Nat Neurosci. 2017 Apr;20(4):602-611. doi: 10.1038/nn.4524. Epub 2017 Mar 6.

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

C Yuen RK¹, Merico D^1,², Bookman M^3,⁴, L Howe J¹, Thiruvahindrapuram B¹, Patel RV¹, Whitney J¹, Deflaux N^3,⁴, Bingham J^3,⁴, Wang Z¹, Pellecchia G¹, Buchanan JA¹, Walker S¹, Marshall CR^1,⁵, Uddin M¹, Zarrei M¹, Deneault E¹, D'Abate L^1,⁶, Chan AJ^1,⁶, Koyanagi S¹, Paton T¹, Pereira SL¹, Hoang N^1,⁷, Engchuan W¹, Higginbotham EJ¹, Ho K¹, Lamoureux S¹, Li W¹, MacDonald JR¹, Nalpathamkalam T¹, Sung WW¹, Tsoi FJ¹, Wei J¹, Xu L¹, Tasse AM⁸, Kirby E⁸, Van Etten W⁹, Twigger S⁹, Roberts W⁷, Drmic I^1,⁷, Jilderda S^1,⁷, Modi BM^1,⁷, Kellam B¹, Szego M^1,¹⁰, Cytrynbaum C^6,^10,^11,¹², Weksberg R^6,^11,¹², Zwaigenbaum L¹³, Woodbury-Smith M^1,¹⁴, Brian J¹⁵, Senman L¹⁵, Iaboni A¹⁵, Doyle-Thomas K¹⁵, Thompson A¹⁴, Chrysler C¹⁴, Leef J¹⁵, Savion-Lemieux T¹⁶, Smith IM¹⁷, Liu X¹⁸, Nicolson R^19,²⁰, Seifer V²¹, Fedele A²¹, Cook EH²², Dager S²³, Estes A²⁴, Gallagher L²⁵, Malow BA²⁶, Parr JR²⁷, Spence SJ²⁸, Vorstman J²⁹, Frey BJ^2,³⁰, Robinson JT³¹, Strug LJ^1,³², Fernandez BA³³, Elsabbagh M¹⁶, Carter MT^12,³⁴, Hallmayer J³⁵, Knoppers BM³⁶, Anagnostou E¹⁵, Szatmari P^37,^38,³⁹, Ring RH⁴⁰, Glazer D^3,⁴, Pletcher MT²¹, Scherer SW^1,^6,⁴¹.

