Abstract
Autism spectrum disorder (ASD) is one of the most heritable neuropsychiatric conditions. The complex genetic landscape of the disorder includes both common and rare variants at hundreds of genetic loci. This marked heterogeneity has thus far hampered efforts to develop genetic diagnostic panels and targeted pharmacological therapies. Here, we give an overview of the current literature on the genetic basis of ASD, and review recent human brain transcriptome studies and their role in identifying convergent pathways downstream of the heterogeneous genetic variants. We also discuss emerging evidence on the involvement of non-coding genomic regions and non-coding RNAs in ASD.
Similar content being viewed by others
References
Volkmar F, Reichow B (2013) Autism in DSM-5: progress and challenges. Mol Autism 4(1):13
Elsabbagh M et al (2012) Global prevalence of autism and other pervasive developmental disorders. Autism Res 5(3):160–179
Bailey A et al (1995) Autism as a strongly genetic disorder: evidence from a British twin study. Psychol Med 25(1):63–77
Steffenburg S et al (1989) A twin study of autism in Denmark, Finland, Iceland, Norway and Sweden. J Child Psychol Psychiatry 30(3):405–416
Hallmayer J et al (2011) Genetic heritability and shared environmental factors among twin pairs with autism. Arch Gen Psychiatry 68(11):1095–1102
De Rubeis S, Buxbaum JD (2015) Recent advances in the genetics of autism spectrum disorder. Curr Neurol Neurosci Rep 15(6):36
Gaugler T et al (2014) Most genetic risk for autism resides with common variation. Nat Genet 46(8):881–885
Sandin S et al (2014) The familial risk of autism. JAMA 311(17):1770–1777
Genome of the Netherlands, C (2014) Whole-genome sequence variation, population structure and demographic history of the Dutch population. Nat Genet 46(8):818–825
Fu W et al (2013) Analysis of 6,515 exomes reveals the recent origin of most human protein-coding variants. Nature 493(7431):216–220
Warrier V et al (2015) A comprehensive meta-analysis of common genetic variants in autism spectrum conditions. Mol Autism 6:49
Wang K et al (2009) Common genetic variants on 5p14.1 associate with autism spectrum disorders. Nature 459(7246):528–533
Weiss L et al (2009) A genome-wide linkage and association scan reveals novel loci for autism. Nature 461(7265):802–808
Anney R et al (2010) A genome-wide scan for common alleles affecting risk for autism. Hum Mol Genet 19(20):4072–4082
Chaste P et al (2015) A genome-wide association study of autism using the Simons Simplex Collection: does reducing phenotypic heterogeneity in autism increase genetic homogeneity? Biol Psychiatry 77(9):775–784
Bernier R et al (2014) Disruptive CHD8 mutations define a subtype of autism early in development. Cell 158(2):263–276
Klei L et al (2012) Common genetic variants, acting additively, are a major source of risk for autism. Mol Autism 3(1):9
Marshall CR et al (2008) Structural variation of chromosomes in autism spectrum disorder. Am J Hum Genet 82(2):477–488
Ivanov A et al (2015) Analysis of intron sequences reveals hallmarks of circular RNA biogenesis in animals. Cell Rep 10(2):170–177
Chahrour M, Zoghbi HY (2007) The story of Rett syndrome: from clinic to neurobiology. Neuron 56(3):422–437
Bhakar A, Dölen G, Bear M (2012) The pathophysiology of fragile X (and what it teaches us about synapses). Annu Rev Neurosci 35:417–443
Tsai P, Sahin M (2011) Mechanisms of neurocognitive dysfunction and therapeutic considerations in tuberous sclerosis complex. Curr Opin Neurol 24(2):106–113
Bourgeron T (2015) From the genetic architecture to synaptic plasticity in autism spectrum disorder. Nat Rev Neurosci 16(9):551–563
Chen JA et al (2015) The emerging picture of autism spectrum disorder: genetics and pathology. Annu Rev Pathol 10:111–144
Glessner J et al (2009) Autism genome-wide copy number variation reveals ubiquitin and neuronal genes. Nature 459(7246):569–573
Morrow EM (2010) Genomic copy number variation in disorders of cognitive development. J Am Acad Child Adolesc Psychiatry 49(11):1091–1104
Pinto D et al (2010) Functional impact of global rare copy number variation in autism spectrum disorders. Nature 466(7304):368–372
Sebat J et al (2007) Strong association of de novo copy number mutations with autism. Science 316(5823):445–449
