02900nas a2200433   4500000000100000000000100001008004100002260001500043653000900058653001500067653001400082653001900096653001100115653003700126653002800163653002100191653002500212653002700237653002200264100002000286700001700306700002000323700002000343700002500363700001700388700001700405700001700422700001500439700002200454700002000476700002500496700002200521700001800543245011100561300001200672490000800684520176000692022001402452       2023                            d  c2023-02-0210abias10aBiomarkers10aCausality10aDrug Discovery10aHumans10aMendelian Randomization Analysis10aMendelian randomization10acausal inference10aGenetic epidemiology10ainstrumental variables10aTarget validation1 aStephen Burgess1 aAmy M. Mason1 aAndrew J. Grant1 aEric A. W. Slob1 aApostolos Gkatzionis1 aVerena Zuber1 aAshish Patel1 aHaodong Tian1 aCunhao Liu1 aWilliam G. Haynes1 aG. Kees Hovingh1 aLotte Bjerre Knudsen1 aJohn C. Whittaker1 aDipender Gill00aUsing genetic association data to guide drug discovery and development: Review of methods and applications  a195-2140 v1103 aEvidence 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.  a1537-6605