Polygenic risk scores (PRS) summarise genetic information into a single number with clinical and research uses. Machine learning (ML) has revolutionised multiple fields, however, the impact of ML on PRSs has been less significant. We explore how ML can improve the generation of PRSs.
Methods:
We train ML models on known PRSs using UK Biobank data. We explore whether the models can recreate human programmed PRSs, including using a single model to generate multiple PRSs, and ML difficulties in PRS generation. We investigate how ML can compensate for missing data and constraints on performance.
Results:
We demonstrate almost perfect generation of multiple PRSs with little loss of performance with reduced quantity of training data. For an example set of missing SNPs the MLP produces predictions that enable separation of cases from population samples with an area under the receiver operating characteristic curve of 0.847 (95% CI: 0.828-0.864) compared to 0.798 (95% CI: 0.779-0.818) for the PRS.
Conclusions:
ML can accurately generate PRSs, including with one model for multiple PRSs. The models are transferable and have high longevity. With certain missing SNPs the ML models can improve on PRS generation. Further improvements likely require use of additional input data.
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