Consensus: a framework for evaluation of uncertain gene variants in laboratory test reporting
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Updated: May 22, 2026

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
Published on: January 16, 2019
David K Crockett1, Perry G Ridge, Andrew R Wilson
1University of Utah School of Medicine, Biomedical Informatics, 26 South 2000 East, Salt Lake City, UT 84112, USA. david.crockett@aruplab.com.
Abstract:
Accurate interpretation of gene testing is a key component in customizing patient therapy. Where confirming evidence for a gene variant is lacking, computational prediction may be employed. A standardized framework, however, does not yet exist for quantitative evaluation of disease association for uncertain or novel gene variants in an objective manner. Here, complementary predictors for missense gene variants were incorporated into a weighted Consensus framework that includes calculated reference intervals from known disease outcomes. Data visualization for clinical reporting is also discussed.
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