Human Genetics

, Volume 135, Issue 7, pp 797–811 | Cite as

Exploiting aberrant mRNA expression in autism for gene discovery and diagnosis

  • Jinting Guan
  • Ence Yang
  • Jizhou Yang
  • Yong Zeng
  • Guoli Ji
  • James J. Cai
Original Investigation


Autism spectrum disorder (ASD) is characterized by substantial phenotypic and genetic heterogeneity, which greatly complicates the identification of genetic factors that contribute to the disease. Study designs have mainly focused on group differences between cases and controls. The problem is that, by their nature, group difference-based methods (e.g., differential expression analysis) blur or collapse the heterogeneity within groups. By ignoring genes with variable within-group expression, an important axis of genetic heterogeneity contributing to expression variability among affected individuals has been overlooked. To this end, we develop a new gene expression analysis method—aberrant gene expression analysis, based on the multivariate distance commonly used for outlier detection. Our method detects the discrepancies in gene expression dispersion between groups and identifies genes with significantly different expression variability. Using this new method, we re-visited RNA sequencing data generated from post-mortem brain tissues of 47 ASD and 57 control samples. We identified 54 functional gene sets whose expression dispersion in ASD samples is more pronounced than that in controls, as well as 76 co-expression modules present in controls but absent in ASD samples due to ASD-specific aberrant gene expression. We also exploited aberrantly expressed genes as biomarkers for ASD diagnosis. With a whole blood expression data set, we identified three aberrantly expressed gene sets whose expression levels serve as discriminating variables achieving >70 % classification accuracy. In summary, our method represents a novel discovery and diagnostic strategy for ASD. Our findings may help open an expression variability-centered research avenue for other genetically heterogeneous disorders.


Autism Spectrum Disorder Mahalanobis Distance Autism Spectrum Disorder Group Autism Spectrum Disorder Patient Autism Spectrum Disorder Sample 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We thank Shannon Ellis and Dan Arking for sharing the data, Oliver Stegle and Tuuli Lappalainen for helping with data normalization, and Steve Horvath for the co-expression network analysis. We thank Rae L. Russell for proofreading and editing this paper. We acknowledge the Texas A&M Institute for Genome Sciences and Society (TIGSS) for providing computing resources and system administration support. This work was supported by the fund of China Scholarship Council to JG, and the National Natural Science Foundation of China (No. 61573296), the Specialized Research Fund for the Doctoral Program of Higher Education of China (No. 20130121130004), the Fundamental Research Funds for the Central Universities in China (Xiamen University: Nos. 201412G009, 201510384106) to GJ.

Compliance with ethical standards

Competing interests

The authors declare that they have no competing interests.

Supplementary material

439_2016_1673_MOESM1_ESM.docx (94 kb)
Supplementary Fig. 1. Results of principal component analysis (PCA) showing the first four principal components (from PC1 to PC4). The distributions of 104 samples (57 controls and 47 ASD samples) on PCA spaces defined by PC1 and 2, PC2 and 3, and PC3 and 4 are shown
439_2016_1673_MOESM2_ESM.docx (609 kb)
Supplementary Fig. 2. Reproducibility of co-expression modules in the non-ASD control group and the breakdown of modules in ASD. Ten example modules are shown with two independent data sets from controls, as well as one data set from ASD samples. Edge width is proportional to the Pearson’s correlation coefficients (ranging 0.5 and 1). Node size is proportional to ΔSSMD for each gene
439_2016_1673_MOESM3_ESM.docx (16 kb)
Supplementary Fig. 3. Distribution of correlation coefficients between t statistics of DE test and ΔSSMD values of genes in 76 significant modules. The kernel density estimate of the distribution is shown with the gray line; values of Spearman correlation coefficient (rho) of modules are shown with orange triangles; rho=0 is shown with the dotted vertical line
439_2016_1673_MOESM4_ESM.docx (16 kb)
Supplementary Fig. 4. Box plot of AUC (area under ROC curve) value against the size of classifier gene set. For each size of the gene set (from 3 to 15), 100 different random gene sets were constructed and tested on the training set and test set for obtaining AUCs. The black and red boxplots denote AUC values tested on the training set (AUC1) and test set (AUC2) varying with the size of classifier gene set, respectively
