Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.
ADVANCED SEARCH
Log in
Top
Molecular Autism
Published in: Molecular Autism 1/2014

Open Access 01-12-2014 | Research

DAWN: a framework to identify autism genes and subnetworks using gene expression and genetics

Authors: Li Liu, Jing Lei, Stephan J Sanders, Arthur Jeremy Willsey, Yan Kou, Abdullah Ercument Cicek, Lambertus Klei, Cong Lu, Xin He, Mingfeng Li, Rebecca A Muhle, Avi Ma’ayan, James P Noonan, Nenad Šestan, Kathryn A McFadden, Matthew W State, Joseph D Buxbaum, Bernie Devlin, Kathryn Roeder

Published in: Molecular Autism | Issue 1/2014

Login to get access

Abstract

Background

De novo loss-of-function (dnLoF) mutations are found twofold more often in autism spectrum disorder (ASD) probands than their unaffected siblings. Multiple independent dnLoF mutations in the same gene implicate the gene in risk and hence provide a systematic, albeit arduous, path forward for ASD genetics. It is likely that using additional non-genetic data will enhance the ability to identify ASD genes.

Methods

To accelerate the search for ASD genes, we developed a novel algorithm, DAWN, to model two kinds of data: rare variations from exome sequencing and gene co-expression in the mid-fetal prefrontal and motor-somatosensory neocortex, a critical nexus for risk. The algorithm casts the ensemble data as a hidden Markov random field in which the graph structure is determined by gene co-expression and it combines these interrelationships with node-specific observations, namely gene identity, expression, genetic data and the estimated effect on risk.

Results

Using currently available genetic data and a specific developmental time period for gene co-expression, DAWN identified 127 genes that plausibly affect risk, and a set of likely ASD subnetworks. Validation experiments making use of published targeted resequencing results demonstrate its efficacy in reliably predicting ASD genes. DAWN also successfully predicts known ASD genes, not included in the genetic data used to create the model.

