0
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      The Brain Gene Registry: a data snapshot

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Monogenic disorders account for a large proportion of population-attributable risk for neurodevelopmental disabilities. However, the data necessary to infer a causal relationship between a given genetic variant and a particular neurodevelopmental disorder is often lacking. Recognizing this scientific roadblock, 13 Intellectual and Developmental Disabilities Research Centers (IDDRCs) formed a consortium to create the Brain Gene Registry (BGR), a repository pairing clinical genetic data with phenotypic data from participants with variants in putative brain genes. Phenotypic profiles are assembled from the electronic health record (EHR) and a battery of remotely administered standardized assessments collectively referred to as the Rapid Neurobehavioral Assessment Protocol (RNAP), which include cognitive, neurologic, and neuropsychiatric assessments, as well as assessments for attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). Co-enrollment of BGR participants in the Clinical Genome Resource’s (ClinGen’s) GenomeConnect enables display of variant information in ClinVar. The BGR currently contains data on 479 participants who are 55% male, 6% Asian, 6% Black or African American, 76% white, and 12% Hispanic/Latine. Over 200 genes are represented in the BGR, with 12 or more participants harboring variants in each of these genes: CACNA1A, DNMT3A, SLC6A1, SETD5, and MYT1L. More than 30% of variants are de novo and 43% are classified as variants of uncertain significance (VUSs). Mean standard scores on cognitive or developmental screens are below average for the BGR cohort. EHR data reveal developmental delay as the earliest and most common diagnosis in this sample, followed by speech and language disorders, ASD, and ADHD. BGR data has already been used to accelerate gene-disease validity curation of 36 genes evaluated by ClinGen’s BGR Intellectual Disability (ID)-Autism (ASD) Gene Curation Expert Panel. In summary, the BGR is a resource for use by stakeholders interested in advancing translational research for brain genes and continues to recruit participants with clinically reported variants to establish a rich and well-characterized national resource to promote research on neurodevelopmental disorders.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s11689-024-09530-3.

          Related collections

          Most cited references30

          • Record: found
          • Abstract: found
          • Article: not found

          Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support.

          Research electronic data capture (REDCap) is a novel workflow methodology and software solution designed for rapid development and deployment of electronic data capture tools to support clinical and translational research. We present: (1) a brief description of the REDCap metadata-driven software toolset; (2) detail concerning the capture and use of study-related metadata from scientific research teams; (3) measures of impact for REDCap; (4) details concerning a consortium network of domestic and international institutions collaborating on the project; and (5) strengths and limitations of the REDCap system. REDCap is currently supporting 286 translational research projects in a growing collaborative network including 27 active partner institutions.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Standards and Guidelines for the Interpretation of Sequence Variants: A Joint Consensus Recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology

            The American College of Medical Genetics and Genomics (ACMG) previously developed guidance for the interpretation of sequence variants. 1 In the past decade, sequencing technology has evolved rapidly with the advent of high-throughput next generation sequencing. By adopting and leveraging next generation sequencing, clinical laboratories are now performing an ever increasing catalogue of genetic testing spanning genotyping, single genes, gene panels, exomes, genomes, transcriptomes and epigenetic assays for genetic disorders. By virtue of increased complexity, this paradigm shift in genetic testing has been accompanied by new challenges in sequence interpretation. In this context, the ACMG convened a workgroup in 2013 comprised of representatives from the ACMG, the Association for Molecular Pathology (AMP) and the College of American Pathologists (CAP) to revisit and revise the standards and guidelines for the interpretation of sequence variants. The group consisted of clinical laboratory directors and clinicians. This report represents expert opinion of the workgroup with input from ACMG, AMP and CAP stakeholders. These recommendations primarily apply to the breadth of genetic tests used in clinical laboratories including genotyping, single genes, panels, exomes and genomes. This report recommends the use of specific standard terminology: ‘pathogenic’, ‘likely pathogenic’, ‘uncertain significance’, ‘likely benign’, and ‘benign’ to describe variants identified in Mendelian disorders. Moreover, this recommendation describes a process for classification of variants into these five categories based on criteria using typical types of variant evidence (e.g. population data, computational data, functional data, segregation data, etc.). Because of the increased complexity of analysis and interpretation of clinical genetic testing described in this report, the ACMG strongly recommends that clinical molecular genetic testing should be performed in a CLIA-approved laboratory with results interpreted by a board-certified clinical molecular geneticist or molecular genetic pathologist or equivalent.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              The REDCap consortium: Building an international community of software platform partners

