Abstract
BackgroundThe American College of Medical Genetics and American College of Pathologists (ACMG/AMP) variant classification guidelines for clinical reporting are widely used in diagnostic laboratories for variant interpretation. The ACMG/AMP guidelines recommend complete concordance of predictions among all in silico algorithms used without specifying the number or types of algorithms. The subjective nature of this recommendation contributes to discordance of variant classification among clinical laboratories and prevents definitive classification of variants.ResultsUsing 14,819 benign or pathogenic missense variants from the ClinVar database, we compared performance of 25 algorithms across datasets differing in distinct biological and technical variables. There was wide variability in concordance among different combinations of algorithms with particularly low concordance for benign variants. We also identify a previously unreported source of error in variant interpretation (false concordance) where concordant in silico predictions are opposite to the evidence provided by other sources. We identified recently developed algorithms with high predictive power and robust to variables such as disease mechanism, gene constraint, and mode of inheritance, although poorer performing algorithms are more frequently used based on review of the clinical genetics literature (2011–2017).ConclusionsOur analyses identify algorithms with high performance characteristics independent of underlying disease mechanisms. We describe combinations of algorithms with increased concordance that should improve in silico algorithm usage during assessment of clinically relevant variants using the ACMG/AMP guidelines.
Highlights
The American College of Medical Genetics and American College of Pathologists (ACMG/Association of Molecular Pathologists (AMP)) variant classification guidelines for clinical reporting are widely used in diagnostic laboratories for variant interpretation
Predictions from in silico algorithms are included as one of the eight evidence criteria recommended for variant interpretation by the American College of Medical Genetics and Genomics (ACMG) and Association of Molecular Pathologists (AMP) [1]
Concordance among in silico algorithms To identify the extent of concordance among in silico algorithms for known pathogenic and benign variants, we obtained 14,819 missense variants from ClinVar for which the rationale for pathogenic or benign assertion has been provided by at least one submitter, primarily clinical laboratories, and annotated these variants with scores and predictions from 25 algorithms using dbNSFP (v3.2) [9] or the respective authors’ websites
Summary
The American College of Medical Genetics and American College of Pathologists (ACMG/AMP) variant classification guidelines for clinical reporting are widely used in diagnostic laboratories for variant interpretation. Many in silico methods have been developed to predict whether amino acid substitutions result in disease. Use of this type of evidence has become a routine part of assessment of novel variants identified through genefocused projects or as a part of whole exome or genome annotation pipelines. Predictions from in silico algorithms are included as one of the eight evidence criteria recommended for variant interpretation by the American College of Medical Genetics and Genomics (ACMG) and Association of Molecular Pathologists (AMP) [1]. In a recent assessment of the ACMG/AMP guidelines by the Clinical Sequence Exploratory Research consortium (CSER), the frequency of use of in silico algorithm evidence for pathogenic and benign variant assertion were 39% and 18%, respectively [4]. The CSER study noted that use of in silico algorithms were one major
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