USING MACHINE LEARNING-BASED TRAIT PREDICTIONS FOR GENETIC ASSOCIATION DISCOVERY

摘要

A method for producing highly accurate, iow cost phenotype labels for a cohort of individual using a machine learning model. The model is trained to predict phenotype labels from routine clinical data. We describe routine clinical data in the form of fundus images and making predictions as to phenotypes associated with eye diseases, such as glaucoma, however the methodology is more generally applicable to phenotype assignment from clinical data. The model is applied to a cohort of interest which includes both genomic data and the same type of routine clinical data. The model produces phenotype labels for each of the members of the cohort of interest. We then conduct a genetic association test (e.g., GW AS) on the cohort of interest using the phenotype labels produced by the model along with associated genomic data and identify genomic information (e.g., specific loci in the genome) associated with the phenotype.