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
“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 measured
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]
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
“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]
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
“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]
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 11,457
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 801–900 of 1,004
Ueoka, I., Kawashima, H., Konishi, A., Aoki, M., Tanaka, R., Yoshida, H., Maeda, T., Ozaki, M., & Yamaguchi, M. (2017) · Experimental Neurology · doi:10.1016/j.expneurol.2017.10.027 · Cited by 31
Germline de novo mutation rates on exons versus introns in humans
Rodriguez-Galindo, M., Casillas, S., Weghorn, D., & Barbadilla, A. (2020) · Nature Communications · doi:10.1038/s41467-020-17162-z · Cited by 32
Chohan, M. O., Esses, S., Haft, J., Ahmari, S. E., & Veenstra-VanderWeele, J. M. (2020) · Psychopharmacology · doi:10.1007/s00213-020-05635-4 · Cited by 30
SENP1 in the Retrosplenial Agranular Cortex Regulates Core Autistic-Like Symptoms in Mice
Yang, K., Shi, Y., Du, X., Wang, J., Zhang, Y., Shan, S., Yuan, Y., Wang, R., Zhou, C., Liu, Y., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109939 · Cited by 32
Sanders, B., D’Andrea, D., Collins, M. O., Rees, E., Steward, T. G. J., Zhu, Y., Chapman, G., Legge, S. E., Pardiñas, A. F., Harwood, A. J., et al. (2022) · Nature Communications · doi:10.1038/s41467-021-27601-0 · Cited by 34
Kukharsky, M. S., Quintiero, A., Matsumoto, T., Matsukawa, K., An, H., Hashimoto, T., Iwatsubo, T., Buchman, V. L., & Shelkovnikova, T. A. (2015) · Molecular Neurodegeneration · doi:10.1186/s13024-015-0014-y · Cited by 29
Novel transcriptional networks regulated by CLOCK in human neuronsPreprint
Fontenot, M. R., Berto, S., Liu, Y., Werthmann, G. C., Douglas, C., Usui, N., Gleason, K., Tamminga, C. A., Takahashi, J. S., & Konopka, G. (2017) · Genes & Development · doi:10.1101/gad.305813.117 · Cited by 30
自閉症関連タンパク質CHD8はC/EBPβと協調して脂肪分化を制御する
Kita, Y. (2018) · Institutional Repositories DataBase (IRDB) · Cited by 30
Chromatin regulators, phenotypic robustness, and autism risk
Suliman, R., Ben-David, E., & Shifman, S. (2014) · Frontiers in Genetics · doi:10.3389/fgene.2014.00081 · Cited by 28
CHD8 mutations increase gliogenesis to enlarge brain size in the nonhuman primate
Li, B., Zhao, H., Tu, Z., Yang, W., Han, R., Wang, L., Luo, X., Pan, M., Chen, X., Zhang, J., et al. (2023) · Cell Discovery · doi:10.1038/s41421-023-00525-3 · Cited by 31
Self-citationBersten, D. C., Bruning, J. B., Peet, D. J., & Whitelaw, M. L. (2014) · PLoS ONE · doi:10.1371/journal.pone.0085768 · Cited by 28
D’Annessa, I., Coletta, A., Sutthibutpong, T., Mitchell, J. S., Chillemi, G., Harris, S. A., & Desideri, A. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku654 · Cited by 29
Martin, E. A., Woodruff, D. M., Rawson, R. L., & Williams, M. E. (2017) · eNeuro · doi:10.1523/eneuro.0088-17.2017 · Cited by 29
Recent genetic and functional insights in autism spectrum disorder
Nakanishi, M., Anderson, M. P., & Takumi, T. (2019) · Current Opinion in Neurology · doi:10.1097/wco.0000000000000718 · Cited by 31
