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Network- and attribute-based classifiers can prioritize genes and pathways for autism spectrum disorders and intellectual disability

Authors

  • Yan Kou,

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    • Yan Kou, M.Sc., is a graduate student at Mount Sinai School of Medicine, New York, working under the supervision of Drs. Ma'ayan and Buxbaum. She is interested in developing and applying systems biological methods to complex human disorders. Yan Kou is a Seaver Graduate Fellow.

  • Catalina Betancur,

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    • Catalina Betancur, M.D., Ph.D., is director of research at the INSERM U952, CNRS UMR 7224, Université Pierre et Marie Curie, in Paris, France. Her work is focused on the elucidation of the genetic bases of autism spectrum disorders.

  • Huilei Xu,

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    • Huilei Xu, B.Sc., is a graduate student at Mount Sinai School of Medicine, New York, working under the supervision of Drs. Ma'ayan and Ihor Lemischka. Huilei Xu is interested in developing and applying data mining methods in computational systems biology with the focus on understanding embryonic stem cell pluripotency and early differentiation.

  • Joseph D Buxbaum,

    Corresponding author
    • Department of Psychiatry, Mount Sinai School of Medicine, One Gustave L Levy Place, Box 1668, New York, NY 10029.
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    • Joseph D Buxbaum, M.Sc., Ph.D., is a Professor in the Departments of Psychiatry, Neuroscience, and Genetics and Genomic Sciences, at the Mount Sinai School of Medicine in New York. His interests are in understanding the causes of, and developing targeted treatment for, neuropsychiatric disorders. Dr. Buxbaum is the Director of the Seaver Autism Center for Research and Treatment as well as Chief for the Center of Excellence in Neurodevelopmental Disorders of the Friedman Brain Institute, both at Mount Sinai.

  • Avi Ma'ayan

    Corresponding author
    • Department of Pharmacology and Systems Therapeutics, Mount Sinai School of Medicine, One Gustave L Levy Place, Box 1215, New York, NY 10029.
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    • Avi Ma'ayan, M.Sc., Ph.D., is an Assistant professor in the Department of Pharmacology and Systems Therapeutics. His interests are in applying graph theory, machine learning and dimensionality reduction methods for integrating omics datasets collected from mammalian sources to better understand biological regulation on a global scale. Dr. Ma'ayan is the Director of the Bioinformatics and Network Analysis Core of the Systems Biology Center New York.


  • Yan Kou, Catalina Betancur, and Huilei Xu contributed equally to this study.

  • How to Cite this Article: Kou Y, Betancur C, Xu H, Buxbaum JD, Ma'ayan A. 2012. Network- and attribute-based classifiers can prioritize genes and pathways for autism spectrum disorders and intellectual disability. Am J Med Genet Part C Semin Med Genet 160C:130–142.

Abstract

Autism spectrum disorders (ASD) are a group of related neurodevelopmental disorders with significant combined prevalence (∼1%) and high heritability. Dozens of individually rare genes and loci associated with high-risk for ASD have been identified, which overlap extensively with genes for intellectual disability (ID). However, studies indicate that there may be hundreds of genes that remain to be identified. The advent of inexpensive massively parallel nucleotide sequencing can reveal the genetic underpinnings of heritable complex diseases, including ASD and ID. However, whole exome sequencing (WES) and whole genome sequencing (WGS) provides an embarrassment of riches, where many candidate variants emerge. It has been argued that genetic variation for ASD and ID will cluster in genes involved in distinct pathways and protein complexes. For this reason, computational methods that prioritize candidate genes based on additional functional information such as protein–protein interactions or association with specific canonical or empirical pathways, or other attributes, can be useful. In this study we applied several supervised learning approaches to prioritize ASD or ID disease gene candidates based on curated lists of known ASD and ID disease genes. We implemented two network-based classifiers and one attribute-based classifier to show that we can rank and classify known, and predict new, genes for these neurodevelopmental disorders. We also show that ID and ASD share common pathways that perturb an overlapping synaptic regulatory subnetwork. We also show that features relating to neuronal phenotypes in mouse knockouts can help in classifying neurodevelopmental genes. Our methods can be applied broadly to other diseases helping in prioritizing newly identified genetic variation that emerge from disease gene discovery based on WES and WGS. © 2012 Wiley Periodicals, Inc.

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