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

Area of Science:

• Genetics
• Epidemiology
• Pharmacology

Background:

• Randomized trials provide reliable drug target validity evidence but are costly and slow.
• Observational studies offer less reliable evidence due to potential biases like confounding and reverse causation.

Purpose of the Study:

• To review statistical methods for Mendelian randomization (MR).
• To showcase MR applications in guiding drug development and clinical interventions.
• To highlight MR's potential in identifying optimal targets, timing, and populations for therapeutic benefit.

Main Methods:

• Mendelian randomization leverages genetic variants as instrumental variables for quasi-experimental analysis.
• Utilizes large-scale observational datasets to investigate drug target effects on biomarkers and disease outcomes.
• Applies statistical methods to summarized genetic association data, often publicly available.

Main Results:

• MR provides rapid evidence on drug target effects, informing prioritization for further investigation.
• Methods can elucidate drug effect mechanisms, identify related biomarkers, and suggest optimal intervention timing and target populations.
• MR's primary limitations include the need for adequately powered studies and suitable genetic proxies.

Conclusions:

• Mendelian randomization is a powerful tool for enhancing the efficiency and reliability of drug target validation.
• It enables more precise and effective clinical development by informing mechanism, timing, and population-specific benefits.
• Widespread adoption is facilitated by the availability of public genetic data, pending study power and proxy availability.