The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
The contribution of de novo coding mutations to autism spectrum disorder
Open accessAbstract
Whole exome sequencing has proven to be a powerful tool for understanding the genetic architecture of human disease. Here we apply it to more than 2,500 simplex families, each having a child with an autistic spectrum disorder. By comparing affected to unaffected siblings, we show that 13% of de novo missense mutations and 43% of de novo likely gene-disrupting (LGD) mutations contribute to 12% and 9% of diagnoses, respectively. Including copy number variants, coding de novo mutations contribute to about 30% of all simplex and 45% of female diagnoses. Almost all LGD mutations occur opposite wild-type alleles. LGD targets in affected females significantly overlap the targets in males of lower intelligence quotient (IQ), but neither overlaps significantly with targets in males of higher IQ. We estimate that LGD mutation in about 400 genes can contribute to the joint class of affected females and males of lower IQ, with an overlapping and similar number of genes vulnerable to contributory missense mutation. LGD targets in the joint class overlap with published targets for intellectual disability and schizophrenia, and are enriched for chromatin modifiers, FMRP-associated genes and embryonically expressed genes. Most of the significance for the latter comes from affected females.
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
“including that, prima facie , most proteins encoded by ASD risk genes are involved either in synaptic structure and function or chromatin modification and regulation of gene expression 38,46,47,58 (Fig. 3).”
From Autism spectrum disorder (2020) · cites this paper as [46]
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
“Rare inherited and de novo variants are major contributors to individual risk for autism spectrum disorder (ASD)”
From Large-Scale Exome Sequencing Study Implicates Both Developmental and Functional Changes in the Neurobiology of Autism (2020) · cites this paper as Iossifov et al., 2014
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
“The application of this approach to exomes or whole genomes in large family samples has since become the most effective strategy for identifying ASD susceptibility genes (De Rubeis et al., 2014; Iossifov et al., 2014; Sanders et al., 2015; Satterstrom et al., 2018).”
From Getting to the cores of autism (2019) · cites this paper as Iossifov et al., 2014
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
“By contrast, de novo mutations, mostly copy number variants (CNVs) and gene disrupting point mutations, have larger individual effects, but collectively explain <5% of the overall liability 1-3 and far less of the heritability.”
From Identification of common genetic risk variants for autism spectrum disorder (2019) · cites this paper as [3]
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
“ASD affects 1-1.5% of individuals and is highly heritable, with both common and rare variants contributing to its etiology 1-4 .”
From Identification of common genetic risk variants for autism spectrum disorder (2019) · cites this paper as [3]
Experimental — accuracy not yet measured
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“However, for the majority of large CNVs, studies have not found evidence for the existence of a single driver gene (De Rubeis et al., 2014; Iossifov et al., 2014).”
From Getting to the cores of autism (2019) · cites this paper as Iossifov et al., 2014
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“For instance, several microdeletions lead to loss of various lengths of the C-terminal portion of Dock4 protein 19, 21, 26, which is responsible for its Rac1-activating ability 27.”
From Autism-like social deficit generated by Dock4 deficiency is rescued by restoration of Rac1 activity and NMDA receptor function. (2021) · cites this paper as [21]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Recent studies have identified a number of DOCK4 variations associated with ASD, including both single nucleotide variations (SNVs) and chromosome microdeletions or duplications (Supplementary Table 1) 18–26.”
From Autism-like social deficit generated by Dock4 deficiency is rescued by restoration of Rac1 activity and NMDA receptor function. (2021) · cites this paper as [21]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Next-generation sequencing identified one intronic114, two missense133,134, and two nonsense variants in autism patients104,114, and the functional consequences of these variants remain to be established (Table 2u).”
From The WAVE complex in developmental and adulthood brain disorders (2025) · cites this paper as [133]
Experimental — accuracy not yet measuredIn the citing paper
“The major risk factors for autism spectrum disorder (ASD) are genetic and include a variety of rare and common alleles, including rare de novo copy number variants (CNVs)1 or protein-truncating SNPs and indels of large effect2 and common polygenic risk that is measured as the sum of thousands of common alleles with small effects3.”
From A phenotypic spectrum of autism is attributable to the combined effects of rare variants, polygenic risk and sex (2022) · cites this paper as [2]
In the citing paper
“The possibility of a “female protective effect” (FPE) against ASD has been described extensively and has received consistent support from the results of genetic studies of de novo variants.4, 5, 6, 7, 8, 9, 10, 11, 12, 13”
From The female protective effect against autism spectrum disorder (2022) · cites this paper as [11]
In the citing paper
“Many types of ASD-associated de novo variants are observed more frequently in female cases.4, 5, 6, 7, 8, 9, 10, 11, 12, 13”
From The female protective effect against autism spectrum disorder (2022) · cites this paper as [11]
In the citing paper
“We also demonstrated that the genetic architecture in this self-reported cohort is similar to published, clinically confirmed ASD cohorts.3,4,7”
From Exome sequencing of 457 autism families recruited online provides evidence for autism risk genes (2019) · cites this paper as [3]
In the citing paper
“Over the past decade, genomic studies focused on de novo, likely gene disrupting (dnLGD) variants (stopgain, frameshift, and essential splice site) have identified ~100 high-confidence ASD risk genes or loci.3,4”
From Exome sequencing of 457 autism families recruited online provides evidence for autism risk genes (2019) · cites this paper as [3]
In the citing paper
“individuals with ASD have a two- to three-fold increase in the number of rare copy number variations (CNVs) and de novo loss-of-function (nonsense, frameshift, and splice site) variants compared to their unaffected siblings11–14.”
From Genome-wide detection of tandem DNA repeats that are expanded in autism (2020) · cites this paper as [12]
In the citing paper
“the application of two SNV callers identified 77 additional de novo SNVs previously missed 9.”
From Excess of rare, inherited truncating mutations in autism (2015) · cites this paper as [9]
In the citing paper
“In addition, we also validated by Sanger sequencing another 132 previously called 9 but not confirmed events, resulting in a total of 209 validated de novo SNVs and indels (Supplementary Table 2).”
From Excess of rare, inherited truncating mutations in autism (2015) · cites this paper as [9]
In the citing paper
“Most of these mutations also decrease IQ and ID is an important comorbidity of ASD (Buja et al., 2018; Iossifov et al., 2014), which is consistent with previous work identifying dozens of known severe, rare medical genetic syndromes associated with ASD (reviewed in (Abrahams and Geschwind, 2008; Geschwind, 2009)).”
From Defining the genetic, genomic, cellular, and diagnostic architectures of psychiatric disorders (2019) · cites this paper as Iossifov et al., 2014
In the citing paper
“Although each gene accounts for only a small fraction of cases, rare de novo variation is predicted to account for ~15% of ASD cases (Iossifov et al., 2014).”
From Defining the genetic, genomic, cellular, and diagnostic architectures of psychiatric disorders (2019) · cites this paper as Iossifov et al., 2014
In the citing paper
“As would be expected for variants with large effects, subjects that carry de novo mutations have lower non-verbal IQs than subjects who do not (Iossifov et al., 2014).”
From Getting to the cores of autism (2019) · cites this paper as Iossifov et al., 2014
In the citing paper
“It is estimated that de novo mutations of genes contribute in approximately 30% of cases, including 25% of boys and 45% of girls (Iossifov et al., 2014).”
From Getting to the cores of autism (2019) · cites this paper as Iossifov et al., 2014
In the citing paper
“Moreover, 20 of these lncRNAs have been shown to interact with microRNA (miRNA)–protein complexes, and 9 with the fragile X mental retardation protein (FMRP), whose mRNA targets are enriched in ASD risk genes9,10.”
From Genome-wide changes in lncRNA, splicing, and regional gene expression patterns in autism (2016) · cites this paper as [9]
In the citing paper
“In this regard, it is notable that the observed transcriptome changes are consistent with an ongoing process that is triggered largely by genetic and prenatal factors3,9,10,23, but that evolves during the first decade of brain development.”
From Genome-wide changes in lncRNA, splicing, and regional gene expression patterns in autism (2016) · cites this paper as [9]
In the citing paper
“We previously computed that genes with de novo (DN) likely gene-disruptive (LGD) mutations in children with autism spectrum disorders (ASD) have high vulnerability: disruptive mutations in many of these genes, the vulnerable autism genes, will have a high likelihood of resulting in ASD.”
From Low load for disruptive mutations in autism genes and their biased transmission (2015) · cites this paper as [10]
In the citing paper
“Moreover, the target genes in affected and unaffected individuals have length distributions that are closely matched (10).”
From Low load for disruptive mutations in autism genes and their biased transmission (2015) · cites this paper as [10]
In the citing paper
“There are 39 such targets (10,18), and we previously estimated that ∼90% of these targets are true causal targets.”
From Low load for disruptive mutations in autism genes and their biased transmission (2015) · cites this paper as [10]
In the citing paper
“All gene classes enriched for autism targets show a decreased load of UR LGD variants, especially the FMRP-associated class, as previously noted (10).”
From Low load for disruptive mutations in autism genes and their biased transmission (2015) · cites this paper as [10]
In the citing paper
“We also find a strong signal for biased transmission in the functional categories of genes previously associated with ASD (10).”
From Low load for disruptive mutations in autism genes and their biased transmission (2015) · cites this paper as [10]
In the citing paper
“We computed that roughly half of the time in males, a DN LGD mutation within an autism gene will produce severe ASD (10).”
From Low load for disruptive mutations in autism genes and their biased transmission (2015) · cites this paper as [10]
In the citing paper
“From recurrence and overlap analysis of DN LGD targets, we estimate ∼500 causative ASD target genes in the affected individuals with lower intelligence quotients (IQ), and these targets are enriched in certain functional classes (10).”
From Low load for disruptive mutations in autism genes and their biased transmission (2015) · cites this paper as [10]
In the citing paper
“The increase in the rate of DN LGD mutation in affected children compared with the rate in their unaffected siblings was used to estimate the proportion (0.42) of 391 observed DN variants in the affected children that contribute to affected status (10).”
From Low load for disruptive mutations in autism genes and their biased transmission (2015) · cites this paper as [10]
In the citing paper
“Although our previous work had shown that embryonic genes were enriched as DN targets (10), their involvement in transmission is greater than we would have predicted.”
From Low load for disruptive mutations in autism genes and their biased transmission (2015) · cites this paper as [10]
In the citing paper
“First, a substantial signal from transmission is seen in the set of “embryonic” genes (10).”
From Low load for disruptive mutations in autism genes and their biased transmission (2015) · cites this paper as [10]
In the citing paper
“Thus, many severe mutations do not converge on one specific phenotype but instead seem to cause a range of clinical disorders74,76,80,81,84,87,91.”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [76]
In the citing paper
“suggesting that females are affected by more severe genetic hits in ASD, an observation that has been replicated in exome-sequencing studies76,117.”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [76]
In the citing paper
“This elevation of protein-compromising URVs was several times larger than an analogously elevated rate for de novo mutations”
From Increased burden of ultra-rare protein-altering variants among 4,877 individuals with schizophrenia (2016) · cites this paper as [13]
In the citing paper
“These data yielded an elevation of about 0.03 such DNMs per exome, based on the difference between rates of 0.185 (95% CI 0.151-0.219) for individuals with schizophrenia12 and 0.156 (95% CI 0.139-0.174) for unaffected individuals13.”
From Increased burden of ultra-rare protein-altering variants among 4,877 individuals with schizophrenia (2016) · cites this paper as [13]
In the citing paper
“Subsets of human genes have been previously identified as “missense constrained” (based on a lack of functional coding variation in controls) or “loss-of-function intolerant” (based on a smaller-than-expected number of loss-of-function mutations in population-scale data)13,21.”
From Increased burden of ultra-rare protein-altering variants among 4,877 individuals with schizophrenia (2016) · cites this paper as [13]
In the citing paper
“ANK2 has been consistently associated with ASD (22,26,80) and also with cardiac arrhythmia (OMIM no.600919) (78).”
From Genetics and genomics of autism spectrum disorder: embracing complexity (2015) · cites this paper as [22]
In the citing paper
“In the case of paternal age, one mediating mechanism lies in the increased rate of de novo SNV and CNV with advancing paternal age (22,24,25,43,44).”
