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Shadrina M, Kalay Ö, Demirkaya-Budak S, LeDuc CA, Chung WK, Turgut D, Budak G, Arslan E, Semenyuk V, Davis-Dusenbery B, Seidman CE, Yost HJ, Jain A, Gelb BD. Automated Identification of Germline de novo Mutations in Family Trios: A Consensus-Based Informatic Approach. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.08.584100. [PMID: 38559260 PMCID: PMC10979888 DOI: 10.1101/2024.03.08.584100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Accurate identification of germline de novo variants (DNVs) remains a challenging problem despite rapid advances in sequencing technologies as well as methods for the analysis of the data they generate, with putative solutions often involving ad hoc filters and visual inspection of identified variants. Here, we present a purely informatic method for the identification of DNVs by analyzing short-read genome sequencing data from proband-parent trios. Our method evaluates variant calls generated by three genome sequence analysis pipelines utilizing different algorithms-GATK HaplotypeCaller, DeepTrio and Velsera GRAF-exploring the assumption that a requirement of consensus can serve as an effective filter for high-quality DNVs. We assessed the efficacy of our method by testing DNVs identified using a previously established, highly accurate classification procedure that partially relied on manual inspection and used Sanger sequencing to validate a DNV subset comprising less confident calls. The results show that our method is highly precise and that applying a force-calling procedure to putative variants further removes false-positive calls, increasing precision of the workflow to 99.6%. Our method also identified novel DNVs, 87% of which were validated, indicating it offers a higher recall rate without compromising accuracy. We have implemented this method as an automated bioinformatics workflow suitable for large-scale analyses without need for manual intervention.
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Affiliation(s)
- Mariya Shadrina
- Mindich Child Health and Development Institute and the Department of Genetics and Genomic Sciences, Icahn School of Medicine, New York, NY, USA
| | - Özem Kalay
- Velsera Inc, 529 Main St, Suite 6610, Charlestown, MA, USA
| | | | - Charles A. LeDuc
- Department of Pediatrics, Columbia University, New York, NY, USA
| | - Wendy K. Chung
- Department of Pediatrics, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Deniz Turgut
- Velsera Inc, 529 Main St, Suite 6610, Charlestown, MA, USA
| | - Gungor Budak
- Velsera Inc, 529 Main St, Suite 6610, Charlestown, MA, USA
| | - Elif Arslan
- Velsera Inc, 529 Main St, Suite 6610, Charlestown, MA, USA
| | | | | | - Christine E. Seidman
- Division of Cardiovascular Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - H. Joseph Yost
- Molecular Medicine Program, University of Utah, Salt Lake City, UT, USA
| | - Amit Jain
- Velsera Inc, 529 Main St, Suite 6610, Charlestown, MA, USA
| | - Bruce D. Gelb
- Mindich Child Health and Development Institute and the Department of Genetics and Genomic Sciences, Icahn School of Medicine, New York, NY, USA
- Department of Pediatrics, Icahn School of Medicine, New York, NY, USA
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2
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Ng JK, Turner TN. HAT: de novo variant calling for highly accurate short-read and long-read sequencing data. Bioinformatics 2024; 40:btad775. [PMID: 38175776 PMCID: PMC10777354 DOI: 10.1093/bioinformatics/btad775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 12/05/2023] [Indexed: 01/06/2024] Open
Abstract
MOTIVATION de novo variants (DNVs) are variants that are present in offspring but not in their parents. DNVs are both important for examining mutation rates as well as in the identification of disease-related variation. While efforts have been made to call DNVs, calling of DNVs is still challenging from parent-child sequenced trio data. We developed Hare And Tortoise (HAT) as an automated DNV detection workflow for highly accurate short-read and long-read sequencing data. Reliable detection of DNVs is important for human genomics and HAT addresses this need. RESULTS HAT is a computational workflow that begins with aligned read data (i.e. CRAM or BAM) from a parent-child sequenced trio and outputs DNVs. HAT detects high-quality DNVs from Illumina short-read whole-exome sequencing, Illumina short-read whole-genome sequencing, and highly accurate PacBio HiFi long-read whole-genome sequencing data. The quality of these DNVs is high based on a series of quality metrics including number of DNVs per individual, percent of DNVs at CpG sites, and percent of DNVs phased to the paternal chromosome of origin. AVAILABILITY AND IMPLEMENTATION https://github.com/TNTurnerLab/HAT.
