Principal Investigator: Dr Michael Blum
Universite Grenoble Alpes, Laboratoire TIMC-IMAG, Domaine de la Merci, Faculte de medecine, La Tronche, Isere 38706, FranceTags: 25589, External population, Learning, Polygenic, prediction, Risk
1a: Polygenic Risk Scores (PRS) combine information from multiple SNPs into a single score for predicting disease risk. Current techniques to compute PRS make strong modeling assumptions. In parallel, statistical learning has been very active and successful at developing model-agnostic approaches for various predictive purposes. Based on several complex traits, our project seeks to evaluate and compare model-agnostic approaches to current techniques that compute PRS. The criterion for comparison is predictive accuracy in particular for individuals coming from underrepresented populations in large-scale genomic data.
1b: The results expected from the current proposal will generate novel computational techniques to improve prognostic for complex health-related traits. The developed tool will help to improve the prevention of a wide range of serious and life-threatening illnesses. For this reason, we think that our project is perfectly in line with the main UK Biobank’s purpose.
1c: Large-scale genomic data can be used to compute risk factors for diseases. Risk factors can then be used in population-based risk screening and stratification programs to identify at-risk individuals. The proposed project seeks to evaluate if modern machine learning methods can improve the predictive accuracy that is derived from genomic data.
1d: We require the full UK Biobank cohort subjects with complete phenotype and genotype information.