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
“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]
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
“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]
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 measured
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
“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 cited source says what the claim attributes to it.
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]
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
“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
“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
“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 701–800 of 1,004
Ku, C., Tan, E. K., & Cooper, D. N. (2013) · Journal of Medical Genetics · doi:10.1136/jmedgenet-2013-101519 · Cited by 41
Diagnosis and Management of Autism Spectrum Disorder in the Era of Genomics
Baker, E. J., & Jeste, S. (2015) · Pediatric Clinics of North America · doi:10.1016/j.pcl.2015.03.003 · Cited by 39
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
Deep Genetic Connection Between Cancer and Developmental Disorders
Qi, H., Dong, C., Chung, W. K., Wang, K., & Shen, Y. (2016) · Human Mutation · doi:10.1002/humu.23040 · Cited by 40
Possible sexually dimorphic role of miRNA and other sncRNA in ASD brain
Schumann, C. M., Sharp, F. R., Ander, B. P., & Stamova, B. S. (2017) · Molecular Autism · doi:10.1186/s13229-017-0117-0 · Cited by 39
Gene-set analysis shows association between FMRP targets and autism spectrum disorder
Jansen, A. G., Dieleman, G. C., Smit, A. B., Verhage, M., Verhulst, F. C., Polderman, T. J. C., & Posthuma, D. (2017) · European Journal of Human Genetics · doi:10.1038/ejhg.2017.55 · Cited by 40
Drosophila Studies on Autism Spectrum Disorders
Tian, Y., Zhang, Z. C., & Han, J. (2017) · Neuroscience Bulletin · doi:10.1007/s12264-017-0166-6 · Cited by 44
Campbell, N. G., Shekar, A., Aguilar, J. I., Peng, D., Navratna, V., Yang, D., Morley, A. N., Duran, A. M., Galli, G., O’Grady, B. J., et al. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1816247116 · Cited by 38
Self-citationSciara, A., Beasley, B., Crawford, J. D., Anderson, E. P., Carrasco, T., Zheng, S., Ordway, G. A., & Chandley, M. (2020) · Autism Research · doi:10.1002/aur.2284 · Cited by 42
Insights on the Role of α- and β-Tubulin Isotypes in Early Brain Development
Tantry, M. S. A., & Santhakumar, K. (2023) · Molecular Neurobiology · doi:10.1007/s12035-023-03302-1 · Cited by 38
Rare deleterious mutations of the gene EFR3A in autism spectrum disorders
Gupta, A. R., Pirruccello, M., Cheng, F., Kang, H., Fernandez, T. V., Baskin, J. M., Choi, M., Liu, L., Ercan‐Sencicek, A. G., Murdoch, J. D., et al. (2014) · Molecular Autism · doi:10.1186/2040-2392-5-31 · Cited by 35
Self-citationWerling, D. M., Lowe, J. K., Luo, R., Cantor, R. M., & Geschwind, D. H. (2014) · Molecular Autism · doi:10.1186/2040-2392-5-13 · Cited by 42
Mass spectrometry as a tool for studying autism spectrum disorder
Woods, A. G., Wetie, A. G. N., Sokolowska, I., Russell, S., Ryan, J. P., Michel, T. M., Thome, J., & Darie, C. C. (2013) · Journal of Molecular Psychiatry · doi:10.1186/2049-9256-1-6 · Cited by 36
The best-laid plans go oft awry: synaptogenic growth factor signaling in neuropsychiatric disease
Williams, A. J., & Umemori, H. (2014) · Frontiers in Synaptic Neuroscience · doi:10.3389/fnsyn.2014.00004 · Cited by 38
The phenotypic manifestations of rare genic CNVs in autism spectrum disorder