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Affiliation(s)
- Jeffrey K Ng
- Department of Genetics, Washington University School of Medicine, St Louis, MO 63110, USA
| | - Tychele N Turner
- Department of Genetics, Washington University School of Medicine, St Louis, MO 63110, USA
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3
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Zhou W, Fischer A, Ogwang MD, Luo W, Kerchan P, Reynolds SJ, Tenge CN, Were PA, Kuremu RT, Wekesa WN, Masalu N, Kawira E, Kinyera T, Otim I, Legason ID, Nabalende H, Ayers LW, Bhatia K, Goedert JJ, Gouveia MH, Cole N, Hicks B, Jones K, Hummel M, Schlesner M, Chagaluka G, Mutalima N, Borgstein E, Liomba GN, Kamiza S, Mkandawire N, Mitambo C, Molyneux EM, Newton R, Glaser S, Kretzmer H, Manning M, Hutchinson A, Hsing AW, Tettey Y, Adjei AA, Chanock SJ, Siebert R, Yeager M, Prokunina-Olsson L, Machiela MJ, Mbulaiteye SM. Mosaic chromosomal alterations in peripheral blood leukocytes of children in sub-Saharan Africa. Nat Commun 2023; 14:8081. [PMID: 38057307 PMCID: PMC10700489 DOI: 10.1038/s41467-023-43881-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 11/22/2023] [Indexed: 12/08/2023] Open
Abstract
In high-income countries, mosaic chromosomal alterations in peripheral blood leukocytes are associated with an elevated risk of adverse health outcomes, including hematologic malignancies. We investigate mosaic chromosomal alterations in sub-Saharan Africa among 931 children with Burkitt lymphoma, an aggressive lymphoma commonly characterized by immunoglobulin-MYC chromosomal rearrangements, 3822 Burkitt lymphoma-free children, and 674 cancer-free men from Ghana. We find autosomal and X chromosome mosaic chromosomal alterations in 3.4% and 1.7% of Burkitt lymphoma-free children, and 8.4% and 3.7% of children with Burkitt lymphoma (P-values = 5.7×10-11 and 3.74×10-2, respectively). Autosomal mosaic chromosomal alterations are detected in 14.0% of Ghanaian men and increase with age. Mosaic chromosomal alterations in Burkitt lymphoma cases include gains on chromosomes 1q and 8, the latter spanning MYC, while mosaic chromosomal alterations in Burkitt lymphoma-free children include copy-neutral loss of heterozygosity on chromosomes 10, 14, and 16. Our results highlight mosaic chromosomal alterations in sub-Saharan African populations as a promising area of research.
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Affiliation(s)
- Weiyin Zhou
- Division of Cancer Epidemiology & Genetics, National Cancer Institute, National Institutes of Health, US Department of Health and Human Services, Bethesda, MD, USA
- Cancer Genomics Research Laboratory, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Anja Fischer
- Institute of Human Genetics, Ulm University and Ulm University Medical Center, Ulm, Germany
| | | | - Wen Luo
- Division of Cancer Epidemiology & Genetics, National Cancer Institute, National Institutes of Health, US Department of Health and Human Services, Bethesda, MD, USA
- Cancer Genomics Research Laboratory, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | | | - Steven J Reynolds
- Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Constance N Tenge
- EMBLEM Study, Moi University College of Health Sciences, Eldoret, Kenya
| | - Pamela A Were
- EMBLEM Study, Academic Model Providing Access To Healthcare (AMPATH), Eldoret, Kenya
| | - Robert T Kuremu
- EMBLEM Study, Moi University College of Health Sciences, Eldoret, Kenya
| | - Walter N Wekesa
- EMBLEM Study, Moi University College of Health Sciences, Eldoret, Kenya
| | | | - Esther Kawira
- EMBLEM Study, Shirati Health, Education, and Development Foundation, Shirati, Tanzania
| | - Tobias Kinyera
- EMBLEM Study, St. Mary's Hospital, Lacor, Gulu, Uganda
- EMBLEM Study, African Field Epidemiology Network, Kampala, Uganda
| | - Isaac Otim
- EMBLEM Study, St. Mary's Hospital, Lacor, Gulu, Uganda
- EMBLEM Study, African Field Epidemiology Network, Kampala, Uganda
| | - Ismail D Legason
- EMBLEM Study, Kuluva Hospital, Arua, Uganda
- EMBLEM Study, African Field Epidemiology Network, Kampala, Uganda
| | - Hadijah Nabalende
- EMBLEM Study, St. Mary's Hospital, Lacor, Gulu, Uganda
- EMBLEM Study, African Field Epidemiology Network, Kampala, Uganda
| | - Leona W Ayers
- Department of Pathology, The Ohio State University, Columbus, OH, USA
| | - Kishor Bhatia
- Division of Cancer Epidemiology & Genetics, National Cancer Institute, National Institutes of Health, US Department of Health and Human Services, Bethesda, MD, USA
| | - James J Goedert
- Division of Cancer Epidemiology & Genetics, National Cancer Institute, National Institutes of Health, US Department of Health and Human Services, Bethesda, MD, USA
| | - Mateus H Gouveia
