Catalyst Awardee

Project Description

AI-driven causal model to determine upstream definitive genetic biomarkers for early detection of Late Onset Alzheimer’s Disease

Victor OK Li, ScD | The University of Hong Kong ; Jacqueline CK Lam, PhD (Co-Lead); Illana Gozes, PhD; Jocelyn Downey, PhD
Competition Sponsor:
Research Grants Council of the Hong Kong Special Administrative Region, China
Award year: 2022


Alzheimer’s disease (AD) is a leading cause of death in China and the fifth leading cause of death worldwide. Early diagnosis is critical to provide timely intervention before irreversible brain damage occurs. 95% of the AD cases occur after age 65 and are considered late-onset AD (LOAD). Although plasma amyloid, phosphorylated Tau, and neurofilaments for individualized risk prediction in mild cognitive impairments (MCIs) are emerging, no causal definitive genetic markers (DGMs) for early detection of LOAD have been established, making accurate early diagnosis and treatment difficult. With the availability of large AD datasets with genetic information and our own Israeli and Hong Kong datasets, it is now possible to analyse the relationship between genetic markers and LOAD. However, (1) traditional statistical or data-driven models only ascertain disease association rather than causation; (2) deciphering disease-driving somatic mutations from inconsequential mutations remains difficult. To overcome these challenges, we will develop an AI-driven causal model to investigate multiple AD pathological pathways and their convergence, utilizing existing AD datasets to efficiently and accurately identify upstream DGMs. To verify the DGMs identified from the AI model, two large ethnically-distinctive LOAD datasets with blood samples from the HK Chinese and Israeli populations developed/accessible by our team will be analysed. Our novelties include the development of an AI-driven causal model which incorporates key AD pathways, somatic mutations, and demographics, to identify what disease-driving biomarkers cause (rather than what associate with) LOAD, paving the way for the early detection and treatment of LOAD.

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