From Genetics and genomics of autism spectrum disorder: embracing complexity (2015) · cites this paper as [22]
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 [22]
In the citing paper
“The model helps in the understanding of the relatively high risk for siblings of individuals diagnosed with ASD (13,46), the increased mutational burden in ASD probands compared with their unaffected siblings (22,28), etiological heterogeneity even within pairs of affected siblings (47,48) and, as we shall discuss further, the variable expressivity of certain loci (49,50) (Fig. 1).”
From Genetics and genomics of autism spectrum disorder: embracing complexity (2015) · cites this paper as [22]
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 [22]
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 [22]
In the citing paper
“CHD2 has been associated with ASD (22,23), ID (73), epilepsy (82,87) and photosensitivity (83).”
From Genetics and genomics of autism spectrum disorder: embracing complexity (2015) · cites this paper as [22]
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 [22]
In the citing paper
“Mutations in CHD2 have been detected in individuals with a diagnosis of ASD (22), ID (73) or epilepsy (82,83,87,89)”
From Genetics and genomics of autism spectrum disorder: embracing complexity (2015) · cites this paper as [22]
In the citing paper
“such rare high-confidence mutations tend to be significantly enriched in genes involved in synaptic functions, transcriptional regulation, and chromatin remodeling functions, and/or are downstream targets of the fragile X syndrome protein (FMRP) complex.1, 3”
From Maternal immune activation dysregulation of the fetal brain transcriptome and relevance to the pathophysiology of autism spectrum disorder (2018) · cites this paper as [3]
In the citing paper
“When considering known ASD-associated missense variants,3 we also found substantial enrichment (33%, 48/145, OR=2.21, P=0.0021) (Figure 1b).”
From Maternal immune activation dysregulation of the fetal brain transcriptome and relevance to the pathophysiology of autism spectrum disorder (2018) · cites this paper as [3]
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 [165]
In the citing paper
“Chromodomain helicase DNA-binding protein 8 (CHD8), encoding a chromatin modifying gene on chromosome 14q11.2, is one of the high-penetrance ASD risk genes recently identified by sequencing studies [83, 165, 261, 347].”
From Autism spectrum disorder: neuropathology and animal models (2017) · cites this paper as [165]
In the citing paper
“Using the WW data, we observe enrichment at an empirical P ≤ .05 for the synaptic co-expression network M13 [48], Mendelian disease genes [11], and both human (HARs) and primate-accelerated regions (PARs) [49]. None of these enrichments exceeded the experiment-wise corrected P ≤ .05.”
In the citing paper
“For example, FMRP biology has also been implicated in all three diagnoses [11].”
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
The Sequence Alignment/Map format and SAMtools
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., & 1000 Genome Project Data Processing Subgroup (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp352 · Cited by 62,041
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
Strong association of de novo copy number mutations with autism
Sebat, J., Lakshmi, B., Malhotra, D., Troge, J., Lese-Martin, C., Walsh, T., Yamrom, B., Yoon, S., Krasnitz, A., Kendall, J., et al. (2007) · Science · doi:10.1126/science.1138659 · Cited by 2,795
Self-citationDNA methylation and epigenetic inheritance
Holliday, R. (1990) · Philosophical transactions of the Royal Society of London. Series B, Biological sciences · doi:10.1098/rstb.1990.0015 · Cited by 2,290
Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs
Cross-Disorder Group of the Psychiatric Genomics Consortium (2013) · Nature Genetics · doi:10.1038/ng.2711 · Cited by 2,263
Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations
O’Roak, B. J., Vives, L., Girirajan, S., Karakoc, E., Krumm, N., Coe, B. P., Levy, R., Ko, A., Lee, C., Smith, J. D., et al. (2012) · Nature · doi:10.1038/nature10989 · Cited by 2,165
Self-citationRate of de novo mutations and the importance of father’s age to disease risk
Kong, A., Frigge, M. L., Masson, G., Besenbacher, S., Sulem, P., Magnusson, G., Gudjonsson, S. A., Sigurdsson, A., Jonasdottir, A., Jonasdottir, A., et al. (2012) · Nature · doi:10.1038/nature11396 · Cited by 2,134
Spatio-temporal transcriptome of the human brain
Kang, H. J., Kawasawa, Y. I., Cheng, F., Zhu, Y., Xu, X., Li, M., Sousa, A. M. M., Pletikos, M., Meyer, K. A., Sedmak, G., et al. (2011) · Nature · doi:10.1038/nature10523 · Cited by 2,084
Self-citationDe 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-citation
Cited by
Sorted by citations
Coming of age: ten years of next-generation sequencing technologies
Goodwin, S., McPherson, J. D., & McCombie, W. R. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2016.49 · Cited by 4,352
Self-citation10 Years of GWAS Discovery: Biology, Function, and Translation
Visscher, P. M., Wray, N. R., Zhang, Q., Sklar, P., McCarthy, M. I., Brown, M. A., & Yang, J. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.06.005 · Cited by 3,719
Predicting splicing from primary sequence with deep learning
Jaganathan, K., Kyriazopoulou Panagiotopoulou, S., McRae, J. F., Darbandi, S. F., Knowles, D., Li, Y. I., Kosmicki, J. A., Arbelaez, J., Cui, W., Schwartz, G. B., et al. (2019) · Cell · doi:10.1016/j.cell.2018.12.015 · Cited by 2,853
Self-citationIdentification of common genetic risk variants for autism spectrum disorder
Autism Spectrum Disorder Working Group of the Psychiatric Genomics Consortium, Grove, J., BUPGEN, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, 23andMe Research Team, Ripke, S., Als, T. D., Mattheisen, M., Walters, R. K., Won, H., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0344-8 · Cited by 2,421
Schizophrenia risk from complex variation of complement component 4
Sekar, A., Bialas, A. R., de Rivera, H., Davis, A., Hammond, T. R., Kamitaki, N., Tooley, K., Presumey, J., Baum, M., Van Doren, V., et al. (2016) · Nature · doi:10.1038/nature16549 · Cited by 2,394
Satterstrom, F. K., Kosmicki, J. A., Wang, J., Breen, M. S., De Rubeis, S., An, J.-Y., Peng, M., Collins, R., Grove, J., Klei, L., et al. (2020) · Cell · doi:10.1016/j.cell.2019.12.036 · Cited by 2,321
“Of the 102 ASDassociated genes, 60 were not discovered by our earlier analyses (De Rubeis et al., 2014; Iossifov et al., 2014; Sanders et al., 2015).”
