The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Patterns and rates of exonic de novo mutations in autism spectrum disorders
Open accessAbstract
Autism spectrum disorders (ASD) are believed to have genetic and environmental origins, yet in only a modest fraction of individuals can specific causes be identified. To identify further genetic risk factors, here we assess the role of de novo mutations in ASD by sequencing the exomes of ASD cases and their parents (n = 175 trios). Fewer than half of the cases (46.3%) carry a missense or nonsense de novo variant, and the overall rate of mutation is only modestly higher than the expected rate. In contrast, the proteins encoded by genes that harboured de novo missense or nonsense mutations showed a higher degree of connectivity among themselves and to previous ASD genes as indexed by protein-protein interaction screens. The small increase in the rate of de novo events, when taken together with the protein interaction results, are consistent with an important but limited role for de novo point mutations in ASD, similar to that documented for de novo copy number variants. Genetic models incorporating these data indicate that most of the observed de novo events are unconnected to ASD; those that do confer risk are distributed across many genes and are incompletely penetrant (that is, not necessarily sufficient for disease). Our results support polygenic models in which spontaneous coding mutations in any of a large number of genes increases risk by 5- to 20-fold. Despite the challenge posed by such models, results from de novo events and a large parallel case–control study provide strong evidence in favour of CHD8 and KATNAL2 as genuine autism risk factors.
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
“TADA requires a mutational model (Sanders et al., 2012; Neale et al., 2012; Samocha et al., 2014) which accounts for gene size and sequence composition to obtain an expectation for mutations per gene, given sample size.”
From Large-Scale Exome Sequencing Study Implicates Both Developmental and Functional Changes in the Neurobiology of Autism (2020) · cites this paper as Neale et al., 2012
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
“Exome sequencing of ASD/ID families has revealed a significant excess of de novo mutations in probands when compared to unaffected siblings and has identified novel candidate genes contributing to the neurological deficits.”
From A de novo convergence of autism genetics and molecular neuroscience (2014) · cites this paper as [25]
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
“Simulations based on the observed number of recurrently disrupted genes have estimated that there are some 600-1200 ASD risk genes (21,24,26,51).”
From Genetics and genomics of autism spectrum disorder: embracing complexity (2015) · cites this paper as [21]
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
“mutations in chromatin complexes have been observed in schizophrenia where synaptic gene regulation is disrupted, and these mutations are also high-confidence genetic risk factors for ASD”
From The clinical use of epigenetics in psychiatry: a narrative review of epigenetic mechanisms, key candidate genes, and precision psychiatry (2025) · cites this paper as [42]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“These studies demonstrate the ability of new sequencing technologies to significantly advance our understanding of the genetic basis of ASD.”
From Activity-dependent neuronal signalling and autism spectrum disorder (2013) · cites this paper as [8]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“One of the INH-M13 hub genes is CHD2, whose de novo variants have been identified in individuals with ASD.42,43”
From hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data (2023) · cites this paper as [43]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“One of the INH-M13 hub genes is CHD2, whose de novo variants have been identified in individuals with ASD.42,43”
From hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data (2023) · cites this paper as [43]
Experimental — accuracy not yet measuredIn the citing paper
“CNVs and SNPs have both been reported to play a major role in autism incidence (36–41).”
From Gene × Environment Interactions in Autism Spectrum Disorders: Role of Epigenetic Mechanisms (2014) · cites this paper as [38]
In the citing paper
“These estimates are similar to published results using somewhat different methods [1], [2].”
From Integrated Model of De Novo and Inherited Genetic Variants Yields Greater Power to Identify Risk Genes (2013) · cites this paper as [2]
In the citing paper
“Exome sequencing has uncovered de novo single nucleotide variations (SNVs) and insertion/deletion (indel) mutations that segregate with ASD.”
From Unifying Views of Autism Spectrum Disorders: A Consideration of Autoregulatory Feedback Loops (2016) · cites this paper as Neale et al., 2012
In the citing paper
“Hypomorphic, biallelic mutations, combined with CNVs and heterozygous stop mutations (Neale et al., 2012; O’Roak et al., 2012; Sanders et al., 2012) which completely inactivate one of two alleles, suggest that a common theme for ASD mutations in general might be partial loss of gene function and/or dosage sensitivity.”
From Using Whole-Exome Sequencing to Identify Inherited Causes of Autism (2013) · cites this paper as Neale et al., 2012
In the citing paper
“ASD is often due to newly arising gene copy number variants (CNVs) — such as a deletion or duplication of a region of a chromosome — or a rare mutation that arises in the germ cell, particularly in sperm of older fathers5–10.”
From Activity-dependent neuronal signalling and autism spectrum disorder (2013) · cites this paper as [8]
In the citing paper
“In addition, very recent exome sequencing of parent-offspring trios has identified de novo exonic mutations in SCN2A, KATNAL2, CHD8, FOXP1, NTNG1, GRIN2B, LAMC3(refs 7–9), extending further the number of genes for which mutation may contribute to ASD.”
From Activity-dependent neuronal signalling and autism spectrum disorder (2013) · cites this paper as [8]
In the citing paper
“References 7– 10 use exome and entire genome sequencing to demonstrate the importance of rare mutations in ASD and identify new genetic mutations associated with ASD.”
From Activity-dependent neuronal signalling and autism spectrum disorder (2013) · cites this paper as [8]
In the citing paper
“and also that the loci hit by these SNVs are highly heterogeneous”
From Sex differences in autism spectrum disorders (2013) · cites this paper as [48]
In the citing paper
“Together, results from these papers demonstrate a significant role for rare SNVs in genetic liability for ASD”
From Sex differences in autism spectrum disorders (2013) · cites this paper as [48]
In the citing paper
“finding a handful of genes that are recurrently hit by these rare variants”
From Sex differences in autism spectrum disorders (2013) · cites this paper as [48]
In the citing paper
“Exome and whole-genome sequencing studies have estimated at least another ∼6% contribution to ASD7–10,15”
From Convergence of genes and cellular pathways dysregulated in autism spectrum disorders (2014) · cites this paper as [8]
In the citing paper
“De novo SNVs in CHD2 have been reported in an ASD subject and in several individuals with a broad spectrum of neurodevelopmental disorders, including ID and epileptic encephalopathy8,49,50 (Figure S6).”
