Abstract

Autism spectrum disorder (ASD) is a neuropsychiatric disorder with strong evidence of genetic contribution, and increased research efforts have resulted in an ever-growing list of ASD candidate genes. However, only a fraction of the hundreds of nominated ASD-related genes have identified de novo or transmitted loss of function (LOF) mutations that can be directly attributed to the disorder. For this reason, a means of prioritizing candidate genes for ASD would help filter out false-positive results and allow researchers to focus on genes that are more likely to be causative. Here we constructed a machine learning model by leveraging a brain-specific functional relationship network (FRN) of genes to produce a genome-wide ranking of ASD risk genes. We rigorously validated our gene ranking using results from two independent sequencing experiments, together representing over 5000 simplex and multiplex ASD families. Finally, through functional enrichment analysis on our highly prioritized candidate gene network, we identified a small number of pathways that are key in early neural development, providing further support for their potential role in ASD.

Details

Title
Brain-specific functional relationship networks inform autism spectrum disorder gene prediction
Author
Duda, Marlena 1   VIAFID ORCID Logo  ; Zhang, Hongjiu 1 ; Hong-Dong, Li 2 ; Wall, Dennis P 3   VIAFID ORCID Logo  ; Burmeister, Margit 4 ; Guan, Yuanfang 5 

1  Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA 
2  Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA; Center for Bioinformatics, School of Information Science and Engineering, Central South University, Changsha, China 
3  Department of Pediatrics, Division of Systems Medicine, Stanford University, Stanford, CA, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA, USA 
4  Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA; Molecular and Behavioral Neuroscience Institute, University of Michigan, Ann Arbor, MI, USA; Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA; Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA 
5  Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA; Department of Internal Medicine, Usniversity of Michigan, Ann Arbor, MI, USA; Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA 
Pages
1-9
Publication year
2018
Publication date
Mar 2018
Publisher
Nature Publishing Group
e-ISSN
21583188
Source type
Scholarly Journal
Language of publication
English
DOI
https://doi.org/10.1038/s41398-018-0098-6
ProQuest document ID
2010831359
Copyright
© 2018. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.