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Sens, D.W. ; Shilova, L. ; Gräf, L. ; Grebenshchikova, M. ; Eskofier, B.M. ; Casale, F.P.

Genetics-driven risk predictions leveraging the Mendelian randomization framework.

Genome Res. 34, 1276-1285 (2024)
Verlagsversion DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
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.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Alzheimers-disease; Association; Insulin; Variants; Resource; Amygdala; Biobank; Tool
Sprache englisch
Veröffentlichungsjahr 2024
HGF-Berichtsjahr 2024
ISSN (print) / ISBN 1088-9051
e-ISSN 1549-5469
Zeitschrift Genome Research
Quellenangaben Band: 34, Heft: 9, Seiten: 1276-1285 Artikelnummer: , Supplement: ,
Verlag Cold Spring Harbor Laboratory Press
Verlagsort 1 Bungtown Rd, Cold Spring Harbor, Ny 11724 Usa
Begutachtungsstatus Peer reviewed
Institut(e) Institute of AI for Health (AIH)
Helmholtz Pioneer Campus (HPC)
POF Topic(s) 30205 - Bioengineering and Digital Health
30201 - Metabolic Health
Forschungsfeld(er) Enabling and Novel Technologies
Pioneer Campus
PSP-Element(e) G-540004-001
G-540008-001
G-510002-001
Förderungen Friedrich-Alexander-Universitaet Erlangen-Nuernberg
Free State of Bavaria's Hightech Agenda through the Institute of AI for Health (AIH)
DOI 10.1101/gr.279252.124
WOS ID 001347161900001
Scopus ID 85206219301
PubMed ID 39332904
Erfassungsdatum 2024-10-30
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