Sherloc: a comprehensive refinement of the ACMG–AMP variant classification criteria
By K. Nykamp, Michael J. Anderson, M. Powers, John Garcia, Blanca Herrera, Y. Ho, Yuya Kobayashi, N. Patil, J. Thusberg, Marjorie Westbrook and 1 more. Genetics in Medicine, 2017
673 citations recorded by the source corpus.
PurposeThe 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.MethodsWe 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.ResultsOur 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.ConclusionSherloc 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.
Subfields: Cancer Research, Genetics, Pathology and Forensic Medicine
View Paper: https://www.semanticscholar.org/paper/317275a71abb584ebed7c12f007859672925c91c
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Citations in the corpus
- Large-scale clinical interpretation of genetic variants using evolutionary data and deep learning (2020, result): “Broadly, this problem is well known [37] but also manifests in somewhat unique ways in our analysis.”