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      Sherloc: a comprehensive refinement of the ACMG–AMP variant classification criteria

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          Abstract

          Purpose

          The 2015 American College of Medical Genetics and Genomics–Association for Molecular Pathology (ACMG–AMP) guidelines were a major step toward establishing a common framework for variant classification. In practice, however, several aspects of the guidelines lack specificity, are subject to varied interpretations, or fail to capture relevant aspects of clinical molecular genetics. A simple implementation of the guidelines in their current form is insufficient for consistent and comprehensive variant classification.

          Methods

          We undertook an iterative process of refining the ACMG–AMP guidelines. We used the guidelines to classify more than 40,000 clinically observed variants, assessed the outcome, and refined the classification criteria to capture exceptions and edge cases. During this process, the criteria evolved through eight major and minor revisions.

          Results

          Our implementation: (i) separated ambiguous ACMG–AMP criteria into a set of discrete but related rules with refined weights; (ii) grouped certain criteria to protect against the overcounting of conceptually related evidence; and (iii) replaced the “clinical criteria” style of the guidelines with additive, semiquantitative criteria.

          Conclusion

          Sherloc builds on the strong framework of 33 rules established by the ACMG–AMP guidelines and introduces 108 detailed refinements, which support a more consistent and transparent approach to variant classification.

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          Most cited references35

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          Cardiovascular disease in Europe 2014: epidemiological update.

          This paper provides an update for 2014 on the burden of cardiovascular disease (CVD), and in particular coronary heart disease (CHD) and stroke, across the countries of Europe. Cardiovascular disease causes more deaths among Europeans than any other condition, and in many countries still causes more than twice as many deaths as cancer. There is clear evidence in most countries with available data that mortality and case-fatality rates from CHD and stroke have decreased substantially over the last 5-10 years but at differing rates. The differing recent trends have therefore led to increasing inequalities in the burden of CVD between countries. For some Eastern European countries, including Russia and Ukraine, the mortality rate for CHD for 55-60 year olds is greater than the equivalent rate in France for people 20 years older.
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            Hereditary breast cancer: new genetic developments, new therapeutic avenues.

            Six genes confer a high risk for developing breast cancer (BRCA1/2, TP53, PTEN, STK11, CDH1). Both BRCA1 and BRCA2 have DNA repair functions, and BRCA1/2 deficient tumors are now being targeted by poly(ADP-ribose) polymerase inhibitors. Other genes conferring an increased risk for breast cancer include ATM, CHEK2, PALB2, BRIP1 and genome-wide association studies have identified lower penetrance alleles including FGFR2, a minor allele of which is associated with breast cancer. We review recent findings related to the function of some of these genes, and discuss how they can be targeted by various drugs. Gaining deeper insights in breast cancer susceptibility will improve our ability to identify those families at increased risk and permit the development of new and more specific therapeutic approaches.
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              New population-based exome data are questioning the pathogenicity of previously cardiomyopathy-associated genetic variants

              Cardiomyopathies are a heterogeneous group of diseases with various etiologies. We focused on three genetically determined cardiomyopathies: hypertrophic (HCM), dilated (DCM), and arrhythmogenic right ventricular cardiomyopathy (ARVC). Eighty-four genes have so far been associated with these cardiomyopathies, but the disease-causing effect of reported variants is often dubious. In order to identify possible false-positive variants, we investigated the prevalence of previously reported cardiomyopathy-associated variants in recently published exome data. We searched for reported missense and nonsense variants in the NHLBI-Go Exome Sequencing Project (ESP) containing exome data from 6500 individuals. In ESP, we identified 94 variants out of 687 (14%) variants previously associated with HCM, 58 out of 337 (17%) variants associated with DCM, and 38 variants out of 209 (18%) associated with ARVC. These findings correspond to a genotype prevalence of 1:4 for HCM, 1:6 for DCM, and 1:5 for ARVC. PolyPhen-2 predictions were conducted on all previously published cardiomyopathy-associated missense variants. We found significant overrepresentation of variants predicted as being benign among those present in ESP compared with the ones not present. In order to validate our findings, seven variants associated with cardiomyopathy were genotyped in a control population and this revealed frequencies comparable with the ones found in ESP. In conclusion, we identified genotype prevalences up to more than one thousand times higher than expected from the phenotype prevalences in the general population (HCM 1:500, DCM 1:2500, and ARVC 1:5000) and our data suggest that a high number of these variants are not monogenic causes of cardiomyopathy.
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                Author and article information

                Journal
                Genet Med
                Genet. Med
                Genetics in Medicine
                Nature Publishing Group
                1098-3600
                1530-0366
                October 2017
                11 May 2017
                : 19
                : 10
                : 1105-1117
                Affiliations
                [1 ]Invitae Corporation , San Francisco, California, USA
                Author notes
                [2]

                Additional members of the Invitae Clinical Genomics Group are listed above the references.

                Article
                gim201737
                10.1038/gim.2017.37
                5632818
                28492532
                c5226329-1cd0-4077-8ba0-2de944eb1374
                Copyright © 2017 The Author(s)

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/

                History
                : 10 November 2016
                : 28 February 2017
                Categories
                Original Research Article

                Genetics
                acmg laboratory guideline,clinical genetic testing,clinical interpretation,variant classification,variant interpretation

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