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Register a new userIntegrating omics and MRI data with kernel-based tests and CNNs to identify rare genetic markers for Alzheimers disease
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For precision medicine and personalized treatment, we need to identify predictive markers of disease. We focus on Alzheimers 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 Alzheimers 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.
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As societies around the world are ageing, the number of Alzheimers disease (AD) patients is rapidly increasing. To date, no low-cost, non-invasive biomarkers have been established to advance the objectivization of AD diagnosis and progression assessment. Here, we utilize Bayesian neural networks to develop a multivariate predictor for AD severity using a wide range of quantitative EEG (QEEG) markers. The Bayesian treatment of neural networks both automatically controls model complexity and provides a predictive distribution over the target function, giving uncertainty bounds for our regression task. It is therefore well suited to clinical neuroscience, where data sets are typically sparse and practitioners require a precise assessment of the predictive uncertainty. We use data of one of the largest prospective AD EEG trials ever conducted to demonstrate the potential of Bayesian deep learning in this domain, while comparing two distinct Bayesian neural network approaches, i.e., Monte Carlo dropout and Hamiltonian Monte Carlo.
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