Conclusions

Validation studies demonstrate that DAWN is effective in predicting ASD genes and subnetworks by leveraging genetic and gene expression data. The findings reported here implicate neurite extension and neuronal arborization as risks for ASD. Using DAWN on emerging ASD sequence data and gene expression data from other brain regions and tissues would likely identify novel ASD genes. DAWN can also be used for other complex disorders to identify genes and subnetworks in those disorders.
Appendix
Available only for authorised users
Literature
1.
Buxbaum JD, Daly MJ, Devlin B, Lehner T, Roeder K, State MW, Barrett J, Bilder D, Boerwinkle E, Brudno M, Burbach P, Buxbaum JD, Camp N, Chahrour M, Cook EH, Coon H, Coppola G, Coulter M, Cutler D, Daly MJ, dePristo M, Devlin B, Eichler EE, Fromer M, Geschwind DH, Gibbs RA, Gill M, Goldberg AP, Haines JL:The autism sequencing consortium: large-scale, high-throughput sequencing in autism spectrum disorders. Neuron. 2012, 76 (6): 1052-1056. 10.1016/j.neuron.2012.12.008.CrossRefPubMed
2.
Sanders SJ, Ercan-Sencicek AG, Hus V, Luo R, Murtha MT, Moreno-De-Luca D, Chu SH, Moreau MP, Gupta AR, Thomson SA, Mason CE, Bilguvar K, Celestino-Soper PB, Choi M, Crawford EL, Davis L, Wright NR, Dhodapkar RM, DiCola M, DiLullo NM, Fernandez TV, Fielding-Singh V, Fishman DO, Frahm S, Garagaloyan R, Goh GS, Kammela S, Klei L, Lowe JK, Lund SC:Multiple recurrentde novo, CNVs, including duplications of the 7q11.23 Williams syndrome region, are strongly associated with autism. Neuron. 2011, 70 (5): 863-885. 10.1016/j.neuron.2011.05.002.PubMedCentralCrossRefPubMed
3.
Pinto D, Pagnamenta AT, Klei L, Anney R, Merico D, Regan R, Conroy J, Magalhaes TR, Correia C, Abrahams BS, Almeida J, Bacchelli E, Bader GD, Bailey AJ, Baird G, Battaglia A, Berney T, Bolshakova N, Bolte S, Bolton PF, Bourgeron T, Brennan S, Brian J, Bryson SE, Carson AR, Casallo G, Casey J, Chung BH, Cochrane L:Functional impact of global rare copy number variation in autism spectrum disorders. Nature. 2010, 466 (7304): 368-372. 10.1038/nature09146.PubMedCentralCrossRefPubMed
4.
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:De novogene disruptions in children on the autistic spectrum. Neuron. 2012, 74 (2): 285-299. 10.1016/j.neuron.2012.04.009.PubMedCentralCrossRefPubMed
5.
Kong A, Frigge ML, Masson G, Besenbacher S, Sulem P, Magnusson G, Gudjonsson SA, Sigurdsson A, Jonasdottir A, Jonasdottir A, Wong WS, Sigurdsson G, Walters GB, Steinberg S, Helgason H, Thorleifsson G, Gudbjartsson DF, Helgason A, Magnusson OT, Thorsteinsdottir U, Stefansson K:Rate ofde novomutations and the importance of father’s age to disease risk. Nature. 2012, 488 (7412): 471-475. 10.1038/nature11396.PubMedCentralCrossRefPubMed
6.
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:Patterns and rates of exonicde novomutations in autism spectrum disorders. Nature. 2012, 485: 242-245. 10.1038/nature11011.PubMedCentralCrossRefPubMed
7.
O’Roak BJ, Deriziotis P, Lee C, Vives L, Schwartz JJ, Girirajan S, Karakoc E, Mackenzie AP, Ng SB, Baker C, Rieder MJ, Nickerson DA, Bernier R, Fisher SE, Shendure J, Eichler EE:Exome sequencing in sporadic autism spectrum disorders identifies severede novomutations. Nat Genet. 2011, 43 (4): 585-589.PubMedCentralCrossRefPubMed
8.
