Dataset Information

Whole genome sequencing resource identifies 18 new candidate genes for autism spectrum disorder.

ABSTRACT: We are performing whole-genome sequencing of families with autism spectrum disorder (ASD) to build a resource (MSSNG) for subcategorizing the phenotypes and underlying genetic factors involved. Here we report sequencing of 5,205 samples from families with ASD, accompanied by clinical information, creating a database accessible on a cloud platform and through a controlled-access internet portal. We found an average of 73.8 de novo single nucleotide variants and 12.6 de novo insertions and deletions or copy number variations per ASD subject. We identified 18 new candidate ASD-risk genes and found that participants bearing mutations in susceptibility genes had significantly lower adaptive ability (P = 6 × 10-4). In 294 of 2,620 (11.2%) of ASD cases, a molecular basis could be determined and 7.2% of these carried copy number variations and/or chromosomal abnormalities, emphasizing the importance of detecting all forms of genetic variation as diagnostic and therapeutic targets in ASD.

SUBMITTER: C Yuen RK

PROVIDER: S-EPMC5501701 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Whole genome sequencing resource identifies 18 new candidate genes for autism spectrum disorder.

C Yuen Ryan K RK Merico Daniele D Bookman Matt M L Howe Jennifer J Thiruvahindrapuram Bhooma B Patel Rohan V RV Whitney Joe J Deflaux Nicole N Bingham Jonathan J Wang Zhuozhi Z Pellecchia Giovanna G Buchanan Janet A JA Walker Susan S Marshall Christian R CR Uddin Mohammed M Zarrei Mehdi M Deneault Eric E D'Abate Lia L Chan Ada J S AJ Koyanagi Stephanie S Paton Tara T Pereira Sergio L SL Hoang Ny N Engchuan Worrawat W Higginbotham Edward J EJ Ho Karen K Lamoureux Sylvia S Li Weili W MacDonald Jeffrey R JR Nalpathamkalam Thomas T Sung Wilson W L WW Tsoi Fiona J FJ Wei John J Xu Lizhen L Tasse Anne-Marie AM Kirby Emily E Van Etten William W Twigger Simon S Roberts Wendy W Drmic Irene I Jilderda Sanne S Modi Bonnie MacKinnon BM Kellam Barbara B Szego Michael M Cytrynbaum Cheryl C Weksberg Rosanna R Zwaigenbaum Lonnie L Woodbury-Smith Marc M Brian Jessica J Senman Lili L Iaboni Alana A Doyle-Thomas Krissy K Thompson Ann A Chrysler Christina C Leef Jonathan J Savion-Lemieux Tal T Smith Isabel M IM Liu Xudong X Nicolson Rob R Seifer Vicki V Fedele Angie A Cook Edwin H EH Dager Stephen S Estes Annette A Gallagher Louise L Malow Beth A BA Parr Jeremy R JR Spence Sarah J SJ Vorstman Jacob J Frey Brendan J BJ Robinson James T JT Strug Lisa J LJ Fernandez Bridget A BA Elsabbagh Mayada M Carter Melissa T MT Hallmayer Joachim J Knoppers Bartha M BM Anagnostou Evdokia E Szatmari Peter P Ring Robert H RH Glazer David D Pletcher Mathew T MT Scherer Stephen W SW

Nature neuroscience 20170306 4

We are performing whole-genome sequencing of families with autism spectrum disorder (ASD) to build a resource (MSSNG) for subcategorizing the phenotypes and underlying genetic factors involved. Here we report sequencing of 5,205 samples from families with ASD, accompanied by clinical information, creating a database accessible on a cloud platform and through a controlled-access internet portal. We found an average of 73.8 de novo single nucleotide variants and 12.6 de novo insertions and deletio ...[more]

PMID: 28263302

Similar Datasets

Project description:Autism spectrum disorder (ASD) is a highly heritable condition caused by a combination of environmental and genetic factors such as de novo and inherited variants, as well as rare or common variants among hundreds of related genes. Previous genome-wide association studies have identified susceptibility genes; however, most ASD-associated genes remain undiscovered. This study aimed to examine rare de novo variants to identify genetic risk factors of ASD using whole exome sequencing (WES), functional characterization, and genetic network analyses of identified variants using Korean familial dataset. We recruited children with ASD and their biological parents. The clinical best estimate diagnosis of ASD was made according to the Diagnostic and Statistical Manual of Mental Disorders (DSM-5TM), using comprehensive diagnostic instruments. The final analyses included a total of 151 individuals from 51 families. Variants were identified and filtered using the GATK Best Practices for bioinformatics analysis, followed by genome alignments and annotation to the reference genome assembly GRCh37 (liftover to GRCh38), and further annotated using dbSNP 154 build databases. To evaluate allele frequencies of de novo variants, we used the dbSNP, gnomAD exome v2.1.1, and genome v3.0. We used Ingenuity Pathway Analysis (IPA, Qiagen) software to construct networks using all identified de novo variants with known autism-related genes to find probable relationships. We identified 36 de novo variants with potential relations to ASD; 27 missense, two silent, one nonsense, one splice region, one splice site, one 5' UTR, and one intronic SNV and two frameshift deletions. We identified six networks with functional relationships. Among the interactions between de novo variants, the IPA assay found that the NF-κB signaling pathway and its interacting genes were commonly observed at two networks. The relatively small cohort size may affect the results of novel ASD genes with de novo variants described in our findings. We did not conduct functional experiments in this study. Because of the diversity and heterogeneity of ASD, the primary purpose of this study was to investigate probable causative relationships between novel de novo variants and known autism genes. Additionally, we based functional relationships with known genes on network analysis rather than on statistical analysis. We identified new variants that may underlie genetic factors contributing to ASD in Korean families using WES and genetic network analyses. We observed novel de novo variants that might be functionally linked to ASD, of which the variants interact with six genetic networks.

Dataset Information

Whole genome sequencing resource identifies 18 new candidate genes for autism spectrum disorder.

Publications

Whole genome sequencing resource identifies 18 new candidate genes for autism spectrum disorder.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets