ArticleGenome biology2017
Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines.
Article in Genome biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 145 papers, 2 of them syntheses that pooled it.
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Who cites it
145 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Variant effect predictors: a systematic review and practical guide.Human genetics · 2024Pooled it
- Standards for the classification of pathogenicity of somatic variants in cancer (oncogenicity): Joint recommendations of Clinical Genome Resource (ClinGen), Cancer Genomics Consortium (CGC), and Variant Interpretation for Cancer Consortium (VICC).Genetics in medicine : official journal of the American College of Medical Genetics · 2022Guideline
- Rare, Damaging DNA Variants inCirculation. Genomic and precision medicine · 2021Trial
- Trial
- Integration of multi-omics data uncovers novel germline susceptibility candidates in early-onset colorectal cancer.European journal of human genetics : EJHG · 2026Article
- Molecular subtyping and POLE mutation spectrum in endometrial carcinoma.The Indian journal of medical research · 2026Article
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- Co-occurring rare germline DNA repair gene variants in BRCA1/BRCA2 implicated hereditary breast cancer families.NPJ breast cancer · 2026Article
- MetaXVP: an interpretable machine learning framework for deep insight into variant pathogenicity and VUS classification.Scientific reports · 2026Article
- Ancestry-specific performance of variant effect predictors in clinical variant classification.bioRxiv : the preprint server for biology · 2026Article
- The Genetic and Molecular Analyses of Rare Candidate GermlineInternational journal of molecular sciences · 2026Article
- In Silico Functional and Structural Analysis ofGenes · 2026Article
- CardioVar: a machine learning framework for pathogenicity prediction of cardiomyopathy genetic variants.Bioinformatics advances · 2026Article
- Functional Screening of NDUFAF6 Variants in Knockout Cells and Complementary Computational Analysis.Journal of clinical laboratory analysis · 2026Article
- The genetic architecture of Parkinson's disease on the Island of Crete.NPJ Parkinson's disease · 2025Article
- Comparison of missing data handling methods for variant pathogenicity predictors.NAR genomics and bioinformatics · 2025Article
- GAMT Deficiency: Clinical Presentation, Treatment, Diagnosis, Animal Models, Preclinical and Clinical Developments.International journal of molecular sciences · 2025Review
- Prediction of human missense variant effects from functional evidence.Research square · 2025Article
- Informatics at the Frontier of Cancer Research.Cancer research · 2025Review
- Improving ALS Molecular Diagnosis Through Functional Assays: Reassessment of aInternational journal of molecular sciences · 2025Article
85 more citing papers are in PubMed but not listed here.
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3 authors.
Funding
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.
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