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Inherited and De Novo Genetic Risk for Autism Impacts Shared Networks

Ruzzo, Elizabeth K. and Pérez-Cano, Laura and Jung, Jae-Yoon and Wang, Lee-kai and Kashef-Haghighi, Dorna and Hartl, Chris and Singh, Chanpreet and Xu, Jin and Hoekstra, Jackson N. and Leventhal, Olivia and Leppä, Virpi M. and Gandal, Michael J. and Paskov, Kelley and Stockham, Nate and Polioudakis, Damon and Lowe, Jennifer K. and Prober, David A. and Geschwind, Daniel H. and Wall, Dennis P. (2019) Inherited and De Novo Genetic Risk for Autism Impacts Shared Networks. Cell, 178 (4). pp. 850-866. ISSN 0092-8674.

[img] MS Excel (Table S1. iHART Sample Information, Related to Figures 1 and 2) - Supplemental Material
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[img] MS Excel (Table S2. RDNV Rates in Multiplex versus Simplex Autism Families, Related to Figure 3) - Supplemental Material
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[img] MS Excel (Table S3. TADA Results, Related to Figure 4) - Supplemental Material
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[img] MS Excel (Table S4. NetSig Significant Genes, Related to Figure 5) - Supplemental Material
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[img] MS Excel (Table S5. Structural Variant Method Details, Related to Figure 1) - Supplemental Material
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We performed a comprehensive assessment of rare inherited variation in autism spectrum disorder (ASD) by analyzing whole-genome sequences of 2,308 individuals from families with multiple affected children. We implicate 69 genes in ASD risk, including 24 passing genome-wide Bonferroni correction and 16 new ASD risk genes, most supported by rare inherited variants, a substantial extension of previous findings. Biological pathways enriched for genes harboring inherited variants represent cytoskeletal organization and ion transport, which are distinct from pathways implicated in previous studies. Nevertheless, the de novo and inherited genes contribute to a common protein-protein interaction network. We also identified structural variants (SVs) affecting non-coding regions, implicating recurrent deletions in the promoters of DLG2 and NR3C2. Loss of nr3c2 function in zebrafish disrupts sleep and social function, overlapping with human ASD-related phenotypes. These data support the utility of studying multiplex families in ASD and are available through the Hartwell Autism Research and Technology portal.