BackgroundMethodInfluentialSelf-citationAccurate proteome-wide missense variant effect prediction with AlphaMissense
Cheng, J., Novati, G., Pan, J., Bycroft, C., Žemgulytė, A., Applebaum, T., Pritzel, A., Wong, L. H., Zielinski, M., Sargeant, T., et al. (2023) · Science · doi:10.1126/science.adg7492 · Cited by 2,163
Lord, C., Brugha, T. S., Charman, T., Cusack, J., Dumas, G., Frazier, T., Jones, E. J. H., Jones, R. M., Pickles, A., State, M. W., et al. (2020) · Nature Reviews Disease Primers · doi:10.1038/s41572-019-0138-4 · Cited by 1,859
Self-citationPrevalence and architecture of de novo mutations in developmental disorders
Study, D. D. D. (2017) · Nature · doi:10.1038/nature21062 · Cited by 1,480
Insights into autism spectrum disorder genomic architecture and biology from 71 risk loci
Sanders, S. J., He, X., Willsey, A. J., Ercan-Sencicek, A. G., Samocha, K. E., Cicek, A. E., Murtha, M. T., Bal, V. H., Bishop, S. L., Dong, S., et al. (2015) · Neuron · doi:10.1016/j.neuron.2015.09.016 · Cited by 1,430
Self-citationShared molecular neuropathology across major psychiatric disorders parallels polygenic overlap
Gandal, M. J., Haney, J. R., Parikshak, N. N., Leppa, V., Ramaswami, G., Hartl, C., Schork, A. J., Appadurai, V., Buil, A., Werge, T. M., et al. (2018) · Science · doi:10.1126/science.aad6469 · Cited by 1,095
Self-citationGene expression elucidates functional impact of polygenic risk for schizophrenia
Fromer, M., Roussos, P., Sieberts, S. K., Johnson, J. S., Kavanagh, D. H., Perumal, T. M., Ruderfer, D. M., Oh, E. C., Topol, A., Shah, H. R., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4399 · Cited by 1,091
InterVar: Clinical Interpretation of Genetic Variants by the 2015 ACMG-AMP Guidelines
Li, Q., & Wang, K. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.01.004 · Cited by 1,057
Clinical application of whole-exome sequencing across clinical indications
Retterer, K., Juusola, J., Cho, M. T., Vitazka, P., Millan, F., Gibellini, F., Vertino-Bell, A., Smaoui, N., Neidich, J., Monaghan, K. G., et al. (2015) · Genetics in Medicine · doi:10.1038/gim.2015.148 · Cited by 1,049
From the genetic architecture to synaptic plasticity in autism spectrum disorder
Bourgeron, T. (2015) · Nature Reviews Neuroscience · doi:10.1038/nrn3992 · Cited by 975
Inferring the molecular and phenotypic impact of amino acid variants with MutPred2
Pejaver, V., Urresti, J., Lugo-Martinez, J., Pagel, K. A., Lin, G. N., Nam, H.-J., Mort, M., Cooper, D. N., Sebat, J., Iakoucheva, L. M., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19669-x · Cited by 923
Contribution of rare inherited and de novo variants in 2,871 congenital heart disease probands
Jin, S. C., Homsy, J., Zaidi, S., Lu, Q., Morton, S., DePalma, S. R., Zeng, X., Qi, H., Chang, W., Sierant, M. C., et al. (2017) · Nature Genetics · doi:10.1038/ng.3970 · Cited by 875
Self-citationDe novo mutations in congenital heart disease with neurodevelopmental and other congenital anomalies
Homsy, J. G., Zaidi, S., Shen, Y., Ware, J. S., Samocha, K. E., Karczewski, K. J., DePalma, S. R., McKean, D. M., Wakimoto, H., Gorham, J., et al. (2015) · Science · doi:10.1126/science.aac9396 · Cited by 855
Self-citationWhole genome sequencing resource identifies 18 new candidate genes for autism spectrum disorder
C Yuen, R. K., Merico, D., Bookman, M., L Howe, J., Thiruvahindrapuram, B., Patel, R. V., Whitney, J., Deflaux, N., Bingham, J., Wang, Z., et al. (2017) · Nature Neuroscience · doi:10.1038/nn.4524 · Cited by 844
Self-citationAdvancing the understanding of autism disease mechanisms through genetics
de la Torre-Ubieta, L., Won, H., Stein, J. L., & Geschwind, D. H. (2016) · Nature Medicine · doi:10.1038/nm.4071 · Cited by 827
Stenson, P. D., Mort, M. E., Ball, E. V., Chapman, M., Evans, K., Azevedo, L., Hayden, M., Heywood, S., Millar, D. S., Phillips, A. D., et al. (2020) · Human Genetics · doi:10.1007/s00439-020-02199-3 · Cited by 823
Genome-wide changes in lncRNA, splicing, and regional gene expression patterns in autism
Parikshak, N. N., Swarup, V., Belgard, T. G., Irimia, M., Ramaswami, G., Gandal, M. J., Hartl, C., Leppa, V., Ubieta, L. D. L. T., Huang, J., et al. (2016) · Nature · doi:10.1038/nature20612 · Cited by 685
“Extended Data Figure 2 | Transcriptome-wide DGE analysis. a , We applied a classification method robust to overfitting (elastic net model 47 ) by training on the RNA-seq data from samples previously analysed in ref.”
InfluentialThe Neurodiversity Approach(es): What Are They and What Do They Mean for Researchers?
