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Related Experiment Videos

JLIN: a java based linkage disequilibrium plotter.

Kim W Carter1, Pamela A McCaskie, Lyle J Palmer

  • 1Laboratory for Genetic Epidemiology, Western Australian Institute for Medical Research and UWA Centre for Medical Research, University of Western Australia, Nedlands, 6009, Western Australia . kcarter@cyllene.uwa.edu.au

BMC Bioinformatics
|February 10, 2006
PubMed
Summary

JLIN is a novel software tool that provides customizable and intuitive visualization of Linkage Disequilibrium (LD) data. It aids researchers in analyzing complex genetic datasets and comparing statistical measures for disease susceptibility studies.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • International efforts focus on identifying genetic polymorphisms linked to complex human diseases.
  • Linkage Disequilibrium (LD) mapping is increasingly utilized for marker analysis in large datasets.
  • Effective data visualization is crucial for analyzing extensive genetic analysis results.

Purpose of the Study:

  • To introduce JLIN (Java Linkage Disequilibrium plotter), a software package for visualizing LD.
  • To provide users with customizable and intuitive LD visualization capabilities.
  • To enhance the analysis of genetic data for complex disease research.

Main Methods:

  • JLIN offers customizable visualization options for Linkage Disequilibrium (LD).
  • Users can select specific visualizations, statistical measures (D', r2, p-values), and measurement ranges.

Related Experiment Videos

  • The software supports various common computing platforms.

    • Main Results:

    • JLIN provides intuitive and customizable visualization of LD across platforms.
    • It allows users to export LD visualizations in multiple common document formats.
    • The software facilitates visual comparison of different statistical measures on datasets.

    Conclusions:

    • JLIN offers unique features that improve upon existing LD visualization tools.
    • It enables users to visually compare and contrast results from various statistical measures.
    • The software supports the analysis of complex genetic data for disease susceptibility studies.