of the plasma proteome on complex diseases” by Jie Zheng et al in Genetics of nature.


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