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Large-scale targeted sequencing identifies risk genes for neurodevelopmental disorders.

Tianyun Wang | Kendra Hoekzema | Davide Vecchio | Huidan Wu | Arvis Sulovari | Bradley P Coe | Madelyn A Gillentine | Amy B Wilfert | Luis A Perez-Jurado | Malin Kvarnung | Yoeri Sleyp | Rachel K Earl | Jill A Rosenfeld | Madeleine R Geisheker | Lin Han | Bing Du | Chris Barnett | Elizabeth Thompson | Marie Shaw | Renee Carroll | Kathryn Friend | Rachael Catford | Elizabeth E Palmer | Xiaobing Zou | Jianjun Ou | Honghui Li | Hui Guo | Jennifer Gerdts | Emanuela Avola | Giuseppe Calabrese | Maurizio Elia | Donatella Greco | Anna Lindstrand | Ann Nordgren | Britt-Marie Anderlid | Geert Vandeweyer | Anke Van Dijck | Nathalie Van der Aa | Brooke McKenna | Miroslava Hancarova | Sarka Bendova | Marketa Havlovicova | Giovanni Malerba | Bernardo Dalla Bernardina | Pierandrea Muglia | Arie van Haeringen | Mariette J V Hoffer | Barbara Franke | Gerarda Cappuccio | Martin Delatycki | Paul J Lockhart | Melanie A Manning | Pengfei Liu | Ingrid E Scheffer | Nicola Brunetti-Pierri | Nanda Rommelse | David G Amaral | Gijs W E Santen | Elisabetta Trabetti | Zdeněk Sedláček | Jacob J Michaelson | Karen Pierce | Eric Courchesne | R Frank Kooy | SPARK Consortium | Magnus Nordenskjöld | Corrado Romano | Hilde Peeters | Raphael A Bernier | Jozef Gecz | Kun Xia | Evan E Eichler
Nature communications | 2020

Most genes associated with neurodevelopmental disorders (NDDs) were identified with an excess of de novo mutations (DNMs) but the significance in case-control mutation burden analysis is unestablished. Here, we sequence 63 genes in 16,294 NDD cases and an additional 62 genes in 6,211 NDD cases. By combining these with published data, we assess a total of 125 genes in over 16,000 NDD cases and compare the mutation burden to nonpsychiatric controls from ExAC. We identify 48 genes (25 newly reported) showing significant burden of ultra-rare (MAF < 0.01%) gene-disruptive mutations (FDR 5%), six of which reach family-wise error rate (FWER) significance (p < 1.25E-06). Among these 125 targeted genes, we also reevaluate DNM excess in 17,426 NDD trios with 6,499 new autism trios. We identify 90 genes enriched for DNMs (FDR 5%; e.g., GABRG2 and UIMC1); of which, 61 reach FWER significance (p < 3.64E-07; e.g., CASZ1). In addition to doubling the number of patients for many NDD risk genes, we present phenotype-genotype correlations for seven risk genes (CTCF, HNRNPU, KCNQ3, ZBTB18, TCF12, SPEN, and LEO1) based on this large-scale targeted sequencing effort.

Pubmed ID: 33004838 RIS Download

Associated grants

  • Agency: NICHD NIH HHS, United States
    Id: P50 HD103524
  • Agency: Howard Hughes Medical Institute, United States
  • Agency: NHGRI NIH HHS, United States
    Id: T32 HG000035
  • Agency: NIMH NIH HHS, United States
    Id: R01 MH109912
  • Agency: NIMH NIH HHS, United States
    Id: R01 MH101221

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This is a list of tools and resources that we have found mentioned in this publication.


Ensembl (tool)

RRID:SCR_002344

Collection of genome databases for vertebrates and other eukaryotic species with DNA and protein sequence search capabilities. Used to automatically annotate genome, integrate this annotation with other available biological data and make data publicly available via web. Ensembl tools include BLAST, BLAT, BioMart and the Variant Effect Predictor (VEP) for all supported species.

