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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
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A data-driven weighting scheme for multivariate phenotypic endpoints recapitulates zebrafish developmental cascades.

Guozhu Zhang1, Kyle R Roell1, Lisa Truong2

  • 1Bioinformatics Research Center, North Carolina State University, Raleigh, NC, USA.

Toxicology and Applied Pharmacology
|November 26, 2016
PubMed
Summary

Zebrafish models now offer a robust way to assess environmental stressor health effects. A new Bayesian method, weighted Aggregate Entropy (wAggE), effectively integrates multiple endpoints for better toxicity detection.

Keywords:
BayesianDevelopmental cascadeHigh-dimensionalMultiple endpointsMultivariateRisk assessmentScoringToxRefDBZebrafish

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Area of Science:

  • Environmental toxicology
  • Zebrafish models
  • High-throughput screening

Background:

  • Zebrafish are valuable models for environmental health studies due to genetic similarity to humans and rapid development.
  • Assessing chemical toxicity requires integrating multiple phenotypic measurements (endpoints).
  • Existing methods for multi-endpoint analysis lack robust integration strategies.

Purpose of the Study:

  • To introduce a Bayesian method, weighted Aggregate Entropy (wAggE), for data-driven weighting of zebrafish endpoints.
  • To develop a robust multi-endpoint summary measure for environmental health effect assessment.
  • To improve the identification of significant morphological effects from chemical exposures.

Main Methods:

  • Implemented a Bayesian approach to quantify the informativeness of 17 zebrafish endpoints.
  • Applied the weighted Aggregate Entropy (wAggE) method to high-throughput screening (HTS) data.
  • Utilized data from zebrafish exposed to 1060 ToxCast chemicals at five concentrations.

Main Results:

  • The empirical weighting scheme in wAggE demonstrated superior performance using Receiver Operating Characteristic (ROC) curves.
  • wAggE showed improved robustness compared to traditional curve-fitting methods.
  • Analysis of endpoint relationships using wAggE successfully recapitulated developmental cascade effects from chemical exposure.

Conclusions:

  • wAggE provides a powerful approach for analyzing multivariate phenotypes in zebrafish.
  • This method enhances the understanding of etiological processes in environmental toxicology.
  • wAggE offers a data-driven strategy for summarizing complex health effects in model organisms.