Evidence mapPaperPMID 36736292Full record

ReviewAmerican journal of human genetics2023

Using genetic association data to guide drug discovery and development: Review of methods and applications.

Stephen Burgess, Amy M Mason, Andrew J Grant, Eric A W Slob, Apostolos Gkatzionis, Verena Zuber, Ashish Patel, Haodong Tian, Cunhao Liu, William G Haynes and 4 more

Abstract readReview
In one paragraph

Review in American journal of human genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
74citing papers in PubMed, 4 pooled it
field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

74 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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  12. Erk5-mediated microglial ferroptosis drives ischemic white matter damage via the Nfatc4-Clptm1l axis.Proceedings of the National Academy of Sciences of the United States of America · 2026
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14 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Stephen BurgessMRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK; Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK. Electronic address: sb452@medschl.cam.ac.uk.
Amy M MasonCardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
Andrew J GrantMRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK.
Eric A W SlobMRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK.
Apostolos GkatzionisMRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.
Verena ZuberDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK; MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK; UK Dementia Research Institute at Imperial College, Imperial College London, London, UK.
Ashish PatelMRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK.
Haodong TianMRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK.
Cunhao LiuMRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK.
William G HaynesNovo Nordisk Research Centre Oxford, Novo Nordisk, Oxford, UK; Radcliffe Department of Medicine, University of Oxford, Oxford, UK.
G Kees HovinghDepartment of Vascular Medicine, Academic Medical Center, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, the Netherlands; Global Chief Medical Office, Novo Nordisk, Copenhagen, Denmark.
Lotte Bjerre KnudsenChief Scientific Advisor Office, Research and Early Development, Novo Nordisk, Copenhagen, Denmark.
John C WhittakerMRC Biostatistics Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK.
Dipender GillDepartment of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK; Chief Scientific Advisor Office, Research and Early Development, Novo Nordisk, Copenhagen, Denmark.

Funding

British Heart Foundation RE/18/4/34215Department of HealthMedical Research Council MC_UU_00002/18Medical Research Council MC_UU_00002/7Medical Research Council MC_UU_00011/3Medical Research Council MR/S019669/1Medical Research Council MR/W029790/1Wellcome Trust 204623Wellcome Trust 204623/Z/16/ZWellcome Trust 225790/Z/22/Z
6 · The paper itself

Abstract

Evidence on the validity of drug targets from randomized trials is reliable but typically expensive and slow to obtain. In contrast, evidence from conventional observational epidemiological studies is less reliable because of the potential for bias from confounding and reverse causation. Mendelian randomization is a quasi-experimental approach analogous to a randomized trial that exploits naturally occurring randomization in the transmission of genetic variants. In Mendelian randomization, genetic variants that can be regarded as proxies for an intervention on the proposed drug target are leveraged as instrumental variables to investigate potential effects on biomarkers and disease outcomes in large-scale observational datasets. This approach can be implemented rapidly for a range of drug targets to provide evidence on their effects and thus inform on their priority for further investigation. In this review, we present statistical methods and their applications to showcase the diverse opportunities for applying Mendelian randomization in guiding clinical development efforts, thus enabling interventions to target the right mechanism in the right population group at the right time. These methods can inform investigators on the mechanisms underlying drug effects, their related biomarkers, implications for the timing of interventions, and the population subgroups that stand to gain the most benefit. Most methods can be implemented with publicly available data on summarized genetic associations with traits and diseases, meaning that the only major limitations to their usage are the availability of appropriately powered studies for the exposure and outcome and the existence of a suitable genetic proxy for the proposed intervention.

Indexed as

Drug DiscoveryMendelian Randomization AnalysisBiasBiomarkersCausalityHumansBiomarkerscausal inferencegenetic epidemiologyinstrumental variablesMendelian randomizationtarget validation

Identifiers

PMID36736292
PMCPMC9943784

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.