Pirone, A., Alexander, J., Koenig, J. B., Cook-Snyder, D. R., Palnati, M., Wickham, R. J., Eden, L. W., Shrestha, N., Reijmers, L. G. J. E., Biederer, T., et al. (2018) · Frontiers in Synaptic Neuroscience · doi:10.3389/fnsyn.2018.00035 · Cited by 28
Therapeutic strategies for autism: targeting three levels of the central dogma of molecular biology
Hong, D., & Iakoucheva, L. M. (2023) · Translational Psychiatry · doi:10.1038/s41398-023-02356-y · Cited by 32
Pleiotropy of autism-associated chromatin regulators
Lasser, M. C., Sun, N., Xu, Y., Wang, S., Drake, S., Law, K., Gonzalez, S., Wang, B., Drury, V., Castillo, O., et al. (2023) · Development · doi:10.1242/dev.201515 · Cited by 31
Lagunas, T., Plassmeyer, S. P., Fischer, A. D., Friedman, R. Z., Rieger, M. A., Selmanovic, D., Sarafinovska, S., Sol, Y. K., Kasper, M. J., Fass, S. B., et al. (2023) · Communications Biology · doi:10.1038/s42003-023-05483-w · Cited by 28
From genetic discovery to future personalized health research
Palotie, A. V., Widén, E., & Ripatti, S. (2012) · New Biotechnology · doi:10.1016/j.nbt.2012.11.013 · Cited by 27
Genetic Variation across Phenotypic Severity of Autism
Claudio Toma, A. (2020) · Trends in Genetics · doi:10.1016/j.tig.2020.01.005 · Cited by 32
Jacobsen, K. K., Kleppe, R., Johansson, S., Zayats, T., & Haavik, J. (2015) · American Journal of Medical Genetics Part B Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32339 · Cited by 28
Zhang, L., & Wei, X. (2022) · Journal of Neuroscience · doi:10.1523/jneurosci.0059-22.2022 · Cited by 36
Vrieze, S. I., Malone, S. M., Vaidyanathan, U., Kwong, A. M., Kang, H. M., Zhan, X., Flickinger, M., Irons, D. E., Jun, G., Locke, A. E., et al. (2014) · Psychophysiology · doi:10.1111/psyp.12350 · Cited by 30
Self-citationGenome-Wide Association Studies of Autism
Glessner, J., Connolly, J. J., & Hakonarson, H. H. (2014) · Current Behavioral Neuroscience Reports · doi:10.1007/s40473-014-0023-0 · Cited by 26
Application of CRISPR/Cas9 to the study of brain development and neuropsychiatric disease
Powell, S. K., Gregory, J. K., Akbarian, S., & Brennand, K. (2017) · Molecular and Cellular Neuroscience · doi:10.1016/j.mcn.2017.05.007 · Cited by 30
A Cell Type-Specific Expression Signature Predicts Haploinsufficient Autism-Susceptibility Genes
Zhang, C., & Shen, Y. (2016) · Human Mutation · doi:10.1002/humu.23147 · Cited by 25
Liang, S., Li, Z., Wang, Y., Li, X., Yang, X., Zhan, X., Huang, Y., Gao, Z., Zhang, M., Sun, C., et al. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00712 · Cited by 28
Kirrel3-Mediated Synapse Formation Is Attenuated by Disease-Associated Missense Variants
Taylor, M. R., Martin, E. A., Sinnen, B. L., Trilokekar, R., Ranza, E., Antonarakis, S. E., & Williams, M. E. (2020) · Journal of Neuroscience · doi:10.1523/jneurosci.3058-19.2020 · Cited by 27
Next Generation Sequencing of 134 Children with Autism Spectrum Disorder and Regression
Yin, J., Chun, C.-A., Zavadenko, N. N., Pechatnikova, N. L., Naumova, O. Y., Doddapaneni, H., Hu, J., Muzny, D. M., Schaaf, C. P., & Grigorenko, E. L. (2020) · Genes · doi:10.3390/genes11080853 · Cited by 28
Self-citationReciprocal control of translation and transcription in autism spectrum disorder
Longo, F., & Klann, E. (2021) · EMBO Reports · doi:10.15252/embr.202052110 · Cited by 29
Biological implications of genetic variations in autism spectrum disorders from genomics studies