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:Sporadic autism exomes reveal a highly interconnected protein network ofde novomutations. Nature. 2012, 485 (7397): 246-250. 10.1038/nature10989.PubMedCentralCrossRefPubMed
9.
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:De novomutations revealed by whole-exome sequencing are strongly associated with autism. Nature. 2012, 485 (1): 82-93.
10.
Anney R, Klei L, Pinto D, Almeida J, Bacchelli E, Baird G, Bolshakova N, Bolte S, Bolton PF, Bourgeron T, Brennan S, Brian J, Casey J, Conroy J, Correia C, Corsello C, Crawford EL, de Jonge M, Delorme R, Duketis E, Duque F, Estes A, Farrar P, Fernandez BA, Folstein SE, Fombonne E, Gilbert J, Gillberg C, Glessner JT, Green A:Individual common variants exert weak effects on risk for autism spectrum disorders. Hum Mol Genet. 2012, 21 (21): 4781-4792. 10.1093/hmg/dds301.PubMedCentralCrossRefPubMed
11.
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, Lese Martin C, Martin DM, Morrow EM, Walsh CA, Melhem NM, Chaste P, Sutcliffe JS, State MW, Roeder K, Cook EH Jr:Common genetic variants, acting additively, are a major source of risk for autism. Mol Autism. 2012, 3 (9):
12.
He X, Sanders SJ, Liu L, De Rubeis S, Lim ET, Sutcliffe JS, Schellenberg GD, Gibbs RA, Daly MJ, Buxbaum JD, State MW, Devlin B, Roeder K:Integrated model ofde novo, and inherited genetic variants yields greater power to identify risk genes. PLoS Genet. 2013, 9 (8): 1003671-10.1371/journal.pgen.1003671.CrossRef
13.
Ben-David E, Shifman S:Combined analysis of exome sequencing points toward a major role for transcription regulation during brain development in autism. Mol Psychiatry. 2012, 18: 1054-1056.CrossRefPubMed
14.
Willsey AJ, Sanders SJ, Li M, Dong S, Tebbenkamp AT, Muhle R, Reilly SK, Lin L, Fertuzinhos S, Miller JA, Murtha M, 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:Coexpression networks implicate human midfetal deep cortical projection neurons in the pathogenesis of autism. Cell. 2013, 155 (5): 997-1007. 10.1016/j.cell.2013.10.020.PubMedCentralCrossRefPubMed
15.
Parikshak NN, Luo R, Zhang A, Won H, Lowe JK, Chandran V, Horvath S, Geschwind DH:Integrative functional genomic analyses implicate specific molecular pathways and circuits in autism. Cell. 2013, 155 (5): 1008-1021. 10.1016/j.cell.2013.10.031.PubMedCentralCrossRefPubMed
16.
Kang HJ, Kawasawa YI, Cheng F, Zhu Y, Xu X, Li M, Sousa AM, Pletikos M, Meyer KA, Sedmak G, Guennel T, Shin Y, Johnson MB, Krsnik Z, Mayer S, Fertuzinhos S, Umlauf S, Lisgo SN, Vortmeyer A, Weinberger DR, Mane S, Hyde TM, Huttner A, Reimers M, Kleinman JE, Sestan N:Spatio-temporal transcriptome of the human brain. Nature. 2011, 478 (7370): 483-489. 10.1038/nature10523.PubMedCentralCrossRefPubMed
17.
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:Multiplex targeted sequencing identifies recurrently mutated genes in autism spectrum disorders. Science. 2012, 338 (6114): 1619-1622. 10.1126/science.1227764.PubMedCentralCrossRefPubMed
18.
Zhang B, Horvath S:A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol. 2005, 4 (1):
19.
Langfelder P, Horvath S:WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008, 9 (559):
20.