Dwyer, P. (2022) · Human Development · doi:10.1159/000523723 · Cited by 672
Fu, J. M., Satterstrom, F. K., Peng, M., Brand, H., Collins, R. L., Dong, S., Wamsley, B., Klei, L., Wang, L., Hao, S. P., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01104-0 · Cited by 640
Self-citationHalladay, A. K., Bishop, S. L., Constantino, J. N., Daniels, A. M., Koenig, K., Palmer, K., Messinger, D. S., Pelphrey, K. A., Sanders, S., Singer, A., et al. (2015) · Molecular Autism · doi:10.1186/s13229-015-0019-y · Cited by 639
Self-citationGenetic Causes and Modifiers of Autism Spectrum Disorder
Rylaarsdam, L. E., & Guemez‐Gamboa, A. (2019) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2019.00385 · Cited by 618
Genetics and Genomics of Congenital Heart Disease
Zaidi, S., & Brueckner, M. K. (2017) · Circulation Research · doi:10.1161/circresaha.116.309140 · Cited by 612
Neurodevelopmental disorders—the history and future of a diagnosticconcept
Morris-Rosendahl, D. J., & Crocq, M. (2020) · Dialogues in Clinical Neuroscience · doi:10.31887/dcns.2020.22.1/macrocq · Cited by 607
A spectral approach integrating functional genomic annotations for coding and noncoding variants
Ionita‐Laza, I., McCallum, K., Xu, B., & Buxbaum, J. D. (2016) · Nature Genetics · doi:10.1038/ng.3477 · Cited by 602
The Autism Spectrum Disorders Working Group of The Psychiatric Genomics Consortium (2017) · Molecular Autism · doi:10.1186/s13229-017-0137-9 · Cited by 587
Excess of rare, inherited truncating mutations in autism
Krumm, N., Turner, T. N., Baker, C., Vives, L., Mohajeri, K., Witherspoon, K., Raja, A., Coe, B. P., Stessman, H. A., He, Z.-X., et al. (2015) · Nature Genetics · doi:10.1038/ng.3303 · Cited by 587
Self-citationStessman, H. A. F., Xiong, B., Coe, B. P., Wang, T., Hoekzema, K., Fenckova, M., Kvarnung, M., Gerdts, J., Trinh, S., Cosemans, N., et al. (2017) · Nature Genetics · doi:10.1038/ng.3792 · Cited by 559
Self-citationAutism spectrum disorder: neuropathology and animal models
Varghese, M., Keshav, N., Jacot-Descombes, S., Warda, T., Wicinski, B., Dickstein, D. L., Harony-Nicolas, H., De Rubeis, S., Drapeau, E., Buxbaum, J. D., et al. (2017) · Acta Neuropathologica · doi:10.1007/s00401-017-1736-4 · Cited by 536
The serotonin system in autism spectrum disorder: From biomarker to animal models
Muller, C. L., Anacker, A. M. J., & Veenstra-VanderWeele, J. (2016) · Neuroscience · doi:10.1016/j.neuroscience.2015.11.010 · Cited by 531
iPSYCH-Broad Autism Group, Weiner, D. J., Psychiatric Genomics Consortium Autism Group, Wigdor, E. M., Ripke, S., Walters, R. K., Kosmicki, J. A., Grove, J., Samocha, K. E., Goldstein, J. I., et al. (2017) · Nature Genetics · doi:10.1038/ng.3863 · Cited by 516
Self-citationWhole-genome sequencing of quartet families with autism spectrum disorder
Yuen, R. K. C., Thiruvahindrapuram, B., Merico, D., Walker, S., Tammimies, K., Hoang, N., Chrysler, C., Nalpathamkalam, T., Pellecchia, G., Liu, Y., et al. (2015) · Nature Medicine · doi:10.1038/nm.3792 · Cited by 514
Gene hunting in autism spectrum disorder: on the path to precision medicine
Geschwind, D. H., & State, M. W. (2015) · The Lancet Neurology · doi:10.1016/s1474-4422(15)00044-7 · Cited by 513
Self-citationDrug development in the era of precision medicine
Dugger, S. A., Platt, A., & Goldstein, D. B. (2018) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2017.226 · Cited by 493
Predicting the clinical impact of human mutation with deep neural networks
Sundaram, L., Gao, H., Padigepati, S. R., McRae, J. F., Li, Y., Kosmicki, J. A., Fritzilas, N., Hakenberg, J., Dutta, A., Shon, J. C., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0167-z · Cited by 505
Trevino, A. E., Müller, F., Andersen, J., Sundaram, L., Kathiria, A., Shcherbina, A., Farh, K., Chang, H. Y., Pașca, A. M., Kundaje, A., et al. (2021) · Cell · doi:10.1016/j.cell.2021.07.039 · Cited by 478
New insights into the generation and role of de novo mutations in health and disease
Acuña‐Hidalgo, R., Veltman, J. A., & Hoischen, A. (2016) · Genome biology · doi:10.1186/s13059-016-1110-1 · Cited by 474
Autism genetics: opportunities and challenges for clinical translation
Vorstman, J. A. S., Parr, J. R., Moreno‐De‐Luca, D., Anney, R., Nürnberger, J. I., & Hallmayer, J. F. (2017) · Nature Reviews Genetics · doi:10.1038/nrg.2017.4 · Cited by 517
Inherited and de novo genetic risk for autism impacts shared networks
Ruzzo, E. K., Pérez-Cano, L., Jung, J.-Y., Wang, L.-K., Kashef-Haghighi, D., Hartl, C., Singh, C., Xu, J., Hoekstra, J. N., Leventhal, O., et al. (2019) · Cell · doi:10.1016/j.cell.2019.07.015 · Cited by 457
Zhou, X., Feliciano, P., Shu, C., Wang, T., Astrovskaya, I., Hall, J. B., Obiajulu, J. U., Wright, J. R., Murali, S. C., Xu, S. X., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01148-2 · Cited by 453
Self-citationDefining the genetic, genomic, cellular, and diagnostic architectures of psychiatric disorders
Sullivan, P. F., & Geschwind, D. H. (2019) · Cell · doi:10.1016/j.cell.2019.01.015 · Cited by 452
Study, S. S., Singh, T., Study, I., Study, D., UK10 K Consortium, Kurki, M. I., Curtis, D., Purcell, S. M., Crooks, L., McRae, J., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4267 · Cited by 452
An xQTL map integrates the genetic architecture of the human brain's transcriptome and epigenome
