Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Box-Cox transformation for QTL mapping.

Runqing Yang1, Nengjun Yi, Shizhong Xu

  • 1School of Agriculture and Biology, Shanghai Jiaotong University, Shanghai, 201101, PR China.

Genetica
|October 10, 2006
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Crop breeding by design: integrating big data for future food security.

National science review·2026
Same author

An Expectation and Maximization Algorithm for Multivariate Genome-wide Association Studies (EMmvGWAS).

Genetics·2026
Same author

Genome-wide association studies and QTL mapping for traits deviating from normal distribution.

National science review·2026
Same author

Optimizing probes for multi-beam ptychography.

Optics express·2026
Same author

Cardiovascular-kidney-metabolic syndrome stages 0-3 and cognitive decline in Chinese adults: a longitudinal analysis from the China health and retirement longitudinal study.

BMC cardiovascular disorders·2026
Same author

GS-Impute: A neural network framework for accurate imputation of low-density markers in across-population genomic selection.

Plant communications·2026
Same journal

Morphological and COI-based identification of species of the Hawkmoth genus Theretra Hübner, 1819 (Lepidoptera: Sphingidae) from Himachal Pradesh, India.

Genetica·2026
Same journal

Advancing genetics and evolution: new Editors-in-Chief in Genetica.

Genetica·2026
Same journal

Origin and diversification of Altai osmans (Oreoleuciscus), Far Eastern phoxinin minnows (Rhynchocypris) and Far Eastern redfins (Tribolodon) of the Leuciscidae family (Actinopterygii): an example of evolutionary tempo variation?

Genetica·2026
Same journal

Chloroplast genome comparison, phylogeny, and molecular evolution of five endemic Potentilla (Rosaceae) species in Mongolia.

Genetica·2026
Same journal

Severe trauma and chronic stress: seeds and fruits of epigenetic sensitivity.

Genetica·2026
Same journal

Molecular evolution and antigenic mapping of classical swine fever virus: a comprehensive analysis of E2 genomic variability and selection dynamics.

Genetica·2026
See all related articles

This study introduces an objective Box-Cox transformation method for quantitative trait loci (QTL) mapping. This approach enhances QTL detection power and works even with normally distributed data.

Area of Science:

  • Quantitative genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • Quantitative trait loci (QTL) mapping typically assumes normally distributed phenotypic data.
  • Violations of this normality assumption necessitate data transformation for accurate QTL analysis.
  • The Box-Cox transformation offers a flexible, generalizable method for data transformation.

Purpose of the Study:

  • To develop a maximum likelihood method for QTL mapping that incorporates the Box-Cox transformation.
  • To estimate the Box-Cox transformation factor simultaneously with QTL parameters.
  • To provide an objective data transformation approach for diverse QTL analysis scenarios.

Main Methods:

  • Developed a maximum likelihood framework for QTL mapping.

Related Experiment Videos

  • Integrated the Box-Cox transformation by treating its factor as an unknown, estimable parameter.
  • Conducted simulation studies to evaluate the method's performance.

    • Main Results:

    • The Box-Cox transformation significantly increased the power of QTL detection.
    • This method demonstrated the potential to replace specialized transformations in QTL mapping.
    • Applying Box-Cox transformation to already normal data did not negatively impact results.

    Conclusions:

    • The proposed maximum likelihood method with Box-Cox transformation offers an objective and powerful tool for QTL analysis.
    • This approach enhances the applicability of QTL mapping to a wider range of data types.
    • The method is robust, improving detection without compromising results on normal data.