De Rubeis S et al (2014) Synaptic, transcriptional and chromatin genes disrupted in autism. Nature 515(7526):209–215
Iossifov I et al (2012) De novo gene disruptions in children on the autistic spectrum. Neuron 74(2):285–299
O’Roak B et al (2012) Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations. Nature 485(7397):246–250
O’Roak BJ et al (2011) Exome sequencing in sporadic autism spectrum disorders identifies severe de novo mutations. Nat Genet 43(6):585–589
Sanders S et al (2012) De novo mutations revealed by whole-exome sequencing are strongly associated with autism. Nature 485(7397):237–241
Neale B et al (2012) Patterns and rates of exonic de novo mutations in autism spectrum disorders. Nature 485(7397):242–245
Sanders S et al (2011) Multiple recurrent de novo CNVs, including duplications of the 7q11.23 Williams syndrome region, are strongly associated with autism. Neuron 70(5):863–885
Sanders SJ et al (2015) Insights into autism spectrum disorder genomic architecture and biology from 71 risk loci. Neuron 87(6):1215–1233
Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10(1):57–63
Colantuoni C et al (2011) Temporal dynamics and genetic control of transcription in the human prefrontal cortex. Nature 478(7370):519–523
Kang HJ et al (2011) Spatio-temporal transcriptome of the human brain. Nature 478(7370):483–489
Hawrylycz MJ et al (2012) An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489(7416):391–399
Langfelder P, Mischel PS, Horvath S (2013) When is hub gene selection better than standard meta-analysis? PLoS One 8(4):e61505
Langfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinform 9:559
Zhang B, Horvath S (2005) A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol 4: article 17
Voineagu I et al (2011) Transcriptomic analysis of autistic brain reveals convergent molecular pathology. Nature 474(7351):380–384
Cantor RM, Lange K, Sinsheimer JS (2010) Prioritizing GWAS results: a review of statistical methods and recommendations for their application. Am J Hum Genet 86(1):6–22
Gupta S et al (2014) Transcriptome analysis reveals dysregulation of innate immune response genes and neuronal activity-dependent genes in autism. Nat Commun 5:5748
Jepson JE et al (2011) Engineered alterations in RNA editing modulate complex behavior in Drosophila: regulatory diversity of adenosine deaminase acting on RNA (ADAR) targets. J Biol Chem 286(10):8325–8337
Tonkin LA et al (2002) RNA editing by ADARs is important for normal behavior in Caenorhabditis elegans. EMBO J 21(22):6025–6035
Eran A et al (2013) Comparative RNA editing in autistic and neurotypical cerebella. Mol Psychiatry 18(9):1041–1048
Parikshak NN et al (2013) Integrative functional genomic analyses implicate specific molecular pathways and circuits in autism. Cell 155(5):1008–1021
Willsey AJ et al (2013) Coexpression networks implicate human midfetal deep cortical projection neurons in the pathogenesis of autism. Cell 155(5):997–1007
Xu X, Nehorai A, Dougherty J (2013) Cell type specific analysis of human brain transcriptome data to predict alterations in cellular composition. Syst Biomed (Austin) 1(3):151–160
Garbett K et al (2008) Immune transcriptome alterations in the temporal cortex of subjects with autism. Neurobiol Dis 30(3):303–311
Pramparo T et al (2015) Cell cycle networks link gene expression dysregulation, mutation, and brain maldevelopment in autistic toddlers. Mol Syst Biol 11(12):841
Miller JA et al (2014) Transcriptional landscape of the prenatal human brain. Nature 508(7495):199–206
Ben-David E, Shifman S (2012) Networks of neuronal genes affected by common and rare variants in autism spectrum disorders. PLoS Genet 8(3):e1002556
Mahfouz A et al (2015) Shared pathways among autism candidate genes determined by co-expression network analysis of the developing human brain transcriptome. J Mol Neurosci 57(4):580–594
Pinto D et al (2014) Convergence of genes and cellular pathways dysregulated in autism spectrum disorders. Am J Hum Genet 94(5):677–694
Hormozdiari F et al (2015) The discovery of integrated gene networks for autism and related disorders. Genome Res 25(1):142–154
Parikshak NN, Gandal MJ, Geschwind DH (2015) Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders. Nat Rev Genet 16(8):441–458
