Penalized inverse-variance weighted estimator for robust Mendelian randomization
This technology is a statistical method that uses a penalized inverse-variance weighted estimator with closed-form correction to improve causal inference in Mendelian randomization studies.
Unmet Need: Reliable causal inference in Mendelian randomization with weak instruments
Current methods for Mendelian randomization (MR), such as the inverse-variance weighted (IVW) estimator, are highly sensitive to weak genetic instruments and horizontal pleiotropy, leading to biased or unreliable causal estimates. These limitations reduce confidence in using MR to prioritize drug targets, validate biomarkers, or guide clinical decision-making, especially for complex traits like cardiometabolic and infectious diseases. There is a critical need for MR approaches that improve accuracy and robustness without sacrificing computational efficiency, enabling reliable causal inference from large-scale genomic data.
The Technology: Penalized Mendelian randomization estimator for unbiased causal inference
This technology is a statistical method that improves the reliability of Mendelian randomization (MR) by incorporating a penalty term into the standard inverse-variance weighted (IVW) estimator to stabilize calculations when genetic instruments are weak. A closed-form correction further reduces bias caused by horizontal pleiotropy, improving the accuracy of causal effect estimates while maintaining computational efficiency. Implemented as an R software package, this method can be integrated into existing MR workflows for large-scale genomic analyses.
This technology has been validated through simulation studies and applied to real-world genetic datasets, including COVID-19 severity and cardiometabolic disease outcomes.
Applications:
- Causal gene target identification for drug development
- Biomarker validation for infectious and cardiometabolic diseases
- Companion diagnostics development for personalized medicine
- Clinical decision support in risk factor assessment
- Integration into genomics-based software platforms for causal inference
- Research tool for statistical genetics and genetic epidemiology
Advantages:
- Reduces bias from weak genetic instruments and pleiotropy
- Provides more accurate and robust causal effect estimates than standard Mendelian randomization (MR) methods
- Maintains computational efficiency for large-scale genomic datasets
- Compatible with existing Mendelian randomization workflows and R-based pipelines
- Validated in both simulation and real-world disease datasets
Lead Inventor:
Patent Information:
Related Publications:
Tech Ventures Reference:
IR CU25374
Licensing Contact: Joan Martinez