Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines

作者：Rajarshi Ghosh, Ninad Oak, Sharon E. Plon · 发表于：Genome biology · 年份：2017 · DOI：10.1186/s13059-017-1353-5 · 被引用次数：247 · 研究领域：Genomics and Rare Diseases、Genetic Associations and Epidemiology、Clinical Reasoning and Diagnostic Skills

BACKGROUND: 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. 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. RESULTS: Using 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). CONCLUSIONS: Our analyses identify algorithms with high performance characteristics independent of u...