The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
The discovery of integrated gene networks for autism and related disorders
Open accessAbstract
Despite considerable genetic heterogeneity underlying neurodevelopmental diseases, there is compelling evidence that many disease genes will map to a much smaller number of biological subnetworks. We developed a computational method, termed MAGI (m erging a ffected g enes into i ntegrated networks), that simultaneously integrates protein–protein interactions and RNA-seq expression profiles during brain development to discover “modules” enriched for de novo mutations in probands. We applied this method to recent exome sequencing of 1116 patients with autism and intellectual disability, discovering two distinct modules that differ in their properties and associated phenotypes. The first module consists of 80 genes associated with Wnt, Notch, SWI/SNF, and NCOR complexes and shows the highest expression early during embryonic development (8–16 post-conception weeks [pcw]). The second module consists of 24 genes associated with synaptic function, including long-term potentiation and calcium signaling with higher levels of postnatal expression. Patients with de novo mutations in these modules are more significantly intellectually impaired and carry more severe missense mutations when compared to probands with de novo mutations outside of these modules. We used our approach to define subsets of the network associated with higher functioning autism as well as greater severity with respect to IQ. Finally, we applied MAGI independently to epilepsy and schizophrenia exome sequencing cohorts and found significant overlap as well as expansion of these modules, suggesting a core set of integrated neurodevelopmental networks common to seemingly diverse human diseases.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“M1 consists of 47 genes associated with Wnt, Notch, SWI/SNF, and NCOR signaling, and showing high expression during embryonic development (8–16 pcw) (Fig. 1, Supplementary Table S1).”
From Distinct selective forces and Neanderthal introgression shaped genetic diversity at genes involved in neurodevelopmental disorders (2017) · cites this paper as [29]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“M2 includes 21 genes associated with synaptic function and mainly expressed at postnatal stages (Fig. 1, Supplementary Table S1).”
From Distinct selective forces and Neanderthal introgression shaped genetic diversity at genes involved in neurodevelopmental disorders (2017) · cites this paper as [29]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“These modules were built through the exome sequencing data of 1116 patients affected by ASD and ID29.”
From Distinct selective forces and Neanderthal introgression shaped genetic diversity at genes involved in neurodevelopmental disorders (2017) · cites this paper as [29]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“We focused in particular on 68 genes which, despite not being an exhaustive catalog of genetic risk loci for ASD and ID, were previously shown to represent functional modules enriched of de novo mutations in probands29.”
From Distinct selective forces and Neanderthal introgression shaped genetic diversity at genes involved in neurodevelopmental disorders (2017) · cites this paper as [29]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Module 1 includes genes associated to transcriptional regulation during brain development, whereas module 2 is composed of synaptic genes involved in long-term potentiation and calcium signaling29.”
From Distinct selective forces and Neanderthal introgression shaped genetic diversity at genes involved in neurodevelopmental disorders (2017) · cites this paper as [29]
The cited source says what the claim attributes to it.
In the citing paper
