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

Quantitative trait associated microarray gene expression data analysis.

Yi Qu1, Shizhong Xu

  • 1Department of Botany and Plant Sciences, University of California, Riverside, USA.

Molecular Biology and Evolution
|May 30, 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

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

Plant communications·2026
Same author

Estimating recombination fraction via Pearson correlation.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik·2026
Same author

A novel polypeptide encoded by circSPIRE1 promotes prostate cancer proliferation and migration by restraining the ubiquitin-dependent degradation of LRP5.

Journal of experimental & clinical cancer research : CR·2025
Same journal

Population Epigenetics: Deciphering DNA Methylation Diversity and its Implications for Health, Disease, and Evolution.

Molecular biology and evolution·2026
Same journal

Genomic signature of repeated transitions to diurnality in spiders.

Molecular biology and evolution·2026
Same journal

Phylogenomic blind spots: The limits of UCE and BUSCO loci in the presence of gene flow.

Molecular biology and evolution·2026
Same journal

seqLens: Optimizing Language Models for Genomic Predictions.

Molecular biology and evolution·2026
Same journal

The transcriptional and translational outcomes for pseudogenes in bacterial endosymbionts.

Molecular biology and evolution·2026
Same journal

800 million years of co-evolution in the green plant lineage - the case of LEUNIG and SEUSS transcriptional co-regulators.

Molecular biology and evolution·2026
See all related articles

This study introduces a new clustering method to link gene expression to quantitative phenotypes, revealing nonlinear associations important for evolutionary insights. The method successfully identifies patterns in Alzheimer's disease and fracture healing data.

Area of Science:

  • Evolutionary biology
  • Genetics
  • Bioinformatics

Background:

  • Phenotypic selection drives genetic change, necessitating study of gene expression-phenotype concordance.
  • Current methods for phenotype-gene expression association are limited to linear correlations.
  • Nonlinear associations and groups of genes with shared patterns are evolutionarily significant.

Purpose of the Study:

  • To develop a novel clustering method for identifying genes related to quantitative phenotypes.
  • To explore nonlinear relationships between gene expression profiles and phenotypes.
  • To provide a tool for analyzing evolutionary relationships between genes and traits.

Main Methods:

  • Utilized orthogonal polynomials within a multivariate Gaussian mixture model for gene clustering.

Related Experiment Videos

  • Employed the expectation-maximization algorithm for parameter estimation.
  • Validated the method with simulated data and applied it to Alzheimer's disease severity and rat fracture healing time-series data.

    • Main Results:

    • Identified significant nonlinear associations between gene expression and phenotypes in both study datasets.
    • Successfully clustered genes based on their relationship to quantitative phenotypes.
    • Demonstrated the method's ability to capture complex patterns in biological data.

    Conclusions:

    • The novel clustering approach effectively reveals nonlinear gene expression-phenotype associations.
    • This method offers a powerful tool for evolutionary genetics research.
    • The findings provide a foundation for further investigation into gene regulation and phenotypic evolution.