Merikangas, A. K., Segurado, R., Heron, E. A., Anney, R., Paterson, A. D., Cook, E. H., Pinto, D., Scherer, S. W., Szatmári, P., Gill, M., et al. (2014) · Molecular Psychiatry · doi:10.1038/mp.2014.150 · Cited by 41
Polycomb and trithorax opposition in development and disease
Poynter, S. T., & Kadoch, C. (2016) · Wiley Interdisciplinary Reviews Developmental Biology · doi:10.1002/wdev.244 · Cited by 43
Cunniff, M. M., Markenscoff-Papadimitriou, E., Ostrowski, J., Rubenstein, J. L. R., & Sohal, V. S. (2020) · eLife · doi:10.7554/elife.54835 · Cited by 40
Parcerisas, A., Ortega-Gascó, A., Pujadas, L., & Soriano, E. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms221810021 · Cited by 38
Mariggiò, M. A., Palumbi, R., Vinella, A., Laterza, R., Petruzzelli, M. G., Peschechera, A., Gabellone, A., Gentile, O., Vincenti, A., & Margari, L. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.705890 · Cited by 40
Doldur-Balli, F., Imamura, T., Veatch, O. J., Gong, N. N., Lim, D. C., Hart, M. P., Abel, T. G., Kayser, M. S., Brodkin, E. S., & Pack, A. I. (2022) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2022.101595 · Cited by 46
Rare variants and the oligogenic architecture of autism
Wang, T., Zhao, P. A., & Eichler, E. E. (2022) · Trends in Genetics · doi:10.1016/j.tig.2022.03.009 · Cited by 37
Matsunami, N., Hensel, C. H., Baird, L. M., Stevens, J., Otterud, B., Leppert, M., Varvil, T., Hadley, D., Glessner, J., Pellegrino, R., et al. (2014) · Molecular Autism · doi:10.1186/2040-2392-5-5 · Cited by 38
Ngo, L., Haas, M., Qu, Z., Li, S., Zenker, J., Teng, K. S.-L., Gunnersen, J. M., Breuss, M. W., Habgood, M. D., Keays, D. A., et al. (2014) · Human Molecular Genetics · doi:10.1093/hmg/ddu238 · Cited by 40
Neurogenomics of speech and language disorders: the road ahead
Deriziotis, P., & Fisher, S. E. (2013) · Genome biology · doi:10.1186/gb-2013-14-4-204 · Cited by 38
Functional characterization of TBR1 variants in neurodevelopmental disorder
den Hoed, J., Sollis, E., Venselaar, H., Estruch, S. B., Deriziotis, P., & Fisher, S. E. (2018) · Scientific Reports · doi:10.1038/s41598-018-32053-6 · Cited by 37
De novo variation in bipolar disorder
Goes, F. S., Pirooznia, M., Tehan, M., Zandi, P. P., McGrath, J. A., Wolyniec, P. S., Nestadt, G., & Pulver, A. E. (2019) · Molecular Psychiatry · doi:10.1038/s41380-019-0611-1 · Cited by 37
Excitatory neuronal CHD8 in the regulation of neocortical development and sensory-motor behaviors
Kweon, H., Jung, W. B., Im, G. H., Ryoo, J., Lee, J.-H., Do, H., Choi, Y., Song, Y.-H., Jung, H., Park, H., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.108780 · Cited by 36
Analysing an allelic series of rare missense variants of CACNA1I in a Swedish schizophrenia cohort
Báez-Nieto, D., Allen, A. S., Akers-Campbell, S., Yang, L., Budnik, N., Pupo, A., Shin, Y.-C., Genovese, G., Liao, M., Pérez‐Palma, E., et al. (2021) · Brain · doi:10.1093/brain/awab443 · Cited by 37
The 3D mutational constraint on amino acid sites in the human proteome
Li, B., Roden, D. M., & Capra, J. A. (2022) · Nature Communications · doi:10.1038/s41467-022-30936-x · Cited by 45
Transcriptional Dysregulation of Neocortical Circuit Assembly in ASD
Kwan, K. Y. (2013) · International review of neurobiology · doi:10.1016/b978-0-12-418700-9.00006-x · Cited by 39
Identifying Genes Responsible for Intellectual Disability in Consanguineous Families
Iqbal, Z., & van Bokhoven, H. (2014) · Human Heredity · doi:10.1159/000360539 · Cited by 39
Neurodevelopmental disorders and genetic testing: Current approaches and future advances