Liu L, Sabo A, Neale BM, Nagaswamy U, Stevens C, Lim E, Bodea CA, Muzny D, Reid JG, Banks E, Coon H, Depristo M, Dinh H, Fennel T, Flannick J, Gabriel S, Garimella K, Gross S, Hawes A, Lewis L, Makarov V, Maguire J, Newsham I, Poplin R, Ripke S, Shakir K, Samocha KE, Wu Y, Boerwinkle E, Buxbaum JD:Analysis of rare, exonic variation amongst subjects with autism spectrum disorders and population controls. PLoS Genet. 2013, 9 (4): 1003443-10.1371/journal.pgen.1003443.CrossRef
21.
Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, Kondrashov AS, Sunyaev SR:A method and server for predicting damaging missense mutations. Nat Methods. 2010, 7 (4): 248-249. 10.1038/nmeth0410-248.PubMedCentralCrossRefPubMed
22.
23.
Li H, Wei Z, Maris J:A hidden Markov random field model for genome-wide association studies. Biostatistics. 2010, 11: 139-150. 10.1093/biostatistics/kxp043.PubMedCentralCrossRefPubMed
24.
Xue L, Zou H, Cai T:Nonconcave penalized composite conditional likelihood estimation of sparse Ising models. Ann Stat. 2012, 40: 1403-1429. 10.1214/12-AOS1017.CrossRef
25.
Benjamini Y, Hochberg Y:Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B Stat Methodol. 1995, 57: 289-300.
26.
Fraley C, Raftery AE:Model-based clustering, discriminant analysis, and density estimation. J Am Stat Assoc. 2002, 97 (458): 611-631. 10.1198/016214502760047131.CrossRef
27.
Stark C, Breitkreutz B-J, Reguly T, Boucher L, Breitkreutz A, Tyers M:BioGRID: a general repository for interaction datasets. Nucleic Acids Res. 2006, 34 (suppl 1): 535-539.CrossRef
28.
Prasad TK, Goel R, Kandasamy K, Keerthikumar S, Kumar S, Mathivanan S, Telikicherla D, Raju R, Shafreen B, Venugopal A, Balakrishnan L, Marimuthu A, Banerjee S, Somanathan DS, Sebastian A, Rani S, Ray S, Harrys Kishore CJ, Kanth S, Ahmed M, Kashyap MK, Mohmood R, Ramachandra YL, Krishna V, Rahiman BA, Mohan S, Ranganathan P, Ramabadran S, Chaerkady R, Pandey A:Human protein reference database – 2009 update. Nucleic Acids Res. 2009, 37 (Database issue): 767-772.CrossRef
29.
Zanzoni A, Montecchi-Palazzi L, Quondam M, Ausiello G, Helmer-Citterich M, Cesareni G:MINT: a Molecular INTeraction database. FEBS Lett. 2002, 513 (1): 135-140. 10.1016/S0014-5793(01)03293-8.CrossRefPubMed
30.
Kerrien S, Aranda B, Breuza L, Bridge A, Broackes-Carter F, Chen C, Duesbury M, Dumousseau M, Feuermann M, Hinz U, Jandrasits C, Jimenez RC, Khadake J, Mahadevan U, Masson P, Pedruzzi I, Pfeiffenberger E, Porras P, Raghunath A, Roechert B, Orchard S, Hermjakob H:The IntAct molecular interaction database in 2012. Nucleic Acids Res. 2012, 40 (Database issue): 841-846.CrossRef
31.
Lachmann A, Ma’ayan A:KEA: kinase enrichment analysis. BMC Bioinformatics. 2009, 25 (5): 684-686. 10.1093/bioinformatics/btp026.CrossRef
32.
33.
Ma’ayan A, Jenkins S, Webb R, Berger S, Purushothaman S, Abul-Husn N, Posner J, Flores T, Iyengar R:SNAVI: desktop application for analysis and visualization of large-scale signaling networks. BMC Syst Biol. 2009, 3 (1): 10-10.1186/1752-0509-3-10.PubMedCentralCrossRefPubMed
34.
Mewes H-W, Frishman D, Güldener U, Mannhaupt G, Mayer K, Mokrejs M, Morgenstern B, Münsterkötter M, Rudd S, Weil B:MIPS: a database for genomes and protein sequences. Nucleic Acids Res. 2002, 30 (1): 31-34. 10.1093/nar/30.1.31.PubMedCentralCrossRefPubMed
35.
Dijkstra EW:A note on two problems in connexion with graphs. Numerische Mathematik. 1959, 1 (1): 269-271. 10.1007/BF01386390.CrossRef
36.
