Document Type
Article
Abstract
The substantial progress in the last few years toward uncovering genetic causes and risk factors for autism spectrum disorders (ASDs) has opened new experimental avenues for identifying the underlying neurobiological mechanism of the condition. The bounty of genetic findings has led to a variety of data-driven exploratory analyses aimed at deriving new insights about the shared features of these genes. These approaches leverage data from a variety of different sources such as co-expression in transcriptomic studies, protein-protein interaction networks, gene ontologies (GOs) annotations, or multi-level combinations of all of these. Here, we review the recurrent themes emerging from these analyses and highlight some of the challenges going forward. Themes include findings that ASD associated genes discovered by a variety of methods have been shown to contain disproportionate amounts of neurite outgrowth/cytoskeletal, synaptic, and more recently Wnt-related and chromatin modifying genes. Expression studies have highlighted a disproportionate expression of ASD gene sets during mid fetal cortical development, particularly for rare variants, with multiple analyses highlighting the striatum and cortical projection and interneurons as well. While these explorations have highlighted potentially interesting relationships among these ASD-related genes, there are challenges in how to best transition these insights into empirically testable hypotheses. Nonetheless, defining shared molecular or cellular pathology downstream of the diverse genes associated with ASDs could provide the cornerstones needed to build toward broadly applicable therapeutic approaches.
Publication Date
1-1-2015
Publication Title
Frontiers in Genetics
Volume
6
Issue
OCT
DOI
10.3389/fgene.2015.00301
Recommended Citation
Kopp, Nathan; Climer, Sharlee; and Dougherty, Joseph D., "Moving from capstones toward cornerstones: Successes and challenges in applying systems biology to identify mechanisms of autism spectrum disorders" (2015). Computer Science Faculty Works. 19.
DOI: https://doi.org/10.3389/fgene.2015.00301
Available at:
https://irl.umsl.edu/cmpsci-faculty/19