Ng, B., White, C. C., Klein, H.-U., Sieberts, S. K., McCabe, C., Patrick, E., Xu, J., Yu, L., Gaiteri, C., Bennett, D. A., et al. (2017) · Nature Neuroscience · doi:10.1038/nn.4632 · Cited by 452
Akbarian, S., Liu, C., Knowles, J. A., Vaccarino, F. M., Farnham, P. J., Crawford, G. E., Jaffe, A. E., Pinto, D., Dracheva, S., Geschwind, D. H., et al. (2015) · Nature Neuroscience · doi:10.1038/nn.4156 · Cited by 451
Self-citationStudy, T. I., Sifrim, A., Study, T. D. D. D., Hitz, M., Wilsdon, A., Breckpot, J., Turki, S. H. A., Thienpont, B., McRae, J. F., Fitzgerald, T. W., et al. (2016) · Nature Genetics · doi:10.1038/ng.3627 · Cited by 449
High Rate of Recurrent De Novo Mutations in Developmental and Epileptic Encephalopathies
Hamdan, F. F., Myers, C. T., Cossette, P., Lemay, P., Spiegelman, D., Laporte, A. D., Nassif, C., Diallo, O., Monlong, J., Cadieux-Dion, M., et al. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.09.008 · Cited by 445
Increased burden of ultra-rare protein-altering variants among 4,877 individuals with schizophrenia
Genovese, G., Fromer, M., Stahl, E. A., Ruderfer, D. M., Chambert, K., Landén, M., Moran, J. L., Purcell, S. M., Sklar, P., Sullivan, P. F., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4402 · Cited by 444
Meta-analysis of 2,104 trios provides support for 10 new genes for intellectual disability
Lelieveld, S. H., Reijnders, M. R. F., Pfundt, R., Yntema, H. G., Kamsteeg, E.-J., de Vries, P., de Vries, B. B. A., Willemsen, M. H., Kleefstra, T., Löhner, K., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4352 · Cited by 442
A complete reference genome improves analysis of human genetic variation
Aganezov, S., Yan, S. M., Soto, D. C., Kirsche, M., Zarate, S., Avdeyev, P., Taylor, D. J., Shafin, K., Shumate, A., Xiao, C., et al. (2022) · Science · doi:10.1126/science.abl3533 · Cited by 441
Self-citationKrishnan, A., Zhang, R., Yao, V., Theesfeld, C. L., Wong, A. K., Tadych, A., Volfovsky, N., Packer, A. I., Lash, A., & Troyanskaya, O. G. (2016) · Nature Neuroscience · doi:10.1038/nn.4353 · Cited by 438
Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders
Parikshak, N. N., Gandal, M. J., & Geschwind, D. H. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3934 · Cited by 430
Genetics and genomics of psychiatric disease
Geschwind, D. H., & Flint, J. (2015) · Science · doi:10.1126/science.aaa8954 · Cited by 424
DNA methylation in human diseases
Jin, Z., & Liu, Y. (2018) · Genes & Diseases · doi:10.1016/j.gendis.2018.01.002 · Cited by 422
Tammimies, K., Marshall, C. R., Walker, S., Kaur, G., Thiruvahindrapuram, B., Lionel, A. C., Yuen, R. K. C., Uddin, M., Roberts, W., Weksberg, R., et al. (2015) · JAMA · doi:10.1001/jama.2015.10078 · Cited by 419
Self-citationCandidate biomarkers in psychiatric disorders: state of the field
Abi‐Dargham, A., Moeller, S. J., Ali, F., DeLorenzo, C., Domschke, K., Horga, G., Jutla, A., Kotov, R., Paulus, M. P., Rubio, J. M., et al. (2023) · World Psychiatry · doi:10.1002/wps.21078 · Cited by 421
Exogenous and evoked oxytocin restores social behavior in the Cntnap2 mouse model of autism
Peñagarikano, O., Lázaro, M. T., Lu, X.-H., Gordon, A., Dong, H., Lam, H. A., Peles, E., Maidment, N. T., Murphy, N. P., Yang, X. W., et al. (2015) · Science Translational Medicine · doi:10.1126/scitranslmed.3010257 · Cited by 378
Targeted capture in evolutionary and ecological genomics
Jones, M. R., & Good, J. M. (2015) · Molecular Ecology · doi:10.1111/mec.13304 · Cited by 370
De novo genic mutations among a Chinese autism spectrum disorder cohort
Wang, T., Guo, H., Xiong, B., Stessman, H. A. F., Wu, H., Coe, B. P., Turner, T. N., Liu, Y., Zhao, W., Hoekzema, K., et al. (2016) · Nature Communications · doi:10.1038/ncomms13316 · Cited by 369
Self-citationGenetic risk for autism spectrum disorders and neuropsychiatric variation in the general population
iPSYCH-SSI-Broad Autism Group, Robinson, E. B., St Pourcain, B., Anttila, V., Kosmicki, J. A., Bulik-Sullivan, B., Grove, J., Maller, J., Samocha, K. E., Sanders, S. J., et al. (2016) · Nature Genetics · doi:10.1038/ng.3529 · Cited by 367
Self-citationCell-Type-Specific Alternative Splicing Governs Cell Fate in the Developing Cerebral Cortex
Zhang, X., Chen, M. H., Wu, X., Kodani, A., Fan, J., Doan, R. N., Ozawa, M., Ma, J., Yoshida, N., Reiter, J. F., et al. (2016) · Cell · doi:10.1016/j.cell.2016.07.025 · Cited by 363
The Dynamic Landscape of Open Chromatin during Human Cortical Neurogenesis
de la Torre-Ubieta, L., Stein, J. L., Won, H., Opland, C. K., Liang, D., Lu, D., & Geschwind, D. H. (2018) · Cell · doi:10.1016/j.cell.2017.12.014 · Cited by 353
Regional missense constraint improves variant deleteriousness prediction
Samocha, K. E., Kosmicki, J. A., Karczewski, K. J., O’Donnell-Luria, A. H., Pierce-Hoffman, E., MacArthur, D. G., Neale, B. M., & Daly, M. J. (2017) · doi:10.1101/148353 · Cited by 351
Mutations in Human Accelerated Regions Disrupt Cognition and Social Behavior
Doan, R. N., Bae, B.-I., Cubelos, B., Chang, C. Y., Hossain, A. A., Al-Saad, S., Mukaddes, N. M., Öner, Ö., Al‐Saffar, M., Balkhy, S., et al. (2016) · Cell · doi:10.1016/j.cell.2016.08.071 · Cited by 357
Cotney, J., Muhle, R. A., Sanders, S. J., Liu, L., Willsey, A. J., Niu, W., Liu, W., Klei, L., Lei, J., Yin, J., et al. (2015) · Nature Communications · doi:10.1038/ncomms7404 · Cited by 350
Self-citationGenomic Patterns of De Novo Mutation in Simplex Autism
