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      • Bilkent Theses
      • Theses - Department of Computer Engineering
      • Dept. of Computer Engineering - Master's degree
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      Spatio-temporal gene discovery for autism spectrum disorder

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      Author
      Norman, Utku
      Advisor
      Çiçek, A. Ercüment
      Date
      2018-08
      Publisher
      Bilkent University
      Language
      English
      Type
      Thesis
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      Abstract
      Whole Exome Sequencing (WES) studies for Autism Spectrum Disorder (ASD) could identify only around six dozen risk genes to date, because the genetic architecture of the disorder is highly complex. To speed the gene discovery process up, a few network-based ASD gene discovery algorithms were proposed. Although these methods use static gene interaction networks, functional clustering of genes is bound to evolve during neurodevelopment and disruptions are likely to have a cascading effect on the future associations. Thus, approaches that disregard the dynamic nature of neurodevelopment are limited. Here, we present a spatiotemporal gene discovery algorithm for ASD, which leverages information from evolving gene coexpression networks of neurodevelopment. The algorithm solves a prize-collecting Steiner forest based problem on coexpression networks, adapted to model neurodevelopment and transfer information from precursor neurodevelopmental windows. The decisions made by the algorithm can be traced back, adding interpretability to the results. We apply the algorithm on WES data of 3,871 samples and identify risk clusters using BrainSpan coexpression networks of early- and mid-fetal periods. On an independent dataset, we show that incorporation of the temporal dimension increases the predictive power: Predicted clusters are hit more (i.e. they contain genes with more disruptive mutations on them) and show higher enrichment in ASD-related functions compared to the state of the art.
      Keywords
      Spatio-temporal networks
      Gene discovery
      Prize-collecting Steiner forest problem
      Autism spectrum disorder
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      http://hdl.handle.net/11693/47739
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      • Dept. of Computer Engineering - Master's degree 511
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