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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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Related Experiment Video

Updated: Aug 19, 2025

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
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Modeling the impact of data sharing on variant classification.

James Casaletto1, Melissa Cline1, Brian Shirts2

  • 1Genomics Institute, University of California, Santa Cruz, Santa Cruz, California, USA.

Journal of the American Medical Informatics Association : JAMIA
|December 1, 2022
PubMed
Summary
This summary is machine-generated.

Sharing clinical data significantly accelerates the classification of variants of uncertain significance (VUS). Data sharing increases VUS classification probability from 25% to 80% within one year, highlighting its critical role in genetic variant interpretation.

Keywords:
benignclassificationgenetic variationmodelingpathogenic

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Area of Science:

  • Genetics
  • Bioinformatics
  • Clinical Genomics

Background:

  • Many genetic variants remain unclassified as variants of uncertain significance (VUS).
  • Clinical observations are crucial for reclassifying VUS.
  • Understanding the timeline for VUS classification is vital for genetic testing and research.

Purpose of the Study:

  • To model the impact of clinical evidence accumulation on variant interpretation.
  • To quantify the time and probability of VUS classification under different data-sharing scenarios.
  • To assess the utility of clinical data sharing in genetic variant classification.

Main Methods:

  • Developed software to model the accumulation of clinical evidence for variant interpretation.
  • Excluded non-clinical evidence types to isolate the impact of patient data.
  • Simulated scenarios of data sharing, data siloing, and sharing only interpretations.

Main Results:

  • Data sharing increased the classification probability of rare pathogenic variants (1/100,000 allele frequency) from <25% to ~80% in one year, and nearly 100% in five years.
  • No data sharing resulted in <25% classification probability for rare variants.
  • Extremely rare variants showed a low probability of classification using only clinical data.

Conclusions:

  • Clinical data sharing is essential for efficient VUS classification.
  • Modeling demonstrates the significant utility of data sharing in genetic variant interpretation.
  • Timelines for variant classification provide valuable insights for stakeholders in genetic testing and research.