Publicly Available Privacy-preserving Benchmarks for Polygenic Prediction

Menno Witteveen, Emil Pedersen, Joeri Meijsen, Michael Riis Andersen, Florian Privé, Doug Speed, Bjarni Vilhjálmsson

Keywords: machine learning, benchmark, polygenic scores, PRS, PGS

Abstract: Recently, several new approaches for creating polygenic scores (PGS) have been developed and this trend shows no sign of abating. However, it has thus far been challenging to determine which approaches are superior, as different studies report seemingly conflicting benchmark results. This heterogeneity in benchmark results is in part due to different outcomes being used, but also due to differences in the genetic variants being used, data preprocessing, and other quality control steps. As a solution, a publicly available benchmark for polygenic prediction is presented here, which allows researchers to both train and test polygenic prediction methods using only summary-level information, thus preserving privacy. Using simulations and real data, we show that model performance can be estimated with accuracy, using only linkage disequilibrium (LD) information and genome-wide association summary statistics for target outcomes. Finally, we make this PGS benchmark - consisting of 8 outcomes, including somatic and psychiatric disorders - publicly available for researchers to download on our PGS benchmark platform (http://www.pgsbenchmark.org). We believe this benchmark can help establish a clear and unbiased standard for future polygenic score methods to compare against.