TY - JOUR
T1 - Using genetic association data to guide drug discovery and development
T2 - Review of methods and applications
AU - Burgess, Stephen
AU - Mason, Amy M.
AU - Grant, Andrew J.
AU - Slob, Eric A. W.
AU - Gkatzionis, Apostolos
AU - Zuber, Verena
AU - Patel, Ashish
AU - Tian, Haodong
AU - Liu, Cunhao
AU - Haynes, William G.
AU - Hovingh, G. Kees
AU - Knudsen, Lotte Bjerre
AU - Whittaker, John C.
AU - Gill, Dipender
N1 - Funding Information: This research was funded by United Kingdom Research and Innovation Medical Research Council ( MC_UU_00002/7 and MC_UU_00011/3 ) and was supported by the National Institute for Health Research Cambridge Biomedical Research Centre ( BRC-1215-20014 ). S.B. is supported by the Wellcome Trust ( 225790/Z/22/Z ). The views expressed are those of the authors and not necessarily those of the National Health Service, the National Institute for Health Research, or the Department of Health and Social Care. D.G. is supported by the British Heart Foundation Center of Research Excellence at Imperial College London ( RE/18/4/34215 ). Funding Information: This research was funded by United Kingdom Research and Innovation Medical Research Council (MC_UU_00002/7 and MC_UU_00011/3) and was supported by the National Institute for Health Research Cambridge Biomedical Research Centre (BRC-1215-20014). S.B. is supported by the Wellcome Trust (225790/Z/22/Z). The views expressed are those of the authors and not necessarily those of the National Health Service, the National Institute for Health Research, or the Department of Health and Social Care. D.G. is supported by the British Heart Foundation Center of Research Excellence at Imperial College London (RE/18/4/34215). W.G.H. G.K.H. L.B.K. and D.G. are employed by Novo Nordisk. Publisher Copyright: © 2022 American Society of Human Genetics
PY - 2023/2/2
Y1 - 2023/2/2
N2 - 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.
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.
KW - Mendelian randomization
KW - causal inference
KW - genetic epidemiology
KW - instrumental variables
KW - target validation
UR - http://www.scopus.com/inward/record.url?scp=85147458334&partnerID=8YFLogxK
U2 - https://doi.org/10.1016/j.ajhg.2022.12.017
DO - https://doi.org/10.1016/j.ajhg.2022.12.017
M3 - Review article
C2 - 36736292
SN - 0002-9297
VL - 110
SP - 195
EP - 214
JO - American journal of human genetics
JF - American journal of human genetics
IS - 2
ER -