From Convergence of genes and cellular pathways dysregulated in autism spectrum disorders (2014) · cites this paper as [8]
In the citing paper
“and three other recent studies by Sanders et al.8, O’Roak et al.7 and Neale et al.23 (65 genes combined from all three; referred to as ‘SON-exomes’);”
From Transcriptome analysis reveals dysregulation of innate immune response genes and neuronal activity-dependent genes in autism (2014) · cites this paper as [23]
In the citing paper
“Genetic investigations in ASD have constructed seed-based networks with literature-curated PPIs to identify the convergence of ASD risk genes71,73.”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [73]
In the citing paper
“In summary, only three genes [CHD8, SCN2A, and synaptic Ras GTPase activating protein 1 (SYNGAP1)] had two independent truncating de novo mutations in any single study, and no gene had more than three mutations.”
From A de novo convergence of autism genetics and molecular neuroscience (2014) · cites this paper as [25]
In the citing paper
“Many strong neurobiological candidates have emerged from the genes disrupted by de novo mutations in these studies, including mutations in previously identified ASD/ID genes.”
From A de novo convergence of autism genetics and molecular neuroscience (2014) · cites this paper as [25]
In the citing paper
“Notably, however, a follow-up case-control study by Neale and colleagues of 935 cases found three additional truncating CHD8 mutations and one splice-site mutation in SCN2A, further strengthening the initial disease associations of these genes [25].”
From A de novo convergence of autism genetics and molecular neuroscience (2014) · cites this paper as [25]
In the citing paper
“We note that the strongest effects are observed for de novo loss-of-function (or truncating) mutations”
From A de novo convergence of autism genetics and molecular neuroscience (2014) · cites this paper as [25]
In the citing paper
“New exome sequencing technology has identified novel rare variants and has found that sporadic cases of ASD/ID are enriched for disruptive de novo mutations.”
From A de novo convergence of autism genetics and molecular neuroscience (2014) · cites this paper as [25]
In the citing paper
“To date, in addition to causal and highly penetrant genes and genomic loci identified before whole-exome sequencing (15,30), over 50 high-risk genes have emerged from the large-scale sequencing studies (21)(22)(23)(24)(25)(26), most under a model of autosomal dominant variation.”
From Genetics and genomics of autism spectrum disorder: embracing complexity (2015) · cites this paper as [21]
In the citing paper
“Rare genetic risk variation identified to date, in contrast, has a very substantial effect on individual risk.Recent whole-exome sequencing analyses of large ASD cohorts (21)(22)(23)(24)(25)(26)(27)(28)(29) have expanded the repertoire of known genes harboring ASD-linked mutations”
From Genetics and genomics of autism spectrum disorder: embracing complexity (2015) · cites this paper as [21]
In the citing paper
“At this point, it is clear that rare microscopically detectable chromosomal rearrangements (14,15), submicroscopic deletions or duplications (CNV or smaller structural variation) (27,(29)(30)(31)(32), single-nucleotide variation (SNV) and small deletions and duplications (indels) (21)(22)(23)(24)(25)(26)(33)(34)(35) all contribute to risk.”
From Genetics and genomics of autism spectrum disorder: embracing complexity (2015) · cites this paper as [21]
In the citing paper
“Recent whole-exome sequencing analyses of large ASD cohorts (21)(22)(23)(24)(25)(26)(27)(28)(29) have expanded the repertoire of known genes harboring ASD-linked mutations that were previously identifiable only by traditional genetic approaches and targeted sequencing (14,15).”
From Genetics and genomics of autism spectrum disorder: embracing complexity (2015) · cites this paper as [21]
In the citing paper
“TBR1 has been implicated in ASD (21,22,25,26) and ID (73).”
From Genetics and genomics of autism spectrum disorder: embracing complexity (2015) · cites this paper as [21]
In the citing paper
“Qualitatively, recent studies of rare variants also find it challenging to distinguish risk variation from the background stochastic noise (12–15).”
From Individual common variants exert weak effects on the risk for autism spectrum disorders (2012) · cites this paper as [14]
In the citing paper
“Albeit rare, de novo mutations have emerged as substantial contributors to ASD risk [83, 165, 166, 254, 261, 309].”