“Module 1 includes genes associated to transcriptional regulation during brain development, whereas module 2 is composed of synaptic genes involved in long-term potentiation and calcium signaling29.”
From Distinct selective forces and Neanderthal introgression shaped genetic diversity at genes involved in neurodevelopmental disorders (2017) · cites this paper as [29]
In the citing paper
“MAGI identified modules containing functionally related genes enriched for deleterious mutations in ASD, many of which are under strong purifying selection, and are also found in epilepsy, schizophrenia and intellectual disability133.”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [133]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Cytoscape: A software Environment for integrated models of biomolecular interaction networks
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003) · Genome Research · doi:10.1101/gr.1239303 · Cited by 51,888
A general framework for estimating the relative pathogenicity of human genetic variants
Kircher, M., Witten, D. M., Jain, P., O'Roak, B. J., Cooper, G. M., & Shendure, J. (2014) · Nature Genetics · doi:10.1038/ng.2892 · Cited by 6,183
Szklarczyk, D., Franceschini, A., Kuhn, M., Simonovic, M., Roth, A. C., Mínguez, P., Doerks, T., Stark, M., Muller, J., Bork, P., et al. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq973 · Cited by 3,469
Human Protein Reference Database--2009 update
Prasad, T. S. K., Goel, R., Kandasamy, K., Keerthikumar, S., Kumar, S., Mathivanan, S., Telikicherla, D., Raju, R., Shafreen, R. B., Venugopal, A., et al. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn892 · Cited by 3,252
Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations
O’Roak, B. J., Vives, L., Girirajan, S., Karakoc, E., Krumm, N., Coe, B. P., Levy, R., Ko, A., Lee, C., Smith, J. D., et al. (2012) · Nature · doi:10.1038/nature10989 · Cited by 2,165
Self-citationRate of de novo mutations and the importance of father’s age to disease risk
Kong, A., Frigge, M. L., Masson, G., Besenbacher, S., Sulem, P., Magnusson, G., Gudjonsson, S. A., Sigurdsson, A., Jonasdottir, A., Jonasdottir, A., et al. (2012) · Nature · doi:10.1038/nature11396 · Cited by 2,134
De novo mutations revealed by whole-exome sequencing are strongly associated with autism
Sanders, S. J., Murtha, M. T., Gupta, A. R., Murdoch, J. D., Raubeson, M. J., Willsey, A. J., Ercan-Sencicek, A. G., DiLullo, N. M., Parikshak, N. N., Stein, J. L., et al. (2012) · Nature · doi:10.1038/nature10945 · Cited by 2,084
FMRP Stalls Ribosomal Translocation on mRNAs Linked to Synaptic Function and Autism
Darnell, J. C., Van Driesche, S. J., Zhang, C., Hung, K. Y. S., Mele, A., Fraser, C. E., Stone, E. F., Chen, C., Fak, J. J., Chi, S. W., et al. (2011) · Cell · doi:10.1016/j.cell.2011.06.013 · Cited by 2,072
Transcriptomic Analysis of Autistic Brain Reveals Convergent Molecular Pathology
Voineagu, I., Wang, X., Johnston, P., Lowe, J. K., Tian, Y., Horvath, S., Mill, J., Cantor, R. M., Blencowe, B. J., & Geschwind, D. H. (2011) · Nature · doi:10.1038/nature10110 · Cited by 1,874
Patterns and rates of exonic de novo mutations in autism spectrum disorders
Neale, B. M., Kou, Y., Liu, L., Ma’ayan, A., Samocha, K. E., Sabo, A., Lin, C.-F., Stevens, C., Wang, L.-S., Makarov, V., et al. (2012) · Nature · doi:10.1038/nature11011 · Cited by 1,734
Flagged first, then by citations
Cited by
Sorted by citations
Genetic studies in intellectual disability and related disorders
Vissers, L. E. L. M., Gilissen, C., & Veltman, J. A. (2016) · Nature Reviews Genetics · doi:10.1038/nrg3999 · Cited by 761
Neurodevelopmental Disorders: From Genetics to Functional Pathways
Parenti, I., Rabaneda, L. G., Schoen, H., & Novarino, G. (2020) · Trends in Neurosciences · doi:10.1016/j.tins.2020.05.004 · Cited by 687