Zhang, Y., Liu, X., Guo, R., Xu, W., Guo, Q. J., Hao, C., Ni, X., & Li, W. (2021) · Bioscience Reports · doi:10.1042/bsr20210593 · Cited by 33
Mutation of key signaling regulators of cerebrovascular development in vein of Galen malformations
Zhao, S., Mekbib, K. Y., van der Ent, M. A., Allington, G., Prendergast, A. E., Chau, J. E., Smith, H. L., Shohfi, J., Ocken, J., Durán, D., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-43062-z · Cited by 28
Tserendulam, B., Schulz, Y., Wolf, S., Rabe, T., Oellerich, T., Urlaub, H., Schaefer, I., & Pauli, S. (2012) · PLoS ONE · doi:10.1371/journal.pone.0052640 · Cited by 28
Loviglio, M. N., Beck, C. R., White, J. J., Leleu, M., Harel, T., Guex, N., Niknejad, A., Bi, W., Chen, E. S., Crespo, I., et al. (2016) · Genome Medicine · doi:10.1186/s13073-016-0359-z · Cited by 30
Self-citationGenetics implicate common mechanisms in autism and schizophrenia: synaptic activity and immunity
Liu, X., Li, Z., Fan, C., Zhang, D., & Chen, J. (2017) · Journal of Medical Genetics · doi:10.1136/jmedgenet-2016-104487 · Cited by 27
Shu, G., Kramár, E. A., López, A. J., Huynh, G., Wood, M. A., & Kwapis, J. L. (2018) · Learning & Memory · doi:10.1101/lm.046920.117 · Cited by 29
Network assisted analysis to reveal the genetic basis of autism
Liu, L., Lei, J., & Roeder, K. (2015) · The Annals of Applied Statistics · doi:10.1214/15-aoas844 · Cited by 30
Self-citationFunctional genomics of human brain development and implications for autism spectrum disorders
Ziats, M. N., Grosvenor, L. P., & Rennert, O. M. (2015) · Translational Psychiatry · doi:10.1038/tp.2015.153 · Cited by 27
Family-based exome sequencing and case-control analysis implicate CEP41 as an ASD gene
Patowary, A., Won, S. Y., Oh, S. J., Nesbitt, R., Archer, M., Nickerson, D. A., Raskind, W. H., Bernier, R., Lee, J. E., & Brkanac, Z. (2019) · Translational Psychiatry · doi:10.1038/s41398-018-0343-z · Cited by 30
Nakagawa, T., Hattori, S., Nobuta, R., Kimura, R., Nakagawa, M., Matsumoto, M., Nagasawa, Y., Funayama, R., Miyakawa, T., Inada, T., et al. (2020) · iScience · doi:10.1016/j.isci.2020.101030 · Cited by 27
Animal Models for OCD Research
Chamberlain, B. L., & Ahmari, S. E. (2021) · Current topics in behavioral neurosciences · doi:10.1007/7854_2020_196 · Cited by 29
Chapman, G., Alsaqati, M., Lunn, S., Singh, T., Linden, S. C., Linden, D. E. J., van den Bree, M. B. M., Ziller, M., Owen, M. J., Hall, J., et al. (2021) · Molecular Psychiatry · doi:10.1038/s41380-021-01182-2 · Cited by 28
Coding and noncoding variants in EBF3 are involved in HADDS and simplex autism
Padhi, E. M., Hayeck, T. J., Cheng, Z., Chatterjee, S., Mannion, B. J., Byrska‐Bishop, M. S., Willems, M., Pinson, L., Redon, S., Bénech, C., et al. (2021) · Human Genomics · doi:10.1186/s40246-021-00342-3 · Cited by 28
Pescosolido, M. F., Yang, U., Sabbagh, M. F., & Morrow, E. M. (2012) · Dialogues in Clinical Neuroscience · doi:10.1080/19585969.2022.12130883 · Cited by 28
CHD8 suppression impacts on histone H3 lysine 36 trimethylation and alters RNA alternative splicing
Kerschbamer, E., Arnoldi, M., Tripathi, T., Pellegrini, M., Maturi, S., Erdin, S., Salviato, E., Leva, F. D., Sebestyén, E., Dassi, E., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac1134 · Cited by 27
Steinberg, K. M., Ramachandran, D., Patel, V. C., Shetty, A. C., Cutler, D. J., & Zwick, M. E. (2012) · Molecular Autism · doi:10.1186/2040-2392-3-8 · Cited by 30