Sherr, E. H., Michelson, D. J., Shevell, M. I., Moeschler, J. B., Gropman, A. L., & Ashwal, S. (2013) · Annals of Neurology · doi:10.1002/ana.23950 · Cited by 36
Autism and EMF? Plausibility of a pathophysiological link – Part I
Herbert, M. R., & Sage, C. (2013) · Pathophysiology · doi:10.1016/j.pathophys.2013.08.001 · Cited by 37
EvoTol: a protein-sequence based evolutionary intolerance framework for disease-gene prioritization
Rackham, O. J. L., Shihab, H. A., Johnson, M. R., & Petretto, E. G. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku1322 · Cited by 35
Prontera, P., Ottaviani, V., Toccaceli, D., Rogaia, D., Ardisia, C., Romani, R., Stangoni, G., Pierini, A., & Donti, E. (2014) · American Journal of Medical Genetics Part A · doi:10.1002/ajmg.a.36741 · Cited by 36
Moreno-Ramos, O. A., Olivares, A. M., Haider, N. B., de Autismo, L. C., & Lattig, M. C. (2015) · PLoS ONE · doi:10.1371/journal.pone.0135927 · Cited by 39
Ulk4 Regulates Neural Stem Cell Pool
Liu, M., Guan, Z., Shen, Q., Flinter, F. A., Domínguez, L., Ahn, J. W., Collier, D. A., O’Brien, T., & Shen, S. (2016) · Stem Cells · doi:10.1002/stem.2423 · Cited by 34
Lammert, D. B., Middleton, F. A., Pan, J. Q., Olson, E. C., & Howell, B. W. (2017) · Journal of Neurochemistry · doi:10.1111/jnc.14045 · Cited by 36
Protein structure and phenotypic analysis of pathogenic and population missense variants in STXBP1
Suri, M., Evers, J. M. G., Laskowski, R. A., O’Brien, S., Baker, K., Clayton‐Smith, J., Dabir, T. A., Josifova, D. J., Joss, S. K., Kerr, B. A., et al. (2017) · Molecular Genetics & Genomic Medicine · doi:10.1002/mgg3.304 · Cited by 36
Copy number variation and neuropsychiatric problems in females and males in the general population
Martin, J., Tammimies, K., Karlsson, R., Lu, Y., Larsson, H., Lichtenstein, P., & Magnusson, P. K. E. (2018) · American Journal of Medical Genetics Part B Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32685 · Cited by 34
Wang, Q., Wu, H. M., Yue, W., Yan, H., Zhang, Y., Tan, L., Deng, W., Chen, Q., Yang, G., Lu, T., et al. (2018) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2018.3039 · Cited by 36
Self-citationAssociation of genes with phenotype in autism spectrum disorder
Nisar, S., Hashem, S., Bhat, A. A., Syed, N., Yadav, S. K., Azeem, M. W., Shahab Uddin, M. S., Bagga, P., Reddy, R. D., & Haris, M. (2019) · Aging · doi:10.18632/aging.102473 · Cited by 39
Fan, C.-X., Gao, Y., Liang, G., Huang, L., Wang, J., Yang, X., Shi, Y., Dräger, U. C., Zhong, M., Gao, T., et al. (2020) · Molecular Autism · doi:10.1186/s13229-020-0318-9 · Cited by 38
Chd8 haploinsufficiency impairs early brain development and protein homeostasis later in life
Jiménez, J. A., Ptacek, T., Tuttle, A. H., Schmid, R. S., Moy, S. S., Simon, J. M., & Zylka, M. J. (2020) · Molecular Autism · doi:10.1186/s13229-020-00369-8 · Cited by 37
A gradient-boosting approach for filtering de novo mutations in parent–offspring trios
Liu, Y., Li, B., Tan, R., Zhu, X., & Wang, Y. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu141 · Cited by 35
Long genes linked to autism spectrum disorders harbor broad enhancer-like chromatin domainsPreprint
Zhao, Y.-T., Kwon, D. Y., Johnson, B. S., Fasolino, M., Lamonica, J. M., Kim, Y. J., Zhao, B. S., He, C., Vahedi, G., Kim, T. H., et al. (2018) · Genome Research · doi:10.1101/gr.233775.117 · Cited by 36
Prioritizing genetic variants in GWAS with lasso using permutation-assisted tuning
Yang, S., Wen, J., Eckert, S. T., Wang, Y., Liu, D. J., Wu, R., Li, R., & Zhan, X. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa229 · Cited by 35
Whole genome sequence study of cannabis dependence in two independent cohorts