Turner, T. N., Coe, B. P., Dickel, D. E., Hoekzema, K., Nelson, B. J., Zody, M. C., Kronenberg, Z. N., Hormozdiari, F., Raja, A. N., Pennacchio, L. A., et al. (2017) · Cell · doi:10.1016/j.cell.2017.08.047 · Cited by 343
Self-citationKosmicki, J. A., Samocha, K. E., Howrigan, D. P., Sanders, S. J., Slowikowski, K., Lek, M., Karczewski, K. J., Cutler, D. J., Devlin, B., Roeder, K., et al. (2017) · Nature Genetics · doi:10.1038/ng.3789 · Cited by 340
Self-citationRecurrent de novo mutations implicate novel genes underlying simplex autism risk
O'Roak, B. J., Stessman, H. A., Boyle, E. A., Witherspoon, K. T., Martin, B., Lee, C., Vives, L., Baker, C., Hiatt, J. B., Nickerson, D. A., et al. (2014) · Nature Communications · doi:10.1038/ncomms6595 · Cited by 336
Self-citationDrug development for neurodevelopmental disorders: lessons learned from fragile X syndrome
Berry-Kravis, E. M., Lindemann, L., Jønch, A. E., Apostol, G., Bear, M. F., Carpenter, R. L., Crawley, J. N., Curie, A., Des Portes, V., Hossain, F., et al. (2018) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2017.221 · Cited by 336
Redin, C., Brand, H., Collins, R. L., Kammin, T., Mitchell, E., Hodge, J. C., Hanscom, C., Pillalamarri, V., Seabra, C. M., Abbott, M.-A., et al. (2017) · Nature Genetics · doi:10.1038/ng.3720 · Cited by 334
The ASD Living Biology: from cell proliferation to clinical phenotype
Courchesne, E., Pramparo, T., Gazestani, V. H., Lombardo, M. V., Pierce, K., & Lewis, N. E. (2019) · Molecular Psychiatry · doi:10.1038/s41380-018-0056-y · Cited by 333
Systematic reanalysis of clinical exome data yields additional diagnoses: implications for providers
Wenger, A. M., Guturu, H., Bernstein, J. A., & Bejerano, G. (2016) · Genetics in Medicine · doi:10.1038/gim.2016.88 · Cited by 333
Neurodevelopmental disease genes implicated by de novo mutation and copy number variation morbidity
Coe, B. P., Stessman, H. A. F., Sulovari, A., Geisheker, M. R., Bakken, T. E., Lake, A. M., Dougherty, J. D., Lein, E. S., Hormozdiari, F., Bernier, R. A., et al. (2019) · Nature Genetics · doi:10.1038/s41588-018-0288-4 · Cited by 330
Self-citationIon Channels in Genetic Epilepsy: From Genes and Mechanisms to Disease-Targeted Therapies
Oyrer, J., Maljevic, S., Scheffer, I. E., Berkovic, S. F., Petrou, S., & Reid, C. A. (2018) · Pharmacological Reviews · doi:10.1124/pr.117.014456 · Cited by 315
Freed, D. N., Aldana, R., Weber, J. A., & Edwards, J. S. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/115717 · Cited by 309
Masini, E., Loi, E., Vega-Benedetti, A. F., Carta, M., Doneddu, G., Fadda, R., & Zavattari, P. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21218290 · Cited by 310
Understanding axon guidance: are we nearly there yet?
Stoeckli, E. T. (2018) · Development · doi:10.1242/dev.151415 · Cited by 302
Lessons learned from studying syndromic autism spectrum disorders
Sztainberg, Y., & Zoghbi, H. Y. (2016) · Nature Neuroscience · doi:10.1038/nn.4420 · Cited by 305
Rates, distribution and implications of postzygotic mosaic mutations in autism spectrum disorder
Lim, E. T., Uddin, M., De Rubeis, S., Chan, Y., Kamumbu, A. S., Zhang, X., D'Gama, A. M., Kim, S. N., Hill, R. S., Goldberg, A. P., et al. (2017) · Nature Neuroscience · doi:10.1038/nn.4598 · Cited by 299
Gene expression in human brain implicates sexually dimorphic pathways in autism spectrum disorders
Werling, D. M., Parikshak, N. N., & Geschwind, D. H. (2016) · Nature Communications · doi:10.1038/ncomms10717 · Cited by 296
Eijsbouts, C. Q., Zheng, T., Kennedy, N. A., Bonfiglio, F., Anderson, C. A., Moutsianas, L., Holliday, J., Shi, J., Shringarpure, S., Agee, M., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00950-8 · Cited by 296
Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk
Zhou, J., Park, C. Y., Theesfeld, C. L., Wong, A. K., Yuan, Y., Scheckel, C., Fak, J. J., Funk, J., Yao, K., Tajima, Y., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0420-0 · Cited by 295
The GABAA Receptor as a Therapeutic Target for Neurodevelopmental Disorders
Braat, S., & Kooy, R. F. (2015) · Neuron · doi:10.1016/j.neuron.2015.03.042 · Cited by 294
Emerging roles and functional mechanisms of PIWI-interacting RNAs
Wang, X., Ramat, A., Simonelig, M., & Liu, M. (2022) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-022-00528-0 · Cited by 300
Werling, D. M., Brand, H., An, J.-Y., Stone, M. R., Zhu, L., Glessner, J. T., Collins, R. L., Dong, S., Layer, R. M., Markenscoff-Papadimitriou, E., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0107-y · Cited by 293
Self-citationDe novo variants in neurodevelopmental disorders with epilepsy
Heyne, H., Singh, T., Stamberger, H., Jamra, R. A., Çağlayan, H., Craiu, D., Jonghe, P. D., Guerrini, R., Helbig, K. L., Koeleman, B. P. C., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0143-7 · Cited by 289
Ben-Shalom, R., Keeshen, C. M., Berrios, K. N., An, J. Y., Sanders, S. J., & Bender, K. J. (2017) · Biological Psychiatry · doi:10.1016/j.biopsych.2017.01.009 · Cited by 288
Self-citationSchafer, S. T., Paquola, A. C. M., Stern, S., Gosselin, D., Ku, M., Pena, M., Kuret, T. J. M., Liyanage, M., Mansour, A. A., Jaeger, B. N., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-018-0295-x · Cited by 288
Innate immune dysfunction and neuroinflammation in autism spectrum disorder (ASD)
Hughes, H. K., Moreno, R. J., & Ashwood, P. (2022) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2022.12.001 · Cited by 293
