ABSTRACT
Autism spectrum disorder (ASD) is a common heterogeneous neurodevelopmental disorder with typical symptoms such as impaired social interaction, language and communication abnormalities and stereotypical behavior. Since the genetics of ASDs is so diverse, information on genome function as provided by transcriptomic data is essential to further our understanding. This is transcriptome is a key link between measuring protein levels and genetic information. Transcriptome-based studies have been often performed by comparing ASD and control groups to identify which genes are dysregulated in the ASD group using statistical techniques. However, these statistical techniques can only find genes solely related to ASD, but cannot reflect relationship among genes which could be the etiology of ASD. In this study, we propose a novel method to find the ASD-associated genes, which are predictive for ASD. For this purpose, we metaanalyze whole-transcriptomic data of previous studies for ASD, which were performed using some expression profiling platforms on different issues of interest. These predictive genes, which can differentiate a sample into either ASD or non-ASD, are selected by an optimization process. Comparing subsets selected from different tissues/platforms, we conclude that tissues contain different gene sets associated with ASD. In addition, a platform can supply other ASD-associated genes of which other platforms cannot. Identified genes are then compared to those which have been well documented in SFARI, which is the most comprehensive and up-to-date data of ASD. Interestingly, we can find two novel genes with evidences from literature, which have not yet been recorded in this database. In summary, meta-analysis on whole-transcriptome data of ASD could shed light on the etiology of ASD.
- C. Lord, M. Rutter, and A. Le Couteur, "Autism Diagnostic Interview-Revised: A revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders," Journal of Autism and Developmental Disorders, vol. 24, no. 5, pp. 659--685, October 01, 1994.Google ScholarCross Ref
- D. L. Christensen, D. A. Bilder, W. Zahorodny, S. Pettygrove, M. S. Durkin, R. T. Fitzgerald, C. Rice, M. Kurzius-Spencer, J. Baio, and M. Yeargin-Allsopp, "Prevalence and Characteristics of Autism Spectrum Disorder Among 4-Year-Old Children in the Autism and Developmental Disabilities Monitoring Network," Journal of Developmental & Behavioral Pediatrics, vol. 37, no. 1, pp. 1--8, 2016.Google ScholarCross Ref
- M. D. Alter, R. Kharkar, K. E. Ramsey, D. W. Craig, R. D. Melmed, T. A. Grebe, R. C. Bay, S. Ober-Reynolds, J. Kirwan, J. J. Jones, J. B. Turner, R. Hen, and D. A. Stephan, "Autism and Increased Paternal Age Related Changes in Global Levels of Gene Expression Regulation," PLOS ONE, vol. 6, no. 2, pp. e16715, 2011.Google ScholarCross Ref
- B. S. Abrahams, and D. H. Geschwind, "Advances in autism genetics: on the threshold of a new neurobiology," Nat Rev Genet, vol. 9, no. 5, pp. 341--355, 05//print, 2008.Google ScholarCross Ref
- J. T. Glessner, J. J. Connolly, and H. Hakonarson, "Genome-Wide Association Studies of Autism," Current Behavioral Neuroscience Reports, vol. 1, no. 4, pp. 234--241, 2014.Google ScholarCross Ref
- I. Voineagu, and V. Eapen, "Converging Pathways in Autism Spectrum Disorders: Interplay between Synaptic Dysfunction and Immune Responses," Frontiers in Human Neuroscience, vol. 7, no. 738, 2013-November-07, 2013.Google Scholar
- A. Ansel, J. P. Rosenzweig, P. D. Zisman, M. Melamed, and B. Gesundheit, "Variation in Gene Expression in Autism Spectrum Disorders: An Extensive Review of Transcriptomic Studies," Frontiers in Neuroscience, vol. 10, no. 601, 2017-January-05, 2017.Google Scholar
- S. W. Kong, C. D. Collins, Y. Shimizu-Motohashi, I. A. Holm, M. G. Campbell, I.-H. Lee, S. J. Brewster, E. Hanson, H. K. Harris, K. R. Lowe, A. Saada, A. Mora, K. Madison, R. Hundley, J. Egan, J. McCarthy, A. Eran, M. Galdzicki, L. Rappaport, L. M. Kunkel, and I. S. Kohane, "Characteristics and Predictive Value of Blood Transcriptome Signature in Males with Autism Spectrum Disorders," PLOS ONE, vol. 7, no. 12, pp. e49475, 2012.Google ScholarCross Ref
- M. Chow, M. Winn, H.-R. Li, C. April, A. Wynshaw-Boris, J.-B. Fan, X.-D. Fu, E. Courchesne, and N. Schork, "Preprocessing and Quality Control Strategies for Illumina DASL Assay-Based Brain Gene Expression Studies with Semi-Degraded Samples," Frontiers in Genetics, vol. 3, no. 11, 2012-February-24, 2012.Google Scholar
- R. Luo, Stephan J. Sanders, Y. Tian, I. Voineagu, N. Huang, Su H. Chu, L. Klei, C. Cai, J. Ou, Jennifer K. Lowe, Matthew E. Hurles, B. Devlin, Matthew W. State, and Daniel H. Geschwind, "Genome-wide Transcriptome Profiling Reveals the Functional Impact of Rare De Novo and Recurrent CNVs in Autism Spectrum Disorders," The American Journal of Human Genetics, vol. 91, no. 1, pp. 38--55.Google ScholarCross Ref
