PRSet: Pathway-based polygenic risk score analyses and software

Polygenic risk scores (PRSs) have been among the leading advances in biomedicine in recent years. As a proxy of genetic liability, PRSs are utilised across multiple fields and applications. While numerous statistical and machine learning methods have been developed to optimise their predictive accuracy, these typically distil genetic liability to a single number based on aggregation of an individual's genome-wide risk alleles. This results in a key loss of information about an individual's genetic profile, which could be critical given the functional sub-structure of the genome and the heterogeneity of complex disease. In this manuscript, we introduce a 'pathway polygenic' paradigm of disease risk, in which multiple genetic liabilities underlie complex diseases, rather than a single genome-wide liability. We describe a method and accompanying software, PRSet, for computing and analysing pathway-based PRSs, in which polygenic scores are calculated across genomic pathways for each individual. We evaluate the potential of pathway PRSs in two distinct ways, creating two major sections: (1) In the first section, we benchmark PRSet as a pathway enrichment tool, evaluating its capacity to capture GWAS signal in pathways. We find that for target sample sizes of >10,000 individuals, pathway PRSs have similar power for evaluating pathway enrichment as leading methods MAGMA and LD score regression, with the distinct advantage of providing individual-level estimates of genetic liability for each pathway-opening up a range of pathway-based PRS applications, (2) In the second section, we evaluate the performance of pathway PRSs for disease stratification. We show that using a supervised disease stratification approach, pathway PRSs (computed by PRSet) outperform two standard genome-wide PRSs (computed by C+T and lassosum) for classifying disease subtypes in 20 of 21 scenarios tested. As the definition and functional annotation of pathways becomes increasingly refined, we expect pathway PRSs to offer key insights into the heterogeneity of complex disease and treatment response, to generate biologically tractable therapeutic targets from polygenic signal, and, ultimately, to provide a powerful path to precision medicine. Author summaryAs proxies of genetic liability, polygenic risk scores (PRSs) are being increasingly applied in multiple fields and designs. However, most leading methods to compute PRSs are based on aggregating genome-wide genotypes to a single number for each individual. While these genome-wide PRSs are demonstrably useful, aggregating risk according to the functional sub-structure of the genome may be more powerful for many PRS applications.Here we introduce a new method and accompanying software, PRSet, to calculate and analyse pathway-based PRSs, in which polygenic scores are computed across different genomic pathways for each individual. We find that pathway-based PRSs have similar power for evaluating pathway enrichment as the leading methods designed for the task (e.g. MAGMA), while pathway PRSs offer the distinct advantage of providing individual-level estimates of genetic liability for each pathway. All applications of genome-wide PRSs are available to pathway-specific PRS, but we expect the latter to offer greater insights into the heterogeneity of complex disease. We therefore investigate the performance of pathway PRSs versus genome-wide PRS methods to stratify patients of heterogeneous diseases into more homogeneous sub-groups, as a proof-of-principle of their potential utility to provide more powerful paths to precision medicine.