- Center for Research on Genomics & Global Health, NHGRI, National Institutes of Health, Bethesda, MD, USA
| | - Nathan Cole
- Division of Cancer Epidemiology & Genetics, National Cancer Institute, National Institutes of Health, US Department of Health and Human Services, Bethesda, MD, USA
- Cancer Genomics Research Laboratory, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Belynda Hicks
- Division of Cancer Epidemiology & Genetics, National Cancer Institute, National Institutes of Health, US Department of Health and Human Services, Bethesda, MD, USA
- Cancer Genomics Research Laboratory, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Kristine Jones
- Division of Cancer Epidemiology & Genetics, National Cancer Institute, National Institutes of Health, US Department of Health and Human Services, Bethesda, MD, USA
- Cancer Genomics Research Laboratory, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Michael Hummel
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Berlin, Germany
- Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Pathology, D-10117, Berlin, Germany
| | - Mathias Schlesner
- Biomedical Informatics, Data Mining and Data Analytics, University of Augsburg, Augsburg, Germany
| | - George Chagaluka
- Departments of Pediatrics and Surgery, College of Medicine, University of Malawi, Blantyre, Malawi
| | - Nora Mutalima
- Epidemiology and Cancer Statistics Group, Department of Health Sciences, University of York, York, UK
- Cancer Epidemiology Unit, University of Oxford, Oxford, UK
| | - Eric Borgstein
- Departments of Pediatrics and Surgery, College of Medicine, University of Malawi, Blantyre, Malawi
| | - George N Liomba
- Departments of Pediatrics and Surgery, College of Medicine, University of Malawi, Blantyre, Malawi
| | - Steve Kamiza
- Departments of Pediatrics and Surgery, College of Medicine, University of Malawi, Blantyre, Malawi
| | - Nyengo Mkandawire
- Departments of Pediatrics and Surgery, College of Medicine, University of Malawi, Blantyre, Malawi
| | - Collins Mitambo
- Research Department, Ministry of Health, P.O. Box 30377, Lilongwe 3, Malawi
| | - Elizabeth M Molyneux
- Departments of Pediatrics and Surgery, College of Medicine, University of Malawi, Blantyre, Malawi
| | - Robert Newton
- Epidemiology and Cancer Statistics Group, Department of Health Sciences, University of York, York, UK
| | - Selina Glaser
- Institute of Human Genetics, Ulm University and Ulm University Medical Center, Ulm, Germany
| | - Helene Kretzmer
- Department of Genome Regulation, Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - Michelle Manning
- Division of Cancer Epidemiology & Genetics, National Cancer Institute, National Institutes of Health, US Department of Health and Human Services, Bethesda, MD, USA
- Cancer Genomics Research Laboratory, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Amy Hutchinson
- Division of Cancer Epidemiology & Genetics, National Cancer Institute, National Institutes of Health, US Department of Health and Human Services, Bethesda, MD, USA
- Cancer Genomics Research Laboratory, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Ann W Hsing
- Stanford Cancer Institute, Stanford University, Stanford, Palo Alto, CA, USA
| | - Yao Tettey
- Department of Pathology, University of Ghana Medical School, College of Health Sciences, P.O. Box KB 52, Korle-Bu, Accra, Ghana
| | - Andrew A Adjei
- Department of Pathology, University of Ghana Medical School, College of Health Sciences, P.O. Box KB 52, Korle-Bu, Accra, Ghana
| | - Stephen J Chanock
- Division of Cancer Epidemiology & Genetics, National Cancer Institute, National Institutes of Health, US Department of Health and Human Services, Bethesda, MD, USA
- Cancer Genomics Research Laboratory, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Reiner Siebert
- Institute of Human Genetics, Ulm University and Ulm University Medical Center, Ulm, Germany
| | - Meredith Yeager
- Division of Cancer Epidemiology & Genetics, National Cancer Institute, National Institutes of Health, US Department of Health and Human Services, Bethesda, MD, USA
- Cancer Genomics Research Laboratory, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Ludmila Prokunina-Olsson
- Division of Cancer Epidemiology & Genetics, National Cancer Institute, National Institutes of Health, US Department of Health and Human Services, Bethesda, MD, USA
| | - Mitchell J Machiela
- Division of Cancer Epidemiology & Genetics, National Cancer Institute, National Institutes of Health, US Department of Health and Human Services, Bethesda, MD, USA
| | - Sam M Mbulaiteye
- Division of Cancer Epidemiology & Genetics, National Cancer Institute, National Institutes of Health, US Department of Health and Human Services, Bethesda, MD, USA.
