Identifying genetic risk variants and developing a deep learning prediction model for disability development in the elderly: a study using the Canadian Longitudinal Study on Aging

Project Summary

Disability in the elderly is a significant health issue with a growing impact on healthcare costs. Identifying the genetic risk variants associated with disability development could advance our understanding of biological mechanisms related to aging. The Canadian Longitudinal Study on Aging (CLSA) provides a unique opportunity to investigate this association due to its comprehensive data collection, including genetic and non-genetic variables. Our study aims to identify genetic variants associated with disability development through genome-wide association studies and build an accurate prediction model for disability using deep learning techniques. We will analyze the CLSA data using copula-based joint survival models, perform genome-wide tests for disability, and develop a deep learning prediction model using genetic and non-genetic information. This research could lead to breakthroughs in early prevention and treatment of disability in the elderly.