439_2016_1673_MOESM5_ESM.docx (25 kb)
Supplementary Fig. 5. Scatter plot of AUC values tested on the test set (AUC2) against AUC values tested on the training set (AUC1) for 100 different random classifier 5-gene sets. Red line denotes the least-squares line of the scatter plot. The Spearman correlation coefficient between AUC1 and AUC2 is 0.32 (P = 1.1 × 10−3). The inset shows the distribution of the Spearman rank correlation coefficients between AUC1 and AUC2 calculated with 1,000 replicates of such 100 random classifier 5-gene sets
439_2016_1673_MOESM6_ESM.docx (23 kb)
Supplementary Table 1. GO term-defined gene sets that tend to be aberrantly expressed in brain tissues of ASD-affected individuals. Gene sets contain genes annotated with GO terms of three sub-ontologies: biological process (BP), molecular function (MF), and cellular component (CC)
439_2016_1673_MOESM7_ESM.docx (34 kb)
Supplementary Table 2. WGCNA co-expression network modules containing genes that tend to be aberrantly expressed in the brains of ASD-affected individuals. Modules are annotated with the DAVID-defined gene function keyword clusters. Representative genes with the corresponding function are shown in bold. Statistics of the preservation between modules built for cases and controls, medianRank and Zsummary, calculated using function modulePreservation of WGCNA are given
439_2016_1673_MOESM8_ESM.docx (27 kb)
Supplementary Table 3. Genes in the three classifier gene sets obtained from blood data set (GEO accession: GSE18123) and corresponding SSMD and ΔSSMD values
439_2016_1673_MOESM9_ESM.docx (19 kb)
Supplementary Table 4. Genes in the three classifier gene sets obtained from brain data set (57 controls and 47 ASD cases) and corresponding SSMD and ΔSSMD values. The performances of classifiers based on gene set I, II, III tested on the training and test sets are also reported including sensitivity (SN), specificity (SP) and accuracy (ACC) values


  1. Abrahams BS, Geschwind DH (2008) Advances in autism genetics: on the threshold of a new neurobiology. Nat Rev Genet 9:341–355. doi: 10.1038/nrg2346 CrossRefPubMedPubMedCentralGoogle Scholar
  2. Baribeau DA, Anagnostou E (2015) Oxytocin and vasopressin: linking pituitary neuropeptides and their receptors to social neurocircuits. Front Neurosci 9:335. doi: 10.3389/fnins.2015.00335 CrossRefPubMedPubMedCentralGoogle Scholar
  3. Belmonte MK, Cook EH Jr, Anderson GM, Rubenstein JL, Greenough WT, Beckel-Mitchener A, Courchesne E, Boulanger LM, Powell SB, Levitt PR, Perry EK, Jiang YH, DeLorey TM, Tierney E (2004) Autism as a disorder of neural information processing: directions for research and targets for therapy. Mol Psychiatry 9:646–663. doi: 10.1038/ PubMedGoogle Scholar
  4. Benfenati F, Valtorta F, Bahler M, Greengard P (1989) Synapsin I, a neuron-specific phosphoprotein interacting with small synaptic vesicles and F-actin. Cell Biol Int Rep 13:1007–1021CrossRefPubMedGoogle Scholar
  5. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate—a practical and powerful approach to multiple testing. J R Stat Soc Ser B Methodol 57:289–300. doi: 10.2307/2346101 Google Scholar
  6. Butler MG, Rafi SK, Hossain W, Stephan DA, Manzardo AM (2015) Whole exome sequencing in females with autism implicates novel and candidate genes. Int J Mol Sci 16:1312–1335. doi: 10.3390/ijms16011312 CrossRefPubMedPubMedCentralGoogle Scholar
  7. Cai JJ, Borenstein E, Petrov DA (2010) Broker genes in human disease. Genome Biol Evol 2:815–825. doi: 10.1093/gbe/evq064 CrossRefPubMedPubMedCentralGoogle Scholar
  8. Cesca F, Baldelli P, Valtorta F, Benfenati F (2010) The synapsins: key actors of synapse function and plasticity. Prog Neurobiol 91:313–348. doi: 10.1016/j.pneurobio.2010.04.006 CrossRefPubMedGoogle Scholar
  9. Chow ML, Pramparo T, Winn ME, Barnes CC, Li HR, Weiss L, Fan JB, Murray S, April C, Belinson H, Fu XD, Wynshaw-Boris A, Schork NJ, Courchesne E (2012) Age-dependent brain gene expression and copy number anomalies in autism suggest distinct pathological processes at young versus mature ages. PLoS Genet 8:e1002592. doi: 10.1371/journal.pgen.1002592 CrossRefPubMedPubMedCentralGoogle Scholar
  10. Clarke RA, Lee S, Eapen V (2012) Pathogenetic model for Tourette syndrome delineates overlap with related neurodevelopmental disorders including autism. Transl Psychiatry 2:e158. doi: 10.1038/tp.2012.75 CrossRefPubMedPubMedCentralGoogle Scholar
  11. Cookson W, Liang L, Abecasis G, Moffatt M, Lathrop M (2009) Mapping complex disease traits with global gene expression. Nat Rev Genet 10:184–194. doi: 10.1038/nrg2537 CrossRefPubMedPubMedCentralGoogle Scholar