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Turner TN. Acorn: an R package for de novo variant analysis. BMC Bioinformatics 2023; 24:330. [PMID: 37660114 PMCID: PMC10475174 DOI: 10.1186/s12859-023-05457-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 08/29/2023] [Indexed: 09/04/2023] Open
Abstract
BACKGROUND The study of de novo variation is important for assessing biological characteristics of new variation and for studies related to human phenotypes. Software programs exist to call de novo variants and programs also exist to test the burden of these variants in genomic regions; however, I am unaware of a program that fits in between these two aspects of de novo variant assessment. This intermediate space is important for assessing the quality of de novo variants and to understand the characteristics of the callsets. For this reason, I developed an R package called acorn. RESULTS Acorn is an R package that examines various features of de novo variants including subsetting the data by individual(s), variant type, or genomic region; calculating features including variant change counts, variant lengths, and presence/absence at CpG sites; and characteristics of parental age in relation to de novo variant counts. CONCLUSIONS Acorn is an R package that fills a critical gap in assessing de novo variants and will be of benefit to many investigators studying de novo variation.
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Affiliation(s)
- Tychele N Turner
- Department of Genetics, Washington University School of Medicine, 4523 Clayton Avenue, Campus Box 8232, St. Louis, MO, 63110, USA.
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5
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Caballero M, Koren A. The landscape of somatic mutations in lymphoblastoid cell lines. CELL GENOMICS 2023; 3:100305. [PMID: 37388907 PMCID: PMC10300552 DOI: 10.1016/j.xgen.2023.100305] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 02/03/2023] [Accepted: 03/28/2023] [Indexed: 07/01/2023]
Abstract
Somatic mutations have important biological ramifications while exerting substantial rate, type, and genomic location heterogeneity. Yet, their sporadic occurrence makes them difficult to study at scale and across individuals. Lymphoblastoid cell lines (LCLs), a model system for human population and functional genomics, harbor large numbers of somatic mutations and have been extensively genotyped. By comparing 1,662 LCLs, we report that the mutational landscape of the genome varies across individuals in terms of the number of mutations, their genomic locations, and their spectra; this variation may itself be modulated by somatic trans-acting mutations. Mutations attributed to the translesion DNA polymerase η follow two different modes of formation, with one mode accounting for the hypermutability of the inactive X chromosome. Nonetheless, the distribution of mutations along the inactive X chromosome appears to follow an epigenetic memory of the active form.
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Affiliation(s)
- Madison Caballero
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, USA
| | - Amnon Koren
- Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY 14853, USA
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6
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Manuel JG, Heins HB, Crocker S, Neidich JA, Sadzewicz L, Tallon L, Turner TN. High Coverage Highly Accurate Long-Read Sequencing of a Mouse Neuronal Cell Line Using the PacBio Revio Sequencer. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.06.543940. [PMID: 37333171 PMCID: PMC10274723 DOI: 10.1101/2023.06.06.543940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Recently, Pacific Biosciences released a new highly accurate long-read sequencer called the Revio System that is projected to generate 30× HiFi whole-genome sequencing for the human genome within one sequencing SMRT Cell. Mouse and human genomes are similar in size. In this study, we sought to test this new sequencer by characterizing the genome and epigenome of the mouse neuronal cell line Neuro-2a. We generated long-read HiFi whole-genome sequencing on three Revio SMRT Cells, achieving a total coverage of 98×, with 30×, 32×, and 36× coverage respectively for each of the three Revio SMRT Cells. We performed several tests on these data including single-nucleotide variant and small insertion detection using GPU-accelerated DeepVariant, structural variant detection with pbsv, methylation detection with pb-CpG-tools, and generating de novo assemblies with the HiCanu and hifiasm assemblers. Overall, we find consistency across SMRT Cells in coverage, detection of variation, methylation, and de novo assemblies for each of the three SMRT Cells.