Stessman, H. A. F., Xiong, B., Coe, B. P., Wang, T., Hoekzema, K., Fenckova, M., Kvarnung, M., Gerdts, J., Trinh, S., Cosemans, N., et al. (2017) · Nature Genetics · doi:10.1038/ng.3792 · Cited by 559
Self-citationInherited and de novo genetic risk for autism impacts shared networks
Ruzzo, E. K., Pérez-Cano, L., Jung, J.-Y., Wang, L.-K., Kashef-Haghighi, D., Hartl, C., Singh, C., Xu, J., Hoekstra, J. N., Leventhal, O., et al. (2019) · Cell · doi:10.1016/j.cell.2019.07.015 · Cited by 457
Autism genetics: opportunities and challenges for clinical translation
Vorstman, J. A. S., Parr, J. R., Moreno‐De‐Luca, D., Anney, R., Nürnberger, J. I., & Hallmayer, J. F. (2017) · Nature Reviews Genetics · doi:10.1038/nrg.2017.4 · Cited by 517
Defining the genetic, genomic, cellular, and diagnostic architectures of psychiatric disorders
Sullivan, P. F., & Geschwind, D. H. (2019) · Cell · doi:10.1016/j.cell.2019.01.015 · Cited by 452
“Despite these limitations, several studies in ASD and SCZ highlight the power of using transcriptional networks based on normal human brain tissue across development or brain regions ormore generalized PPIs (Hormozdiari et al., 2015; Li et al., 2014; Lin et al., 2015) to identify molecular pathways, developmental epoc…”
BackgroundSystems biology and gene networks in neurodevelopmental and neurodegenerative disorders
Parikshak, N. N., Gandal, M. J., & Geschwind, D. H. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3934 · Cited by 430
Krishnan, A., Zhang, R., Yao, V., Theesfeld, C. L., Wong, A. K., Tadych, A., Volfovsky, N., Packer, A. I., Lash, A., & Troyanskaya, O. G. (2016) · Nature Neuroscience · doi:10.1038/nn.4353 · Cited by 438
Mammalian SWI/SNF Chromatin Remodeling Complexes: Emerging Mechanisms and Therapeutic Strategies
Centore, R. C., Sandoval, G. J., Soares, L. M. M., Kadoch, C., & Chan, H. M. (2020) · Trends in Genetics · doi:10.1016/j.tig.2020.07.011 · Cited by 400
Neurodevelopmental disease genes implicated by de novo mutation and copy number variation morbidity
Coe, B. P., Stessman, H. A. F., Sulovari, A., Geisheker, M. R., Bakken, T. E., Lake, A. M., Dougherty, J. D., Lein, E. S., Hormozdiari, F., Bernier, R. A., et al. (2019) · Nature Genetics · doi:10.1038/s41588-018-0288-4 · Cited by 330
Self-citationGenomic Patterns of De Novo Mutation in Simplex Autism
Turner, T. N., Coe, B. P., Dickel, D. E., Hoekzema, K., Nelson, B. J., Zody, M. C., Kronenberg, Z. N., Hormozdiari, F., Raja, A. N., Pennacchio, L. A., et al. (2017) · Cell · doi:10.1016/j.cell.2017.08.047 · Cited by 343
Self-citationWhole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk
Zhou, J., Park, C. Y., Theesfeld, C. L., Wong, A. K., Yuan, Y., Scheckel, C., Fak, J. J., Funk, J., Yao, K., Tajima, Y., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0420-0 · Cited by 295
Germline Chd8 haploinsufficiency alters brain development in mouse
Gompers, A. L., Su-Feher, L., Ellegood, J., Copping, N. A., Riyadh, M. A., Stradleigh, T. W., Pride, M. C., Schaffler, M. D., Wade, A. A., Catta-Preta, R., et al. (2017) · Nature Neuroscience · doi:10.1038/nn.4592 · Cited by 266
Computational network biology: Data, models, and applications
Liu, C., Ma, Y., Zhao, J., Nussinov, R., Zhang, Y., Cheng, F., & Zhang, Z. (2019) · Physics Reports · doi:10.1016/j.physrep.2019.12.004 · Cited by 230
Calpain-1 and Calpain-2: The Yin and Yang of Synaptic Plasticity and Neurodegeneration
Baudry, M., & Bi, X. (2016) · Trends in Neurosciences · doi:10.1016/j.tins.2016.01.007 · Cited by 225
Doorenweerd, N., Mahfouz, A. E., van Putten, M., Kaliyaperumal, R., Hoen, P. A. C. ’., Hendriksen, J. G. M., Aartsma-Rus, A. M., Verschuuren, J. J. G. M., Niks, E. H., Reinders, M. J. T., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-12981-5 · Cited by 235
Li, J., Cai, T., Jiang, Y. W., Chen, H., He, X., Chen, C., Li, X., Shao, Q., Xia, R., Li, Z., et al. (2015) · Molecular Psychiatry · doi:10.1038/mp.2015.40 · Cited by 212
