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

      Reclassification of Variants Following Renal Genetics Testing: Uncommon Yet Impactful for Diagnosis and Management

      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

          Introduction

          Genetic testing is increasingly utilized in nephrology practice, but limited real-world data exist on variant reclassification following renal genetics testing.

          Methods

          A cohort of patients at the Cleveland Clinic Renal Genetics Clinic who underwent genetic testing through clinical laboratories was assessed with their clinical and laboratory data analyzed.

          Results

          Between January 2019 and June 2023, 425 new patients with variable kidney disorders from 413 pedigrees completed genetic testing through 10 clinical laboratories, including 255 (60%) females with median (25th, 75th percentiles) age of 36 (22–54) years. Multigene panel was the most frequently used modality followed by single-gene testing, exome sequencing (ES), chromosomal microarray (CMA), and genome sequencing (GS). At initial report, 52% of patients had ≥1 variants of uncertain significance (VUS) with or without concurrent pathogenic variant(s). Twenty amendments were issued across 19 pedigrees involving 19 variants in 17 genes. The overall variant reclassification rate was 5%, with 63% being upgrades and 32% downgrades. Of the reclassified variants, 79% were initially reported as VUS. The median time-to-amendments from initial reports was 8.4 (4–27) months. Following the variant reclassifications, 60% of the patients received a new diagnosis or a change in diagnosis. Among these, 67% of patients received significant changes in clinical management.

          Conclusion

          Variant reclassification following genetic testing is infrequent but important for diagnosis and management of patients with suspected genetic kidney disease. The majority of variant reclassifications involve VUS and are upgrades in clinically issued amended reports. Further studies are needed to investigate the predictors of such events.

          Graphical abstract

          Related collections

          Most cited references37

          • 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: found
            Is Open Access

            Global, regional, and national burden of chronic kidney disease, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017

            Summary Background Health system planning requires careful assessment of chronic kidney disease (CKD) epidemiology, but data for morbidity and mortality of this disease are scarce or non-existent in many countries. We estimated the global, regional, and national burden of CKD, as well as the burden of cardiovascular disease and gout attributable to impaired kidney function, for the Global Burden of Diseases, Injuries, and Risk Factors Study 2017. We use the term CKD to refer to the morbidity and mortality that can be directly attributed to all stages of CKD, and we use the term impaired kidney function to refer to the additional risk of CKD from cardiovascular disease and gout. Methods The main data sources we used were published literature, vital registration systems, end-stage kidney disease registries, and household surveys. Estimates of CKD burden were produced using a Cause of Death Ensemble model and a Bayesian meta-regression analytical tool, and included incidence, prevalence, years lived with disability, mortality, years of life lost, and disability-adjusted life-years (DALYs). A comparative risk assessment approach was used to estimate the proportion of cardiovascular diseases and gout burden attributable to impaired kidney function. Findings Globally, in 2017, 1·2 million (95% uncertainty interval [UI] 1·2 to 1·3) people died from CKD. The global all-age mortality rate from CKD increased 41·5% (95% UI 35·2 to 46·5) between 1990 and 2017, although there was no significant change in the age-standardised mortality rate (2·8%, −1·5 to 6·3). In 2017, 697·5 million (95% UI 649·2 to 752·0) cases of all-stage CKD were recorded, for a global prevalence of 9·1% (8·5 to 9·8). The global all-age prevalence of CKD increased 29·3% (95% UI 26·4 to 32·6) since 1990, whereas the age-standardised prevalence remained stable (1·2%, −1·1 to 3·5). CKD resulted in 35·8 million (95% UI 33·7 to 38·0) DALYs in 2017, with diabetic nephropathy accounting for almost a third of DALYs. Most of the burden of CKD was concentrated in the three lowest quintiles of Socio-demographic Index (SDI). In several regions, particularly Oceania, sub-Saharan Africa, and Latin America, the burden of CKD was much higher than expected for the level of development, whereas the disease burden in western, eastern, and central sub-Saharan Africa, east Asia, south Asia, central and eastern Europe, Australasia, and western Europe was lower than expected. 1·4 million (95% UI 1·2 to 1·6) cardiovascular disease-related deaths and 25·3 million (22·2 to 28·9) cardiovascular disease DALYs were attributable to impaired kidney function. Interpretation Kidney disease has a major effect on global health, both as a direct cause of global morbidity and mortality and as an important risk factor for cardiovascular disease. CKD is largely preventable and treatable and deserves greater attention in global health policy decision making, particularly in locations with low and middle SDI. Funding Bill & Melinda Gates Foundation.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              Purposeful selection of variables in logistic regression

              Background The main problem in many model-building situations is to choose from a large set of covariates those that should be included in the "best" model. A decision to keep a variable in the model might be based on the clinical or statistical significance. There are several variable selection algorithms in existence. Those methods are mechanical and as such carry some limitations. Hosmer and Lemeshow describe a purposeful selection of covariates within which an analyst makes a variable selection decision at each step of the modeling process. Methods In this paper we introduce an algorithm which automates that process. We conduct a simulation study to compare the performance of this algorithm with three well documented variable selection procedures in SAS PROC LOGISTIC: FORWARD, BACKWARD, and STEPWISE. Results We show that the advantage of this approach is when the analyst is interested in risk factor modeling and not just prediction. In addition to significant covariates, this variable selection procedure has the capability of retaining important confounding variables, resulting potentially in a slightly richer model. Application of the macro is further illustrated with the Hosmer and Lemeshow Worchester Heart Attack Study (WHAS) data. Conclusion If an analyst is in need of an algorithm that will help guide the retention of significant covariates as well as confounding ones they should consider this macro as an alternative tool.
                Bookmark

                Author and article information

                Contributors
                Journal
                Kidney Int Rep
                Kidney Int Rep
                Kidney International Reports
                Elsevier
                2468-0249
                06 February 2024
                May 2024
                06 February 2024
                : 9
                : 5
                : 1441-1450
                Affiliations
                [1 ]Case Western Reserve University School of Medicine, Cleveland, Ohio, USA
                [2 ]Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, Ohio, USA
                [3 ]Center for Personalized Genetic Healthcare, Medical Specialties Institute, Cleveland Clinic, Cleveland, Ohio, USA
                [4 ]Department of Pathology and Laboratory Medicine, University of Cincinnati, Cincinnati, Ohio, USA
                [5 ]Department of Kidney Medicine, Medical Specialties Institute, Cleveland Clinic, Cleveland, Ohio, USA
                [6 ]Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, Cleveland, Ohio, USA
                Author notes
                [] Correspondence: Xiangling Wang, Cleveland Clinic Center for Personalized Genetic Healthcare, Department of Kidney Medicine, 9500 Euclid Avenue, Cleveland, Ohio 44195, USA. WANGX8@ 123456ccf.org
                Article
                S2468-0249(24)00067-6
                10.1016/j.ekir.2024.01.055
                11068948
                38707809
                6088dbb4-8896-4a7f-9954-e1f6679f8818
                © 2024 International Society of Nephrology. Published by Elsevier Inc.

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 3 November 2023
                : 8 January 2024
                : 29 January 2024
                Categories
                Clinical Research

                diagnosis and management,kidney disease,renal genetics,variant of uncertain significance,variant reclassification

                Comments

                Comment on this article