  12. Corradi A, Fadda M, Piton A, Patry L, Marte A, Rossi P, Cadieux-Dion M, Gauthier J, Lapointe L, Mottron L, Valtorta F, Rouleau GA, Fassio A, Benfenati F, Cossette P (2014) SYN2 is an autism predisposing gene: loss-of-function mutations alter synaptic vesicle cycling and axon outgrowth. Hum Mol Genet 23:90–103. doi: 10.1093/hmg/ddt401 CrossRefPubMedGoogle Scholar
  13. Croft D, Mundo AF, Haw R, Milacic M, Weiser J, Wu G, Caudy M, Garapati P, Gillespie M, Kamdar MR, Jassal B, Jupe S, Matthews L, May B, Palatnik S, Rothfels K, Shamovsky V, Song H, Williams M, Birney E, Hermjakob H, Stein L, D’Eustachio P (2014) The Reactome pathway knowledgebase. Nucleic Acids Res 42:D472–D477. doi: 10.1093/nar/gkt1102 CrossRefPubMedGoogle Scholar
  14. De Rubeis S, Pasciuto E, Li KW, Fernandez E, Di Marino D, Buzzi A, Ostroff LE, Klann E, Zwartkruis FJ, Komiyama NH, Grant SG, Poujol C, Choquet D, Achsel T, Posthuma D, Smit AB, Bagni C (2013) CYFIP1 coordinates mRNA translation and cytoskeleton remodeling to ensure proper dendritic spine formation. Neuron 79:1169–1182. doi: 10.1016/j.neuron.2013.06.039 CrossRefPubMedPubMedCentralGoogle Scholar
  15. De Rubeis S, He X, Goldberg AP, Poultney CS, Samocha K, Cicek AE, Kou Y, Liu L, Fromer M, Walker S, Singh T, Klei L, Kosmicki J, Shih-Chen F, Aleksic B, Biscaldi M, Bolton PF, Brownfeld JM, Cai J, Campbell NG, Carracedo A, Chahrour MH, Chiocchetti AG, Coon H, Crawford EL, Curran SR, Dawson G, Duketis E, Fernandez BA, Gallagher L, Geller E, Guter SJ, Hill RS, Ionita-Laza J, Jimenz Gonzalez P, Kilpinen H, Klauck SM, Kolevzon A, Lee I, Lei I, Lei J, Lehtimaki T, Lin CF, Ma’ayan A, Marshall CR, McInnes AL, Neale B, Owen MJ, Ozaki N, Parellada M, Parr JR, Purcell S, Puura K, Rajagopalan D, Rehnstrom K, Reichenberg A, Sabo A, Sachse M, Sanders SJ, Schafer C, Schulte-Ruther M, Skuse D, Stevens C, Szatmari P, Tammimies K, Valladares O, Voran A, Li-San W, Weiss LA, Willsey AJ, Yu TW, Yuen RK, DDD Study, Homozygosity Mapping Collaborative for Autism, UK10K Consortium, Cook EH, Freitag CM, Gill M, Hultman CM, Lehner T, Palotie A, Schellenberg GD, Sklar P, State MW, Sutcliffe JS, Walsh CA, Scherer SW, Zwick ME, Barett JC, Cutler DJ, Roeder K, Devlin B, Daly MJ, Buxbaum JD (2014) Synaptic, transcriptional and chromatin genes disrupted in autism. Nature 515:209–215. doi: 10.1038/nature13772 CrossRefPubMedPubMedCentralGoogle Scholar
  16. Devlin B, Scherer SW (2012) Genetic architecture in autism spectrum disorder. Curr Opin Genet Dev 22:229–237. doi: 10.1016/j.gde.2012.03.002 CrossRefPubMedGoogle Scholar
  17. Dey SS, Foley JE, Limsirichai P, Schaffer DV, Arkin AP (2015) Orthogonal control of expression mean and variance by epigenetic features at different genomic loci. Mol Syst Biol 11:806. doi: 10.15252/msb.20145704 CrossRefPubMedPubMedCentralGoogle Scholar
  18. Dinwiddie DL, Soden SE, Saunders CJ, Miller NA, Farrow EG, Smith LD, Kingsmore SF (2013) De novo frameshift mutation in ASXL3 in a patient with global developmental delay, microcephaly, and craniofacial anomalies. BMC Med Genomics 6:32. doi: 10.1186/1755-8794-6-32 CrossRefPubMedPubMedCentralGoogle Scholar
  19. Ecker S, Pancaldi V, Rico D, Valencia A (2015) Higher gene expression variability in the more aggressive subtype of chronic lymphocytic leukemia. Genome Med 7:8. doi: 10.1186/s13073-014-0125-z CrossRefPubMedPubMedCentralGoogle Scholar
  20. Elsabbagh M, Johnson MH (2010) Getting answers from babies about autism. Trends Cogn Sci 14:81–87. doi: 10.1016/j.tics.2009.12.005 CrossRefPubMedGoogle Scholar
  21. Ercan-Sencicek AG, Stillman AA, Ghosh AK, Bilguvar K, O’Roak BJ, Mason CE, Abbott T, Gupta A, King RA, Pauls DL, Tischfield JA, Heiman GA, Singer HS, Gilbert DL, Hoekstra PJ, Morgan TM, Loring E, Yasuno K, Fernandez T, Sanders S, Louvi A, Cho JH, Mane S, Colangelo CM, Biederer T, Lifton RP, Gunel M, State MW (2010) l-Histidine decarboxylase and Tourette’s syndrome. N Engl J Med 362:1901–1908. doi: 10.1056/NEJMoa0907006 CrossRefPubMedPubMedCentralGoogle Scholar
  22. Fatemi SH, Folsom TD, Reutiman TJ, Sidwell RW (2008) Viral regulation of aquaporin 4, connexin 43, microcephalin and nucleolin. Schizophr Res 98:163–177. doi: 10.1016/j.schres.2007.09.031 CrossRefPubMedGoogle Scholar
  23. Flint J, Timpson N, Munafo M (2014) Assessing the utility of intermediate phenotypes for genetic mapping of psychiatric disease. Trends Neurosci 37:733–741. doi: 10.1016/j.tins.2014.08.007 CrossRefPubMedGoogle Scholar
  24. Frye RE, Huffman LC, Elliott GR (2010) Tetrahydrobiopterin as a novel therapeutic intervention for autism. Neurotherapeutics 7:241–249. doi: 10.1016/j.nurt.2010.05.004 CrossRefPubMedPubMedCentralGoogle Scholar