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Affiliation(s)
- Juana G. Manuel
- Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Hillary B. Heins
- Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Sandra Crocker
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Julie A. Neidich
- Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110, USA
- Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Lisa Sadzewicz
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Luke Tallon
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Tychele N. Turner
- Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA
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7
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Ng JK, Turner TN. HAT: de novo variant calling for highly accurate short-read and long-read sequencing data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.27.525940. [PMID: 36747667 PMCID: PMC9900919 DOI: 10.1101/2023.01.27.525940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/13/2023]
Abstract
Motivation de novo variant (DNV) calling is challenging from parent-child sequenced trio data. We developed Hare And Tortoise (HAT) to work as an automated workflow to detect DNVs in highly accurate short-read and long-read sequencing data. Reliable detection of DNVs is important for human genetics studies (e.g., autism, epilepsy). Results HAT is a workflow to detect DNVs from short-read and long read sequencing data. This workflow begins with aligned read data (i.e., CRAM or BAM) from a parent-child sequenced trio and outputs DNVs. HAT detects high-quality DNVs from short-read whole-exome sequencing, short-read whole-genome sequencing, and highly accurate long-read sequencing data.
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Affiliation(s)
- Jeffrey K. Ng
- Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Tychele N. Turner
- Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA
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8
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Byrska-Bishop M, Evani US, Zhao X, Basile AO, Abel HJ, Regier AA, Corvelo A, Clarke WE, Musunuri R, Nagulapalli K, Fairley S, Runnels A, Winterkorn L, Lowy E, Paul Flicek, Germer S, Brand H, Hall IM, Talkowski ME, Narzisi G, Zody MC. High-coverage whole-genome sequencing of the expanded 1000 Genomes Project cohort including 602 trios. Cell 2022; 185:3426-3440.e19. [PMID: 36055201 PMCID: PMC9439720 DOI: 10.1016/j.cell.2022.08.004] [Citation(s) in RCA: 228] [Impact Index Per Article: 114.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 06/21/2022] [Accepted: 08/03/2022] [Indexed: 01/05/2023]
Abstract
The 1000 Genomes Project (1kGP) is the largest fully open resource of whole-genome sequencing (WGS) data consented for public distribution without access or use restrictions. The final, phase 3 release of the 1kGP included 2,504 unrelated samples from 26 populations and was based primarily on low-coverage WGS. Here, we present a high-coverage 3,202-sample WGS 1kGP resource, which now includes 602 complete trios, sequenced to a depth of 30X using Illumina. We performed single-nucleotide variant (SNV) and short insertion and deletion (INDEL) discovery and generated a comprehensive set of structural variants (SVs) by integrating multiple analytic methods through a machine learning model. We show gains in sensitivity and precision of variant calls compared to phase 3, especially among rare SNVs as well as INDELs and SVs spanning frequency spectrum. We also generated an improved reference imputation panel, making variants discovered here accessible for association studies.
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Affiliation(s)
| | | | - Xuefang Zhao
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA,Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA,Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | | | - Haley J. Abel
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63108, USA,Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Allison A. Regier
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63108, USA,Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
| | | | - Wayne E. Clarke
- New York Genome Center, New York, NY 10013, USA,Outlier Informatics Inc., Saskatoon, SK S7H 1L4, Canada
| | | | | | - Susan Fairley
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | | | | | - Ernesto Lowy
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | | | - Paul Flicek
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | | | - Harrison Brand
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA,Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA,Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Ira M. Hall
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63108, USA,Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA,Center for Genomic Health, Yale University School of Medicine, New Haven, CT 06510, USA,Department of Genetics, Yale University School of Medicine, New Haven, CT 06520, USA
| | - Michael E. Talkowski
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA,Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA,Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | | | - Michael C. Zody
- New York Genome Center, New York, NY 10013, USA,Corresponding author
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