Gizer, I. R., Bizon, C., Gilder, D. A., Ehlers, C. L., & Wilhelmsen, K. C. (2017) · Addiction Biology · doi:10.1111/adb.12489 · Cited by 36
Evgrafov, O. V., Armoskus, C., Wrobel, B., Spitsyna, V. N., Souaiaia, T., Herstein, J. S., Walker, C., Nguyen, J. D. K., Camarena, A., Weitz, J., et al. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2020.01.005 · Cited by 34
Study, C., Nambot, S., Faivre, L. O., Mirzaa, G., Thévenon, J., Bruel, A., Mosca‐Boidron, A., Masurel, A., Goldenberg, A., Meur, N. L., et al. (2020) · European Journal of Human Genetics · doi:10.1038/s41431-020-0571-6 · Cited by 35
Genetic landscape of autism spectrum disorder in Vietnamese children
Kiên, T. T., Le, V. S., Bui, H. T. P., Do, D. H., Ly, H. T. T., Nguyen, H. T., Dao, L. T. M., Nguyen, T. H., Vu, D. M., Ha, L. T., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-61695-8 · Cited by 37
Canalization and Robustness in Human Genetics and Disease
Gibson, G. C., & Lacek, K. A. (2020) · Annual Review of Genetics · doi:10.1146/annurev-genet-022020-022327 · Cited by 34
Rare and de novo coding variants in chromodomain genes in Chiari I malformation
Sadler, B. E., Wilborn, J., Antunes, L., Kuensting, T., Hale, A. T., Gannon, S. R., McCall, K., Cruchaga, C., Harms, M. B., Voisin, N., et al. (2020) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2020.12.001 · Cited by 37
Developmental disabilities across the world: A scientometric review from 1936 to 2020
Carollo, A., Bonassi, A., Lim, M., Gabrieli, G., Setoh, P., Dimitriou, D., Aryadoust, V., & Esposito, G. (2021) · Research in Developmental Disabilities · doi:10.1016/j.ridd.2021.104031 · Cited by 35
Gillotin, S., Sahni, V., Lepko, T., Hanspal, M. A., Swartz, J., Alexopoulou, Z., & Marshall, F. H. (2021) · Ageing Research Reviews · doi:10.1016/j.arr.2021.101447 · Cited by 34
Pescosolido, M. F., Uzun, E. D. G., Nagpal, S., & Morrow, E. M. (2013) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1016/j.jaac.2013.01.003 · Cited by 35
A Genome Scan for Loci Shared by Autism Spectrum Disorder and Language Impairment
Bartlett, C. W., Hou, L., Flax, J. F., Hare, A., Cheong, S. Y., Fermano, Z., Zimmerman-Bier, B., Cartwright, C., Azaro, M. A., Buyske, S. G., et al. (2013) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2013.12081103 · Cited by 35
Zemet, R., den Veyver, I. B. V., & Stankiewicz, P. (2022) · Prenatal Diagnosis · doi:10.1002/pd.6144 · Cited by 35
Thongkorn, S., Kanlayaprasit, S., Kasitipradit, K., Lertpeerapan, P., Panjabud, P., Hu, V. W., Jindatip, D., & Sarachana, T. (2023) · Biology of Sex Differences · doi:10.1186/s13293-023-00496-w · Cited by 33
Shi, X., Lu, C., Corman, A., Nikish, A., Zhou, Y., Platt, R. J., Iossifov, I. H., Zhang, F., Pan, J. Q., & Sanjana, N. E. (2023) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2023.09.004 · Cited by 35
Large-scale neurophysiology and single-cell profiling in human neuroscience
Lee, A. T., Chang, E. F., Paredes, M. F., & Nowakowski, T. J. (2024) · Nature · doi:10.1038/s41586-024-07405-0 · Cited by 35
Helsmoortel, C., Vandeweyer, G., Ordoukhanian, P. T., Nieuwerburgh, F. V., der Aa, N. V., & Kooy, R. F. (2014) · Clinical Genetics · doi:10.1111/cge.12470 · Cited by 34
Next‐generation sequencing in schizophrenia and other neuropsychiatric disorders
Schreiber, M. A., Dorschner, M. O., & Tsuang, D. W. (2013) · American Journal of Medical Genetics Part B Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32156 · Cited by 34
Coherent Somatic Mutation in Autoimmune Disease
Ross, K. A. (2014) · PLoS ONE · doi:10.1371/journal.pone.0101093 · Cited by 34