  25. Garbett K, Ebert PJ, Mitchell A, Lintas C, Manzi B, Mirnics K, Persico AM (2008) Immune transcriptome alterations in the temporal cortex of subjects with autism. Neurobiol Dis 30:303–311. doi: 10.1016/j.nbd.2008.01.012 CrossRefPubMedPubMedCentralGoogle Scholar
  26. Gaugler T, Klei L, Sanders SJ, Bodea CA, Goldberg AP, Lee AB, Mahajan M, Manaa D, Pawitan Y, Reichert J, Ripke S, Sandin S, Sklar P, Svantesson O, Reichenberg A, Hultman CM, Devlin B, Roeder K, Buxbaum JD (2014) Most genetic risk for autism resides with common variation. Nat Genet 46:881–885. doi: 10.1038/ng.3039 CrossRefPubMedPubMedCentralGoogle Scholar
  27. Geschwind DH (2008) Autism: many genes, common pathways? Cell 135:391–395. doi: 10.1016/j.cell.2008.10.016 CrossRefPubMedPubMedCentralGoogle Scholar
  28. Geschwind DH (2011) Genetics of autism spectrum disorders. Trends Cogn Sci 15:409–416. doi: 10.1016/j.tics.2011.07.003 CrossRefPubMedPubMedCentralGoogle Scholar
  29. Geschwind DH, State MW (2015) Gene hunting in autism spectrum disorder: on the path to precision medicine. Lancet Neurol 14:1109–1120. doi: 10.1016/S1474-4422(15)00044-7 CrossRefPubMedPubMedCentralGoogle Scholar
  30. Glessner JT, Wang K, Cai G, Korvatska O, Kim CE, Wood S, Zhang H, Estes A, Brune CW, Bradfield JP, Imielinski M, Frackelton EC, Reichert J, Crawford EL, Munson J, Sleiman PM, Chiavacci R, Annaiah K, Thomas K, Hou C, Glaberson W, Flory J, Otieno F, Garris M, Soorya L, Klei L, Piven J, Meyer KJ, Anagnostou E, Sakurai T, Game RM, Rudd DS, Zurawiecki D, McDougle CJ, Davis LK, Miller J, Posey DJ, Michaels S, Kolevzon A, Silverman JM, Bernier R, Levy SE, Schultz RT, Dawson G, Owley T, McMahon WM, Wassink TH, Sweeney JA, Nurnberger JI, Coon H, Sutcliffe JS, Minshew NJ, Grant SF, Bucan M, Cook EH, Buxbaum JD, Devlin B, Schellenberg GD, Hakonarson H (2009) Autism genome-wide copy number variation reveals ubiquitin and neuronal genes. Nature 459:569–573. doi: 10.1038/nature07953 CrossRefPubMedPubMedCentralGoogle Scholar
  31. Guglielmi L, Servettini I, Caramia M, Catacuzzeno L, Franciolini F, D’Adamo MC, Pessia M (2015) Update on the implication of potassium channels in autism: K(+) channelautism spectrum disorder. Front Cell Neurosci 9:34. doi: 10.3389/fncel.2015.00034 CrossRefPubMedPubMedCentralGoogle Scholar
  32. Gupta S, Ellis SE, Ashar FN, Moes A, Bader JS, Zhan J, West AB, Arking DE (2014) Transcriptome analysis reveals dysregulation of innate immune response genes and neuronal activity-dependent genes in autism. Nat Commun 5:5748. doi: 10.1038/ncomms6748 CrossRefPubMedPubMedCentralGoogle Scholar
  33. Hansen KD, Irizarry RA, Wu Z (2012) Removing technical variability in RNA-seq data using conditional quantile normalization. Biostatistics 13:204–216. doi: 10.1093/biostatistics/kxr054 CrossRefPubMedPubMedCentralGoogle Scholar
  34. Heinzen EL, Ge D, Cronin KD, Maia JM, Shianna KV, Gabriel WN, Welsh-Bohmer KA, Hulette CM, Denny TN, Goldstein DB (2008) Tissue-specific genetic control of splicing: implications for the study of complex traits. PLoS Biol 6:e1. doi: 10.1371/journal.pbio.1000001 CrossRefPubMedGoogle Scholar
  35. Hormozdiari F, Penn O, Borenstein E, Eichler EE (2015) The discovery of integrated gene networks for autism and related disorders. Genome Res 25:142–154. doi: 10.1101/gr.178855.114 CrossRefPubMedPubMedCentralGoogle Scholar
  36. Hulse AM, Cai JJ (2013) Genetic variants contribute to gene expression variability in humans. Genetics 193:95–108. doi: 10.1534/genetics.112.146779 CrossRefPubMedPubMedCentralGoogle Scholar
  37. Iossifov I, Ronemus M, Levy D, Wang Z, Hakker I, Rosenbaum J, Yamrom B, Lee YH, Narzisi G, Leotta A, Kendall J, Grabowska E, Ma B, Marks S, Rodgers L, Stepansky A, Troge J, Andrews P, Bekritsky M, Pradhan K, Ghiban E, Kramer M, Parla J, Demeter R, Fulton LL, Fulton RS, Magrini VJ, Ye K, Darnell JC, Darnell RB, Mardis ER, Wilson RK, Schatz MC, McCombie WR, Wigler M (2012) De novo gene disruptions in children on the autistic spectrum. Neuron 74:285–299. doi: 10.1016/j.neuron.2012.04.009 CrossRefPubMedPubMedCentralGoogle Scholar
  38. Iossifov I, O’Roak BJ, Sanders SJ, Ronemus M, Krumm N, Levy D, Stessman HA, Witherspoon KT, Vives L, Patterson KE, Smith JD, Paeper B, Nickerson DA, Dea J, Dong S, Gonzalez LE, Mandell JD, Mane SM, Murtha MT, Sullivan CA, Walker MF, Waqar Z, Wei L, Willsey AJ, Yamrom B, Lee YH, Grabowska E, Dalkic E, Wang Z, Marks S, Andrews P, Leotta A, Kendall J, Hakker I, Rosenbaum J, Ma B, Rodgers L, Troge J, Narzisi G, Yoon S, Schatz MC, Ye K, McCombie WR, Shendure J, Eichler EE, State MW, Wigler M (2014) The contribution of de novo coding mutations to autism spectrum disorder. Nature 515:216–221. doi: 10.1038/nature13908 CrossRefPubMedPubMedCentralGoogle Scholar
