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Mendelian randomization for studying the effects of perturbing drug targets

  • Dipender Gill*
  • , Marios K. Georgakis
  • , Venexia M. Walker
  • , A. Floriaan Schmidt
  • , Apostolos Gkatzionis
  • , Daniel F. Freitag
  • , Chris Finan
  • , Aroon D. Hingorani
  • , Joanna M. M. Howson
  • , Stephen Burgess
  • , Daniel I. Swerdlow
  • , George Davey Smith
  • , Michael V. Holmes
  • , Martin Dichgans
  • , Robert A. Scott
  • , Jie Zheng
  • , Bruce M. Psaty
  • , Neil M. Davies
  • *Corresponding author for this work
  • Imperial College London
  • Novo Nordisk Research Centre Oxford
  • St. George's University of London
  • Adult Critical Care, St George’s University Hospitals NHS Foundation Trust and St George’s University of London, London, UK
  • Ludwig Maximilian University of Munich
  • University of Bristol
  • Population Health Sciences, Bristol, United Kingdom
  • University of Pennsylvania
  • University College London
  • University Medical Center Utrecht
  • University of Cambridge
  • Bayer AG
  • British Heart Foundation
  • University College London Hospitals, London, UK
  • University of Oxford
  • Munich Cluster for Systems Neurology (SyNergy)
  • German Center for Neurodegenerative Diseases
  • GlaxoSmithKline
  • University of Washington
  • Kaiser Permanente
  • Norwegian University of Science and Technology

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Abstract

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.
Original languageEnglish
Article number16
JournalWellcome Open Research
Volume6
DOIs
Publication statusPublished - 2021
Externally publishedYes

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