Author information

1: The Centre for Applied Genomics, Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Canada.
2: Deep Genomics Inc., Toronto, Canada.
3: Google, Mountain View, California, USA.
4: Verily Life Sciences, South San Francisco, California, USA.
5: Genome Diagnostics, Department of Paediatric Laboratory Medicine, The Hospital for Sick Children, Toronto, Canada.
6: Department of Molecular Genetics, University of Toronto, Toronto, Canada.
7: Autism Research Unit, The Hospital for Sick Children, Toronto, Canada.
8: Public Population Project in Genomics and Society, McGill University, Montreal, Canada.
9: BioTeam Inc., Middleton, Massachusetts, USA.
10: Dalla Lana School of Public Health and the Department of Family and Community Medicine, University of Toronto, Toronto, Ontario, Canada.
11: Program in Genetics and Genome Biology, The Hospital for Sick Children, Toronto, Canada.
12: Division of Clinical and Metabolic Genetics, The Hospital for Sick Children, Toronto, Canada.
13: Department of Pediatrics, University of Alberta, Edmonton, Canada.
14: Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, Canada.
15: Bloorview Research Institute, University of Toronto, Toronto, Canada. .
16: Department of Psychiatry, McGill University, Montreal, Canada.
17: Departments of Pediatrics and of Psychology &Neuroscience, Dalhousie University and Autism Research Centre, IWK Health Centre, Halifax, Canada.
18: Department of Psychiatry, Queen's University, Kinston, Canada.
19: Children's Health Research Institute, London, Ontario, Canada.
20: Western University, London, Ontario, Canada.
21: Autism Speaks, New York, New York, USA.
22: Institute for Juvenile Research, Department of Psychiatry, University of Illinois at Chicago, Chicago, Illinois, USA.
23: Department of Radiology, University of Washington, Seattle, Washington, USA.
24: Department of Speech and Hearing Sciences, University of Washington, Seattle, Washington, USA.
25: Department of Psychiatry, School of Medicine, Trinity College Dublin, Dublin, Ireland.
26: Sleep Disorders Division, Department of Neurology, Vanderbilt University School of Medicine, Nashville, Tennessee, USA.
27: Institute of Neuroscience, Newcastle University, Newcastle Upon Tyne, UK.
28: Department of Neurology, Boston Children's Hospital, Boston, Massachusetts, USA.
29: Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands.
30: Department of Electrical and Computer Engineering and Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Canada.
31: Department of Medicine, University of California San Diego, La Jolla, California, USA.
32: Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Canada.
33: Disciplines of Genetics and Medicine, Memorial University of Newfoundland and Provincial Medical Genetic Program, Eastern Health, St. John's, Canada.
34: Regional Genetics Program, The Children's Hospital of Eastern Ontario, Ottawa, Canada.
35: Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California, USA.
36: Centre of Genomics and Policy, McGill University, Montreal, Canada.
37: Child Youth and Family Services, Centre for Addiction and Mental Health, Toronto, Canada.
38: Department of Psychiatry, University of Toronto, Toronto, Canada.
39: Department of Psychiatry, The Hospital for Sick Children, Toronto, Canada.
40: Department of Pharmacology &Physiology, Drexel University College of Medicine, Philadelphia, Pennsylvania, USA.
41: McLaughlin Centre, University of Toronto, Toronto, Canada.

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.

PMID:: 28263302
PMCID:: PMC5501701
DOI:: 10.1038/nn.4524

[Indexed for MEDLINE]

Free PMC Article

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Figure 1

Schematic of sample and data processing in MSSNG

An executive committee oversees the project. The parameters for DNA sample selection and (genetic and phenotypic) data are managed by the committee, including consenting and ethics protocols. Coded identifiers’ for samples selected for WGS are posted as they are identified at MSSNG portal (http://mss.ng/research), so the ASD research community can monitor progress. Phenome data include subject information (identity number, year of birth, sex), family code (proband, parent, sibling), results of diagnostic tests (e.g. ADOS, ADI-R, age at diagnosis, functional assessments, intelligence tests, body measurements and dysmorphic features). The database accommodates as much of this information as is available for each sample, but that varies widely. Future plans include incorporation of fields for co-morbidities, related conditions, exposures, extended family history, interventions, and other parameters that become apparent. WGS technologies were Complete Genomics and Illumina HiSeq (2000 and X). WGS data are transferred to Google Genomics for data processing through the Google Cloud. Ref-blocked gVCFs were generated and stored in Google Cloud Storage, which were also processed for de novo mutation detection in the local cluster (for Complete Genomics data using filtering method) and Google Compute Engine (for Illumina data using DenovoGear). The Ref-blocked gVCFs and the de novo mutations were annotated through the Google Compute Engine (using Annovar), which can be accessed through the BigQuery tables. Quality controls of the genomic data were performed in the local cluster and the Google Cloud. The processed genetic data and the phenotypic data are accessible through the MSSNG Portal interface. The MSSNG database is designed to support incremental addition of data without changes in architecture, scaling to at least tens of thousands. New WGS and phenotype data are continually added to MSSNG as new batches of 1,000 samples are processed. DACO: Data access committee; UPD: Uniparental disomy; Ti/Tv: Transition to transversion ratio; IBS: Identity by state.

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

Nat Neurosci. 2017 Apr;20(4):602-611.