Publicly Available Privacy-preserving Benchmarks for Polygenic Prediction
Menno J. Witteveen, Emil M. Pedersen, Joeri J. Meijsen, Michael Riis Andersen, Florian Privé, Doug Speed, Bjarni Jóhann Vilhjálmsson
This paper presents a publicly available privacy-preserving benchmark for polygenic prediction using summary-level data, enabling accurate model performance estimation without access to individual-level genetic data.
bioRxiv · Oct 10, 2022 · PreprintGenetics