Mendelian randomization for studying the effects of perturbing drug targets

Dipender Gill(St George's, University of London), Marios K. Georgakis(University of Cambridge), Venexia Walker(University of Bristol), Amand F. Schmidt(University of Washington), Apostolos Gkatzionis(Kaiser Permanente Washington Health Research Institute), Daniel F. Freitag(Imperial College London), Chris Finan(British Heart Foundation), Aroon D. Hingorani(British Heart Foundation), Joanna M. M. Howson(Novo Nordisk (United Kingdom)), Stephen Burgess(Kaiser Permanente Washington Health Research Institute), Daniel I. Swerdlow(German Center for Neurodegenerative Diseases), George Davey Smith(University of Bristol), Michael V. Holmes(University of Oxford), Martin Dichgans(German Center for Neurodegenerative Diseases), Jie Zheng(University of Bristol), Bruce M. Psaty(Kaiser Permanente Washington Health Research Institute), Neil M Davies(Norwegian University of Science and Technology)
Wellcome Open Research
January 28, 2021
Cited by 282Open Access
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Abstract

<ns4:p>Drugs whose targets have genetic evidence to support efficacy and safety are more likely to be approved after clinical development. In this paper, we provide an overview of how natural sequence variation in the genes that encode drug targets can be used in Mendelian randomization analyses to offer insight into mechanism-based efficacy and adverse effects. Large databases of summary level genetic association data are increasingly available and can be leveraged to identify and validate variants that serve as proxies for drug target perturbation. As with all empirical research, Mendelian randomization has limitations including genetic confounding, its consideration of lifelong effects, and issues related to heterogeneity across different tissues and populations. When appropriately applied, Mendelian randomization provides a useful empirical framework for using population level data to improve the success rates of the drug development pipeline.</ns4:p>


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