Discovering Genetic Signatures Associated with Alzheimer's Disease in Tiled Whole Genome Sequence Data: Results from the Artificial Intelligence for Alzheimer's Disease (AI4AD) Consortium
Zaranek, S. W.; Zaranek, A. W.; Amstutz, P.; Bao, J.; Chen, J.; Clegg, T.; Craft, H.; Jo, T.; Lee, B.; Nho, K.; Thomopoulos, S. I.; Davatzikos, C.; Shen, L.; Huang, H.; Thompson, P. M.; Saykin, A. J.; The Alzheimer's Disease Neuroimaging Initiative as a consortium author for the AI4AD Initiative,
Show abstract
Currently, the ability to analyze large-scale whole genome sequence (WGS) data is limited due to both the size of the data and the inability of many existing tools to scale. To address this challenge, we use data "tiling" to efficiently partition whole genome sequences into smaller segments resulting in a simple numeric matrix of small integers. This lossless representation is particularly suitable for machine learning (ML) models. As an example of the benefits of tiling, we showcase results from tiled data as part of the Artificial Intelligence for Alzheimers Disease (AI4AD) consortium. AI4AD is a coordinated initiative to develop transformative AI approaches for high throughput analysis of next generation sequencing and related imaging, AD biomarker, and cognitive data. The collective effort integrates imaging, genomic, biomarker, and cognitive data to address fundamental barriers in AD prevention and drug discovery. One of the projects initial aims is to discover new genetic signatures in WGS data that can be used to understand AD risk and progression in conjunction with imaging, biomarker and cognitive data. We tiled and analyzed 15,000+ genomes from the Alzheimers Disease Sequencing Project (ADSP) and the Alzheimers Disease Neuroimaging Initiative (ADNI). We tile 11,762 genomes, a subset of the release which does not include family-based datasets (AD Cases: 4,983, age range: 50-90 years, mean age: 73.8 years). We illustrate the use of tiled data in ML classification methods to predict phenotypes. Specifically, we identify and prioritize tile variants/genetic variants that are possible genetic signatures for AD. The model shows added predictive value from variants of genes previously found to be associated with AD risk, age of onset, neurofibrillary tangle measurements, and other AD-related traits-including the APOE variant (rs429358).
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