Genetic study of proteins is a breakthrough in the development of medicine for complex diseases.


Comparing the genetic predicted causal relationship of proteins on human diseases with historical drug development programs, this study showed for the first time that protein-disease pairs with genetic predictive evidence are more likely to have approved drugs for the same indication. To support open science, the Working Group created a graphical database, the EpigraphyDB Protein Favus Browser (www.epigraphdb.org/pqtl/), making more than 220,000 pairs of protein-disease partners open to the public. is. The team shared the analysis protocol with the public via GitHub (https://github.com/MRCIEU/epigraphdb-pqtl). Sincerely: Dr. G. Zheng

An innovative genetic study of blood protein levels, led by researchers at the MRC Integrative Epidemiology Unit (MRC-IEU) at the University of Bristol, has shown how drug targets are prioritized by identifying the effects of proteins on proteins. Genetic data can be used to support. Diseases.


Working in collaboration with pharmaceutical companies, Bristol researchers have developed a comprehensive analysis pipeline using genetic prediction of protein levels to prioritize drug targets, and this approach to reducing drug development failure rates Has set the capacity of.

Genetic studies of proteins are in their infancy. The purpose of this research published in Nature genetics, Establish if genetic prediction of protein target effects could predict drug test success. Dr. Ji Zheng, Professor Tom Gant and colleagues from the University of Bristol worked with pharmaceutical companies to establish multi-disciplinary collaborations to address this scientific question.

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

The lead author, Dr. Zheng stated that their predicted effect of proteins on human diseases can be used to estimate the effects of drugs targeting these proteins.

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

“This study lays the foundation for a solid methodology for future genetic studies. The next step is for the analytical protocol to be used by the study’s pharmaceutical partners in the initial drug target validation pipeline. We hope these findings support further drug development will do?” Increasing the success rate of drug trials, reducing drug costs and benefiting patients, ”said Dr. Zheng.

Tom Gaunt, Professor of Health and Biomedical Informatics, University of Bristol, and a member of the NIHR Bristol Biomedical Research Center, said: “Our study used publicly available data published by many researchers around the world (MRC-IEU OpenGas Collided with the database)), and indeed demonstrates the ability to share open data in enabling novel discoveries in health research. We have shown that this reuse of existing data provides medicines with anticipated benefits for health and society. Development provides an efficient approach to reduce costs. ”


The new computational tool enables predicting major functional sites in proteins based on structure


more information:
Phenome-wide Mendelian randomization mapping the effect of plasma proteome on complex diseases, Nature genetics (2020). DOI: 10.1038 / s41588-020-0682-6, www.nature.com/articles/s41588-020-0682-6

Provided by the University of Bristol

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