- O. Bousquet, and L. Bottou, "The tradeoffs of large scale learning." pp. 161--168. Google ScholarDigital Library
- L. Bottou, "Large-scale machine learning with stochastic gradient descent," Proceedings of COMPSTAT'2010, pp. 177--186: Springer, 2010.Google Scholar
- T. Barrett, D. B. Troup, S. E. Wilhite, P. Ledoux, D. Rudnev, C. Evangelista, I. F. Kim, A. Soboleva, M. Tomashevsky, K. A. Marshall, K. H. Phillippy, P. M. Sherman, R. N. Muertter, and R. Edgar, "NCBI GEO: archive for high-throughput functional genomic data," Nucleic acids research, vol. 37, no. suppl 1, pp. D885--D890, January 1, 2009, 2009.Google ScholarCross Ref
- T. Barrett, D. B. Troup, S. E. Wilhite, P. Ledoux, D. Rudnev, C. Evangelista, I. F. Kim, A. Soboleva, M. Tomashevsky, and R. Edgar, "NCBI GEO: mining tens of millions of expression profiles - database and tools update," Nucleic acids research, vol. 35, no. suppl 1, pp. D760--D765, November 11, 2006, 2006.Google Scholar
- C. Lord, M. Rutter, S. Goode, J. Heemsbergen, H. Jordan, L. Mawhood, and E. Schopler, "Austism diagnostic observation schedule: A standardized observation of communicative and social behavior," Journal of Autism and Developmental Disorders, vol. 19, no. 2, pp. 185--212, June 01, 1989.Google ScholarCross Ref
- I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, "Gene Selection for Cancer Classification using Support Vector Machines," Machine Learning, vol. 46, no. 1, pp. 389--422, January 01, 2002. Google ScholarDigital Library
- F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, and V. Dubourg, "Scikit-learn: Machine learning in Python," Journal of Machine Learning Research, vol. 12, no. Oct, pp. 2825--2830, 2011. Google ScholarDigital Library
- S. Banerjee-Basu, and A. Packer, "SFARI Gene: an evolving database for the autism research community," Disease Models & Mechanisms, vol. 3, no. 3--4, pp. 133--135, 2010.Google Scholar
- B. S. Abrahams, D. E. Arking, D. B. Campbell, H. C. Mefford, E. M. Morrow, L. A. Weiss, I. Menashe, T. Wadkins, S. Banerjee-Basu, and A. Packer, "SFARI Gene 2.0: a community-driven knowledgebase for the autism spectrum disorders (ASDs)," Molecular Autism, vol. 4, no. 1, pp. 36, October 03, 2013.Google ScholarCross Ref
- J. A. Laurence, and S. H. Fatemi, "Glial fibrillary acidic protein is elevated in superior frontal, parietal and cerebellar cortices of autistic subjects," The Cerebellum, vol. 4, no. 3, pp. 206--210, 2005/09/01, 2005.Google ScholarCross Ref
- I. Cetin, I. Tezdig, M. C. Tarakcioglu, M. T. Kadak, O. F. Demirel, and O. F. Ozer, "Serum levels of glial fibrillary acidic protein and Nogo-A in children with autism spectrum disorders," Biomarkers, vol. 21, no. 7, pp. 614--618, 2016/10/02, 2016.Google ScholarCross Ref
- J. Wang, Q. Zou, R. Han, Y. Li, and Y. Wang, "Serum levels of Glial fibrillary acidic protein in Chinese children with autism spectrum disorders," International Journal of Developmental Neuroscience, vol. 57, no. Supplement C, pp. 41--45, 2017/04/01/, 2017.Google Scholar
- K. Garbett, P. J. Ebert, A. Mitchell, C. Lintas, B. Manzi, K. Mirnics, and A. M. Persico, "Immune transcriptome alterations in the temporal cortex of subjects with autism," Neurobiology of Disease, vol. 30, no. 3, pp. 303--311, 2008/06/01/, 2008.Google ScholarCross Ref
- R. M. Piro, "Network medicine: linking disorders," Human Genetics, vol. 131, no. 12, pp. 1811--1820, 2012.Google ScholarCross Ref
- A.-L. Barabasi, N. Gulbahce, and J. Loscalzo, "Network medicine: a network-based approach to human disease," Nat Rev Genet, vol. 12, no. 1, pp. 56--68, 2011.Google Scholar
- D.-H. Le, "Network-based ranking methods for prediction of novel disease associated microRNAs," Computational Biology and Chemistry, vol. 58, pp. 139--148, 10//, 2015. Google ScholarDigital Library
- D.-H. Le, and Y.-K. Kwon, "Neighbor-favoring weight reinforcement to improve random walk-based disease gene prioritization," Computational Biology and Chemistry, vol. 44, no. 0, pp. 1--8, 2013. Google ScholarDigital Library
- E. A. Demetriou, A. Lampit, D. S. Quintana, S. L. Naismith, Y. J. C. Song, J. E. Pye, I. Hickie, and A. J. Guastella, "Autism spectrum disorders: a meta-analysis of executive function," Mol Psychiatry, 04/25/online, 2017.Google Scholar
- T. A. S. D. W. G. o. T. P. G. Consortium, "Meta-analysis of GWAS of over 16,000 individuals with autism spectrum disorder highlights a novel locus at 10q24.32 and a significant overlap with schizophrenia," Molecular Autism, vol. 8, no. 1, pp. 21, May 22, 2017.Google ScholarCross Ref
Index Terms
- Meta-analysis of whole-transcriptome data for prediction of novel genes associated with autism spectrum disorder
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