Format

Send to

Choose Destination
Genome Biol. 2017 Nov 28;18(1):225. doi: 10.1186/s13059-017-1353-5.

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

Ghosh R1,2, Oak N1,2, Plon SE3,4.

Author information

1
Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA.
2
Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
3
Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA. splon@bcm.edu.
4
Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA. splon@bcm.edu.

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.

KEYWORDS:

ACMG; ClinVar; Clinical genetics; Diagnostics; In silico algorithm; ROC; Variant interpretation

PMID:
29179779
PMCID:
PMC5704597
DOI:
10.1186/s13059-017-1353-5
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for BioMed Central Icon for PubMed Central
Loading ...
Support Center