From Autism spectrum disorder: neuropathology and animal models (2017) · cites this paper as [254]
In the citing paper
“Recent exome sequencing studies only reported one de novo SHANK2 mutation out of 965 patients [32], [36], [37]”
From Meta-analysis of SHANK Mutations in Autism Spectrum Disorders: A Gradient of Severity in Cognitive Impairments (2014) · cites this paper as [37]
In the citing paper
“no truncating coding-sequence variation within SHANK1 and SHANK3”
From Meta-analysis of SHANK Mutations in Autism Spectrum Disorders: A Gradient of Severity in Cognitive Impairments (2014) · cites this paper as [37]
In the citing paper
“However, a central finding from these papers was that only a minority of cases had a de novo putatively functional variant”
From Genetic architectures of psychiatric disorders: the emerging picture and its implications (2012) · cites this paper as [84]
In the citing paper
“Three recent papers describe the results from exome sequencing of ~600 trios, and identify roles for de novo exonic mutations in SCN2A, KATNAL2, and CHD8 in the pathogenesis of ASD. 83–85”
From Genetic architectures of psychiatric disorders: the emerging picture and its implications (2012) · cites this paper as [84]
In the citing paper
“Intriguingly, all three studies noted an increased rate of de novo exonic mutations in older parents (with the mutations generally being of paternal origin), 83–85”
From Genetic architectures of psychiatric disorders: the emerging picture and its implications (2012) · cites this paper as [84]
In the citing paper
“Unexpectedly, simplex and multiplex ASD pedigrees show relatively similar de novo mutation rates for SV 80,81 and exonic variation. 83–85”
From Genetic architectures of psychiatric disorders: the emerging picture and its implications (2012) · cites this paper as [84]
In the citing paper
“pathway analyses reported in two of the studies found that genes containing de novo exonic variation were more closely connected in reference to protein-protein interaction databases. 83,84”
From Genetic architectures of psychiatric disorders: the emerging picture and its implications (2012) · cites this paper as [84]
In the citing paper
“estimates from de novo exonic mutations (similar to those from SV data) suggest that ASD is highly polygenic (estimates ranged from 400–1000 genes). 84,85”
From Genetic architectures of psychiatric disorders: the emerging picture and its implications (2012) · cites this paper as [84]
In the citing paper
“The published results for ASD 83–85 and unpublished data on SCZ suggest these issues will be important and underscore the need for sequencing studies to have the same emphasis on statistical rigor and large sample sizes that has enabled GWAS to realize success for multiple psychiatric disorders. 154”
From Genetic architectures of psychiatric disorders: the emerging picture and its implications (2012) · cites this paper as [84]
In the citing paper
“Many studies of complex neuropsychiatric disorders have also concluded that disease-causing variants are often clustered in protein-interaction networks with a high degree of connectivity among themselves and that these clustered networks are often enriched with functional pathways relevant to brain functions [72–80].”
From An integrated analysis of genes and functional pathways for aggression in human and rodent models (2019) · cites this paper as [72]
In the citing paper
“These three studies report de novo mutations with a twofold to fourfold increase in de novo nonsense variants among affected subjects over that expected by chance”
From Autism risk factors: genes, environment, and gene-environment interactions (2012) · cites this paper as [36]
In the citing paper
“two of these studies report that spontaneous changes are correlated with paternal age”
From Autism risk factors: genes, environment, and gene-environment interactions (2012) · cites this paper as [36]
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
Fast and accurate short read alignment with Burrows-Wheeler transform
Li, H., & Durbin, R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp324 · Cited by 58,607
The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data
McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., Garimella, K., Altshuler, D., Gabriel, S., Daly, M., et al. (2010) · Genome Research · doi:10.1101/gr.107524.110 · Cited by 27,895
Self-citationA method and server for predicting damaging missense mutations.
Adzhubei, I. A., Schmidt, S., Peshkin, L., Ramensky, V. E., Gerasimova, A., Bork, P., Kondrashov, A. S., & Sunyaev, S. R. (2010) · Nature Methods · doi:10.1038/nmeth0410-248 · Cited by 13,194
Self-citationFast and accurate long-read alignment with Burrows-Wheeler transform.
Li, H., & Durbin, R. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp698 · Cited by 12,063
A framework for variation discovery and genotyping using next-generation DNA sequencing data
DePristo, M. A., Banks, E., Poplin, R. E., Garimella, K. V., Maguire, J., Hartl, C., Philippakis, A., del Angel, G., Rivas, M. A., Hanna, M., et al. (2011) · Nature Genetics · doi:10.1038/ng.806 · Cited by 11,508
Self-citationRare-Variant Association Testing for Sequencing Data with the Sequence Kernel Association Test
Wu, M. C., Lee, S., Cai, T., Li, Y., Boehnke, M., & Lin, X. (2011) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2011.05.029 · Cited by 2,373
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
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
Self-citationFunctional impact of global rare copy number variation in autism spectrum disorders
Pinto, D., Pagnamenta, A. T., Klei, L., Anney, R., Merico, D., Regan, R., Conroy, J., Magalhaes, T. R., Correia, C., Abrahams, B. S., et al. (2010) · Nature · doi:10.1038/nature09146 · Cited by 2,013 · cited 1×
“Copy number variants (CNVs), often de novo and covering multiple adjacent genes, have been identified as conferring risk 3,4 .”
BackgroundSelf-citationGenetic Heritability and Shared Environmental Factors Among Twin Pairs With Autism
Hallmayer, J. (2011) · Archives of General Psychiatry · doi:10.1001/archgenpsychiatry.2011.76 · Cited by 1,970
Cited by
Sorted by citations
Items 501–600 of 1,004
Characterization of intellectual disability and autism comorbidity through gene panel sequencing
Aspromonte, M. C., Bellini, M., Gasparini, A., Carraro, M., Bettella, E., Polli, R., Cesca, F., Bigoni, S., Boni, S., Carlet, O., et al. (2019) · Human Mutation · doi:10.1002/humu.23822 · Cited by 69
Bertoli‐Avella, A. M., Kandaswamy, K. K., Khan, S., Ordonez‐Herrera, N., Tripolszki, K., Beetz, C., Rocha, M. E., Urzi, A., Hotakainen, R., Leubauer, A., et al. (2021) · Genetics in Medicine · doi:10.1038/s41436-021-01159-0 · Cited by 62
Translational Genetics for Diagnosis of Human Disorders of Sex Development
Baxter, R. M., & Vilain, E. J. N. (2013) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-091212-153417 · Cited by 62
The female protective effect in autism spectrum disorder is not mediated by a single genetic locus
Gockley, J., Willsey, A. J., Dong, S., Dougherty, J. D., Constantino, J. N., & Sanders, S. J. (2015) · Molecular Autism · doi:10.1186/s13229-015-0014-3 · Cited by 61
“ASD is highly heritable [1], and progress has been made in identifying specific genetic loci [2-8] and the pathological mechanisms they target [9-11].”