  39. Jalbrzikowski M, Lazaro MT, Gao F, Huang A, Chow C, Geschwind DH, Coppola G, Bearden CE (2015) Transcriptome profiling of peripheral blood in 22q11.2 deletion syndrome reveals functional pathways related to psychosis and autism spectrum disorder. PLoS One 10:e0132542. doi: 10.1371/journal.pone.0132542 CrossRefPubMedPubMedCentralGoogle Scholar
  40. Klaiman C, Huffman L, Masaki L, Elliott GR (2013) Tetrahydrobiopterin as a treatment for autism spectrum disorders: a double-blind, placebo-controlled trial. J Child Adolesc Psychopharmacol 23:320–328. doi: 10.1089/cap.2012.0127 CrossRefPubMedGoogle Scholar
  41. Klei L, Sanders SJ, Murtha MT, Hus V, Lowe JK, Willsey AJ, Moreno-De-Luca D, Yu TW, Fombonne E, Geschwind D, Grice DE, Ledbetter DH, Lord C, Mane SM, Martin CL, Martin DM, Morrow EM, Walsh CA, Melhem NM, Chaste P, Sutcliffe JS, State MW, Cook EH Jr, Roeder K, Devlin B (2012) Common genetic variants, acting additively, are a major source of risk for autism. Mol Autism 3:9. doi: 10.1186/2040-2392-3-9 CrossRefPubMedPubMedCentralGoogle Scholar
  42. Kong SW, Collins CD, Shimizu-Motohashi Y, Holm IA, Campbell MG, Lee IH, Brewster SJ, Hanson E, Harris HK, Lowe KR, Saada A, Mora A, Madison K, Hundley R, Egan J, McCarthy J, Eran A, Galdzicki M, Rappaport L, Kunkel LM, Kohane IS (2012) Characteristics and predictive value of blood transcriptome signature in males with autism spectrum disorders. PLoS One 7:e49475. doi: 10.1371/journal.pone.0049475 CrossRefPubMedPubMedCentralGoogle Scholar
  43. Konganti K, Wang G, Yang E, Cai JJ (2013) SBEToolbox: a Matlab toolbox for biological network analysis. Evol Bioinform Online 9:355–362. doi: 10.4137/EBO.S12012 CrossRefPubMedPubMedCentralGoogle Scholar
  44. Langfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9:559. doi: 10.1186/1471-2105-9-559 CrossRefPubMedPubMedCentralGoogle Scholar
  45. Langfelder P, Luo R, Oldham MC, Horvath S (2011) Is my network module preserved and reproducible? PLoS Comput Biol 7:e1001057. doi: 10.1371/journal.pcbi.1001057 CrossRefPubMedPubMedCentralGoogle Scholar
  46. Lazaro MT, Golshani P (2015) The utility of rodent models of autism spectrum disorders. Curr Opin Neurol 28:103–109. doi: 10.1097/WCO.0000000000000183 CrossRefPubMedPubMedCentralGoogle Scholar
  47. Lee HJ, Song JY, Kim JW, Jin SY, Hong MS, Park JK, Chung JH, Shibata H, Fukumaki Y (2005) Association study of polymorphisms in synaptic vesicle-associated genes, SYN2 and CPLX2, with schizophrenia. Behav Brain Funct 1:15. doi: 10.1186/1744-9081-1-15 CrossRefPubMedPubMedCentralGoogle Scholar
  48. Lee H, Lin MC, Kornblum HI, Papazian DM, Nelson SF (2014) Exome sequencing identifies de novo gain of function missense mutation in KCND2 in identical twins with autism and seizures that slows potassium channel inactivation. Hum Mol Genet 23:3481–3489. doi: 10.1093/hmg/ddu056 CrossRefPubMedPubMedCentralGoogle Scholar
  49. Li J, Liu Y, Kim T, Min R, Zhang Z (2010) Gene expression variability within and between human populations and implications toward disease susceptibility. PLoS Comput Biol 6:e1000910. doi: 10.1371/journal.pcbi.1000910 CrossRefPubMedPubMedCentralGoogle Scholar
  50. Li J, Shi M, Ma Z, Zhao S, Euskirchen G, Ziskin J, Urban A, Hallmayer J, Snyder M (2014) Integrated systems analysis reveals a molecular network underlying autism spectrum disorders. Mol Syst Biol 10:774. doi: 10.15252/msb.20145487 CrossRefPubMedPubMedCentralGoogle Scholar
  51. Liberzon A, Subramanian A, Pinchback R, Thorvaldsdottir H, Tamayo P, Mesirov JP (2011) Molecular signatures database (MSigDB) 3.0. Bioinformatics 27:1739–1740. doi: 10.1093/bioinformatics/btr260 CrossRefPubMedPubMedCentralGoogle Scholar
  52. Lim MM, Bielsky IF, Young LJ (2005) Neuropeptides and the social brain: potential rodent models of autism. Int J Dev Neurosci 23:235–243. doi: 10.1016/j.ijdevneu.2004.05.006 CrossRefPubMedGoogle Scholar
  53. Lionel AC, Crosbie J, Barbosa N, Goodale T, Thiruvahindrapuram B, Rickaby J, Gazzellone M, Carson AR, Howe JL, Wang Z, Wei J, Stewart AF, Roberts R, McPherson R, Fiebig A, Franke A, Schreiber S, Zwaigenbaum L, Fernandez BA, Roberts W, Arnold PD, Szatmari P, Marshall CR, Schachar R, Scherer SW (2011) Rare copy number variation discovery and cross-disorder comparisons identify risk genes for ADHD. Sci Transl Med 3:95ra75. doi: 10.1126/scitranslmed.3002464 CrossRefPubMedGoogle Scholar
  54. Mahalanobis PC (1936) On the generalised distance in statistics. Proc Natl Inst Sci India 2:49–55. doi:citeulike-article-id:4155812Google Scholar
  55. Mar JC, Matigian NA, Mackay-Sim A, Mellick GD, Sue CM, Silburn PA, McGrath JJ, Quackenbush J, Wells CA (2011) Variance of gene expression identifies altered network constraints in neurological disease. PLoS Genet 7:e1002207. doi: 10.1371/journal.pgen.1002207 CrossRefPubMedPubMedCentralGoogle Scholar
