Evaluation of in silico algorithms for use with ACMG/AMP clinical variant interpretation guidelines
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Abstract
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 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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Score intervals defined by tool developers, and ACMG/AMP recommendations that require the consensus of multiple predictors, lack quantitative support.
“However, score intervals defined by tool developers, and ACMG/AMP recommendations that require the consensus of multiple predictors, lack quantitative support.”
From Calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for PP3/BP4 criteria (2022) · cites this paper as [3]
The Discussion says the ACMG/AMP recommendation requires concordance among multiple algorithms without specifying their number or choice, and reports that using more algorithms increases discordant calls. The Results say cutoffs for some algorithms are absent or arbitrarily inferred, but the paper does not establish that developer-defined score intervals generally lack quantitative support. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThis is in agreement with previous studies.
“These fractions were considerably larger than 4.41%, our estimate of the prior probability of pathogenicity (prevalence of pathogenic variants) in the gnomAD set, suggesting a high false positive rate with respect to PP3, in agreement with previous studies.3”
From Calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for PP3/BP4 criteria (2022) · cites this paper as [3]
Several studies have attempted to benchmark methods to identify tools and tool-threshold combinations that are the most accurate and appropriate for clinically relevant variants.
“Several studies have attempted to benchmark methods to identify tools and tool-threshold combinations that are the most accurate and appropriate for clinically relevant variants.3,38,39,40,41”
From Calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for PP3/BP4 criteria (2022) · cites this paper as [3]
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- Authors
- Ghosh, R., Oak, N., & Plon, S. E.
- Affiliations
- Baylor College of Medicine (US)
- Venue
- Genome biology
- Published
- 2017-11-28
- Volume
- 18
- Issue
- 1
- Pages
- 225-225
- ISSN
- 1465-6906 (ISSN-L)
- 1474-760X
- PMC
- PMC5704597
- OpenAlex
- W2949886398
- Cited by
- 233
- References
- 41
- Citations by year
- 2026: 16 · 2025: 22 · 2024: 20 · 2023: 24 · 2022: 31 · 2021: 30 · 2020: 30 · 2019: 36 · 2018: 22 · 2017: 1
- FWCI
- 16.16
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Genome biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Human Genome Research Institute (5 U01 HG007436)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- clinical variant interpretation, missense mutations, ClinVar, pathogenic variants, benign variants, disease mechanisms, mode of inheritance, algorithm comparison
- MeSH terms
- Algorithms, Humans, Software, Genetic Variation, Reproducibility of Results, Genome, Human, Guidelines as Topic, Computational Biology, Databases, Genetic, Genetic Association Studies
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