Sequencing ASMT Identifies Rare Mutations in Chinese Han Patients with Autism
Wang, L., Li, J., Ruan, Y., Lu, T., Liu, C., Jia, M., Yue, W., Liu, J., Bourgeron, T., & Zhang, D. (2013) · PLoS ONE · doi:10.1371/journal.pone.0053727 · Cited by 36
Self-citationZhu, X., Padmanabhan, R., Copeland, B. R., Bridgers, J., Ren, Z., Kamalakaran, S., O’Driscoll-Collins, A., Berkovic, S. F., Scheffer, I. E., Poduri, A., et al. (2017) · PLoS Genetics · doi:10.1371/journal.pgen.1007104 · Cited by 35
Torrico, B., Shaw, A. D., Mosca, R., Vivó-Luque, N., Hervás, A., Fernàndez-Castillo, N., Aloy, P., Bayés, M., Fullerton, J. M., Cormand, B., et al. (2019) · Journal of Psychiatry and Neuroscience · doi:10.1503/jpn.180184 · Cited by 34
Mohajeri, K., Yadav, R., D’haene, E., Boone, P. M., Erdin, S., Gao, D., Moysés‐Oliveira, M., Bhavsar, R., Currall, B., O’Keefe, K., et al. (2022) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2022.09.015 · Cited by 30
The Genetics of Neuropsychiatric Diseases: Looking In and Beyond the Exome
Heinzen, E. L., Neale, B. M., Traynelis, S. F., Allen, A. S., & Goldstein, D. B. (2015) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-071714-034136 · Cited by 33
Self-citationTranscriptional Regulation of Olfactory Bulb Neurogenesis
Díaz‐Guerra, E., Pignatelli, J., Nieto‐Estévez, V., & Vicario‐Abejón, C. (2013) · The Anatomical Record · doi:10.1002/ar.22733 · Cited by 33
Buxbaum, J. D., Bolshakova, N., Brownfeld, J. M., Anney, R., Bender, P. K., Bernier, R., Cook, E. H., Coon, H. H., Cuccaro, M. L., Freitag, C. M., et al. (2014) · Molecular Autism · doi:10.1186/2040-2392-5-34 · Cited by 32
Self-citationNebel, R. A., Zhao, D., Pedrosa, E., Kirschen, J., Lachman, H. M., Zheng, D., & Abrahams, B. S. (2016) · PLoS ONE · doi:10.1371/journal.pone.0148039 · Cited by 34
Breckpot, J., Vercruyssen, M., Weyts, E., Vandevoort, S., D'Haenens, G., Buggenhout, G. V., Leempoels, L., Brischoux‐Boucher, E., Maldergem, L. V., Renieri, A., et al. (2016) · European Journal of Medical Genetics · doi:10.1016/j.ejmg.2016.08.003 · Cited by 33
Incorporating Functional Information in Tests of Excess De Novo Mutational Load
Jiang, Y., Han, Y., Petrovski, S., Owzar, K., Goldstein, D. B., & Allen, A. S. (2015) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2015.06.013 · Cited by 30
Self-citationSekido, R. (2014) · Advances in genetics · doi:10.1016/b978-0-12-800222-3.00007-3 · Cited by 34
Eom, T.-Y., Stanco, A., Guo, J., Wilkins, G., Deslauriers, D., Yan, J. E., Monckton, C., Blair, J., Oon, E., Perez, A., et al. (2014) · Developmental Cell · doi:10.1016/j.devcel.2014.11.022 · Cited by 32
Yook, C., Kim, K., Kim, D., Kang, H., Kim, S.-G., Kim, E., & Kim, S. Y. (2019) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2019.00241 · Cited by 33
CHD1, a multifaceted epigenetic remodeler in prostate cancer
Li, H., Gigi, L., & Zhao, D. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1123362 · Cited by 33
CHD8 intragenic deletion associated with autism spectrum disorder
Stolerman, E. S., Smith, B. T., Chaubey, A., & Jones, J. R. (2016) · European Journal of Medical Genetics · doi:10.1016/j.ejmg.2016.02.010 · Cited by 30
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
Autism Spectrum Disorder-Related Syndromes: Modeling with Drosophila and Rodents
Ueoka, I., Pham, H. T. N., Matsumoto, K., & Yamaguchi, M. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20174071 · Cited by 39
Malvaez, M. (2019) · Journal of Neuroscience Research · doi:10.1002/jnr.24552 · Cited by 37
Linking Autism Risk Genes to Disruption of Cortical Development