  56. Neale BM, Kou Y, Liu L, Ma’ayan A, Samocha KE, Sabo A, Lin CF, Stevens C, Wang LS, Makarov V, Polak P, Yoon S, Maguire J, Crawford EL, Campbell NG, Geller ET, Valladares O, Schafer C, Liu H, Zhao T, Cai G, Lihm J, Dannenfelser R, Jabado O, Peralta Z, Nagaswamy U, Muzny D, Reid JG, Newsham I, Wu Y, Lewis L, Han Y, Voight BF, Lim E, Rossin E, Kirby A, Flannick J, Fromer M, Shakir K, Fennell T, Garimella K, Banks E, Poplin R, Gabriel S, DePristo M, Wimbish JR, Boone BE, Levy SE, Betancur C, Sunyaev S, Boerwinkle E, Buxbaum JD, Cook EH Jr, Devlin B, Gibbs RA, Roeder K, Schellenberg GD, Sutcliffe JS, Daly MJ (2012) Patterns and rates of exonic de novo mutations in autism spectrum disorders. Nature 485:242–245. doi: 10.1038/nature11011 CrossRefPubMedPubMedCentralGoogle Scholar
  57. O’Roak BJ, Vives L, Fu W, Egertson JD, Stanaway IB, Phelps IG, Carvill G, Kumar A, Lee C, Ankenman K, Munson J, Hiatt JB, Turner EH, Levy R, O’Day DR, Krumm N, Coe BP, Martin BK, Borenstein E, Nickerson DA, Mefford HC, Doherty D, Akey JM, Bernier R, Eichler EE, Shendure J (2012a) Multiplex targeted sequencing identifies recurrently mutated genes in autism spectrum disorders. Science 338:1619–1622. doi: 10.1126/science.1227764 CrossRefPubMedPubMedCentralGoogle Scholar
  58. O’Roak BJ, Vives L, Girirajan S, Karakoc E, Krumm N, Coe BP, Levy R, Ko A, Lee C, Smith JD, Turner EH, Stanaway IB, Vernot B, Malig M, Baker C, Reilly B, Akey JM, Borenstein E, Rieder MJ, Nickerson DA, Bernier R, Shendure J, Eichler EE (2012b) Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations. Nature 485:246–250. doi: 10.1038/nature10989 CrossRefPubMedPubMedCentralGoogle Scholar
  59. Parikshak NN, Luo R, Zhang A, Won H, Lowe JK, Chandran V, Horvath S, Geschwind DH (2013) Integrative functional genomic analyses implicate specific molecular pathways and circuits in autism. Cell 155:1008–1021. doi: 10.1016/j.cell.2013.10.031 CrossRefPubMedPubMedCentralGoogle Scholar
  60. Persico AM, Bourgeron T (2006) Searching for ways out of the autism maze: genetic, epigenetic and environmental clues. Trends Neurosci 29:349–358. doi: 10.1016/j.tins.2006.05.010 CrossRefPubMedGoogle Scholar
  61. Pettem KL, Yokomaku D, Takahashi H, Ge Y, Craig AM (2013) Interaction between autism-linked MDGAs and neuroligins suppresses inhibitory synapse development. J Cell Biol 200:321–336. doi: 10.1083/jcb.201206028 CrossRefPubMedPubMedCentralGoogle Scholar
  62. Pinto D, Delaby E, Merico D, Barbosa M, Merikangas A, Klei L, Thiruvahindrapuram B, Xu X, Ziman R, Wang Z, Vorstman JA, Thompson A, Regan R, Pilorge M, Pellecchia G, Pagnamenta AT, Oliveira B, Marshall CR, Magalhaes TR, Lowe JK, Howe JL, Griswold AJ, Gilbert J, Duketis E, Dombroski BA, De Jonge MV, Cuccaro M, Crawford EL, Correia CT, Conroy J, Conceicao IC, Chiocchetti AG, Casey JP, Cai G, Cabrol C, Bolshakova N, Bacchelli E, Anney R, Gallinger S, Cotterchio M, Casey G, Zwaigenbaum L, Wittemeyer K, Wing K, Wallace S, van Engeland H, Tryfon A, Thomson S, Soorya L, Roge B, Roberts W, Poustka F, Mouga S, Minshew N, McInnes LA, McGrew SG, Lord C, Leboyer M, Le Couteur AS, Kolevzon A, Jimenez Gonzalez P, Jacob S, Holt R, Guter S, Green J, Green A, Gillberg C, Fernandez BA, Duque F, Delorme R, Dawson G, Chaste P, Cafe C, Brennan S, Bourgeron T, Bolton PF, Bolte S, Bernier R, Baird G, Bailey AJ, Anagnostou E, Almeida J, Wijsman EM, Vieland VJ, Vicente AM, Schellenberg GD, Pericak-Vance M, Paterson AD, Parr JR, Oliveira G, Nurnberger JI, Monaco AP, Maestrini E, Klauck SM, Hakonarson H, Haines JL, Geschwind DH, Freitag CM, Folstein SE, Ennis S et al (2014) Convergence of genes and cellular pathways dysregulated in autism spectrum disorders. Am J Hum Genet 94:677–694. doi: 10.1016/j.ajhg.2014.03.018 CrossRefPubMedPubMedCentralGoogle Scholar
  63. Pramparo T, Pierce K, Lombardo MV, Carter Barnes C, Marinero S, Ahrens-Barbeau C, Murray SS, Lopez L, Xu R, Courchesne E (2015) Prediction of autism by translation and immune/inflammation coexpressed genes in toddlers from pediatric community practices. JAMA Psychiatry 72:386–394. doi: 10.1001/jamapsychiatry.2014.3008 CrossRefPubMedGoogle Scholar
  64. Ramanathan S, Woodroffe A, Flodman PL, Mays LZ, Hanouni M, Modahl CB, Steinberg-Epstein R, Bocian ME, Spence MA, Smith M (2004) A case of autism with an interstitial deletion on 4q leading to hemizygosity for genes encoding for glutamine and glycine neurotransmitter receptor sub-units (AMPA 2, GLRA3, GLRB) and neuropeptide receptors NPY1R, NPY5R. BMC Med Genet 5:10. doi: 10.1186/1471-2350-5-10 CrossRefPubMedPubMedCentralGoogle Scholar
  65. Rousseeuw PJ, Van Driessen K (1999) A fast algorithm for the minimum covariance determinant estimator. Technometrics 41:212–223. doi: 10.2307/1270566 CrossRefGoogle Scholar
