Classification of Alzheimer’s disease with Genetic Data and Artificial Intelligence

Alzheimer's disease(AD) is the most common cause of dementia, accounting for 60% to 80% of cases that affect over six million people in the United States. The disease gradually progresses from mild cognitive impairment(MCI) to dementia, which takes more than a decade. Identifying individuals who have a high risk of AD earlier is essential for AD prevention and intervention. As the heritability of AD is high(up to 79%), genetic data should be powerful to identify individuals at high risk. Indeed, polygenic risk score (PRS), designed to estimate individual genetic liability by integrating large GWAS summary statistics and individual genotype data, has been shown to be promising for AD risk prediction(AUCs up to 84%). However, the prediction accuracy using a single PRS is still not sufficient for MCI and AD classification in clinical practice.
We hypothesize that convolution neural network(CNN) models can improve the classification of AD and MCI by multiple integrating PRSs from multiple traits, multi-omics data (genotyping data, scRNA-seq), clinical data, and imaging data. The objective is to develop advanced AI algorithms and build data-driven models for disease risk assessment, earlier identifying individuals with high risk for MCI and AD. Our long-term goal is to develop and validate a prediction model that can be translated into clinical practice. Our CNN model has recently shown an improved performance for AD with PRSs from multiple traits(AUC 92.4%). We want to extend our approach to predicting AD and MCI in different ethnic groups and validate the results with independent datasets. To this end, we would like to apply for multi-omics data in NG00067.v9 from https://dss.niagads.org/datasets/ng00067/. With an extensive experience in genetic studies on complex disorders and disease modeling, we are confident that we will achieve the specified goals and promote the integration of genetic data with AI algorithms, facilitating data-driven, personalized care of AD. We expect to finish this study within 2 years with publication and grant application. We have IRB approval and will follow the rules for data sharing and acknowledgment.

Alzheimer’s disease (AD), the most common form of dementia, that usually develops from mild cognitive impairment to dementia. There is currently no treatment to slow the progression of this disorder. But earlier identification of the individuals with higher risk maybe critical to prevent the disease. We propose a new approach to create models for classification of AD and MCI with artificial intelligence and genetic data. This study will have a significant value in personalized medicine for AD risk assessment, classification, and earlier intervention.

We don’t have the planned collaboration with researchers outside Cleveland Clinic in the current analytic plans.