BackgroundMicrotubules and Neurodevelopmental Disease: The Movers and the Makers
Breuss, M. W., & Keays, D. A. (2013) · Advances in experimental medicine and biology · doi:10.1007/978-94-007-7687-6_5 · Cited by 61
The struggle to find reliable results in exome sequencing data: filtering out Mendelian errors
Patel, Z. H., Kottyan, L. C., Lázaro, S., Williams, M. S., Ledbetter, D. H., Tromp, G., Rupert, A., Kohram, M., Wagner, M., Husami, A., et al. (2014) · Frontiers in Genetics · doi:10.3389/fgene.2014.00016 · Cited by 61
De novo SCN2A splice site mutation in a boy with Autism spectrum disorder
Tavassoli, T., Kolevzon, A. L., Wang, A. T., Curchack, J. T., Halpern, D. B., Schwartz, L., Soffes, S., Bush, L., Grodberg, D., Cai, G., et al. (2014) · BMC Medical Genetics · doi:10.1186/1471-2350-15-35 · Cited by 62
Self-citationUsing ALoFT to determine the impact of putative loss-of-function variants in protein-coding genes
Balasubramanian, S., Fu, Y., Pawashe, M., McGillivray, P. D., Jin, M., Liu, J., Karczewski, K. J., MacArthur, D. G., & Gerstein, M. (2017) · Nature Communications · doi:10.1038/s41467-017-00443-5 · Cited by 62
Estruch, S. B., Graham, S. A., Quevedo, M., Vino, A., Dekkers, D. H. W., Deriziotis, P., Sollis, E., Demmers, J., Poot, R. A., & Fisher, S. E. (2018) · Human Molecular Genetics · doi:10.1093/hmg/ddy035 · Cited by 67
Soler, J., Fañanás, L., Parellada, M., Krebs, M., Rouleau, G. A., & Fatjó‐Vilas, M. (2018) · Journal of Psychiatry and Neuroscience · doi:10.1503/jpn.170066 · Cited by 61
What we can learn from a genetic rodent model about autism
Möhrle, D., Fernández, M. C., Peñagarikano, O., Frick, A., Allman, B. L., & Schmid, S. (2019) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2019.12.015 · Cited by 60
Chapman, N. H., Nato, A. Q., Bernier, R., Ankenman, K., Sohi, H. K., MUNSON, J. A., Patowary, A., Archer, M., Blue, E., Webb, S. J., et al. (2015) · Human Genetics · doi:10.1007/s00439-015-1585-y · Cited by 61
Genetic research in autism spectrum disorders
Robinson, E. B., Neale, B. M., & Hyman, S. E. (2015) · Current Opinion in Pediatrics · doi:10.1097/mop.0000000000000278 · Cited by 60
Self-citationGlobal increases in both common and rare copy number load associated with autism
Girirajan, S., Johnson, R. L., Tassone, F., Balciuniene, J., Katiyar, N., Fox, K., Baker, C. A., Srikanth, A., Yeoh, K. H., Khoo, S. J., et al. (2013) · Human Molecular Genetics · doi:10.1093/hmg/ddt136 · Cited by 65
Transcriptomic Insight Into the Polygenic Mechanisms Underlying Psychiatric Disorders
Hernandez, L. M., Kim, M., Hoftman, G. D., Haney, J. R., de la Torre-Ubieta, L., Paşaniuc, B., & Gandal, M. J. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2020.06.005 · Cited by 59
Shohat, S., Ben‐David, E., & Shifman, S. (2017) · Cell Reports · doi:10.1016/j.celrep.2017.02.007 · Cited by 58
Magdalon, J., Sánchez-Sánchez, S. M., Griesi‐Oliveira, K., & Sertie, A. L. (2017) · International Journal of Molecular Sciences · doi:10.3390/ijms18030659 · Cited by 60
Dougherty, J. D., Marrus, N. M., Maloney, S. E., Yip, B. H. K., Sandin, S., Turner, T. N., Selmanovic, D., Kroll, K. L., Gutmann, D. H., Constantino, J. N., et al. (2022) · Neuron · doi:10.1016/j.neuron.2022.06.020 · Cited by 73
A role for genetic susceptibility in sporadic focal segmental glomerulosclerosis
Yu, H., Artomov, M., Brähler, S., Stander, M. C., Shamsan, G. A., Sampson, M. G., White, J. M., Kretzler, M., Miner, J. H., Jain, S. R., et al. (2016) · Journal of Clinical Investigation · doi:10.1172/jci82592 · Cited by 59
Self-citationJansen, S., Hoischen, A., Coe, B. P., Carvill, G. L., Esch, H. V., Bosch, D. G. M., Andersen, U. A., Baker, C. A., Bauters, M., Bernier, R., et al. (2017) · European Journal of Human Genetics · doi:10.1038/s41431-017-0039-5 · Cited by 64
Herborg, F., Andreassen, T. F., Berlin, F., Loland, C. J., & Gether, U. (2018) · Journal of Biological Chemistry · doi:10.1074/jbc.ra118.001753 · Cited by 56
Regulation of neuronal connectivity in the mammalian brain by chromatin remodeling
Goodman, J. V., & Bonni, A. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.04.010 · Cited by 64
Phenotypic expansion of POGZ‐related intellectual disability syndrome (White‐Sutton syndrome)
Batzir, N. A., Posey, J. E., Song, X., Coban‐Akdemir, Z., Rosenfeld, J. A., Brown, C., Chen, E., Holtrop, S., Mizerik, E. A., Moreno, M. N., et al. (2019) · American Journal of Medical Genetics Part A · doi:10.1002/ajmg.a.61380 · Cited by 60
Desachy, G., Croen, L. A., Torres, A. R., Kharrazi, M., Delorenze, G. N., Windham, G. C., Yoshida, C. K., & Weiss, L. A. (2015) · Molecular Psychiatry · doi:10.1038/mp.2014.179 · Cited by 54
Chuang, H.-C., Huang, T.-N., & Hsueh, Y.-P. (2014) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2014.00280 · Cited by 56
