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.
Article first published online: 12 APR 2012
Copyright © 2012 Wiley Periodicals, Inc.
American Journal of Medical Genetics Part C: Seminars in Medical Genetics
Special Issue: Autism and Intellectual Disability: Two Sides of the Same Coin
Volume 160C, Issue 2, pages 130–142, 15 May 2012
How to Cite
Kou, Y., Betancur, C., Xu, H., Buxbaum, J. D. and 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., 160C: 130–142. doi: 10.1002/ajmg.c.31330
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.
- Issue published online: 19 APR 2012
- Article first published online: 12 APR 2012
- NIH. Grant Numbers: P50GM071558-03, R01DK088541-01A1, RC2LM010994-01, R01MH089025-02
- high-throughput sequencing;
- massively parallel sequencing;
- gene discovery;
- neurodevelopmental disorders;
- intellectual disability;
- support vector machine
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.