Prenatal Origins of ASD: The When, What, and How of ASD Development
Courchesne, E., Gazestani, V., & Lewis, N. E. (2020) · Trends in Neurosciences · doi:10.1016/j.tins.2020.03.005 · Cited by 200
Co-expression networks reveal the tissue-specific regulation of transcription and splicingPreprint
Saha, A., Kim, Y., Gewirtz, A. D., Jo, B., Gao, C., McDowell, I. C., Engelhardt, B. E., & Battle, A. J. (2017) · Genome Research · doi:10.1101/gr.216721.116 · Cited by 173
The landscape of DNA methylation amid a perfect storm of autism aetiologies
Ciernia, A. V., & LaSalle, J. M. (2016) · Nature reviews. Neuroscience · doi:10.1038/nrn.2016.41 · Cited by 168
Identification of disease treatment mechanisms through the multiscale interactome
Ruiz, C., Zitnik, M., & Leskovec, J. (2021) · Nature Communications · doi:10.1038/s41467-021-21770-8 · Cited by 172
Autism spectrum disorder: insights into convergent mechanisms from transcriptomics
Quesnel-Vallières, M., Weatheritt, R. J., Cordes, S. P., & Blencowe, B. J. (2018) · Nature Reviews Genetics · doi:10.1038/s41576-018-0066-2 · Cited by 167
Disruption of POGZ Is Associated with Intellectual Disability and Autism Spectrum Disorders
Stessman, H. A. F., Willemsen, M. H., Fencková, M., Penn, O., Hoischen, A., Xiong, B., Wang, T., Hoekzema, K., Vives, L., Vogel, I., et al. (2016) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2016.02.004 · Cited by 160
Self-citationNeocortical neurogenesis and the etiology of autism spectrum disorder
Packer, A. (2016) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.03.002 · Cited by 138
Recurrent de novo mutations in neurodevelopmental disorders: properties and clinical implications
Wilfert, A. B., Sulovari, A., Turner, T. N., Coe, B. P., & Eichler, E. E. (2017) · Genome Medicine · doi:10.1186/s13073-017-0498-x · Cited by 144
Self-citationKumar, S., Reynolds, K., Ji, Y., Gu, R., Rai, S., & Zhou, C. J. (2019) · Journal of Neurodevelopmental Disorders · doi:10.1186/s11689-019-9268-y · Cited by 147
Histone deacetylase 3 associates with MeCP2 to regulate FOXO and social behavior
Nott, A., Cheng, J., Gao, F., Lin, Y.-T., Gjoneska, E., Ko, T., Minhas, P., Zamudio, A. V., Meng, J., Zhang, F., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4347 · Cited by 110
Mutations and Modeling of the Chromatin Remodeler CHD8 Define an Emerging Autism Etiology
Barnard, R., Pomaville, M. B., & O’Roak, B. J. (2015) · Frontiers in Neuroscience · doi:10.3389/fnins.2015.00477 · Cited by 111
Krishnan, M. L., Van Steenwinckel, J., Schang, A.-L., Yan, J., Arnadottir, J., Le Charpentier, T., Csaba, Z., Dournaud, P., Cipriani, S., Auvynet, C., et al. (2017) · Nature Communications · doi:10.1038/s41467-017-00422-w · Cited by 105
Valproic acid exposure sequentially activates Wnt and mTOR pathways in rats
Qin, L., Dai, X., & Yin, Y. (2016) · Molecular and Cellular Neuroscience · doi:10.1016/j.mcn.2016.06.004 · Cited by 101
Higdon, R., Earl, R. K., Stanberry, L. I., Hudac, C. M., Montague, E., Stewart, E., Janko, I., Choiniere, J., Broomall, W., Kolker, N., et al. (2015) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2015.0020 · Cited by 107
Genetic variants in Alzheimer disease — molecular and brain network approaches
Gaiteri, C., Mostafavi, S., Honey, C. J., Jager, P. L. D., & Bennett, D. A. (2016) · Nature Reviews Neurology · doi:10.1038/nrneurol.2016.84 · Cited by 97
Waterhouse, L. H., London, E., & Gillberg, C. (2016) · Review Journal of Autism and Developmental Disorders · doi:10.1007/s40489-016-0085-x · Cited by 106
Sex differences in brain plasticity: a new hypothesis for sex ratio bias in autism
Mottron, L., Duret, P., Mueller, S., Moore, R. D., d’Arc, B. F., Jacquemont, S., & Xiong, L. (2015) · Molecular Autism · doi:10.1186/s13229-015-0024-1 · Cited by 104
The Genetics of Autism Spectrum Disorders