Berger SI, Posner JM, Ma’ayan A:Genes2Networks: connecting lists of gene symbols using mammalian protein interactions databases. BMC Bioinformatics. 2007, 8 (1): 372-10.1186/1471-2105-8-372.PubMedCentralCrossRefPubMed
37.
38.
Chen EY, Tan CM, Kou Y, Duan Q, Wang Z, Meirelles GV, Clark NR, Ma’ayan A:Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics. 2013, 14 (128):
39.
Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G:Gene ontology: tool for the unification of biology. Nat Genet. 2000, 25 (1): 25-29. 10.1038/75556.PubMedCentralCrossRefPubMed
40.
Blake JA, Bult CJ, Eppig JT, Kadin JA, Richardson JE, the Mouse GenomeDatabaseGroup:The mouse genome database genotypes: phenotypes. Nucleic Acids Res. 2009, 37 (suppl 1): 712-719.CrossRef
41.
42.
Betancur C:Etiological heterogeneity in autism spectrum disorders: more than 100 genetic and genomic disorders and still counting. Brain Res. 2011, 1380: 42-77.CrossRefPubMed
43.
Ramocki MB, Zoghbi HY:Failure of neuronal homeostasis results in common neuropsychiatric phenotypes. Nature. 2008, 455 (7215): 912-918. 10.1038/nature07457.PubMedCentralCrossRefPubMed
44.
45.
Strauss KA, Puffenberger EG, Huentelman MJ, Gottlieb S, Dobrin SE, Parod JM, Stephan DA, Morton DH:Recessive symptomatic focal epilepsy and mutant contactin-associated protein-like 2. N Engl J Med. 2206, 354 (3): 1370-1377.
46.
47.
Chahrour MH, Y u TW, Lim ET, Ataman B, Coulter ME, Hill RS, Stevens CR, Schubert CR, Greenberg ME, Gabriel SB, Walsh CA:Whole-exome sequencing and homozygosity analysis implicate depolarization-regulated neuronal genes in autism. PLoS Genet. 2012, 8 (4): 1002635-10.1371/journal.pgen.1002635.CrossRef
48.
Bacon C, Rappold GA:The distinct and overlapping phenotypic spectra ofFOXP1andFOXP2in cognitive disorders. Hum Genet. 2012, 131 (11): 1687-1698. 10.1007/s00439-012-1193-z.PubMedCentralCrossRefPubMed
49.
Eden S, Rohatgi R, Podtelejnikov AV, Mann M, Kirschner MW:Mechanism of regulation of WAVE1-induced actin nucleation by rac1 and nck. Nature. 2002, 418 (6899): 790-793. 10.1038/nature00859.CrossRefPubMed
50.
Khodosevich K, Monyer H:Signaling involved in neurite outgrowth of postnatally born subventricular zone neurons in vitro. BMC Neuroscience. 2010, 11 (1):
51.
Hamilton AM, Zito K:Breaking it down: the ubiquitin proteasome system in neuronal morphogenesis. Neural Plast. 2013, 2013: 196848-PubMedCentralPubMed
52.
Reiterer V, Maier S, Sitte HH, Kriz A, Rüegg MA, Hauri H-P, Freissmuth M, Farhan H:Sec24-and ARFGAP1-dependent trafficking of GABA transporter-1 is a prerequisite for correct axonal targeting. J Neurosci. 2008, 28 (47): 12453-12464. 10.1523/JNEUROSCI.3451-08.2008.PubMedCentralCrossRefPubMed
53.
Lai CS, Fisher SE, Hurst JA, Vargha-Khadem F, Monaco AP:A forkhead-domain gene is mutated in a severe speech and language disorder. Nature. 2001, 413 (6855): 519-523. 10.1038/35097076.CrossRefPubMed
54.
Feuk L, Kalervo A, Lipsanen-Nyman M, Skaug J, Nakabayashi K, Finucane B, Hartung D, Innes M, Kerem B, Nowaczyk MJ, Rivlin J, Roberts W, Senman L, Summers A, Szatmari P, Wong V, Vincent JB, Zeesman S, Osborne LR, Cardy JO, Kere J, Scherer SW, Hannula-Jouppi K:Absence of a paternally inheritedFOXP2gene in developmental verbal dyspraxia. Am J Hum Genet. 2006, 79 (5): 965-972. 10.1086/508902.PubMedCentralCrossRefPubMed
55.