Murdoch, J. D., Gupta, A. R., Sanders, S., Walker, M. F., Keaney, J. F., Fernandez, T. V., Murtha, M. T., Anyanwu, S. I., Ober, G., Raubeson, M. J., et al. (2015) · PLoS Genetics · doi:10.1371/journal.pgen.1004852 · Cited by 63
Self-citationPrem, S., Millonig, J. H., & DiCicco-Bloom, E. M. (2020) · Advances in neurobiology · doi:10.1007/978-3-030-45493-7_5 · Cited by 59
Suliman-Lavie, R., Title, B., Cohen, Y., Hamada, N., Tal, M., Tal, N., Monderer‐Rothkoff, G., Gudmundsdottir, B., Gudmundsson, K. O., Keller, J. R., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19577-0 · Cited by 58
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
Trust in standards: Transitioning clinical exome sequencing from bench to bedside
Timmermans, S. (2014) · Social Studies of Science · doi:10.1177/0306312714559323 · Cited by 58
Dong, T., He, J., Wang, S., Wang, L., Cheng, Y., & Zhong, Y. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1602152113 · Cited by 62
Self-citationChen, 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
Self-citationThe population genetics of human disease: The case of recessive, lethal mutations
Amorim, C. E. G., Gao, Z., Baker, Z., Diesel, J. F., Simons, Y. B., Haque, I. S., Pickrell, J. K., & Przeworski, M. (2017) · PLoS Genetics · doi:10.1371/journal.pgen.1006915 · Cited by 58
Enriched expression of genes associated with autism spectrum disorders in human inhibitory neurons
Wang, P., Zhao, D., Lachman, H. M., & Zheng, D. (2018) · Translational Psychiatry · doi:10.1038/s41398-017-0058-6 · Cited by 61
Phenotypic and genetic spectrum of epilepsy with myoclonic atonic seizures
Tang, S. S., Addis, L., Smith, A. B., Topp, S. D., Pendziwiat, M., Mei, D., Parker, A. P. J., Agrawal, S., Hughes, E., Lascelles, K., et al. (2020) · Epilepsia · doi:10.1111/epi.16508 · Cited by 61
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
Advancing parental age and autism: multifactorial pathways
Lee, B. K., & McGrath, J. J. (2015) · Trends in Molecular Medicine · doi:10.1016/j.molmed.2014.11.005 · Cited by 62
Wang, Q., Lu, Q., & Zhao, H. (2015) · Frontiers in Genetics · doi:10.3389/fgene.2015.00149 · Cited by 62
The 16p11.2 homologs fam57ba and doc2a generate certain brain and body phenotypes
McCammon, J. M., Blaker, A. L., Chen, X., & Sive, H. L. (2017) · Human Molecular Genetics · doi:10.1093/hmg/ddx255 · Cited by 57
Dinwiddie, D. L., Soden, S. E., Saunders, C., Miller, N., Farrow, E., Smith, L. D., & Kingsmore, S. F. (2013) · BMC Medical Genomics · doi:10.1186/1755-8794-6-32 · Cited by 60
Chromodomain Helicase DNA-Binding Proteins in Stem Cells and Human Developmental Diseases
Micucci, J. A., Sperry, E. D., & Martin, D. M. (2015) · Stem Cells and Development · doi:10.1089/scd.2014.0544 · Cited by 56
Gilling, M., Rasmussen, H. B., Calløe, K., Sequeira, A. F., Baretto, M., Oliveira, G., Almeida, J., Lauritsen, M. B., Ullmann, R., Boonen, S. E., et al. (2013) · Frontiers in Genetics · doi:10.3389/fgene.2013.00054 · Cited by 55
Oligodendrocyte dysfunction due to Chd8 mutation gives rise to behavioral deficits in mice
Kawamura, A., Katayama, Y., Nishiyama, M., Shoji, H., Tokuoka, K., Ueta, Y., Miyata, M., Isa, T., Miyakawa, T., Hayashi‐Takagi, A., et al. (2020) · Human Molecular Genetics · doi:10.1093/hmg/ddaa036 · Cited by 58
The chromatin remodeling enzyme Chd4 regulates genome architecture in the mouse brain
Goodman, J. V., Yamada, T., Yang, Y., Kong, L., Wu, D. Y., Zhao, G., Gabel, H. W., & Bonni, A. (2020) · Nature Communications · doi:10.1038/s41467-020-17065-z · Cited by 57
Hanly, C., Shah, H., Au, P. Y. B., & Murias, K. (2020) · Clinical Genetics · doi:10.1111/cge.13882 · Cited by 59
Díaz‐Caneja, C. M., State, M. W., Hagerman, R. J., Jacquemont, S., Marín, Ó., Bagni, C., Umbricht, D., Simonoff, E. A., Andres-Trelles, F., Kaale, A., et al. (2021) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2021.02.020 · Cited by 56
Pintacuda, G., Hsu, Y.-H. H., Tsafou, K., Li, K. W., Martín, J. M., Riseman, J., Biagini, J. C., Ching, J. K. T., Mena, D., Gonzalez‐Lozano, M. A., et al. (2023) · Cell Genomics · doi:10.1016/j.xgen.2022.100250 · Cited by 53
Targeted Exon Capture and Sequencing in Sporadic Amyotrophic Lateral Sclerosis
Couthouis, J., Raphael, A. R., Daneshjou, R., & Gitler, A. D. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004704 · Cited by 58
Protection of the Gametes Embryo/Fetus From Prenatal Radiation Exposure
Brent, R. L. (2014) · Health Physics · doi:10.1097/hp.0000000000000235 · Cited by 57
Bonora, E., Graziano, C., Minopoli, F., Bacchelli, E., Magini, P., Diquigiovanni, C., Lomartire, S., Bianco, F., Vargiolu, M., Parchi, P., et al. (2014) · EMBO Molecular Medicine · doi:10.1002/emmm.201303235 · Cited by 54
