Imperial College London
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DrDipenderGill

Faculty of Medicine, School of Public Health

Clinical Research Fellow
 
 
 
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Contact

 

+44 (0)7904 843 810dipender.gill

 
 
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Location

 

School of a Public HealthMedical SchoolSt Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@article{Burgess:2023:10.1016/j.ajhg.2022.12.017,
author = {Burgess, S and Mason, AM and Grant, AJ and Slob, EAW and Gkatzionis, A and Zuber, V and Patel, A and Tian, H and Liu, C and Haynes, WG and Hovingh, GK and Knudsen, LB and Whittaker, JC and Gill, D},
doi = {10.1016/j.ajhg.2022.12.017},
journal = {American Journal of Human Genetics},
pages = {195--214},
title = {Using genetic association data to guide drug discovery and development: review of methods and applications},
url = {http://dx.doi.org/10.1016/j.ajhg.2022.12.017},
volume = {110},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Burgess,S
AU  - Mason,AM
AU  - Grant,AJ
AU  - Slob,EAW
AU  - Gkatzionis,A
AU  - Zuber,V
AU  - Patel,A
AU  - Tian,H
AU  - Liu,C
AU  - Haynes,WG
AU  - Hovingh,GK
AU  - Knudsen,LB
AU  - Whittaker,JC
AU  - Gill,D
DO  - 10.1016/j.ajhg.2022.12.017
EP  - 214
PY  - 2023///
SN  - 0002-9297
SP  - 195
TI  - Using genetic association data to guide drug discovery and development: review of methods and applications
T2  - American Journal of Human Genetics
UR  - http://dx.doi.org/10.1016/j.ajhg.2022.12.017
UR  - https://www.ncbi.nlm.nih.gov/pubmed/36736292
UR  - https://www.sciencedirect.com/science/article/pii/S0002929722005511
UR  - http://hdl.handle.net/10044/1/102902
VL  - 110
ER  -
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