Literature DB >> 33644404

Mendelian randomization for studying the effects of perturbing drug targets.

Dipender Gill1,2,3,4,5, Marios K Georgakis6, Venexia M Walker7,8,9, A Floriaan Schmidt10,11, Apostolos Gkatzionis12, Daniel F Freitag13, Chris Finan10,14,15, Aroon D Hingorani10,14,15, Joanna M M Howson3, Stephen Burgess12,16, Daniel I Swerdlow10, George Davey Smith7,8,17, Michael V Holmes18, Martin Dichgans6,19,20, Robert A Scott21, Jie Zheng7,8, Bruce M Psaty22,23, Neil M Davies7,8,24.   

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. Copyright:
© 2021 Gill D et al.

Entities:  

Keywords:  Drugs; Genetics; Mendelian randomization

Year:  2021        PMID: 33644404      PMCID: PMC7903200          DOI: 10.12688/wellcomeopenres.16544.2

Source DB:  PubMed          Journal:  Wellcome Open Res        ISSN: 2398-502X


  94 in total

1.  Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets.

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Journal:  Nat Genet       Date:  2016-03-28       Impact factor: 38.330

2.  The use of phenome-wide association studies (PheWAS) for exploration of novel genotype-phenotype relationships and pleiotropy discovery.

Authors:  S A Pendergrass; K Brown-Gentry; S M Dudek; E S Torstenson; J L Ambite; C L Avery; S Buyske; C Cai; M D Fesinmeyer; C Haiman; G Heiss; L A Hindorff; C-N Hsu; R D Jackson; C Kooperberg; L Le Marchand; Y Lin; T C Matise; L Moreland; K Monroe; A P Reiner; R Wallace; L R Wilkens; D C Crawford; M D Ritchie
Journal:  Genet Epidemiol       Date:  2011-05-18       Impact factor: 2.135

3.  Estimated Research and Development Investment Needed to Bring a New Medicine to Market, 2009-2018.

Authors:  Olivier J Wouters; Martin McKee; Jeroen Luyten
Journal:  JAMA       Date:  2020-03-03       Impact factor: 157.335

4.  Mendelian randomization analysis with multiple genetic variants using summarized data.

Authors:  Stephen Burgess; Adam Butterworth; Simon G Thompson
Journal:  Genet Epidemiol       Date:  2013-09-20       Impact factor: 2.135

5.  GeneHancer: genome-wide integration of enhancers and target genes in GeneCards.

Authors:  Simon Fishilevich; Ron Nudel; Noa Rappaport; Rotem Hadar; Inbar Plaschkes; Tsippi Iny Stein; Naomi Rosen; Asher Kohn; Michal Twik; Marilyn Safran; Doron Lancet; Dana Cohen
Journal:  Database (Oxford)       Date:  2017-01-01       Impact factor: 3.451

6.  Low-density lipoproteins cause atherosclerotic cardiovascular disease: pathophysiological, genetic, and therapeutic insights: a consensus statement from the European Atherosclerosis Society Consensus Panel.

Authors:  Jan Borén; M John Chapman; Ronald M Krauss; Chris J Packard; Jacob F Bentzon; Christoph J Binder; Mat J Daemen; Linda L Demer; Robert A Hegele; Stephen J Nicholls; Børge G Nordestgaard; Gerald F Watts; Eric Bruckert; Sergio Fazio; Brian A Ference; Ian Graham; Jay D Horton; Ulf Landmesser; Ulrich Laufs; Luis Masana; Gerard Pasterkamp; Frederick J Raal; Kausik K Ray; Heribert Schunkert; Marja-Riitta Taskinen; Bart van de Sluis; Olov Wiklund; Lale Tokgozoglu; Alberico L Catapano; Henry N Ginsberg
Journal:  Eur Heart J       Date:  2020-06-21       Impact factor: 29.983

7.  Adjustment for index event bias in genome-wide association studies of subsequent events.

Authors:  Frank Dudbridge; Richard J Allen; Nuala A Sheehan; A Floriaan Schmidt; James C Lee; R Gisli Jenkins; Louise V Wain; Aroon D Hingorani; Riyaz S Patel
Journal:  Nat Commun       Date:  2019-04-05       Impact factor: 14.919

8.  Repurposing antihypertensive drugs for the prevention of Alzheimer's disease: a Mendelian randomization study.

Authors:  Venexia M Walker; Patrick G Kehoe; Richard M Martin; Neil M Davies
Journal:  Int J Epidemiol       Date:  2020-08-01       Impact factor: 7.196

9.  Variation in PCSK9 and HMGCR and Risk of Cardiovascular Disease and Diabetes.

Authors:  Brian A Ference; Jennifer G Robinson; Robert D Brook; Alberico L Catapano; M John Chapman; David R Neff; Szilard Voros; Robert P Giugliano; George Davey Smith; Sergio Fazio; Marc S Sabatine
Journal:  N Engl J Med       Date:  2016-12-01       Impact factor: 91.245

10.  Are drug targets with genetic support twice as likely to be approved? Revised estimates of the impact of genetic support for drug mechanisms on the probability of drug approval.

Authors:  Emily A King; J Wade Davis; Jacob F Degner
Journal:  PLoS Genet       Date:  2019-12-12       Impact factor: 5.917

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Journal:  Diabetologia       Date:  2022-01-26       Impact factor: 10.122

Review 2.  Using Human Genetics to Understand Mechanisms in Ischemic Stroke Outcome: From Early Brain Injury to Long-Term Recovery.

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Review 3.  Target Discovery for Drug Development Using Mendelian Randomization.

Authors:  Daniel S Evans
Journal:  Methods Mol Biol       Date:  2022

4.  Integration of innovative statistical methods using genetic data provides pharmacological insight and facilitates drug development.

Authors:  Andrew J Grant
Journal:  Br J Clin Pharmacol       Date:  2021-11-07       Impact factor: 3.716

Review 5.  Combining evidence from Mendelian randomization and colocalization: Review and comparison of approaches.

Authors:  Verena Zuber; Nastasiya F Grinberg; Dipender Gill; Ichcha Manipur; Eric A W Slob; Ashish Patel; Chris Wallace; Stephen Burgess
Journal:  Am J Hum Genet       Date:  2022-04-21       Impact factor: 11.043

6.  Growth Factors and Their Roles in Multiple Sclerosis Risk.

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7.  Prioritizing the Role of Major Lipoproteins and Subfractions as Risk Factors for Peripheral Artery Disease.

Authors:  Michael G Levin; Verena Zuber; Venexia M Walker; Derek Klarin; Julie Lynch; Rainer Malik; Aaron W Aday; Leonardo Bottolo; Aruna D Pradhan; Martin Dichgans; Kyong-Mi Chang; Daniel J Rader; Philip S Tsao; Benjamin F Voight; Dipender Gill; Stephen Burgess; Scott M Damrauer
Journal:  Circulation       Date:  2021-06-18       Impact factor: 29.690

8.  Morning Cortisol and Circulating Inflammatory Cytokine Levels: A Mendelian Randomisation Study.

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Journal:  Genes (Basel)       Date:  2022-01-08       Impact factor: 4.096

9.  Leveraging human genetic data to investigate the cardiometabolic effects of glucose-dependent insulinotropic polypeptide signalling.

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Journal:  Diabetologia       Date:  2021-09-09       Impact factor: 10.122

Review 10.  The evolution of mendelian randomization for investigating drug effects.

Authors:  Dipender Gill; Stephen Burgess
Journal:  PLoS Med       Date:  2022-02-03       Impact factor: 11.613

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