Deep exon resequencing of DLGAP2 as a candidate gene of autism spectrum disorders
Chien, W., Gau, S. S., Liao, H., Chiu, Y., Wu, Y., Huang, Y., Tsai, W., Tsai, H.-M., & Chen, C. (2013) · Molecular Autism · doi:10.1186/2040-2392-4-26 · Cited by 57
Self-citationSingle-neuron and genetic correlates of autistic behavior in macaque
Yoshida, K., Go, Y., Kushima, I., Toyoda, A., Fujiyama, A., Imai, H., Saito, N., Iriki, A., Ozaki, N., & Isoda, M. (2016) · Science Advances · doi:10.1126/sciadv.1600558 · Cited by 55
Ostrowski, P. J., Zachariou, A., Loveday, C., Beleza‐Meireles, A., Bértoli, M., Dean, J., Douglas, A. G. L., Ellis, I., Foster, A. C., Graham, J. M., et al. (2019) · American Journal of Medical Genetics Part C Seminars in Medical Genetics · doi:10.1002/ajmg.c.31749 · Cited by 55
Noh, H. J., Ponting, C. P., Boulding, H. C., Meader, S., Betancur, C., Buxbaum, J. D., Pinto, D., Marshall, C. R., Lionel, A. C., Scherer, S. W., et al. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003523 · Cited by 54
Self-citationCreating a neural specific chromatin landscape by npBAF and nBAF complexes
Staahl, B. T., & Crabtree, G. R. (2013) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2013.09.003 · Cited by 52
Point mutations as a source of de novo genetic disease
de Ligt, J., Veltman, J. A., & Vissers, L. E. L. M. (2013) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2013.01.007 · Cited by 55
Genetics of Complex Traits in Psychiatry
Gelernter, J. E. (2014) · Biological Psychiatry · doi:10.1016/j.biopsych.2014.08.005 · Cited by 59
Wu, J., Li, Y., & Jiang, R. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004237 · Cited by 54
Tokita, M., Chen, C., Chitayat, D. A., Macnamara, E. F., Rosenfeld, J. A., Hanchard, N. A., Lewis, A. M., Brown, C., Marom, R., Shao, Y., et al. (2018) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2018.06.005 · Cited by 56
Ellingford, R. A., Panasiuk, M. J., de Meritens, E. R., Shaunak, R., Naybour, L., Browne, L., Basson, M. A., & Andreae, L. C. (2021) · Molecular Psychiatry · doi:10.1038/s41380-021-01070-9 · Cited by 53
Guidetti, C., Salvini, E., Viri, M., Deidda, F., Amoruso, A., Visciglia, A., Drago, L., Calgaro, M., Vitulo, N., Pane, M., et al. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11185263 · Cited by 54
Hussein, Y., Tripathi, U., Choudhary, A., Nayak, R., Peles, D., Rosh, I., Rabinski, T., Djamus, J., Vatine, G. D., Spiegel, R., et al. (2023) · Translational Psychiatry · doi:10.1038/s41398-023-02535-x · Cited by 54
FAT1 cadherin acts upstream of Hippo signalling through TAZ to regulate neuronal differentiation
Ahmed, A. F., de Bock, C. E., Lincz, L. F., Pundavela, J., Zouikr, I., Sontag, E., Hondermarck, H., & Thorne, R. F. (2015) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-015-1955-6 · Cited by 53
T‐Brain‐1 – A Potential Master Regulator in Autism Spectrum Disorders
Chuang, H., Huang, T., & Hsueh, Y. (2015) · Autism Research · doi:10.1002/aur.1456 · Cited by 56
Haploinsufficiency predictions without study bias
Steinberg, J., Honti, F., Meader, S., & Webber, C. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv474 · Cited by 52
Postnatal Loss of Mef2c Results in Dissociation of Effects on Synapse Number and Learning and Memory
Adachi, M., Lin, P.-Y., Pranav, H., & Monteggia, L. M. (2015) · Biological Psychiatry · doi:10.1016/j.biopsych.2015.09.018 · Cited by 55
Discovery of rare variants for complex phenotypes
Kosmicki, J. A., Churchhouse, C. L., Rivas, M. A., & Neale, B. M. (2016) · Human Genetics · doi:10.1007/s00439-016-1679-1 · Cited by 54
Self-citationNazeen, S., Palmer, N., Berger, B., & Kohane, I. S. (2016) · Genome biology · doi:10.1186/s13059-016-1084-z · Cited by 55
AutismKB 2.0: a knowledgebase for the genetic evidence of autism spectrum disorder
Yang, C., Li, J., Wu, Q., Yang, X., Huang, A. Y., Zhang, J., Ye, A. Y., Dou, Y., Yan, L., Zhou, W., et al. (2018) · Database · doi:10.1093/database/bay106 · Cited by 55
Leveraging large genomic datasets to illuminate the pathobiology of autism spectrum disorders
Searles, V. B., Wang, B., & State, M. W. (2020) · Neuropsychopharmacology · doi:10.1038/s41386-020-0768-y · Cited by 53
Lanz, T. A., Guilmette, E., Gosink, M. M., Fischer, J. E., Fitzgerald, L. W., Stephenson, D. T., & Pletcher, M. T. (2013) · Molecular Autism · doi:10.1186/2040-2392-4-45 · Cited by 52
Porciúncula, L. O., Goto‐Silva, L., Ledur, P. F., & Rehen, S. K. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.674563 · Cited by 54
Velmeshev, D., Magistri, M., & Faghihi, M. A. (2013) · Molecular Autism · doi:10.1186/2040-2392-4-32 · Cited by 55
Autism: Oxytocin, serotonin, and social reward
Dölen, G. (2015) · Social Neuroscience · doi:10.1080/17470919.2015.1087875 · Cited by 57
