Spatio-temporal gene discovery for autism spectrum disorder
Author
Norman, Utku
Advisor
Çiçek, A. Ercüment
Date
2018-08Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
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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 networksGene discovery
Prize-collecting Steiner forest problem
Autism spectrum disorder