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Spark (tool)

RRID:SCR_006207

A clustering and visualization tool that enables the interactive exploration of genome-wide data, with a specialization in epigenomics data. Spark is also available as a service within the Epigenome toolset of the Genboree Workbench. The approach utilizes data clusters as a high-level visual guide and supports interactive inspection of individual regions within each cluster. The cluster view links to gene ontology analysis tools and the detailed region view connects to existing genome browser displays taking advantage of their wealth of annotation and functionality.

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OMIM (tool)

RRID:SCR_006437

Online catalog of human genes and genetic disorders, for clinical features, phenotypes and genes. Collection of human genes and genetic phenotypes, focusing on relationship between phenotype and genotype. Referenced overviews in OMIM contain information on all known mendelian disorders and variety of related genes. It is updated daily, and entries contain copious links to other genetics resources.

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1000 Genomes Project and AWS (tool)

RRID:SCR_008801

A dataset containing the full genomic sequence of 1,700 individuals, freely available for research use. The 1000 Genomes Project is an international research effort coordinated by a consortium of 75 companies and organizations to establish the most detailed catalogue of human genetic variation. The project has grown to 200 terabytes of genomic data including DNA sequenced from more than 1,700 individuals that researchers can now access on AWS for use in disease research free of charge. The dataset containing the full genomic sequence of 1,700 individuals is now available to all via Amazon S3. The data can be found at: http://s3.amazonaws.com/1000genomes The 1000 Genomes Project aims to include the genomes of more than 2,662 individuals from 26 populations around the world, and the NIH will continue to add the remaining genome samples to the data collection this year. Public Data Sets on AWS provide a centralized repository of public data hosted on Amazon Simple Storage Service (Amazon S3). The data can be seamlessly accessed from AWS services such Amazon Elastic Compute Cloud (Amazon EC2) and Amazon Elastic MapReduce (Amazon EMR), which provide organizations with the highly scalable compute resources needed to take advantage of these large data collections. AWS is storing the public data sets at no charge to the community. Researchers pay only for the additional AWS resources they need for further processing or analysis of the data. All 200 TB of the latest 1000 Genomes Project data is available in a publicly available Amazon S3 bucket. You can access the data via simple HTTP requests, or take advantage of the AWS SDKs in languages such as Ruby, Java, Python, .NET and PHP. Researchers can use the Amazon EC2 utility computing service to dive into this data without the usual capital investment required to work with data at this scale. AWS also provides a number of orchestration and automation services to help teams make their research available to others to remix and reuse. Making the data available via a bucket in Amazon S3 also means that customers can crunch the information using Hadoop via Amazon Elastic MapReduce, and take advantage of the growing collection of tools for running bioinformatics job flows, such as CloudBurst and Crossbow.

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KING (tool)

RRID:SCR_009251

Software toolset that makes use of high-throughput SNP data typically seen in a genome-wide association study (GWAS) for applications such as family relationship inference and population structure identification (entry from Genetic Analysis Software)

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FreeBayes (tool)

RRID:SCR_010761

A Bayesian genetic variant detector designed to find small polymorphisms, specifically SNPs, indels, MNPs, and complex events smaller than the length of a short-read sequencing alignment.

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Genome Aggregation Database (tool)

RRID:SCR_014964

Database that aggregates exome and genome sequencing data from large-scale sequencing projects. The gnomAD data set contains individuals sequenced using multiple exome capture methods and sequencing chemistries. Raw data from the projects have been reprocessed through the same pipeline, and jointly variant-called to increase consistency across projects.

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Combined Annotation Dependent Depletion (tool)

RRID:SCR_018393

Web tool for predicting deleteriousness of variants throughout human genome. Software tool for scoring deleteriousness of single nucleotide variants as well as insertion and deletions variants in human genome.

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