An interaction network of mental disorder proteins in neural stem cells
Moen, M. J., Adams, H. H. H., Brandsma, J. H., Dekkers, D. H. W., Akinci, U., Karkampouna, S., Quevedo, M., Kockx, C. E. M., Özgür, Z., van IJcken, W. F. J., et al. (2017) · Translational Psychiatry · doi:10.1038/tp.2017.52 · Cited by 27
Next-Generation Sequencing in Autism Spectrum DisorderPreprint
Sanders, S. (2018) · Cold Spring Harbor Perspectives in Medicine · doi:10.1101/cshperspect.a026872 · Cited by 30
State, M. W., & Thapar, A. (2015) · Wiley eBooks · doi:10.1002/9781118381953.ch24 · Cited by 27
Faja, S., & Dawson, G. (2017) · Wiley eBooks · doi:10.1002/9781394258932.ch22 · Cited by 29
Vardarajan, B. N., Eran, A., Jung, J.-Y., Kunkel, L. M., & Wall, D. P. (2013) · Translational Psychiatry · doi:10.1038/tp.2013.38 · Cited by 26
Lima, L. D. A., dos Santos, A. C. F., Belangero, S. I., Gadelha, A., Bressan, R. A., Salum, G. A., Pan, P. M., Moriyama, T. S., Graeff‐Martins, A. S., Tamanaha, A. C., et al. (2016) · Scientific Reports · doi:10.1038/srep22851 · Cited by 27
Exploiting aberrant mRNA expression in autism for gene discovery and diagnosis
Guan, J., Yang, E., Yang, J., Zeng, Y., Ji, G., & Cai, J. J. (2016) · Human Genetics · doi:10.1007/s00439-016-1673-7 · Cited by 28
Brain transcriptome atlases: a computational perspective
Mahfouz, A. E., Huisman, S. M. H., Lelieveldt, B. P. F., & Reinders, M. J. T. (2016) · Brain Structure and Function · doi:10.1007/s00429-016-1338-2 · Cited by 27
Millan, M. I. P., Vishnopolska, S. A., Daly, A. Z., Bustamante, J. P., Seilicovich, A., Bergadá, I., Braslavsky, D. G., Keselman, A. C., Lemons, R. M., Mortensen, A. H., et al. (2018) · Molecular Genetics & Genomic Medicine · doi:10.1002/mgg3.395 · Cited by 25
Alexander, J., Pirone, A., & Jacob, M. H. (2020) · Frontiers in Synaptic Neuroscience · doi:10.3389/fnsyn.2020.00014 · Cited by 25
Reilly, J., Gallagher, L., Leader, G., & Shen, S. (2020) · PLoS ONE · doi:10.1371/journal.pone.0242773 · Cited by 25
Neuroadaptations and TGF-β signaling: emerging role in models of neuropsychiatric disorders
Mitra, S., Werner, C. T., & Dietz, D. (2021) · Molecular Psychiatry · doi:10.1038/s41380-021-01186-y · Cited by 29
Mani, S., Jindal, D., & Singh, M. (2022) · Current Gene Therapy · doi:10.2174/1566523222666220328142427 · Cited by 30
Rhythmic Relating: Bidirectional Support for Social Timing in Autism Therapies
Daniel, S. W., Wimpory, D. C., Delafield‐Butt, J. T., Malloch, S. N., Holck, U., Geretsegger, M., Tortora, S., Osborne, N., Schögler, B., Koch, S. C., et al. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.793258 · Cited by 34
Yuan, B., Wang, M., Wu, X., Cheng, P., Zhang, R., Zhang, R., Yu, S., Zhang, J., Du, Y., Wang, X., et al. (2023) · Neuroscience Bulletin · doi:10.1007/s12264-023-01037-6 · Cited by 28
Etiological Heterogeneity in Autism Spectrum Disorders
Betancur, C., & Coleman, M. (2012) · Elsevier eBooks · doi:10.1016/b978-0-12-391924-3.00008-9 · Cited by 28
Self-citationQuantitative Profiling of Brain Lipid Raft Proteome in a Mouse Model of Fragile X Syndrome
Kalinowska, M., Castillo, C., & Francesconi, A. (2015) · PLOS ONE · doi:10.1371/journal.pone.0121464 · Cited by 26
Campbell, N. G., Zhu, C.-B., Lindler, K. M., Yaspan, B. L., Kistner‐Griffin, E., Hewlett, W. A., Tate, C. G., Blakely, R., & Sutcliffe, J. S. (2013) · Molecular Autism · doi:10.1186/2040-2392-4-28 · Cited by 28