Fundamental Elements in Autism: From Neurogenesis and Neurite Growth to Synaptic Plasticity
Gilbert, J. P., & Man, H. (2017) · Frontiers in Cellular Neuroscience · doi:10.3389/fncel.2017.00359 · Cited by 286
Cheroni, C., Caporale, N., & Testa, G. (2020) · Molecular Autism · doi:10.1186/s13229-020-00370-1 · Cited by 290
COMPASS and SWI/SNF complexes in development and disease
Cenik, B. K., & Shilatifard, A. (2020) · Nature Reviews Genetics · doi:10.1038/s41576-020-0278-0 · Cited by 280
Histone Lysine Methylases and Demethylases in the Landscape of Human Developmental Disorders
Faùndes, V., Newman, W. G., Bernardini, L., Canham, N. L. E., Clayton‐Smith, J., Dallapiccola, B., Davies, S. J., Demos, M. K., Goldman, A. P., Gill, H., et al. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.11.013 · Cited by 274
Self-citationPrecise temporal regulation of alternative splicing during neural development
Weyn‐Vanhentenryck, S. M., Feng, H., Ustianenko, D., Duffié, R., Yan, Q., Jacko, M., Martinez, J. C., Goodwin, M., Zhang, X., Hengst, U., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-04559-0 · Cited by 272
Turner, T. N., Hormozdiari, F., Duyzend, M., McClymont, S. A., Hook, P. W., Iossifov, I. H., Raja, A. N., Baker, C. A., Hoekzema, K., Stessman, H. A. F., et al. (2015) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2015.11.023 · Cited by 272
Self-citationExtreme Polygenicity of Complex Traits Is Explained by Negative Selection
O'Connor, L. J., Schoech, A. P., Hormozdiari, F., Gazal, S., Patterson, N., & Price, A. L. (2019) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2019.07.003 · Cited by 268
Germline Chd8 haploinsufficiency alters brain development in mouse
Gompers, A. L., Su-Feher, L., Ellegood, J., Copping, N. A., Riyadh, M. A., Stradleigh, T. W., Pride, M. C., Schaffler, M. D., Wade, A. A., Catta-Preta, R., et al. (2017) · Nature Neuroscience · doi:10.1038/nn.4592 · Cited by 266
Metadata
- Authors
- Iossifov, I., O’Roak, B. J., Sanders, S. J., Ronemus, M., Krumm, N., Levy, D., Stessman, H. A., Witherspoon, K. T., Vives, L., Patterson, K. E., Smith, J. D., Paeper, B., Nickerson, D. A., Dea, J., Dong, S., Gonzalez, L. E., Mandell, J. D., Mane, S. M., Murtha, M. T., Sullivan, C. A., Walker, M. F., Waqar, Z., Wei, L., Willsey, A. J., Yamrom, B., Lee, Y.-H., Grabowska, E., Dalkic, E., Wang, Z., Marks, S., Andrews, P., Leotta, A., Kendall, J., Hakker, I., Rosenbaum, J., Ma, B., Rodgers, L., Troge, J., Narzisi, G., Yoon, S., Schatz, M. C., Ye, K., McCombie, W. R., Shendure, J., Eichler, E. E., State, M. W., & Wigler, M.
- Affiliations
- Cold Spring Harbor Laboratory (US)
- Oregon Health & Science University (US)
- University of California, San Francisco (US)
- University of Washington (US)
- Peking University (CN)
- Yale University (US)
- Albert Einstein College of Medicine (US)
- Howard Hughes Medical Institute (US)
- Corresponding author
- Shendure, Jay
- Eichler, Evan E.
- State, Matthew W.
- Wigler, Michael
- ORCID
- Iossifov, Ivan
- O’Roak, Brian J.
- Sanders, Stephan J.
- Ronemus, Michael
- Krumm, Niklas
- Stessman, Holly A.
- Patterson, Karynne E.
- Smith, Joshua D.
- Nickerson, Deborah A.
- Dea, Jeanselle
- Dong, Shan
- Mandell, Jeffrey D.
- Mane, Shrikant M.
- Sullivan, Catherine A.
- Wei, Liping
- Willsey, A. Jeremy
- Yamrom, Boris
- Rodgers, Linda
- Narzisi, Giuseppe
- Schatz, Michael C.
- Ye, Kenny
- McCombie, W. Richard
- Shendure, Jay
- Eichler, Evan E.
- State, Matthew W.
- Wigler, Michael
- Venue
- Nature
- Published
- 2014-10-29
- Volume
- 515
- Issue
- 7526
- Pages
- 216-221
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC4313871
- OpenAlex
- W2008627757
- Cited by
- 2,585
- References
- 51
- Citations by year
- 2026: 125 · 2025: 178 · 2024: 204 · 2023: 209 · 2022: 263 · 2021: 266 · 2020: 298 · 2019: 232 · 2018: 202 · 2017: 237 · 2016: 210 · 2015: 146 · 2014: 12
- FWCI
- 112.46
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Cold Spring Harbor Laboratory Institutional Repository (Cold Spring Harbor Laboratory) (acceptedVersion)
- Zonguldak Bülent Ecevit University Institutional Repository (submittedVersion, cc-by-nc-nd)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- Howard Hughes Medical Institute
- National Institutes of Health (1rc2hl103010-01, UL1TR000142)
- Canadian Institutes of Health Research
- National Heart, Lung, and Blood Institute (RC2HL102925, RC2HL102926, RC2HL102923, UC2HL102924, RC2HL-103010, UC2HL102925, UC2HL102923, UC2HL103010, RC2HL102924, UC2HL102926)
- National Institute of Mental Health (R01MH101221)
- National Institute of General Medical Sciences (T32GM007266)
- National Center for Advancing Translational Sciences
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (U54HD083091)
- 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, de novo mutations, simplex families, missense mutations, copy number variation, sex differences, intellectual disability, schizophrenia, chromatin modifiers, exome sequencing, genetic architecture, affected siblings, synaptic genes, chromatin remodeling
- MeSH terms
- Child, Child Development Disorders, Pervasive, Female, Genes, Humans, Intelligence Tests, Male, Mutation, Reproducibility of Results, Cluster Analysis, Open Reading Frames, Genetic Predisposition to Disease, Exome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