  66. Sanders SJ, Murtha MT, Gupta AR, Murdoch JD, Raubeson MJ, Willsey AJ, Ercan-Sencicek AG, DiLullo NM, Parikshak NN, Stein JL, Walker MF, Ober GT, Teran NA, Song Y, El-Fishawy P, Murtha RC, Choi M, Overton JD, Bjornson RD, Carriero NJ, Meyer KA, Bilguvar K, Mane SM, Sestan N, Lifton RP, Gunel M, Roeder K, Geschwind DH, Devlin B, State MW (2012) De novo mutations revealed by whole-exome sequencing are strongly associated with autism. Nature 485:237–241. doi: 10.1038/nature10945 CrossRefPubMedPubMedCentralGoogle Scholar
  67. Sandin S, Lichtenstein P, Kuja-Halkola R, Larsson H, Hultman CM, Reichenberg A (2014) The familial risk of autism. JAMA 311:1770–1777. doi: 10.1001/jama.2014.4144 CrossRefPubMedPubMedCentralGoogle Scholar
  68. Sawicka K, Zukin RS (2012) Dysregulation of mTOR signaling in neuropsychiatric disorders: therapeutic implications. Neuropsychopharmacology 37:305–306. doi: 10.1038/npp.2011.210 CrossRefPubMedGoogle Scholar
  69. Schaaf CP, Sabo A, Sakai Y, Crosby J, Muzny D, Hawes A, Lewis L, Akbar H, Varghese R, Boerwinkle E, Gibbs RA, Zoghbi HY (2011) Oligogenic heterozygosity in individuals with high-functioning autism spectrum disorders. Hum Mol Genet 20:3366–3375. doi: 10.1093/hmg/ddr243 CrossRefPubMedPubMedCentralGoogle Scholar
  70. Schubert D, Martens GJ, Kolk SM (2015) Molecular underpinnings of prefrontal cortex development in rodents provide insights into the etiology of neurodevelopmental disorders. Mol Psychiatry 20:795–809. doi: 10.1038/mp.2014.147 CrossRefPubMedGoogle Scholar
  71. Sebat J, Lakshmi B, Malhotra D, Troge J, Lese-Martin C, Walsh T, Yamrom B, Yoon S, Krasnitz A, Kendall J, Leotta A, Pai D, Zhang R, Lee YH, Hicks J, Spence SJ, Lee AT, Puura K, Lehtimaki T, Ledbetter D, Gregersen PK, Bregman J, Sutcliffe JS, Jobanputra V, Chung W, Warburton D, King MC, Skuse D, Geschwind DH, Gilliam TC, Ye K, Wigler M (2007) Strong association of de novo copy number mutations with autism. Science 316:445–449. doi: 10.1126/science.1138659 CrossRefPubMedPubMedCentralGoogle Scholar
  72. Smedley D, Haider S, Durinck S, Pandini L, Provero P, Allen J, Arnaiz O, Awedh MH, Baldock R, Barbiera G, Bardou P, Beck T, Blake A, Bonierbale M, Brookes AJ, Bucci G, Buetti I, Burge S, Cabau C, Carlson JW, Chelala C, Chrysostomou C, Cittaro D, Collin O, Cordova R, Cutts RJ, Dassi E, Di Genova A, Djari A, Esposito A, Estrella H, Eyras E, Fernandez-Banet J, Forbes S, Free RC, Fujisawa T, Gadaleta E, Garcia-Manteiga JM, Goodstein D, Gray K, Guerra-Assuncao JA, Haggarty B, Han DJ, Han BW, Harris T, Harshbarger J, Hastings RK, Hayes RD, Hoede C, Hu S, Hu ZL, Hutchins L, Kan Z, Kawaji H, Keliet A, Kerhornou A, Kim S, Kinsella R, Klopp C, Kong L, Lawson D, Lazarevic D, Lee JH, Letellier T, Li CY, Lio P, Liu CJ, Luo J, Maass A, Mariette J, Maurel T, Merella S, Mohamed AM, Moreews F, Nabihoudine I, Ndegwa N, Noirot C, Perez-Llamas C, Primig M, Quattrone A, Quesneville H, Rambaldi D, Reecy J, Riba M, Rosanoff S, Saddiq AA, Salas E, Sallou O, Shepherd R, Simon R, Sperling L, Spooner W, Staines DM, Steinbach D, Stone K, Stupka E, Teague JW, Dayem Ullah AZ, Wang J, Ware D et al (2015) The BioMart community portal: an innovative alternative to large, centralized data repositories. Nucleic Acids Res 43:W589–W598. doi: 10.1093/nar/gkv350 CrossRefPubMedPubMedCentralGoogle Scholar
  73. Somel M, Khaitovich P, Bahn S, Paabo S, Lachmann M (2006) Gene expression becomes heterogeneous with age. Curr Biol 16:R359–R360. doi: 10.1016/j.cub.2006.04.024 CrossRefPubMedGoogle Scholar
  74. Stegle O, Parts L, Durbin R, Winn J (2010) A Bayesian framework to account for complex non-genetic factors in gene expression levels greatly increases power in eQTL studies. PLoS Comput Biol 6:e1000770. doi: 10.1371/journal.pcbi.1000770 CrossRefPubMedPubMedCentralGoogle Scholar
  75. Stevens HE, Smith KM, Maragnoli ME, Fagel D, Borok E, Shanabrough M, Horvath TL, Vaccarino FM (2010) Fgfr2 is required for the development of the medial prefrontal cortex and its connections with limbic circuits. J Neurosci 30:5590–5602. doi: 10.1523/JNEUROSCI.5837-09.2010 CrossRefPubMedPubMedCentralGoogle Scholar
  76. Tabuchi K, Blundell J, Etherton MR, Hammer RE, Liu X, Powell CM, Sudhof TC (2007) A neuroligin-3 mutation implicated in autism increases inhibitory synaptic transmission in mice. Science 318:71–76. doi: 10.1126/science.1146221 CrossRefPubMedPubMedCentralGoogle Scholar