Self-citationPisansky, M. T., Young, A. E., O’Connor, M. B., Gottesman, I. I., Bagchi, A., & Gewirtz, J. C. (2017) · Translational Psychiatry · doi:10.1038/tp.2017.111 · Cited by 26
Ciernia, A. V., Kramár, E. A., Matheos, D. P., Havekes, R., Hemstedt, T. J., Magnan, C. N., Sakata, K., Tran, A. V., Azzawi, S., López, A. J., et al. (2017) · Learning & Memory · doi:10.1101/lm.044602.116 · Cited by 28
Correlation of mutated gene and signalling pathways in ASD
Apte, M., & Kumar, A. B. (2023) · IBRO Neuroscience Reports · doi:10.1016/j.ibneur.2023.03.011 · Cited by 30
Identifying Essential Cell Types and Circuits in Autism Spectrum Disorders
Maloney, S. E., Rieger, M. A., & Dougherty, J. D. (2013) · International review of neurobiology · doi:10.1016/b978-0-12-418700-9.00003-4 · Cited by 26
A framework for the detection of de novo mutations in family-based sequencing data
Francioli, L. C., Cretu-Stancu, M., Garimella, K. V., Fromer, M., Kloosterman, W. P., Samocha, K. E., Neale, B. M., Daly, M. J., Banks, E., DePristo, M. A., et al. (2016) · European Journal of Human Genetics · doi:10.1038/ejhg.2016.147 · Cited by 27
Self-citationSerologic Markers of Autism Spectrum Disorder
Khramova, T., Kaysheva, A. L., Ivanov, Y. D., Pleshakova, T. O., Iourov, I. Y., Vorsanova, S. G., Yurov, Y. B., Schetkin, A. A., & Archakov, A. I. (2017) · Journal of Molecular Neuroscience · doi:10.1007/s12031-017-0950-9 · Cited by 25
Timpano, S., & Picketts, D. J. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00885 · Cited by 28
Pintacuda, G., Martín, J. M., & Eggan, K. C. (2021) · Molecular Autism · doi:10.1186/s13229-021-00417-x · Cited by 26
Neuronal activity-induced BRG1 phosphorylation regulates enhancer activation
Kim, B., Luo, Y., Zhan, X., Zhang, Z., Shi, X., Yi, J., Xuan, Z., & Wu, J. I. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109357 · Cited by 28
Ronald, A., & Hoekstra, R. A. (2014) · Springer eBooks · doi:10.1007/978-1-4614-9509-3_2 · Cited by 24
Biological pathways and networks implicated in psychiatric disorders
Sullivan, P. F., & Posthuma, D. (2014) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2014.09.003 · Cited by 25
Calvo, A., Moglia, C., Canosa, A., Brunetti, M., Barberis, M., Traynor, B. J., Carrara, G., Valentini, C., Restagno, G., & Chiò, A. (2014) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2013.12.028 · Cited by 25
Advancing paternal age and psychiatric disorders
Frans, E. M., MacCabe, J. H., & Reichenberg, A. (2015) · World Psychiatry · doi:10.1002/wps.20190 · Cited by 28
Mutations in TBR1 gene leads to cortical malformations and intellectual disability
Végas, N., Cavallin, M., Kleefstra, T., de Boer, L., Philbert, M., Maillard, C., Boddaert, N., Münnich, A., Hubert, L., Béry, A., et al. (2018) · European Journal of Medical Genetics · doi:10.1016/j.ejmg.2018.09.012 · Cited by 25
Autism‐linked CHD gene expression patterns during development predict multi‐organ disease phenotypes
Kasah, S., Oddy, C., & Basson, M. A. (2018) · Journal of Anatomy · doi:10.1111/joa.12889 · Cited by 25
Significant Strain Variation in the Mutation Spectra of Inbred Laboratory Mice
Dumont, B. L. (2019) · Molecular Biology and Evolution · doi:10.1093/molbev/msz026 · Cited by 27
Gene expression in maturing neurons: regulatory mechanisms and related neurodevelopmental disorders.