Item Type:Article
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Singh, Chanpreet0000-0002-2697-3033
Prober, David A.0000-0002-7371-4675
Geschwind, Daniel H.0000-0003-2896-3450
Additional Information:© 2019 Elsevier. Received 15 November 2018, Revised 8 April 2019, Accepted 11 July 2019, Available online 8 August 2019. We thank Stephanie A. Arteaga, Stephanie N. Kravitz, Cheyenne L. Schloffman, Min Sun, Tor Solli-Nowlan, T. Chang, Hyejung Won, Sasha Sharma, Marlena Duda, Greg Madden McInnes, Ravina Jain, Valentí Moncunill, Josep M. Mercader, Montserrat Puiggròs, Hailey H. Choi, Anika Gupta, and David Torrents for technical support and Hannah Hurley and Amina Kinkhabwala for assistance with zebrafish experiments. We are grateful to The Hartwell Foundation for supporting the creation of the iHART database. We are grateful to the Simons Foundation for additional support for genome sequencing. We are grateful to the PRACE Research Infrastructure resource MareNostrum III based in Spain at the Barcelona Supercomputing Center. We thank the New York Genome Center for conducting sequencing and initial quality control. We thank A. Gordon, J. Huang, J. Sebat, and D. Antaki for help with resolving the DLG2 structural variant. We thank Amazon Web Services for their grant support for the computational infrastructure and storage for the iHART database. We thank J. Sul for helpful discussions and for suggesting a machine learning approach. This work has been supported by grants from The Hartwell Foundation and the NIH (U24MH081810, R01MH064547, NS101158, NS070911, NS101665, NS095824, S10OD011939, P30AG10161, R01AG17917, and U01AG61356) and from the Stanford Precision Health and Integrated Diagnostics Center and from the Stanford Bio-X Center. We are grateful to all of the families at the participating SSC sites as well as the principal investigators (A. Beaudet, R. Bernier, J. Constantino, E. Cook, E. Fombonne, D. Geschwind, R. Goin-Kochel, E. Hanson, D. Grice, A. Klin, D. Ledbetter, C. Lord, C. Martin, D. Martin, R. Maxim, J. Miles, O. Ousley, K. Pelphrey, B. Peterson, J. Piggot, C. Saulnier, M. State, W. Stone, J. Sutcliffe, C. Walsh, Z. Warren, and E. Wijsman). We appreciate obtaining access to genetic data on SFARI Base. Approved researchers can obtain the SSC population dataset described in this study ( by applying at Author Contributions: E.K.R. and L.P.C. contributed to the analytical plans, performed analyses, and interpreted results. J.K.L. selected and submitted samples for sequencing. E.K.R., J.Y.J., L.K.W., and J.K.L. performed quality control checks. L.K.W. wrote scripts for data processing and helped interpret results. L.P.C., D.K.H., J.Y.J., E.K.R., and D.P.W. developed ARC. C.H. interpreted results and ran TADA simulations. E.K.R. and C.H. ran high-risk inherited simulations. J.Y.J. and D.P.W. designed the access systems. J.Y.J. performed joint genotyping, VCF annotation, and data transfers. L.P.C. and D.K.H. processed SVs, and L.P.C. wrote the SV cross-algorithm comparison pipeline. D.P. performed single-cell analyses. M.J.G. analyzed phenotypes. V.M.L. helped with array-based CNV analyses. C.S., J.X., and D.A.P. performed and analyzed zebrafish experiments. D.P.W. identified and supplied funding. E.K.R. and D.H.G. took the lead in writing the manuscript, and all authors reviewed, edited, and approved the manuscript. D.H.G. and D.P.W. supervised the experimental design and analysis and interpreted results. Data and Code Availability: The whole-genome sequencing data generated during this study are available from the Hartwell Foundation’s Autism Research and Technology Initiative (iHART) following request and approval of the data use agreement available at Access to the whole-genome sequencing data generated in this study will be subject to approval by Autism Speaks and AGRE. Details about the format of the data, access options, and access instructions are included at We also freely provide the code for ARC (Artifact Removal by Classifier), our random forest supervised model developed to distinguish true rare de novo variants from LCL-specific genetic aberrations or other types of artifacts such as sequencing and mapping errors, together with a full tutorial at Interactive genotype/phenotype search engine: To facilitate sharing of iHART data with the broader autism research community and patients, we implemented a set of online data access methods to preview and search genetic variants and phenotypic traits ( Zebrafish data: The zebrafish datasets generated and analyzed in this study, and the code used to generate the data, are available upon request. The authors declare no competing interests.
Funding AgencyGrant Number
Hartwell FoundationUNSPECIFIED
NIHU24 MH081810
Stanford Precision Health and Integrated Diagnostics CenterUNSPECIFIED
Stanford Bio-X CenterUNSPECIFIED
Subject Keywords:genetics; autism; ASD; multiplex families; machine learning; inherited; de novo
Issue or Number:4
Record Number:CaltechAUTHORS:20190808-135241284
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Official Citation:Elizabeth K. Ruzzo, Laura Pérez-Cano, Jae-Yoon Jung, Lee-kai Wang, Dorna Kashef-Haghighi, Chris Hartl, Chanpreet Singh, Jin Xu, Jackson N. Hoekstra, Olivia Leventhal, Virpi M. Leppä, Michael J. Gandal, Kelley Paskov, Nate Stockham, Damon Polioudakis, Jennifer K. Lowe, David A. Prober, Daniel H. Geschwind, Dennis P. Wall, Inherited and De Novo Genetic Risk for Autism Impacts Shared Networks, Cell, Volume 178, Issue 4, 2019, Pages 850-866.e26, ISSN 0092-8674, (
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:97709
Deposited By: George Porter
Deposited On:09 Aug 2019 14:47
Last Modified:03 Oct 2019 21:34

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