Huguet, G., Benabou, M., & Bourgeron, T. (2016) · Research and perspectives in endocrine interactions · doi:10.1007/978-3-319-27069-2_11 · Cited by 105
Nguyen, H. T., Bryois, J., Kim, A., Dobbyn, A. L., Huckins, L. M., Muñoz‐Manchado, A. B., Ruderfer, D. M., Genovese, G., Fromer, M., Xu, X., et al. (2017) · Genome Medicine · doi:10.1186/s13073-017-0497-y · Cited by 87
Network-based approaches for analysis of complex biological systems
Chasman, D. A., Siahpirani, A. F., & Roy, S. (2016) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2016.04.007 · Cited by 89
Radio, F. C., Pang, K., Ciolfi, A., Levy, M. A., Hernández, A., Pedace, L., Pantaleoni, F., Liu, Z., de Boer, E., Jackson, A., et al. (2021) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2021.01.015 · Cited by 94
Placental methylome analysis from a prospective autism study
Schroeder, D. I., Schmidt, R. J., Crary-Dooley, F. K., Walker, C. K., Ozonoff, S. J., Tancredi, D. J., Hertz‐Picciotto, I., & LaSalle, J. M. (2016) · Molecular Autism · doi:10.1186/s13229-016-0114-8 · Cited by 83
Berto, S., Usui, N., Konopka, G., & Fogel, B. L. (2016) · Human Molecular Genetics · doi:10.1093/hmg/ddw110 · Cited by 80
Exploiting single-cell expression to characterize co-expression replicability
Crow, M., Paul, A., Ballouz, S., Huang, Z. J., & Gillis, J. (2016) · Genome Biology · doi:10.1186/s13059-016-0964-6 · Cited by 76
TBR1 regulates autism risk genes in the developing neocortex
Notwell, J. H., Heavner, W. E., Darbandi, S. F., Katzman, S., McKenna, W. L., Ortiz-Londono, C. F., Tastad, D., Eckler, M. J., Rubenstein, J. L. R., McConnell, S. K., et al. (2016) · Genome Research · doi:10.1101/gr.203612.115 · Cited by 74
The novel lncRNA lnc-NR2F1 is pro-neurogenic and mutated in human neurodevelopmental disorders
Ang, C. E., Ma, Q., Wapinski, O. L., Fan, S., Flynn, R. A., Lee, Q. Y., Coe, B. P., Onoguchi, M., Olmos, V. H., Do, B. T., et al. (2019) · eLife · doi:10.7554/elife.41770 · Cited by 76
Self-citationSynaptic Wnt/GSK3βSignaling Hub in Autism
Caracci, M. O., Ávila, M. E., & Ferrari, G. V. D. (2016) · Neural Plasticity · doi:10.1155/2016/9603751 · Cited by 78
Dunaway, K. W., Islam, M. S., Coulson, R. L., Lopez, S. J., Ciernia, A. V., Chu, R. G., Yasui, D. H., Pessah, I. N., Lott, P. C., Mordaunt, C. E., et al. (2016) · Cell Reports · doi:10.1016/j.celrep.2016.11.058 · Cited by 70
Butler, M. G., Rafi, S. K., & Manzardo, A. M. (2015) · International Journal of Molecular Sciences · doi:10.3390/ijms16036464 · Cited by 78
Identification of Neuropsychiatric Copy Number Variants in a Health Care System Population
Martin, C. L., Wain, K. E., Oetjens, M. T., Tolwinski, K., Palen, E., Hare-Harris, A. E., Habegger, L., Maxwell, E. K., Reid, J. G., Walsh, L. K., et al. (2020) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2020.2159 · Cited by 70
The Role of De Novo Noncoding Regulatory Mutations in Neurodevelopmental Disorders
Turner, T. N., & Eichler, E. E. (2018) · Trends in Neurosciences · doi:10.1016/j.tins.2018.11.002 · Cited by 74
Self-citationGenetic heterogeneity in autism: From single gene to a pathway perspective
An, J., & Claudianos, C. (2016) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.06.013 · Cited by 68
Brain-specific functional relationship networks inform autism spectrum disorder gene prediction
Duda, M., Zhang, H., Li, H., Wall, D. P., Burmeister, M., & Guan, Y. (2018) · Translational Psychiatry · doi:10.1038/s41398-018-0098-6 · Cited by 65
Zhang, Y., Xiang, Z., Jia, Y., Xue-ling, H., Wang, L., & Cui, W. (2019) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2019.109644 · Cited by 64
De novo Mutations (DNMs) in Autism Spectrum Disorder (ASD): Pathway and Network Analysis
Alonso-González, A., Rodriguez‐Fontenla, C., & Carracedo, Á. (2018) · Frontiers in Genetics · doi:10.3389/fgene.2018.00406 · Cited by 73
