Background: 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. Results: Here we introduce PRSice-2, an efficient and scalable software for automating and simplifying polygenic risk score 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 polygenic score software, LDpred and lassosum, while having comparable predictive power. Conclusion: 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; for example, 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

The application of polygenic risk scores (PRS) has become routine in genetic epidemiological studies. Among a range of applications, PRS are commonly used to assess shared aetiology among different phenotypes and to evaluate the predictive power of genetic data, while they are also now being exploited as part of study design, in which experiments are performed on individuals, or their biological samples (eg. tissues, cells), at the tails of the PRS distribution and contrasted. As GWAS sample sizes increase and PRS become more powerful, they are also set to play a key role in personalised medicine. Despite their growing application and importance, there are limited guidelines for performing PRS analyses, which can lead to inconsistency between studies and misinterpretation of results. Here we provide detailed guidelines for performing polygenic risk score analyses relevant to different methods for their calculation, outlining standard quality control steps and offering recommendations for best-practice. We also discuss different methods for the calculation of PRS, common misconceptions regarding the interpretation of results and future challenges.