Integrating genetic correlation, polygenic risk scores, and Mendelian randomization to identify modifiable risk factors for Alzheimer’s disease

To reduce the population prevalence of Alzheimer’s disease (AD), it is critical to identify risk factors that modify AD risk. Methods of causal inference that exploit genomic information, such as genetic correlation (rg), polygenic risk scores (PRS) and Mendelian randomization (MR), can overcome some of the limitations of observational studies such as confounding and reverse causation. Here we use rg, PRS, and MR to investigate causal associations between twenty‐two risk factors and eleven AD outcomes.