Lin, Y., Afshar, S., Rajadhyaksha, A. M., Potash, J. B., & Han, S. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.500064 · Cited by 61
Casanova, M., Sharp, J. L., Chakraborty, H., Sumi, N. S., & Casanova, M. F. (2016) · Molecular Autism · doi:10.1186/s13229-016-0082-z · Cited by 66
Shohat, S., Ben‐David, E., & Shifman, S. (2017) · Cell Reports · doi:10.1016/j.celrep.2017.02.007 · Cited by 58
Autism Spectrum Disorder: Signaling Pathways and Prospective Therapeutic Targets
Baranova, J., Dragunas, G., Botelho, M. C. S., Ayub, A. L. P., Bueno-Alves, R., Alencar, R. R., Papaiz, D. D., Sogayar, M. C., Ulrich, H., & Correa, R. G. (2020) · Cellular and Molecular Neurobiology · doi:10.1007/s10571-020-00882-7 · Cited by 63
Mahfouz, A. E., Ziats, M. N., Rennert, O. M., Lelieveldt, B. P. F., & Reinders, M. J. T. (2015) · Journal of Molecular Neuroscience · doi:10.1007/s12031-015-0641-3 · Cited by 63
Chen, R., Davis, L. K., Guter, S. J., Wei, Q., Jacob, S., Potter, M. H., Cox, N. J., Cook, E. H., Sutcliffe, J. S., & Li, B. (2017) · Molecular Autism · doi:10.1186/s13229-017-0130-3 · Cited by 59
Current perspectives in autism spectrum disorder: from genes to therapy
Chahrour, M., O'Roak, B. J., Santini, E., Samaco, R. C., Kleiman, R. J., & Manzini, M. C. (2016) · The Journal of Neuroscience · doi:10.1523/jneurosci.2335-16.2016 · Cited by 54
Pathogenic WDFY3 variants cause neurodevelopmental disorders and opposing effects on brain size
Duc, D. L., Giulivi, C., Hiatt, S. M., Napoli, E., Panoutsopoulos, A. A., Crescenzo, A. H. D., Kotzaeridou, U., Syrbe, S., Anagnostou, E. A., Azage, M., et al. (2019) · Brain · doi:10.1093/brain/awz198 · Cited by 59
Cerebral organoids as tools to identify the developmental roots of autism
Chan, W. K., Griffiths, R., Price, D. J., & Mason, J. O. (2020) · Molecular Autism · doi:10.1186/s13229-020-00360-3 · Cited by 58
Autism spectrum disorders: Integration of the genome, transcriptome and the environment
Vijayakumar, N. T., & Judy, M. V. (2016) · Journal of the Neurological Sciences · doi:10.1016/j.jns.2016.03.026 · Cited by 56
Nash, A., Aumann, T. D., Pigoni, M., Lichtenthaler, S. F., Takeshima, H., Munro, K. M., & Gunnersen, J. M. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhz230 · Cited by 52
Li, J., Ma, Z., Shi, M., Malty, R. H., Aoki, H., Minić, Z., Phanse, S., Jin, K., Wall, D. P., Zhang, Z., et al. (2015) · Cell Systems · doi:10.1016/j.cels.2015.11.002 · Cited by 52
Tocco, C., Bertacchi, M., & Studer, M. (2021) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2021.767965 · Cited by 53
Multiplex gene and phenotype network to characterize shared genetic pathways of epilepsy and autism
Peng, J. J., Zhou, Y., & Wang, K. (2021) · Scientific Reports · doi:10.1038/s41598-020-78654-y · Cited by 47
Dissecting the genetic basis of comorbid epilepsy phenotypes in neurodevelopmental disorders
Chow, J. C., Jensen, M., Amini, H., Hormozdiari, F., Penn, O., Shifman, S., Girirajan, S., & Hormozdiari, F. (2019) · Genome Medicine · doi:10.1186/s13073-019-0678-y · Cited by 45
Self-citationFrom Rare Copy Number Variants to Biological Processes in ADHD
Harich, B., van der Voet, M., Klein, M., Čížek, P., Fencková, M., Schenck, A., & Franke, B. (2020) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2020.19090923 · Cited by 44
Genetic Influences on the Developing Young Brain and Risk for Neuropsychiatric Disorders
Alex, A. M., Buß, C., Davis, E. P., Campos, G. D. L., Donald, K. A., Fair, D. A., Gaab, N., Gao, W., Gilmore, J. H., Girault, J. B., et al. (2023) · Biological Psychiatry · doi:10.1016/j.biopsych.2023.01.013 · Cited by 45
Convergent Evolution and the Origin of Complex Phenotypes in the Age of Systems Biology
Washburn, J. D., Bird, K. A., Conant, G. C., & Pires, J. C. (2016) · International Journal of Plant Sciences · doi:10.1086/686009 · Cited by 43
Massive datasets and machine learning for computational biomedicine: trends and challenges
