Connecting the Genetic Dots of Autism Through Systems Biology

Friday, May 18, 2012
Sheraton Hall (Sheraton Centre Toronto)
9:00 AM
D. Wall1,2,3, J. Y. Jung4, T. Nelson4 and K. St. Gabriel5, (1)Harvard Medical School, Harvard Medical School, Boston, MA, (2)Pathology/Center for Biomedical Informatics, Harvard Medical School, Boston, MA, (3)Pediatrics, Harvard Medical School, Boston, MA, (4)Harvard Medical School, Boston, MA, (5)Center for Biomedical Informatics, Harvard Medical School, Boston, MA
Background:  

Although it is clear that autism is one of the most heritable mental disorders, the genetic etiology of autism remains elusive. More than 700 genes have been tied to autism, each of which is involved in numerous biological pathways and a variety of different protein and gene level interactions. It is difficult for a single researcher to grasp the complexity of the autism gene space. Given this, comparing autism susceptibility genes with candidate genes of other disorders having shared phenotypic traits with autism can shed light on our understanding of molecular mechanisms of autism.

Objectives:

To build and visualize the genetic system of autism within the context of all other human diseases and conditions, principally including neurodevelopmental disorders that share behaviors with autism, and to leverage the similarities and differences to generate and test hypotheses related to the genetic causes of autism.

Methods:  

We built a web resource called Autworks. In Autworks, we combined gene/protein interactions, gene-disease associations and cross-disorder information with powerful visualization and computational tools. We integrated and cross-checked candidate gene information and their interactions from twelve external resources including PubMed, GeneCards, HuGE, and protein interaction databases. For cross-disorder analysis, we computed the enrichment of genetic overlap between candidate genes identified with autism and those linked to over 3,700 other human disorders. Each disorder or cluster of disorders can be further investigated as lists of genes and gene properties (biological processes, variants with known deleterious phenotypes, etc.) or visualized as entire networks of interacting proteins. Sophisticated graph analytics enhance the power of Autworks' network visualization tools. Researchers can upload their own gene sets to test hypotheses using the tools provided by Autworks.

Results:

Autworks' enrichment results enable researchers to identify disorders with statistically significant genetic similarity to autism, visualize the network of interacting genes within these disorders, and analyze their own sets of genes using the same tools. These features enable rapid hypothesis generation and hypothesis testing. Both are key to prioritizing known autism candidates and identifying new candidates worthy of further experimentation.

Conclusions:

Despite decades of research, we still do not understand the causes of autism. What we do know is that autism has a strong genetic component and that its genetic roots are both numerous and variable. We have has designed a research tool to harness this genetic complexity and to place it within the context of other, related human disorders. This context helps gauge the importance of known genes while pinpointing new genes worthy of further study. With Autworks it is possible to search through all genes that have been implicated in autism to-date, to examine the complete genetic system of autism at once, or to study autism within a network of human diseases and disorders.  Each path may help our community better understand the genetics of autism and may reveal new insights that lead to diagnostic markers and targets for therapeutic intervention.  Autworks is accessible here: http://autworks.hms.harvard.edu.

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