Delphi: A Deep-learning Framework for Polygenic Risk Prediction

Polygenic risk scores (PRS) are relative measures of an individual's genetic propensity to a particular trait or disease. Most PRS methods assume that mutation effects scale linearly with the number of alleles and are constant across individuals. While these assumptions simplify computation, they increase error, particularly for less-represented racial groups. We developed and provide Delphi (deep learning for phenotype inference), a deep-learning method that relaxes these assumptions to produce more predictive PRS. In contrast to other methods, Delphi can integrate up to hundreds of thousands of SNPs as input. We compare our results to a standard, linear PRS model, lasso regression, and a gradient-boosted trees-based method. We show that deep learning can be an effective approach to genetic risk prediction. We report a relative increase in the percentage variance explained compared to the state-of-the-art by 11.4% for body mass index, 18.9% for systolic blood pressure, 7.5% for LDL, 35% for C-reactive protein, 16.2% for height, 29.6% for pulse rate; in addition, Delphi provides 2% absolute explained variance for blood glucose while other tested methods were non-predictive. Furthermore, we show that Delphi tends to increase the weight of high-effect mutations. This work demonstrates an effective deep learning method for modeling genetic risk that also showed to generalize well when evaluated on individuals from non-European ancestries. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement the Swiss National Science Foundation (Sinergia CRSII5_202276/1). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: North West Multi-centre Research Ethics Committee (MREC) approved the UK Biobank. UK Biobank gave approval for this work (application number 80108). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data referred to in the present work is available by application at https://www.ukbiobank.ac.uk/.