@article{3266,
  keywords = {bias, Biomarkers, Causality, Drug Discovery, Humans, Mendelian Randomization Analysis, Mendelian randomization, causal inference, Genetic epidemiology, instrumental variables, Target validation},
  author = {Stephen Burgess and Amy M. Mason and Andrew J. Grant and Eric A. W. Slob and Apostolos Gkatzionis and Verena Zuber and Ashish Patel and Haodong Tian and Cunhao Liu and William G. Haynes and G. Kees Hovingh and Lotte Bjerre Knudsen and John C. Whittaker and Dipender Gill},
  title = {Using genetic association data to guide drug discovery and development: Review of methods and applications},
  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.},
  year = {2023},
  journal = {American Journal of Human Genetics},
  volume = {110},
  pages = {195-214},
  month = {2023-02-02},
  issn = {1537-6605},
  doi = {10.1016/j.ajhg.2022.12.017},
  language = {eng},
}