Ding, B. (2015) · PubMed · Cited by 24
Cross-Disorder Comparison of Four Neuropsychiatric CNV Loci
Moreno‐De‐Luca, D., Moreno-De-Luca, A., Cubells, J. F., & Sanders, S. (2014) · Current Genetic Medicine Reports · doi:10.1007/s40142-014-0045-7 · Cited by 26
Uddin, M. J., Codner, D., Hasan, S. M. M., Scherer, S. W., O’Rielly, D. D., & Rahman, P. (2015) · Scientific Reports · doi:10.1038/srep10314 · Cited by 25
Mosca, E., Bersanelli, M., Gnocchi, M., Moscatelli, M., Castellani, G., Milanesi, L., & Mezzelani, A. (2017) · Frontiers in Genetics · doi:10.3389/fgene.2017.00129 · Cited by 25
Modeling Neuropsychiatric and Neurodegenerative Diseases With Induced Pluripotent Stem Cells
LaMarca, E. A., Powell, S. K., Akbarian, S., & Brennand, K. (2018) · Frontiers in Pediatrics · doi:10.3389/fped.2018.00082 · Cited by 29
Monoallelic and biallelic mutations inRELNunderlie a graded series of neurodevelopmental disorders
Donato, N. D., Guerrini, R., Billington, C. J., Barkovich, A. J., Dinkel, P., Freri, E., Heide, M., Gershon, E. S., Gertler, T. S., Hopkin, R. J., et al. (2022) · Brain · doi:10.1093/brain/awac164 · Cited by 24
Neurotransmitter Systems in Autism Spectrum Disorder
Çetin, F. H., Tunca, H., Güney, E., & İşeri, E. (2015) · InTech eBooks · doi:10.5772/59122 · Cited by 25
Wu, L., Schaid, D. J., Sicotte, H., Wieben, E. D., Li, H., & Petersen, G. M. (2014) · Journal of Medical Genetics · doi:10.1136/jmedgenet-2014-102697 · Cited by 25
Gabriele, S., Lombardi, F., Sacco, R., Napolioni, V., Altieri, L., Tirindelli, M. C., Gregorj, C., Bravaccio, C., Rousseau, F., & Persico, A. M. (2014) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2014.07.021 · Cited by 26
Structural Architecture of SNP Effects on Complex Traits
Gamazon, E. R., Cox, N. J., & Davis, L. K. (2014) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2014.09.009 · Cited by 26
Campbell, M. G., Kohane, I. S., & Kong, S. W. (2013) · BMC Medical Genomics · doi:10.1186/1755-8794-6-34 · Cited by 25
Tsutiya, A., Nakano, Y., Hansen‐Kiss, E., Kelly, B., Nishihara, M., Goshima, Y., Corsmeier, D., White, P., Herman, G. E., & Ohtani‐Kaneko, R. (2017) · Scientific Reports · doi:10.1038/s41598-017-16782-8 · Cited by 26
Clair, D. S., & Johnstone, M. (2018) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2017.0037 · Cited by 24
Wang, L., Li, J., Shuang, M., Lu, T., Wang, Z., Zhang, T., Yue, W., Jia, M., Ruan, Y., Liu, J., et al. (2018) · Translational Psychiatry · doi:10.1038/s41398-018-0197-4 · Cited by 27
Self-citationZhang‐James, Y., Vaudel, M., Mjaavatten, O., Berven, F. S., Haavik, J., & Faraone, S. V. (2019) · ADHD Attention Deficit and Hyperactivity Disorders · doi:10.1007/s12402-018-0281-x · Cited by 26
Anesthesia Neurotoxicity in the Developing Brain
Clausen, N. G., Hansen, T. G., & Disma, N. M. (2019) · Clinics in Perinatology · doi:10.1016/j.clp.2019.08.002 · Cited by 24
Reelin Signaling Controls the Preference for Social Novelty in Zebrafish
Vecchia, E. D., Donato, V. D., Young, A. M. J., Bene, F. D., & Norton, W. (2019) · Frontiers in Behavioral Neuroscience · doi:10.3389/fnbeh.2019.00214 · Cited by 24
Huang, T., Hsu, T., Lin, M.-H., Chuang, H., Hu, H., Sun, C., Tao, M., Lin, J. Y., & Hsueh, Y. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.08.082 · Cited by 26
Janeczko, D., Hołowczuk, M., Orzeł, A., Klatka, B., & Semczuk, A. (2019) · Biomedical Reports · doi:10.3892/br.2019.1266 · Cited by 26
Nita, A., Muto, Y., Katayama, Y., Matsumoto, A., Nishiyama, M., & Nakayama, K. I. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.108688 · Cited by 24
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
