Demystifying inconsistent two-sample mendelian randomization estimations using selection diagram
Lei Hou, Yuanyuan Yu, Zhi Geng, Fuzhong Xue, Hongkai Li · 2025
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Description
Lei Hou and co-authors untangle why two-sample Mendelian randomization can give clashing answers. The method uses genes as natural experiments to infer cause. It draws exposure data from one group and outcome data from another. If the two groups run on different local rules, the estimate can warp. Their 2025 paper draws that warp with selection diagrams, maps of what shapes the data.
Two grades of clash are named and defined, complete and partial. A test tells when neither can occur, for both smooth and yes-no outcomes. A ratio gauges how far an estimate strays from the truth. Breaking one key assumption, monotonicity, brings the worst grade. A waist-to-hip and diabetes case shows the trap in real data.
Catalog record
| CREATOR | Lei Hou, Yuanyuan Yu, Zhi Geng, Fuzhong Xue, Hongkai Li |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 33 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |