Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Likelihood-based calibration improves the clinical utility of JAG1 functional data for variant classification.

American journal of human genetics - 6 Aug 2026

Hayeck Tristan J, Sottolano Christopher J, Blair Justin J, Xenakis Markos N, Spinner Nancy B, Gilbert Melissa A

Abstract excerpt

Multiplexed assays of variant effects (MAVEs) represent a powerful approach to providing functional information for variants at scale. To harness the full utility of these systems, assay readout must be translated into a language that is accommodating to the clinical genomics community. We previously performed a MAVE to characterize variants in JAG1, the primary cause of the autosomal-dominant, multisystemic...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Alagille Syndrome
Share to Calibration

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to JAG1 gene

Share to Genetic Variation

Share to Jagged-1 Protein

Share to Mutation, Missense

Source

Open in PubMed Open full text

Identifiers

PMID: 42442366
DOI: 10.1016/j.ajhg.2026.06.016
PMCID: PMC13439524

Genes

JAG1

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Likelihood-based calibration improves the clinical utility of JAG1 functional data for variant classification.PMID 42442366Read ↗

Functional characterization of 2,832 JAG1 variants supports reclassification for Alagille syndrome and improves guidance for clinical variant interpretationSemantic proximity Read ↗

Gene-based calibration of high-throughput functional assays for clinical variant classificationSemantic proximity Read ↗

Clinical evidence yield as a framework for evaluating computational predictors and multiplexed assays of variant effectSemantic proximity Read ↗

Calibration of additional computational tools expands ClinGen recommendation options for variant classification with PP3/BP4 criteria.Semantic proximity Read ↗

Calibration of additional computational tools expands ClinGen recommendation options for variant classification with PP3/BP4 criteriaSemantic proximity Read ↗

acmgscaler: An R package and Colab for standardised gene-level variant effect score calibration within the ACMG/AMP frameworkSemantic proximity Read ↗

Quantifying the potential of functional evidence to reclassify variants of uncertain significance in the categorical and Bayesian interpretation frameworks.Semantic proximity Read ↗

Gene- and domain-aware calibration increases the clinical utility of variant effect predictorsSemantic proximity Read ↗

The Impact of Clinical and Molecular Variant Properties on Calibration and Performance of Variant Effect Prediction ToolsSemantic proximity Read ↗

Gene- and domain-aware calibration increases the clinical utility of variant effect predictorsSemantic proximity Read ↗

A scalable approach to resolving variants of uncertain significanceSemantic proximity Read ↗

Recommendations for application of the functional evidence PS3/BS3 criterion using the ACMG/AMP sequence variant interpretation framework.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.