PRSice-2: Polygenic Risk Score software for biobank-scale data

TLDR

Polygenic risk score analyses are central to biomedical research, and biobank projects now provide unprecedented large‑scale genetic and phenotypic data for discovery. The authors present PRSice‑2, a scalable software designed to automate PRS analyses on biobank‑scale data. PRSice‑2, written in C++ with an R plotting script, processes genotyped and imputed data, offers inflation‑free empirical P‑values, supports multiple inheritance models, and evaluates several continuous and binary traits simultaneously. Compared with PRSice‑1, LDpred, and lassosum, PRSice‑2 is markedly faster, more memory‑efficient, and retains comparable predictive power, making it valuable for increasingly large and sophisticated PRS applications.

Abstract

Polygenic risk score (PRS) analyses have become an integral part of biomedical research, exploited to gain insights into shared aetiology among traits, to control for genomic profile in experimental studies, and to strengthen causal inference, among a range of applications. Substantial efforts are now devoted to biobank projects to collect large genetic and phenotypic data, providing unprecedented opportunity for genetic discovery and applications. To process the large-scale data provided by such biobank resources, highly efficient and scalable methods and software are required.Here we introduce PRSice-2, an efficient and scalable software program for automating and simplifying PRS analyses on large-scale data. PRSice-2 handles both genotyped and imputed data, provides empirical association P-values free from inflation due to overfitting, supports different inheritance models, and can evaluate multiple continuous and binary target traits simultaneously. We demonstrate that PRSice-2 is dramatically faster and more memory-efficient than PRSice-1 and alternative PRS software, LDpred and lassosum, while having comparable predictive power.PRSice-2's combination of efficiency and power will be increasingly important as data sizes grow and as the applications of PRS become more sophisticated, e.g., when incorporated into high-dimensional or gene set-based analyses. PRSice-2 is written in C++, with an R script for plotting, and is freely available for download from http://PRSice.info.