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      Clinical impact of genomic testing in patients with suspected monogenic kidney disease

      research-article
      , MBBS 1 , 2 , 3 , 4 , , DM 3 , 4 , 5 , 6 , , PhD 1 , 2 , , PhD 7 , 8 , , PhD 3 , 7 , , PhD 9 , , BMedSci 10 , , BN 11 , , MBChB 12 , 13 , , MGenCouns 11 , , MBBS 13 , 14 , , MMed 12 , 13 , , PhD 6 , , MGenCouns 7 , , PhD 15 , , MD 4 , 16 , , MGenCouns 12 , , PhD 1 , 2 , , MBBS 17 , , MBBS 15 , , PhD 11 , 17 , , MBiomedSc 3 , 7 , , MGenCouns 18 , , MBBS 18 , 19 , , BSciAg 3 , 4 , , MGenCouns 7 , 11 , , MBBS 5 , 6 , , MGenCouns 3 , 6 , , PhD 3 , 4 , 20 , 21 , , MD(Res) 3 , 4 , 5 , 22 ,
      Genetics in Medicine
      Nature Publishing Group US
      chronic kidney disease, exome sequencing, genetic kidney disease

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          Abstract

          Purpose

          To determine the diagnostic yield and clinical impact of exome sequencing (ES) in patients with suspected monogenic kidney disease.

          Methods

          We performed clinically accredited singleton ES in a prospectively ascertained cohort of 204 patients assessed in multidisciplinary renal genetics clinics at four tertiary hospitals in Melbourne, Australia.

          Results

          ES identified a molecular diagnosis in 80 (39%) patients, encompassing 35 distinct genetic disorders. Younger age at presentation was independently associated with an ES diagnosis ( p < 0.001). Of those diagnosed, 31/80 (39%) had a change in their clinical diagnosis. ES diagnosis was considered to have contributed to management in 47/80 (59%), including negating the need for diagnostic renal biopsy in 10/80 (13%), changing surveillance in 35/80 (44%), and changing the treatment plan in 16/80 (20%). In cases with no change to management in the proband, the ES result had implications for the management of family members in 26/33 (79%). Cascade testing was subsequently offered to 40/80 families (50%).

          Conclusion

          In this pragmatic pediatric and adult cohort with suspected monogenic kidney disease, ES had high diagnostic and clinical utility. Our findings, including predictors of positive diagnosis, can be used to guide clinical practice and health service design.

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          Most cited references42

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          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.
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            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.
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              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.
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                Author and article information

                Contributors
                Cathy.Quinlan@rch.org.au
                Journal
                Genet Med
                Genet Med
                Genetics in Medicine
                Nature Publishing Group US (New York )
                1098-3600
                1530-0366
                17 September 2020
                17 September 2020
                2021
                : 23
                : 1
                : 183-191
                Affiliations
                [1 ]GRID grid.416060.5, ISNI 0000 0004 0390 1496, Department of Nephrology, , Monash Medical Centre, ; Melbourne, Australia
                [2 ]GRID grid.1002.3, ISNI 0000 0004 1936 7857, School of Clinical Sciences, , Monash University, ; Melbourne, Australia
                [3 ]GRID grid.1058.c, ISNI 0000 0000 9442 535X, Murdoch Children’s Research Institute, ; Melbourne, Australia
                [4 ]The KidGen Collaborative, Australian Genomics Health Alliance, Melbourne, Australia
                [5 ]GRID grid.1008.9, ISNI 0000 0001 2179 088X, Department of Pediatrics, , University of Melbourne, ; Melbourne, Australia
                [6 ]GRID grid.507857.8, Victorian Clinical Genetics Services, ; Melbourne, Australia
                [7 ]Melbourne Genomics Health Alliance, Melbourne, Australia
                [8 ]GRID grid.1008.9, ISNI 0000 0001 2179 088X, Department of Pediatrics, Faculty of Medicine Dentistry & Health Sciences, , The University of Melbourne, ; Melbourne, Australia
                [9 ]GRID grid.410678.c, Department of Nephrology, , Austin Health, ; Melbourne, Australia
                [10 ]GRID grid.1055.1, ISNI 0000000403978434, Cancer Genetics and Genomics Program, Peter MacCallum Cancer Centre, ; Melbourne, Australia
                [11 ]GRID grid.416153.4, ISNI 0000 0004 0624 1200, Department of Genomic Medicine, , Royal Melbourne Hospital, ; Melbourne, Australia
                [12 ]GRID grid.419789.a, ISNI 0000 0000 9295 3933, Monash Genetics, Monash Health, ; Melbourne, Australia
                [13 ]GRID grid.1002.3, ISNI 0000 0004 1936 7857, Department of Pediatrics, , Monash University, ; Melbourne, Australia
                [14 ]GRID grid.460788.5, Department of Nephrology, , Monash Children’s Hospital, ; Melbourne, Australia
                [15 ]GRID grid.429299.d, ISNI 0000 0004 0452 651X, Department of Nephrology, , Melbourne Health, ; Melbourne, Australia
                [16 ]GRID grid.416100.2, ISNI 0000 0001 0688 4634, Genetic Health Queensland, Royal Brisbane and Women’s Hospital, ; Brisbane, Australia
                [17 ]GRID grid.1008.9, ISNI 0000 0001 2179 088X, Department of Medicine, , University of Melbourne, ; Melbourne, Australia
                [18 ]GRID grid.410678.c, Clinical Genetics Service, Austin Health, ; Melbourne, Australia
                [19 ]GRID grid.1009.8, ISNI 0000 0004 1936 826X, School of Medicine and Menzies Institute for Medical Research, , University of Tasmania, ; Hobart, Australia
                [20 ]GRID grid.416100.2, ISNI 0000 0001 0688 4634, Kidney Health Service and Conjoint Renal Research Laboratory, Royal Brisbane and Women’s Hospital, ; Brisbane, Australia
                [21 ]GRID grid.1003.2, ISNI 0000 0000 9320 7537, Institute for Molecular Bioscience and Faculty of Medicine, , The University of Queensland, ; Brisbane, Australia
                [22 ]GRID grid.416107.5, ISNI 0000 0004 0614 0346, Department of Pediatric Nephrology, , Royal Children’s Hospital, ; Melbourne, Australia
                Author information
                http://orcid.org/0000-0002-9268-8505
                Article
                963
                10.1038/s41436-020-00963-4
                7790755
                32939031
                f9e86685-9b84-48df-871a-04fd86f2a75e
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, and provide a link to the Creative Commons license. You do not have permission under this license to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

                History
                : 9 April 2020
                : 25 August 2020
                : 31 August 2020
                Funding
                Funded by: FundRef https://doi.org/10.13039/, Melbourne Genomics;
                Categories
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                © American College of Medical Genetics and Genomics 2021

                Genetics
                chronic kidney disease,exome sequencing,genetic kidney disease
                Genetics
                chronic kidney disease, exome sequencing, genetic kidney disease

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