Variantval

Variantval is a scalar metric used in data analysis to quantify the estimated impact or importance of a specific variant within a defined model or dataset. It is designed to enable direct comparison of variants and to support prioritization in downstream analyses.

Definition: Variantval is the value produced by applying a scoring framework to a variant V within a model M built from features F(V). The meaning of the value depends on the scheme, but it generally reflects predicted effect magnitude, pathogenicity, or contribution to phenotype. Values are typically nonnegative and may be normalized to a standard range, such as 0 to 1.

Computation: Variantval can be obtained by a trained predictive model that maps F(V) to a real-valued score, or by a rule-based aggregation of feature contributions. Common features include sequence conservation, predicted functional impact, allele frequency, and functional annotations. Confidence measures may accompany the score.

Interpretation and usage: Higher variantval indicates greater estimated impact or priority, but interpretation depends on calibration and context. Users often apply thresholds to classify variants into low, medium, or high priority within the same scoring framework.

Applications and limitations: Variantval is used for prioritizing variants in sequencing studies, filtering large datasets, and informing downstream analyses. Limitations include sensitivity to data quality, feature availability, and model choice, as well as challenges in cross-study comparability.

See also: variant score, pathogenicity prediction, risk score.