              The Research Electronic Data Capture (REDCap) data management platform was developed in 2004 to address an institutional need at Vanderbilt University, then shared with a limited number of adopting sites beginning in 2006. Given bi-directional benefit in early sharing experiments, we created a broader consortium sharing and support model for any academic, non-profit, or government partner wishing to adopt the software. Our sharing framework and consortium-based support model have evolved over time along with the size of the consortium (currently more than 3200 REDCap partners across 128 countries). While the "REDCap Consortium" model represents only one example of how to build and disseminate a software platform, lessons learned from our approach may assist other research institutions seeking to build and disseminate innovative technologies.
                Bookmark

                Author and article information

                Contributors
                dbaldri@wustl.edu
                Journal
                J Neurodev Disord
                J Neurodev Disord
                Journal of Neurodevelopmental Disorders
                BioMed Central (London )
                1866-1947
                1866-1955
                17 April 2024
                17 April 2024
                2024
                : 16
                : 17
                Affiliations
                [1 ]Department of Pediatrics, Washington University School of Medicine in St. Louis, ( https://ror.org/03x3g5467) St. Louis, MO USA
                [2 ]Institute for Informatics, Data Science and Biostatistics, Washington University School of Medicine in St. Louis, ( https://ror.org/03x3g5467) St. Louis, MO USA
                [3 ]Department of Pediatrics, Albert Einstein College of Medicine, ( https://ror.org/05cf8a891) Bronx, NY USA
                [4 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Neurology, , Boston Children’s Hospital, Harvard Medical School, ; Boston, MA USA
                [5 ]Rosamund Stone Zander Translational Neuroscience Center, Boston Children’s Hospital, ( https://ror.org/00dvg7y05) Boston, MA USA
                [6 ]Departments of Pediatrics and Neuroscience, Albert Einstein College of Medicine, ( https://ror.org/05cf8a891) Bronx, NY USA
                [7 ]Department of Psychiatry, Washington University School of Medicine in St. Louis, ( https://ror.org/03x3g5467) St. Louis, MO USA
                [8 ]Autism and Developmental Medicine Institute, ( https://ror.org/00sq30w29) Geisinger, Danville, PA USA
                [9 ]GRID grid.467415.5, ISNI 0000 0004 0458 1279, Department of Genomic Health, , Geisinger, ; Danville, PA USA
                [10 ]GRID grid.428158.2, ISNI 0000 0004 0371 6071, Division of Behavioral and Mental Health, Departments of Psychiatry and Pediatrics, , Children’s Healthcare of Atlanta, Emory University, ; Atlanta, GA USA
                [11 ]The Carolina Institute for Developmental Disabilities, University of North Carolina, ( https://ror.org/0130frc33) Chapel Hill, NC USA
                [12 ]Department of Neurology, Washington University School of Medicine in St. Louis, ( https://ror.org/03x3g5467) St. Louis, MO USA
                Author information
                http://orcid.org/0000-0002-6027-6020
                Article
                9530
                10.1186/s11689-024-09530-3
                11022437
                38632549
                f39605ce-4397-49a9-848d-ac5ebda77713
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 17 October 2023
                : 27 March 2024
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100006108, National Center for Advancing Translational Sciences;
                Award ID: U01TR002764
                Award ID: UL1TR002345
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100009633, Eunice Kennedy Shriver National Institute of Child Health and Human Development;
                Award ID: P50HD105352
                Award ID: P50HD105351
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100000051, National Human Genome Research Institute;
                Award ID: U24HG006834
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100008746, National Cancer Center;
                Award ID: P30CA091842
                Categories
                Research
                Custom metadata
                © BioMed Central Ltd., part of Springer Nature 2024

                Neurosciences
                brain gene registry,neurodevelopmental disorders,electronic health records
                Neurosciences
                brain gene registry, neurodevelopmental disorders, electronic health records

                Comments

                Comment on this article