  77. Tammimies K, Marshall CR, Walker S, Kaur G, Thiruvahindrapuram B, Lionel AC, Yuen RK, Uddin M, Roberts W, Weksberg R, Woodbury-Smith M, Zwaigenbaum L, Anagnostou E, Wang Z, Wei J, Howe JL, Gazzellone MJ, Lau L, Sung WW, Whitten K, Vardy C, Crosbie V, Tsang B, D’Abate L, Tong WW, Luscombe S, Doyle T, Carter MT, Szatmari P, Stuckless S, Merico D, Stavropoulos DJ, Scherer SW, Fernandez BA (2015) Molecular Diagnostic Yield of Chromosomal Microarray Analysis and Whole-Exome Sequencing in Children With Autism Spectrum Disorder. JAMA 314:895–903. doi: 10.1001/jama.2015.10078 CrossRefPubMedGoogle Scholar
  78. Tierney E, Bukelis I, Thompson RE, Ahmed K, Aneja A, Kratz L, Kelley RI (2006) Abnormalities of cholesterol metabolism in autism spectrum disorders. Am J Med Genet B Neuropsychiatr Genet 141B:666–668. doi: 10.1002/ajmg.b.30368 CrossRefPubMedPubMedCentralGoogle Scholar
  79. Turner TN, Hormozdiari F, Duyzend MH, McClymont SA, Hook PW, Iossifov I, Raja A, Baker C, Hoekzema K, Stessman HA, Zody MC, Nelson BJ, Huddleston J, Sandstrom R, Smith JD, Hanna D, Swanson JM, Faustman EM, Bamshad MJ, Stamatoyannopoulos J, Nickerson DA, McCallion AS, Darnell R, Eichler EE (2016) Genome Sequencing of Autism-Affected Families Reveals Disruption of Putative Noncoding Regulatory DNA. Am J Hum Genet 98:58–74. doi: 10.1016/j.ajhg.2015.11.023 CrossRefPubMedGoogle Scholar
  80. Verboven S, Hubert M (2005) LIBRA: a MATLAB library for robust analysis. Chemometr Intell Lab Syst 75:127–136. doi: 10.1016/j.chemolab.2004.06.003 CrossRefGoogle Scholar
  81. Voineagu I, Wang X, Johnston P, Lowe JK, Tian Y, Horvath S, Mill J, Cantor RM, Blencowe BJ, Geschwind DH (2011) Transcriptomic analysis of autistic brain reveals convergent molecular pathology. Nature 474:380–384. doi: 10.1038/nature10110 CrossRefPubMedPubMedCentralGoogle Scholar
  82. Walsh P, Elsabbagh M, Bolton P, Singh I (2011) In search of biomarkers for autism: scientific, social and ethical challenges. Nat Rev Neurosci 12:603–612. doi: 10.1038/nrn3113 CrossRefPubMedGoogle Scholar
  83. Wang K, Zhang H, Ma D, Bucan M, Glessner JT, Abrahams BS, Salyakina D, Imielinski M, Bradfield JP, Sleiman PM, Kim CE, Hou C, Frackelton E, Chiavacci R, Takahashi N, Sakurai T, Rappaport E, Lajonchere CM, Munson J, Estes A, Korvatska O, Piven J, Sonnenblick LI, Alvarez Retuerto AI, Herman EI, Dong H, Hutman T, Sigman M, Ozonoff S, Klin A, Owley T, Sweeney JA, Brune CW, Cantor RM, Bernier R, Gilbert JR, Cuccaro ML, McMahon WM, Miller J, State MW, Wassink TH, Coon H, Levy SE, Schultz RT, Nurnberger JI, Haines JL, Sutcliffe JS, Cook EH, Minshew NJ, Buxbaum JD, Dawson G, Grant SF, Geschwind DH, Pericak-Vance MA, Schellenberg GD, Hakonarson H (2009) Common genetic variants on 5p14.1 associate with autism spectrum disorders. Nature 459:528–533. doi: 10.1038/nature07999 CrossRefPubMedPubMedCentralGoogle Scholar
  84. Wang G, Yang E, Brinkmeyer-Langford CL, Cai JJ (2014) Additive, epistatic, and environmental effects through the lens of expression variability QTL in a twin cohort. Genetics 196:413–425. doi: 10.1534/genetics.113.157503 CrossRefPubMedGoogle Scholar
  85. Waterhouse L, Gillberg C (2014) Why autism must be taken apart. J Autism Dev Disord 44:1788–1792. doi: 10.1007/s10803-013-2030-5 CrossRefPubMedGoogle Scholar
  86. Weiss LA, Arking DE, Gene Discovery Project of Johns Hopkins and the Autism Consortium, Daly MJ, Chakravarti A (2009) A genome-wide linkage and association scan reveals novel loci for autism. Nature 461:802–808. doi: 10.1038/nature08490 CrossRefPubMedPubMedCentralGoogle Scholar
  87. Willsey AJ, State MW (2015) Autism spectrum disorders: from genes to neurobiology. Curr Opin Neurobiol 30:92–99. doi: 10.1016/j.conb.2014.10.015 CrossRefPubMedGoogle Scholar
  88. Willsey AJ, Sanders SJ, Li M, Dong S, Tebbenkamp AT, Muhle RA, Reilly SK, Lin L, Fertuzinhos S, Miller JA, Murtha MT, Bichsel C, Niu W, Cotney J, Ercan-Sencicek AG, Gockley J, Gupta AR, Han W, He X, Hoffman EJ, Klei L, Lei J, Liu W, Liu L, Lu C, Xu X, Zhu Y, Mane SM, Lein ES, Wei L, Noonan JP, Roeder K, Devlin B, Sestan N, State MW (2013) Coexpression networks implicate human midfetal deep cortical projection neurons in the pathogenesis of autism. Cell 155:997–1007. doi: 10.1016/j.cell.2013.10.020 CrossRefPubMedPubMedCentralGoogle Scholar
  89. Zeng Y, Wang G, Yang E, Ji G, Brinkmeyer-Langford CL, Cai JJ (2015) Aberrant gene expression in humans. PLoS Genet 11:e1004942. doi: 10.1371/journal.pgen.1004942 CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Department of AutomationXiamen UniversityXiamenChina
  2. 2.Department of Veterinary Integrative BiosciencesTexas A&M UniversityCollege StationUSA
  3. 3.Institute for Systems Biomedicine, School of Basic Medical Sciences, Peking University Health Science CenterBeijingChina
  4. 4.Innovation Center for Cell Signaling Network, Xiamen UniversityXiamenChina
  5. 5.Interdisciplinary Program of GeneticsTexas A&M UniversityCollege StationUSA

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