| Title: | Genetics-driven risk predictions leveraging the Mendelian randomization framework |
| Journal: | Genome Research |
| Published: | 27 Sep 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/39332904/ |
| DOI: | https://doi.org/10.1101/gr.279252.124 |
Publication 13571
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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.</p>
10 Keywords
- Alzheimer Disease
- Diabetes Mellitus, Type 2
- Genetic Predisposition to Disease
- Genome-Wide Association Study
- Humans
- Mendelian Randomization Analysis
- Parkinson Disease
- Polymorphism, Single Nucleotide
- Risk Assessment
- Risk Factors
6 Authors
- Daniel Sens
- Liubov Shilova
- Ludwig Gräf
- Maria Grebenshchikova
- Bjoern M. Eskofier
- Francesco Paolo Casale
1 Application
| Application ID | Title |
|---|---|
| 87065 | Computational and machine learning tools for the analysis of genetic and medical imaging data to study human health and disease |
Enabling scientific discoveries that improve human health