Mattick JS (2001) Non‐coding RNAs: the architects of eukaryotic complexity. 2:986–991
Briggs JA et al (2015) Mechanisms of long non-coding RNAs in mammalian nervous system development, plasticity, disease, and evolution. Neuron 88(5):861–877
Morris KV, Mattick JS (2014) The rise of regulatory RNA. Nat Rev Genet 15(6):423–437
Roberts TC, Morris KV, Wood MJ (2014) The role of long non-coding RNAs in neurodevelopment, brain function and neurological disease. Philos Trans R Soc Lond B Biol Sci 369(1652)
Lin M et al (2011) RNA-Seq of human neurons derived from iPS cells reveals candidate long non-coding RNAs involved in neurogenesis and neuropsychiatric disorders. PLoS One 6(9):e23356
Ng SY, Johnson R, Stanton LW (2012) Human long non-coding RNAs promote pluripotency and neuronal differentiation by association with chromatin modifiers and transcription factors. EMBO J 31(3):522–533
Varela MA, Roberts TC, Wood MJ (2013) Epigenetics and ncRNAs in brain function and disease: mechanisms and prospects for therapy. Neurotherapeutics 10(4):621–631
Feng J et al (2006) The Evf-2 noncoding RNA is transcribed from the Dlx-5/6 ultraconserved region and functions as a Dlx-2 transcriptional coactivator. Genes Dev 20(11):1470–1484
Young TL, Matsuda T, Cepko CL (2005) The noncoding RNA taurine upregulated gene 1 is required for differentiation of the murine retina. Curr Biol 15(6):501–512
Issler O, Chen A (2015) Determining the role of microRNAs in psychiatric disorders. Nat Rev Neurosci 16(4):201–212
Kerin T et al (2012) A noncoding RNA antisense to moesin at 5p14.1 in autism. Sci Transl Med 4(128):128ra40
Schizophrenia Working Group of the Psychiatric Genomics, C. (2014) Biological insights from 108 schizophrenia-associated genetic loci. Nature 511(7510):421–427
Ziats MN, Rennert OM (2013) Aberrant expression of long noncoding RNAs in autistic brain. J Mol Neurosci 49(3):589–593
Stamova B et al (2015) Specific regional and age-related small noncoding RNA expression patterns within superior temporal gyrus of typical human brains are less distinct in autism brains. J Child Neurol 30(14):1930–1946
Ziats MN, Rennert OM (2014) Identification of differentially expressed microRNAs across the developing human brain. Mol Psychiatry 19(7):848–852
Wall DP et al (2010) Genotator: a disease-agnostic tool for genetic annotation of disease. BMC Med Genomics 3:50
Maston GA et al (2012) Characterization of enhancer function from genome-wide analyses. Annu Rev Genomics Hum Genet 13:29–57
Wenger AM et al (2013) The enhancer landscape during early neocortical development reveals patterns of dense regulation and co-option. PLoS Genet 9(8):e1003728
Kim TK et al (2010) Widespread transcription at neuronal activity-regulated enhancers. Nature 465(7295):182–187
Andersson R et al (2014) An atlas of active enhancers across human cell types and tissues. Nature 507(7493):455–461
Lam MT et al (2014) Enhancer RNAs and regulated transcriptional programs. Trends Biochem Sci 39(4):170–182
Wu H et al (2014) Tissue-specific RNA expression marks distant-acting developmental enhancers. PLoS Genet 10(9):e1004610
Kharchenko PV et al (2011) Comprehensive analysis of the chromatin landscape in Drosophila melanogaster. Nature 471(7339):480–485
Hah N et al (2013) Enhancer transcripts mark active estrogen receptor binding sites. Genome Res 23(8):1210–1223
Melo CA et al (2013) eRNAs are required for p53-dependent enhancer activity and gene transcription. Mol Cell 49(3):524–535
Mousavi K et al (2013) eRNAs promote transcription by establishing chromatin accessibility at defined genomic loci. Mol Cell 51(5):606–617
Schaukowitch K et al (2014) Enhancer RNA facilitates NELF release from immediate early genes. Mol Cell 56(1):29–42
Lam MT et al (2013) Rev-Erbs repress macrophage gene expression by inhibiting enhancer-directed transcription. Nature 498(7455):511–515
Pnueli L et al (2015) RNA transcribed from a distal enhancer is required for activating the chromatin at the promoter of the gonadotropin alpha-subunit gene. Proc Natl Acad Sci USA 112(14):4369–4374
Hsieh CL et al (2014) Enhancer RNAs participate in androgen receptor-driven looping that selectively enhances gene activation. Proc Natl Acad Sci USA 111(20):7319–7324