Huang, T., & Hsueh, Y. (2015) · Frontiers in Neuroscience · doi:10.3389/fnins.2015.00406 · Cited by 54
The clinical presentation caused by truncating CHD8 variants
Douzgou, S., Liang, H. W., Metcalfe, K., Somarathi, S., Tischkowitz, M., Mohamed, W., Kini, U., McKee, S., Yates, L., Bertoli, M., et al. (2019) · Clinical Genetics · doi:10.1111/cge.13554 · Cited by 52
Du, X., Gao, X., Liu, X., Shen, L., Wang, K., Fan, Y., Sun, Y., Luo, X., Liu, H., Wang, L., et al. (2018) · Frontiers in Genetics · doi:10.3389/fgene.2018.00594 · Cited by 57
Self-citationWhole genome sequencing in support of wellness and health maintenance
Patel, C. J., Sivadas, A., Tabassum, R., Preeprem, T., Zhao, J., Arafat, D., Chen, R., Morgan, A. A., Martin, G. S., Brigham, K. L., et al. (2013) · Genome Medicine · doi:10.1186/gm462 · Cited by 53
A Population-Specific Uncommon Variant in GRIN3A Associated with Schizophrenia
Takata, A., Iwayama, Y., Fukuo, Y., Ikeda, M., Okochi, T., Maekawa, M., Toyota, T., Yamada, K., Hattori, E., Ohnishi, T., et al. (2012) · Biological Psychiatry · doi:10.1016/j.biopsych.2012.10.024 · Cited by 51
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
Devanna, P., Chen, X., Ho, J., Gajewski, D., Smith, S. D., Gialluisi, A., Francks, C., Fisher, S. E., Newbury, D. F., & Vernes, S. C. (2017) · Molecular Psychiatry · doi:10.1038/mp.2017.30 · Cited by 52
Chromosomal microarray analysis in the genetic evaluation of 279 patients with syndromic obesity
D’Angelo, C. S., Varela, M. C., de Castro, C. I. E., Otto, P. A., Pérez, A. B. A., Lourenço, C. M., Kim, C. A., Bertola, D. R., Kok, F., Garcia-Alonso, L., et al. (2018) · Molecular Cytogenetics · doi:10.1186/s13039-018-0363-7 · Cited by 53
Kurki, M., Saarentaus, E., Pietiläinen, O. P. H., Gormley, P., Lal, D., Kerminen, S., Torniainen‐Holm, M., Hämäläinen, E., Rahikkala, E., Keski‐Filppula, R., et al. (2019) · Nature Communications · doi:10.1038/s41467-018-08262-y · Cited by 56
Self-citationRecent Advances in Understanding the Genetic Architecture of Autism
Dias, C., & Walsh, C. A. (2020) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-121219-082309 · Cited by 56
The autism-associated protein CHD8 is required for cerebellar development and motor function
Kawamura, A., Katayama, Y., Kakegawa, W., Ino, D., Nishiyama, M., Yuzaki, M., & Nakayama, K. I. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.108932 · Cited by 53
The germline mutational process in rhesus macaque and its implications for phylogenetic dating
Bergeron, L. A., Besenbacher, S., Bakker, J., Zheng, J., Li, P., Pacheco, G., Sinding, M. S., Kamilari, M., Gilbert, M. T. P., Schierup, M. H., et al. (2021) · GigaScience · doi:10.1093/gigascience/giab029 · Cited by 48
Extended Blood Group Molecular Typing and Next-Generation Sequencing
Liu, Z., Liu, M., Mercado, T., Illoh, O., & Davey, R. (2014) · Transfusion Medicine Reviews · doi:10.1016/j.tmrv.2014.08.003 · Cited by 49
Inferring causality and functional significance of human coding DNA variants
Sunyaev, S. R. (2012) · Human Molecular Genetics · doi:10.1093/hmg/dds385 · Cited by 52
Self-citationMabb, A. M., Simon, J. M., King, I. F. G., Lee, H.-M., An, L., Philpot, B. D., & Zylka, M. J. (2016) · PLoS ONE · doi:10.1371/journal.pone.0156439 · Cited by 52
The emerging roles of MicroRNAs in autism spectrum disorders
Fregeac, J., Colleaux, L., & Nguyen, L. S. (2016) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.10.018 · Cited by 52
Livingstone, M., Folkman, L., Yang, Y., Zhang, P., Mort, M. E., Cooper, D. N., Liu, Y., Stantić, B., & Zhou, Y. (2017) · Human Mutation · doi:10.1002/humu.23283 · Cited by 50
Tatsumoto, S., Go, Y., Fukuta, K., Noguchi, H., Hayakawa, T., Tomonaga, M., Hirai, H., Matsuzawa, T., Agata, K., & Fujiyama, A. (2017) · Scientific Reports · doi:10.1038/s41598-017-13919-7 · Cited by 47
Systematic reconstruction of autism biology from massive genetic mutation profiles
Luo, W., Zhang, C., Jiang, Y., & Brouwer, C. (2018) · Science Advances · doi:10.1126/sciadv.1701799 · Cited by 49
Katanin-like protein Katnal2 is required for ciliogenesis and brain development in Xenopus embryos
Willsey, H. R., Walentek, P., Exner, C. R. T., Xu, Y., Lane, A. B., Harland, R. M., Heald, R., & Santama, N. (2018) · Developmental Biology · doi:10.1016/j.ydbio.2018.08.002 · Cited by 49
Zhang, Z., Cao, M., Chang, C.-W., Wang, C., Shi, X., Zhan, X., Birnbaum, S. G., Bezprozvanny, I., Huber, K. M., & Wu, J. I. (2016) · Molecular and Cellular Biology · doi:10.1128/mcb.00534-15 · Cited by 48
The Development of the Mesoprefrontal Dopaminergic System in Health and Disease
Islam, K. U. S., Meli, N., & Blaess, S. (2021) · Frontiers in Neural Circuits · doi:10.3389/fncir.2021.746582 · Cited by 55