Hamdan FF, Daoud H, Rochefort D, Piton A, Gauthier J, Langlois M, Foomani G, Dobrzeniecka S, Krebs MO, Joober R, Lafreniere RG, Lacaille JC, Mottron L, Drapeau P, Beauchamp MH, Phillips MS, Fombonne E, Rouleau GA, Michaud JL:De novo, mutations inFOXP2in cases with intellectual disability, autism, and language impairment. Am J Hum Genet. 2010, 87 (5): 671-678. 10.1016/j.ajhg.2010.09.017.PubMedCentralCrossRefPubMed
56.
Talkowski ME, Rosenfeld JA, Blumenthal I, Pillalamarri V, Chiang C, Heilbut A, Ernst C, Hanscom C, Rossin E, Lindgren AM, Pereira S, Ruderfer D, Kirby A, Ripke S, Harris DJ, Lee JH, Ha K, Kim HG, Solomon BD, Gropman AL, Lucente D, Sims K, Ohsumi TK, Borowsky ML, Loranger S, Quade B, Lage K, Miles J, Wu BL, Shen Y:Sequencing chromosomal abnormalities reveals neurodevelopmental loci that confer risk across diagnostic boundaries. Cell. 2012, 149 (3): 525-537. 10.1016/j.cell.2012.03.028.PubMedCentralCrossRefPubMed
57.
Depienne C, Fedirko E, Jothy A-C, Viveweger C, Hahn-Barma V, Brice A, Durr A, Ribaï P:Mental deficiency in three families withSPG4spastic paraplegia. Eur J Hum Genet. 2007, 16 (1): 97-104.PubMed
58.
Errico A, Ballabio A, Rugarli EI:Spastin, the protein mutated in autosomal dominant hereditary spastic paraplegia, is involved in microtubule dynamics. Hum Mol Genet. 2002, 11 (2): 153-163. 10.1093/hmg/11.2.153.CrossRefPubMed
59.
Zhang C, Li D, Ma Y, Yan J, Yang B, Li P, Yu A, Lu C, Ma X:Role of spastin and protrudin in neurite outgrowth. J Cell Biochem. 2012, 113 (7): 2296-2307. 10.1002/jcb.24100.CrossRefPubMed
60.
Renbaum P, Kellerman E, Jaron R, Geiger D, Segel R, Lee M, King MC, Levy-Lahad E:Spinal muscular atrophy with pontocerebellar hypoplasia is caused by a mutation in theVRK1gene. Am J Hum Genet. 2009, 85 (2): 281-289. 10.1016/j.ajhg.2009.07.006.PubMedCentralCrossRefPubMed
61.
Govek E-E, Newey SE, Van Aelst L:The role of Rho GTPases in neuronal development. Genes Dev. 2005, 19: 1-49. 10.1101/gad.1256405.CrossRefPubMed
62.
63.
McNeill EM, Klöckner-Bormann M, Roesler EC, Talton LE, Moechars D, Clagett-Dame M:Nav2hypomorphic mutant mice are ataxic and exhibit abnormalities in cerebellar development. Dev Biol. 2011, 353 (2): 331-343. 10.1016/j.ydbio.2011.03.008.PubMedCentralCrossRefPubMed
64.
Di Giovanni S, Faden AI, Yakovlev A, Duke-Cohan JS, Finn T, Thouin M, Knoblach S, De Biase A, Bregman BS, Hoffman EP:Neuronal plasticity after spinal cord injury: identification of a gene cluster driving neurite outgrowth. FASEB J. 2005, 19 (1): 153-154.PubMed
65.
Huang X, Hu J, Li Y, Zhuyun Yang Z, Zhu H, Zhou L, Ma K, Schachner M, Xiao Z, Li Y:The cell adhesion molecule L1 regulates the expression of FGF21 and enhances neurite outgrowth. Brain Res. 2013, 1530: 13-21.CrossRefPubMed
66.
Du Y, Weed SA, Xiong W-C, Marshall TD, Parsons JT:Identification of a novel cortactin SH3 domain-binding protein and its localization to growth cones of cultured neurons. Mol Cell Biol. 1998, 18: 5838-5851.PubMedCentralCrossRefPubMed
67.
Yu W, Polepalli J, Wagh D, Rajadas J, Malenka R, Lu B:A critical role for the PAR-1/MARK-tau axis in mediating the toxic effects of Aβon synapses and dendritic spines. Hum Mol Genet. 2012, 21 (6): 1384-1390. 10.1093/hmg/ddr576.PubMedCentralCrossRefPubMed
68.
