Autism is a complex disorder with a wide spectrum of phenotypes. Although it is clearly heritable, the molecular agents responsible remain elusive. More than 100 genes have been tied to Autism, each of which is involved in numerous different biological processes and in a variety of different molecular interactions. No single researcher can completely grasp the complexity of this Autism gene space, and perhaps for this reason, few genes have emerged as promising markers or targets for therapeutic intervention. Our plan is provide a way to grasp this complexity by shifting the focus from single genes to the entire genetic system of Autism. Thus, we will build the complete network of molecular interactions for all Autism candidate genes using bioinformatic methods that integrate multiple sources of genomic and bibliomic information. While this network will be a powerful enabler of new discoveries in Autism, it will not be enough to fully grasp the genetic underpinnings of the various behaviors indicative of the disorder. Hope for that however lies in the behavioral similarities between Autism and numerous other neurological disorders. These behavioral similarities suggest that there are common molecular mechanisms that if understood could help provide a clearer genotype-phenotype map of the Autism spectrum. Our plan is to capitalize on these similarities by conducting a comprehensive comparative analysis of the Autism network with the networks of more than 400 neurological disorders. Our work will result in a systems level view of Autism and its most similar neurological disorders that will not only help to see emergent trends that clarify the genetic basis of the spectrum, but will also help to prioritize known Autism candidates and reveal new candidates worthy of investigation. All of our work will be made freely accessible in a web-based tool that allows complete navigation through the Autism network and the networks of all other related neurological disorders. Wall &Kohane Abstract 1

Public Health Relevance

The genetic component of Autism remains unknown, but current research indicates that it is most likely the result of combined effects of many different genetic variants in possibly hundreds of genes. Grasping the complexity of this genetic land scape is a significant challenge for Autism researchers, and requires sophisticated bioinformatic solutions that are readily accessible to all members of the research community. We propose to build a web-based system called Autworks that is at once an up-to-date central clearing house for all information relevant to the genetic component of Autism and a powerful research tool that allows researchers to view the genetic component of Autism as a network and in light of research results on related neurological disorders.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Project (R01)
Project #
Application #
Study Section
Neurotechnology Study Section (NT)
Program Officer
Senthil, Geetha
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Harvard University
Schools of Medicine
United States
Zip Code
Gafni, Erik; Luquette, Lovelace J; Lancaster, Alex K et al. (2014) COSMOS: Python library for massively parallel workflows. Bioinformatics 30:2956-8
Duda, M; Kosmicki, J A; Wall, D P (2014) Testing the accuracy of an observation-based classifier for rapid detection of autism risk. Transl Psychiatry 4:e424
Fusaro, Vincent A; Daniels, Jena; Duda, Marlena et al. (2014) The potential of accelerating early detection of autism through content analysis of YouTube videos. PLoS One 9:e93533
Vardarajan, B N; Eran, A; Jung, J-Y et al. (2013) Haplotype structure enables prioritization of common markers and candidate genes in autism spectrum disorder. Transl Psychiatry 3:e262
Wall, Dennis P; Pivovarov, Rimma; Tong, Mark et al. (2010) Genotator: a disease-agnostic tool for genetic annotation of disease. BMC Med Genomics 3:50