Robust Mendelian Randomization Methods to Identify Causal Risk Factors for Alzheimer’s Disease

Funding Source: UW–Madison’s Data Science Initiative  Read more

Principal Investigators: Hyunseung Kang and Qiongshi Lu
Co-Investigator: Corinne Engleman

More than five million Americans are living with Alzheimer’s disease, a disease without a cure. Epidemiological studies that directly test associations between risk factors and Alzheimer’s disease are difficult to conduct because identified associations are in many cases confounded. The time period in which people are affected by risk factors may have ended years before clinical symptoms are observed.

This project will implement Mendelian randomization—using genetic instrumental variables to make inferences about causal effects based on observational data — to identify risk factors for Alzheimer’s. The project relies on integration with the Wisconsin Registry for Alzheimer’s Prevention.

The research is expected to produce an atlas of causal risk factors, including complex human traits, genes and their tissue-dependent transcriptional activities, serum and metabolites, for Alzheimer’s disease. These results can be used to guide future studies and therapeutics development. Methods developed in this project will be released as publicly accessible software packages.