Андреевич, К. А., Pardalos, P. M., & Karakitsiou, A. (2018) · Annals of Operations Research · doi:10.1007/s10479-018-2891-2 · Cited by 46
Post-Developmental Roles of Notch Signaling in the Nervous System
Salazar, J. L., Yang, S.-A., & Yamamoto, S. (2020) · Biomolecules · doi:10.3390/biom10070985 · Cited by 43
A novel de novo POGZ mutation in a patient with intellectual disability
Bo, T., Zou, Y., Zhang, Y., Zhang, R., Ou, J., Shen, Y., Zhao, J., Luo, X., Guo, J., Zeng, L., et al. (2016) · Journal of Human Genetics · doi:10.1038/jhg.2015.156 · Cited by 39
Medina, M. A., Andrade, V., Caracci, M. O., Ávila, M. E., Verdugo, D. A., Vargas, M. F., Ugarte, G. D., Reyes, A. E., Opazo, C., & Ferrari, G. V. D. (2018) · Translational Psychiatry · doi:10.1038/s41398-018-0093-y · Cited by 40
Brown, E., Lautz, J. D., Davis, T. R., Gniffke, E. P., VanSchoiack, A., Neier, S. C., Tashbook, N., Nicolini, C., Fahnestock, M. L., Schrum, A. G., et al. (2018) · Molecular Autism · doi:10.1186/s13229-018-0229-1 · Cited by 42
Golovina, E., Fadason, T., Lints, T., Walker, C. G., Vickers, M. H., & O'Sullivan, J. M. (2021) · Scientific Reports · doi:10.1038/s41598-021-95447-z · Cited by 37
Upadhyay, J., Patra, J., Tiwari, N., Salankar, N., Ansari, M. N., & Ahmad, W. (2021) · Cells · doi:10.3390/cells10040958 · Cited by 36
Autism throughout genetics: Perusal of the implication of ion channels
Daghsni, M., Rima, M., Fajloun, Z., Ronjat, M., Brusés, J. L., Mrad, R., & WAARD, M. M. D. (2018) · Brain and Behavior · doi:10.1002/brb3.978 · Cited by 35
Arnett, A. B., Wang, T., Eichler, E. E., & Bernier, R. (2021) · Journal of Neurodevelopmental Disorders · doi:10.1186/s11689-021-09371-4 · Cited by 33
Self-citationWidespread genotype-phenotype correlations in intellectual disability
Casanova, E. L., Gerstner, Z., Sharp, J. L., Casanova, M. F., & Feltus, F. A. (2018) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2018.00535 · Cited by 31
A comparative study of the genetic components of three subcategories of autism spectrum disorder
Li, J., Hu, S., Zhang, K., Shi, L., Zhang, Y., Zhao, T., Wang, L., He, X., Xia, K., Liu, C., et al. (2018) · Molecular Psychiatry · doi:10.1038/s41380-018-0081-x · Cited by 30
Hierarchical cortical transcriptome disorganization in autism
Lombardo, M. V., Courchesne, E., Lewis, N. E., & Pramparo, T. (2017) · Molecular Autism · doi:10.1186/s13229-017-0147-7 · Cited by 35
Weigel, B., Tegethoff, J. F., Grieder, S. D., Lim, B., Nagarajan, B., Liu, Y.-C., Truberg, J., Papageorgiou, D., Adrian-Segarra, J. M., Schmidt, L. K., et al. (2023) · Molecular Psychiatry · doi:10.1038/s41380-023-01959-7 · Cited by 31
Tuand, K., Stijnen, P., Volders, K., Declercq, J., Nuytens, K., Meulemans, S. M. P., & Creemers, J. W. M. (2016) · PLoS ONE · doi:10.1371/journal.pone.0151954 · Cited by 29
Marbach, F., Stoyanov, G., Erger, F., Stratakis, C. A., Settas, N., London, E., Rosenfeld, J. A., Torti, E. E., Haldeman‐Englert, C. R., Sklirou, E., et al. (2021) · Genetics in Medicine · doi:10.1038/s41436-021-01152-7 · Cited by 28
Annamneedi, A., Gora, C., Dudas, A., Leray, X., Bozon, V., Crépieux, P., & Pellissier, L. P. (2023) · British Journal of Pharmacology · doi:10.1111/bph.16216 · Cited by 29
Epistasis in Neuropsychiatric Disorders
Webber, C. (2017) · Trends in Genetics · doi:10.1016/j.tig.2017.01.009 · Cited by 29
Coding and noncoding variants in EBF3 are involved in HADDS and simplex autism
Padhi, E. M., Hayeck, T. J., Cheng, Z., Chatterjee, S., Mannion, B. J., Byrska‐Bishop, M. S., Willems, M., Pinson, L., Redon, S., Bénech, C., et al. (2021) · Human Genomics · doi:10.1186/s40246-021-00342-3 · Cited by 28
Exploiting aberrant mRNA expression in autism for gene discovery and diagnosis
Guan, J., Yang, E., Yang, J., Zeng, Y., Ji, G., & Cai, J. J. (2016) · Human Genetics · doi:10.1007/s00439-016-1673-7 · Cited by 28
