Integrating omics and MRI data with kernel-based tests and CNNs to identify rare genetic markers for Alzheimer's disease
This work addresses the challenge of identifying rare genetic markers for Alzheimer's disease, which is crucial for precision medicine, though it appears incremental as it builds on existing kernel-based methods and CNNs.
The study tackled the identification of rare genetic markers for Alzheimer's disease by combining MRI data with genome sequencing, using CNNs to derive brain traits and novel kernel-based tests for association analysis. Results showed that CNNs provided fast and precise trait derivation, and the new kernels improved power in detecting associations with very rare variants.
For precision medicine and personalized treatment, we need to identify predictive markers of disease. We focus on Alzheimer's disease (AD), where magnetic resonance imaging scans provide information about the disease status. By combining imaging with genome sequencing, we aim at identifying rare genetic markers associated with quantitative traits predicted from convolutional neural networks (CNNs), which traditionally have been derived manually by experts. Kernel-based tests are a powerful tool for associating sets of genetic variants, but how to optimally model rare genetic variants is still an open research question. We propose a generalized set of kernels that incorporate prior information from various annotations and multi-omics data. In the analysis of data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we evaluate whether (i) CNNs yield precise and reliable brain traits, and (ii) the novel kernel-based tests can help to identify loci associated with AD. The results indicate that CNNs provide a fast, scalable and precise tool to derive quantitative AD traits and that new kernels integrating domain knowledge can yield higher power in association tests of very rare variants.