Nussinov, R., Yavuz, B. R., Arici, M. K., Demirel, H. C., Zhang, M., Liu, Y., Tsai, C., Jang, H., & Tunçbağ, N. (2023) · Biophysical Reviews · doi:10.1007/s12551-023-01054-9 · Cited by 47
Epigenetic Mechanisms in Autism Spectrum Disorder
Zhubi, A., Cook, E. H., Guidotti, A., & Grayson, D. R. (2014) · International review of neurobiology · doi:10.1016/b978-0-12-801311-3.00006-8 · Cited by 56
Topoisomerase 1 inhibition reversibly impairs synaptic function
Mabb, A. M., Kullmann, P. H. M., Twomey, M. A., Miriyala, J., Philpot, B. D., & Zylka, M. J. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1413204111 · Cited by 49
Early Identification of Autism Spectrum Disorder: Current Challenges and Future Global Directions
Barbaro, J., & Halder, S. (2016) · Current Developmental Disorders Reports · doi:10.1007/s40474-016-0078-6 · Cited by 53
Metadata
- Authors
- 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., Polak, P., Yoon, S., Maguire, J., Crawford, E. L., Campbell, N. G., Geller, E. T., Valladares, O., Schafer, C., Liu, H., Zhao, T., Cai, G., Lihm, J., Dannenfelser, R., Jabado, O., Peralta, Z., Nagaswamy, U., Muzny, D., Reid, J. G., Newsham, I., Wu, Y., Lewis, L., Han, Y., Voight, B. F., 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, J. R., Boone, B. E., Levy, S. E., Betancur, C., Sunyaev, S., Boerwinkle, E., Buxbaum, J. D., Cook Jr, E. H., Devlin, B., Gibbs, R. A., Roeder, K., Schellenberg, G. D., Sutcliffe, J. S., & Daly, M. J.
- Affiliations
- Harvard University (US)
- Icahn School of Medicine at Mount Sinai (US)
- Carnegie Mellon University (US)
- Broad Institute (US)
- Baylor College of Medicine (US)
- University of Pennsylvania (US)
- Vanderbilt University (US)
- Johns Hopkins University (US)
- HudsonAlpha Institute for Biotechnology (US)
- Centre National de la Recherche Scientifique (FR)
- University of Pittsburgh (US)
- Corresponding author
- Buxbaum, Joseph D.
- Roeder, Kathryn
- Daly, Mark J.
- ORCID
- Neale, Benjamin M.
- Liu, Li
- Ma’ayan, Avi
- Samocha, Kaitlin E.
- Sabo, Aniko
- Lin, Chiao-Feng
- Stevens, Christine
- Makarov, Vladimir
- Polak, Paz
- Geller, Evan T.
- Valladares, Otto
- Liu, Han
- Zhao, Tuo
- Lihm, Jayon
- Dannenfelser, Ruth
- Muzny, Donna
- Reid, Jeffrey G.
- Newsham, Irene
- Wu, Yuanqing
- Han, Yi
- Voight, Benjamin F.
- Lim, Elaine
- Rossin, Elizabeth
- Flannick, Jason
- Fromer, Menachem
- Fennell, Tim
- Garimella, Kiran
- Banks, Eric
- Gabriel, Stacey
- Levy, Shawn E.
- Betancur, Catalina
- Sunyaev, Shamil
- Boerwinkle, Eric
- Buxbaum, Joseph D.
- Devlin, Bernie
- Gibbs, Richard A.
- Roeder, Kathryn
- Schellenberg, Gerard D.
- Sutcliffe, James S.
- Daly, Mark J.
- Venue
- Nature
- Published
- 2012-04-04
- Volume
- 485
- Issue
- 7397
- Pages
- 242-245
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC3613847
- OpenAlex
- W2024663595
- Semantic Scholar
- 7597cd398ad2874b3c231d70f97956fb53b1be57
- Cited by
- 1,734
- References
- 27
- Citations by year
- 2026: 32 · 2025: 44 · 2024: 56 · 2023: 56 · 2022: 75 · 2021: 99 · 2020: 110 · 2019: 121 · 2018: 125 · 2017: 144 · 2016: 158 · 2015: 180 · 2014: 245 · 2013: 194 · 2012: 92
- FWCI
- 116.56
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- HAL (submittedVersion, other-oa)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Vanderbilt University (UL1 RR024975)
- National Institutes of Health (MH057881, HD015052, HD055751, RR024975)
- Vanderbilt Kennedy Center, Vanderbilt University Medical Center
- National Institute of Mental Health (R01MH061009, R01MH089025, R01MH089175, R01MH089004, R01MH089208, R01MH057881, MH057881, R01MH089482, R01MH084676)
- National Human Genome Research Institute (U54HG003067, HG003273, U54HG003273)
- National Institute of General Medical Sciences (T32GM007753, P50GM071558)
- National Center for Research Resources (RR024975, KL2RR024977, TL1RR024978, UL1RR024975)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (P30HD015052, P50HD055751)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Autism Spectrum Disorder Research
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetics and Neurodevelopmental Disorders
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Keywords
- autism spectrum disorder, genetic heterogeneity, locus heterogeneity, polygenic risk, parent-child trios, exome sequencing, protein-protein interaction network, SCN2A, CHD8, β-catenin signaling, p53 pathway, mutation rate, penetrance, copy number variation, genetic risk
- MeSH terms
- Autistic Disorder, DNA-Binding Proteins, Exons, Family Health, Humans, Models, Genetic, Mutation, Phenotype, Transcription Factors, Poisson Distribution, Case-Control Studies, Genetic Predisposition to Disease, Multifactorial Inheritance, Exome, Protein Interaction Maps
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