Genetics-driven risk predictions leveraging the Mendelian randomization framework
- Daniel Sens1,2,
- Liubov Shilova1,2,3,
- Ludwig Gräf1,4,
- Maria Grebenshchikova1,5,
- Bjoern M. Eskofier1,3 and
- Francesco Paolo Casale1,2,4
- 1Institute of AI for Health, Helmholtz Zentrum München—German Research Center for Environmental Health, 85764 Neuherberg, Germany;
- 2Helmholtz Pioneer Campus, Helmholtz Zentrum München—German Research Center for Environmental Health, 85764 Neuherberg, Germany;
- 3Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany;
- 4School of Computation, Information and Technology, Technical University of Munich, 85748 Garching, Germany;
- 5School of Management, Technical University of Munich, 80333 Munich, Germany
Abstract
Accurate predictive models of future disease onset are crucial for effective preventive healthcare, yet longitudinal data sets linking early risk factors to subsequent health outcomes are limited. To overcome this challenge, we introduce a novel framework, Predictive Risk modeling using Mendelian Randomization (PRiMeR), which utilizes genetic effects as supervisory signals to learn disease risk predictors without relying on longitudinal data. To do so, PRiMeR leverages risk factors and genetic data from a healthy cohort, along with results from genome-wide association studies of diseases of interest. After training, the learned predictor can be used to assess risk for new patients solely based on risk factors. We validate PRiMeR through comprehensive simulations and in future type 2 diabetes predictions in UK Biobank participants without diabetes, using follow-up onset labels for validation. Moreover, we apply PRiMeR to predict future Alzheimer's disease onset from brain imaging biomarkers and future Parkinson's disease onset from accelerometer-derived traits. Overall, with PRiMeR we offer a new perspective in predictive modeling, showing it is possible to learn risk predictors leveraging genetics rather than longitudinal data.
Footnotes
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[Supplemental material is available for this article.]
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Article published online before print. Article, supplemental material, and publication date are at https://www.genome.org/cgi/doi/10.1101/gr.279252.124.
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Freely available online through the Genome Research Open Access option.
- Received March 4, 2024.
- Accepted September 3, 2024.
This article, published in Genome Research, is available under a Creative Commons License (Attribution 4.0 International), as described at http://creativecommons.org/licenses/by/4.0/.
