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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Genet. | doi: 10.3389/fgene.2019.00726

Integration of machine learning methods to dissect genetically imputed transcriptomic profiles in Alzheimer’s Disease.

  • 1Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Germany
  • 2University of Bonn, Germany
  • 3Department of Computer Science, Faculty of Computer Science & Technology, University of Cambridge, United Kingdom
  • 4National Research Council Research Area Milan, Italy

The genetic component of many common traits is associated with the gene expression and
several variants act as expression quantitative loci, regulating the gene expression in a tissue-
specific manner. In this work, we applied tissue-specific cis-eQTL gene expression prediction
models on the genotype of 808 samples including controls, patients with mild cognitive impairment, and subjects with Alzheimer Disease. We then dissected the imputed transcriptomic profiles by means of different unsupervised and supervised machine learning approaches to identify potential biological associations (all code is available at https://github.com/imerelli/DeepNeuro). Our analysis suggests that unsupervised and supervised methods can provide complementary information, which can be integrated for a better characterization of the underlying biological system. In particular, a variational autoencoder representation of the transcriptomic profiles, followed by a support vector machine classification, has been used for tissue-specific gene prioritizations. Interestingly, the achieved gene prioritization can be efficiently integrated as a feature selection step for improving the accuracy of deep learning classifier networks. The identified gene-tissue information suggests a potential role for inflammatory and regulatory processes in gut-brain axis related tissues. In line with the expected low heritability that can be apportioned to eQTL variants, we were able to achieve only relatively low prediction capability with deep learning classification models. However, our analysis revealed that the classification power strongly depends on the network structure, with recurrent neural networks being the best performing network class. Interestingly, cross-tissue analysis suggests a potentially greater role of models trained in brain tissues also by considering dementia-related endophenotypes. Overall, the present analysis suggests that the combination of supervised and unsupervised machine learning techniques can be used for the evaluation of high dimensional omics data.

Keywords: eQTL, gene expression imputation, GTEx, Variational autoencoder, Support vector machine, deep learning, recurrent neural network, Alzheheimer's disease

Received: 18 Apr 2019; Accepted: 10 Jul 2019.

Copyright: © 2019 Maj, Azevedo, Giansanti, Borisov, Dimitri, Spasov, Lio' and Merelli. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mx. Carlo Maj, Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Bonn, 53127, North Rhine-Westphalia, Germany, cmaj@uni-bonn.de