Li W et al (2013) Functional roles of enhancer RNAs for oestrogen-dependent transcriptional activation. Nature 498(7455):516–520
Sigova AA et al (2015) Transcription factor trapping by RNA in gene regulatory elements. Science 350(6263):978–981
Telese F et al (2015) LRP8-Reelin-regulated neuronal enhancer signature underlying learning and memory formation. Neuron 86(3):696–710
Adelman K, Lis JT (2012) Promoter-proximal pausing of RNA polymerase II: emerging roles in metazoans. Nat Rev Genet 13(10):720–731
Yao P et al (2015) Coexpression networks identify brain region-specific enhancer RNAs in the human brain. Nat Neurosci 18(8):1168–1174
Chadwick LH (2012) The NIH roadmap epigenomics program data resource. Epigenomics 4(3):317–324
Jeck WR et al (2013) Circular RNAs are abundant, conserved, and associated with ALU repeats. RNA 19(2):141–157
Conn SJ et al (2015) The RNA binding protein quaking regulates formation of circRNAs. Cell 160(6):1125–1134
Zhang Y et al (2013) Circular intronic long noncoding RNAs. Mol Cell 51(6):792–806
Li Z et al (2015) Exon–intron circular RNAs regulate transcription in the nucleus. Nat Struct Mol Biol 22(3):256–264
Jeck WR, Sharpless NE (2014) Detecting and characterizing circular RNAs. Nat Biotechnol 32(5):453–461
Cheng J, Metge F, Dieterich C (2016) Specific identification and quantification of circular RNAs from sequencing data. Bioinformatics 32(7):1094–1096
Szabo L et al (2015) Statistically based splicing detection reveals neural enrichment and tissue-specific induction of circular RNA during human fetal development. Genome Biol 16:126
Ashwal-Fluss R et al (2014) circRNA biogenesis competes with pre-mRNA splicing. Mol Cell 56(1):55–66
Westholm JO et al (2014) Genome-wide analysis of Drosophila circular RNAs reveals their structural and sequence properties and age-dependent neural accumulation. Cell Rep 9(5):1966–1980
Rybak-Wolf A et al (2015) Circular RNAs in the mammalian brain are highly abundant, conserved, and dynamically expressed. Mol Cell
You X et al (2015) Neural circular RNAs are derived from synaptic genes and regulated by development and plasticity. Nat Neurosci 18(4):603–610
Kuhn A et al (2011) Population-specific expression analysis (PSEA) reveals molecular changes in diseased brain. Nat Methods 8(11):945–947
Gaujoux R, Seoighe C (2013) Cell mix: a comprehensive toolbox for gene expression deconvolution. Bioinformatics 29(17):2211–2212
Gong T, Szustakowski JD (2013) DeconRNASeq: a statistical framework for deconvolution of heterogeneous tissue samples based on mRNA-Seq data. Bioinformatics 29(8):1083–1085
Zhong Y et al (2013) Digital sorting of complex tissues for cell type-specific gene expression profiles. BMC Bioinform 14(1):89
Jaffe AE et al (2016) Mapping DNA methylation across development, genotype and schizophrenia in the human frontal cortex. Nat Neurosci 19(1):40–47
Dolmetsch R, Geschwind DH (2011) The human brain in a dish: the promise of iPSC-derived neurons. Cell 145(6):831–834
Hjelm BE et al (2013) In vitro-differentiated neural cell cultures progress towards donor-identical brain tissue. Hum Mol Genet 22(17):3534–3546
Darmanis S et al (2015) A survey of human brain transcriptome diversity at the single cell level. Proc Natl Acad Sci USA 112(23):7285–7290
Macosko EZ et al (2015) Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161(5):1202–1214
Akbarian S et al (2015) The PsychENCODE project. Nat Neurosci 18(12):1707–1712
BrainSeq, A.H.B.G.C.E.a.d.l.o., A.H.B.G.C. BrainSeq (2015) BrainSeq: neurogenomics to drive novel target discovery for neuropsychiatric disorders. Neuron 88(6):1078–1083
Acknowledgments
This work was supported by an ARC DECRA fellowship (DE140101033) an NHMRC Project Grant (APP1062510) to IV, and an UNSW Brain Sciences Grant-in-Aid to A.G.
Author information
Authors and Affiliations
Corresponding author
Additional information
A. Gokoolparsadh and G. J. Sutton equally contributed.
Rights and permissions
About this article
Cite this article
Gokoolparsadh, A., Sutton, G.J., Charamko, A. et al. Searching for convergent pathways in autism spectrum disorders: insights from human brain transcriptome studies. Cell. Mol. Life Sci. 73, 4517–4530 (2016). https://doi.org/10.1007/s00018-016-2304-0
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00018-016-2304-0