Brain transcriptome atlases: a computational perspective
Mahfouz, A. E., Huisman, S. M. H., Lelieveldt, B. P. F., & Reinders, M. J. T. (2016) · Brain Structure and Function · doi:10.1007/s00429-016-1338-2 · Cited by 27
Zhang, Y., Cui, W., Zhai, Q., Zhang, T., & Wen, X. (2017) · Molecular Medicine Reports · doi:10.3892/mmr.2017.6787 · Cited by 27
Molecular subtyping and improved treatment of neurodevelopmental disease
Stessman, H. A. F., Turner, T. N., & Eichler, E. E. (2016) · Genome Medicine · doi:10.1186/s13073-016-0278-z · Cited by 25
Self-citationCorrelation of mutated gene and signalling pathways in ASD
Apte, M., & Kumar, A. B. (2023) · IBRO Neuroscience Reports · doi:10.1016/j.ibneur.2023.03.011 · Cited by 30
Learning impairments and molecular changes in the brain caused by β-catenin loss
Wickham, R. J., Alexander, J., Eden, L. W., Valencia‐Yang, M., Rodríguez, J. L., Aubrey, J. R., & Jacob, M. H. (2019) · Human Molecular Genetics · doi:10.1093/hmg/ddz115 · Cited by 26
Target Genes of Autism Risk Loci in Brain Frontal Cortex
Sun, Y., Yao, X., March, M., Meng, X., Li, J., Wei, Z., Abou‐Sleiman, P. M., Hakonarson, H. H., Xia, Q., & Li, J. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00707 · Cited by 23
Timpano, S., & Picketts, D. J. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00885 · Cited by 28
Wang, W., Han, R., Zhang, M., Wang, Y., Wang, T., Wang, Y., Shang, X., & Peng, J. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab459 · Cited by 26
In vivo imaging translocator protein (TSPO) in autism spectrum disorder
Simpson, D., Gharehgazlou, A., Silva, T. M. S., Labrie‐Cleary, C., Wilson, A. A., Meyer, J. H., Mizrahi, R., & Rusjan, P. (2022) · Neuropsychopharmacology · doi:10.1038/s41386-022-01306-4 · Cited by 29
Worms on the spectrum - C. elegans models in autism research
Schmeißer, K., & Parker, J. A. (2017) · Experimental Neurology · doi:10.1016/j.expneurol.2017.04.007 · Cited by 26
Goetzl, L. M., Thompson-Felix, T. M., Darbinian, N., Merabova, N., Merali, S., Merali, C., Sanserino, K. A., Tatevosian, T., Fant, B., & Wimmer, M. E. (2019) · Genes Brain & Behavior · doi:10.1111/gbb.12583 · Cited by 23
Metadata
- Authors
- Hormozdiari, F., Penn, O., Borenstein, E., & Eichler, E. E.
- Affiliations
- University of Washington (US)
- Howard Hughes Medical Institute (US)
- Venue
- Genome Research
- Publisher
- Cold Spring Harbor Laboratory
- Published
- 2014-11-05
- Volume
- 25
- Issue
- 1
- Pages
- 142-154
- ISSN
- 1088-9051 (ISSN-L)
- 1549-5469
- PMC
- PMC4317170
- OpenAlex
- W2106870542
- Cited by
- 249
- References
- 53
- Citations by year
- 2026: 5 · 2025: 11 · 2024: 11 · 2023: 16 · 2022: 20 · 2021: 20 · 2020: 29 · 2019: 27 · 2018: 23 · 2017: 27 · 2016: 43 · 2015: 16 · 2014: 1
- Type
- posted content
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Genome Research (publishedVersion)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by-nc)
- Funders
- Simons Foundation (303241)
- NIH/NIMH (R01MH101221)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetics and Neurodevelopmental Disorders
- Life Sciences › Neuroscience › Cognitive Neuroscience › Autism Spectrum Disorder Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- autism spectrum disorder, intellectual disability, de novo mutations, gene regulatory networks, Three Kings, protein-protein interaction, RNA-seq expression, brain development, Wnt signaling, Notch signaling, SWI/SNF complex, synaptic function, long-term potentiation, calcium signaling, epilepsy, schizophrenia, neurodevelopmental disorders, missense mutations, disease severity
- MeSH terms
- Algorithms, Autistic Disorder, Epilepsy, Humans, Phenotype, Schizophrenia, Cohort Studies, Cluster Analysis, Databases, Factual, Sequence Analysis, RNA, Genetic Heterogeneity, Mutation, Missense, Gene Regulatory Networks, Exome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