García-Forn, M., Boitnott, A., Akpinar, Z., & Rubeis, S. D. (2020) · Cells · doi:10.3390/cells9112500 · Cited by 35
A Candidate Gene Association Study Further Corroborates Involvement of Contactin Genes in Autism
Poot, M. (2014) · Molecular Syndromology · doi:10.1159/000362891 · Cited by 36
Sex-biased gene expression in the developing brain: implications for autism spectrum disorders
Ziats, M. N., & Rennert, O. M. (2013) · Molecular Autism · doi:10.1186/2040-2392-4-10 · Cited by 35
Bartholdi, D., Stray‐Pedersen, A., Azzarello-Burri, S. M., Kibæk, M., Kirchhoff, M., Oneda, B., Rødningen, O. K., Schmitt‐Mechelke, T., Rauch, A. M., & Kjærgaard, S. (2014) · American Journal of Medical Genetics Part A · doi:10.1002/ajmg.a.36439 · Cited by 32
Liu, Y., Liang, Y., Çiçek, A. E., Li, Z., Li, J., Muhle, R., Krenzer, M., Mei, Y., Wang, Y., Knoblauch, N., et al. (2018) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2018.03.023 · Cited by 34
Self-citationTranslating human genetics into mouse: The impact of ultra‐rapid in vivo genome editing
Aida, T., Imahashi, R., & Tanaka, K. (2013) · Development Growth & Differentiation · doi:10.1111/dgd.12101 · Cited by 33
Koshimizu, E., Miyatake, S., Okamoto, N., Nakashima, M., Tsurusaki, Y., Miyake, N., Saitsu, H., & Matsumoto, N. (2013) · PLoS ONE · doi:10.1371/journal.pone.0074167 · Cited by 33
Brick, D. J., Nethercott, H. E., Montesano, S., Bañuelos, M. G., Stover, A. E., Schutte, S. S., O’Dowd, D. K., Hagerman, R. J., Ono, M. Y., Hessl, D. R., et al. (2014) · Stem Cells Translational Medicine · doi:10.5966/sctm.2014-0073 · Cited by 31
Genetic architecture, epigenetic influence and environment exposure in the pathogenesis of Autism
Yu, L., Wu, Y., & Wu, B.-L. (2015) · Science China Life Sciences · doi:10.1007/s11427-015-4941-1 · Cited by 31
Self-citationSomatic mutations in disorders with disrupted brain connectivity
Lee, J. H. (2016) · Experimental & Molecular Medicine · doi:10.1038/emm.2016.53 · Cited by 29
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
Self-citationDissanayake, C., Searles, J., Barbaro, J., Sadka, N., & Lawson, L. P. (2019) · Autism Research · doi:10.1002/aur.2074 · Cited by 31
Comprehensive molecular testing in patients with high functioning autism spectrum disorder
Álvarez‐Mora, M. I., Calvo, R. M., Navarro, O. P., Madrigal, I., Quintela, I., Amigo, J., Martinez-Elurbe, D., Linder-Lucht, M., Laín, G. A., Carracedo, Á., et al. (2016) · Mutation research. Fundamental and molecular mechanisms of mutagenesis · doi:10.1016/j.mrfmmm.2015.12.006 · Cited by 32
Autism Linked to Increased Oncogene Mutations but Decreased Cancer Rate
Darbro, B. W., Singh, R., Zimmerman, M. B., Mahajan, V. B., & Bassuk, A. G. (2016) · PLoS ONE · doi:10.1371/journal.pone.0149041 · Cited by 31
Chen, L., Zhou, A. S., & West, A. E. (2017) · Journal of Neurophysiology · doi:10.1152/jn.00067.2017 · Cited by 35
Gao, Y., Shonai, D., Trn, M., Zhao, J., Soderblom, E. J., Garcia-Moreno, S. A., Gersbach, C. A., Wetsel, W. C., Dawson, G., Velmeshev, D., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-51037-x · Cited by 33
Self-citationGlutamatergic candidate genes in autism spectrum disorder: an overview
Chiocchetti, A. G., Bour, H. S., & Freitag, C. M. (2014) · Journal of Neural Transmission · doi:10.1007/s00702-014-1161-y · Cited by 31
A common molecular signature in ASD gene expression: following Root 66 to autism
Díaz-Beltrán, L., Esteban, F. J., & Wall, D. P. (2016) · Translational Psychiatry · doi:10.1038/tp.2015.112 · Cited by 30
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