Use this application to create a new PRS using state-of-the-art algorithms

Different PRS methods perform better on different diseases and datasets, that's why we've implemented the three most powerful PRS algorithms to provide standard predictive performance benchmarking metrics on your data. For the three PRS methods, we calculate the AUC of the ROC curve, the fold increased risk compared to the remainder of the PRS distribution, and the odds ratio per standard deviation. This provides you with the information necessary to choose the most predictive PRS for the disease and population of interest.



Use this application to validate the predictive power of your PRS

PRSs are population dependent, that's why we've developed an easy way to validate the predictive power of a PRS in a specific population. This is the ideal analysis if you have genotype or sequence data from a new population and want to test the predictive performance of different PRSs.


Use this application to compute an individual's PRS

The most recent PRSs use thousands or even millions of genetic variants from across the genome. To match your input data to all of these variants requires a computationally intensive data enrichment step called imputation. We use industry standard and rigorously tested statistical imputation methods to enrich an individual's genotype or sequence data to millions of genetic variants to optimise the power of the PRS. Allelica's software deals with the bioinformatic complexity of the entire analysis whilst remaining transparent about our methods, so you only need to upload your genotype data and you'll receive a customisable report with comprehensive information on the individual's PRS and genetic risk of developing the disease.