Steiner P, Sarria J, Huni B, Marsault R, Catsicas S, Hirling H:Overexpression of neuronal sec1 enhances axonal branching in hippocampal neurons. Neuroscience. 2002, 113 (4): 893-905. 10.1016/S0306-4522(02)00225-7.CrossRefPubMed
69.
Lage K, Hansen NT, Karlberg EO, Eklund AC, Roque FS, Donahoe PK, Szallasi Z, Jensen TS, Brunak S:A large-scale analysis of tissue-specific pathology and gene expression of human disease genes and complexes. Proc Natl Acad Sci. 2087, 105 (52): 0-20875.
70.
Judson MC, Eagleson KL, Wang L, Levitt P:Evidence of cell-nonautonomous changes in dendrite and dendritic spine morphology in the met-signaling-deficient mouse forebrain. J Comp Neurol. 2010, 518 (21): 4463-4478. 10.1002/cne.22467.PubMedCentralCrossRefPubMed
71.
Devlin B, Scherer SW:Genetic architecture in autism spectrum disorder. Curr Opin Genet Dev. 2012, 22 (3): 229-237. 10.1016/j.gde.2012.03.002.CrossRefPubMed
72.
Shipman SL, Nicoll RA:Dimerization of postsynaptic neuroligin drives synaptic assembly via transsynaptic clustering of neurexin. Proc Natl Acad Sci. 19432, 109 (47):
73.
Zoghbi HY:Postnatal neurodevelopmental disorders: meeting at the synapse?. Science. 2003, 302 (5646): 826-830. 10.1126/science.1089071.CrossRefPubMed
74.
Bourgeron T:A synaptic trek to autism. Curr Opin Neurobiol. 2009, 19 (2): 231-234. 10.1016/j.conb.2009.06.003.CrossRefPubMed
75.
Poelmans G, Franke B, Pauls D, Glennon J, Buitelaar J:AKAPs integrate genetic findings for autism spectrum disorders. Trans Psychiatry. 2013, 3 (6): 270-10.1038/tp.2013.48.CrossRef
76.
77.
McFadden K, Minshew NJ:Evidence for dysregulation of axonal growth and guidance in the etiology of asd. Front Hum Neurosci. 2013,7,
78.
Chaste P, 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, Lese Martin C, Martin DM, Morrow EM, Walsh CA, Sutcliffe JS, State MW, Devlin B, Cook EH, Kim SJ:Adjusting head circumference for covariates in autism: clinical correlates of a highly heritable continuous trait. Biol Psychiatry. 2013, 74: 576-584. 10.1016/j.biopsych.2013.04.018.PubMedCentralCrossRefPubMed
79.
Metadata
Title
DAWN: a framework to identify autism genes and subnetworks using gene expression and genetics
Authors
Li Liu
Jing Lei
Stephan J Sanders
Arthur Jeremy Willsey
Yan Kou
Abdullah Ercument Cicek
Lambertus Klei
Cong Lu
Xin He
Mingfeng Li
Rebecca A Muhle
Avi Ma’ayan
James P Noonan
Nenad Šestan
Kathryn A McFadden
Matthew W State
Joseph D Buxbaum
Bernie Devlin
Kathryn Roeder
Publication date
01-12-2014
Publisher
BioMed Central
Published in
Molecular Autism / Issue 1/2014
Electronic ISSN: 2040-2392
DOI
https://doi.org/10.1186/2040-2392-5-22

Other articles of this Issue 1/2014

Molecular Autism 1/2014 Go to the issue

Research

An examination of autism spectrum traits in adolescents with anorexia nervosa and their parents

Research

N-ethylmaleimide-sensitive factor interacts with the serotonin transporter and modulates its trafficking: implications for pathophysiology in autism

Research

Exome sequencing of extended families with autism reveals genes shared across neurodevelopmental and neuropsychiatric disorders

Research

Gender differences in emotionality and sociability in children with autism spectrum disorders

Research

Abnormal lateralization of functional connectivity between language and default mode regions in autism

Research

STX1A and Asperger syndrome: a replication study