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

Bayesian point estimation of quantitative trait loci.

S A Sisson1, M A Hurn

  • 1Department of Mathematics and Computer Science, University of Puerto Rico, Río Piedras, Puerto Rico. scott@maths.unsw.edu.au

Biometrics
|March 23, 2004
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

Sequential Monte Carlo without likelihoods.

Proceedings of the National Academy of Sciences of the United States of America·2007
Same author

Using approximate Bayesian computation to estimate tuberculosis transmission parameters from genotype data.

Genetics·2006
Same author

Bayesian fused classification of medical images.

IEEE transactions on medical imaging·1996
Same journal

Fast penalized generalized estimating equations for large longitudinal functional datasets.

Biometrics·2026
Same journal

Causally-interpretable random-effects meta-analysis.

Biometrics·2026
Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026
Same journal

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026
Same journal

Finite mixtures of linear quantile regressions with concomitant variables: a solution to endogeneity in longitudinal data modeling.

Biometrics·2026
Same journal

Discussion on "INTACT: a method for integration of longitudinal physical activity data from multiple sources" by Jingru Zhang, Erjia Cui, Hongzhe Li, and Haochang Shou.

Biometrics·2026
See all related articles

This study introduces a Bayesian approach to estimate quantitative trait loci (QTL) locations and numbers. The method aids in identifying genetic markers associated with traits, crucial for comparative mapping.

Area of Science:

  • Genetics and Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Quantitative trait loci (QTL) are chromosomal regions influencing complex traits.
  • Identifying the number and location of QTL is challenging due to unknown encoding sites.
  • Associations between molecular markers and phenotypes are key to QTL mapping.

Purpose of the Study:

  • To develop and apply a Bayesian model for estimating the number and location of QTL.
  • To focus on identifying the best candidate markers segregating for a trait.
  • To provide a framework for comparative mapping studies.

Main Methods:

  • Utilized a Bayesian statistical model for simple experimental designs.
  • Developed a novel loss function for estimating both the number and location of QTL.

Related Experiment Videos

  • Applied the model to both simulated and real genetic datasets.

    • Main Results:

    • Demonstrated the utility of the Bayesian model in estimating QTL parameters.
    • Successfully identified significant candidate markers associated with quantitative traits.
    • Validated the proposed loss function's effectiveness in QTL analysis.

    Conclusions:

    • The proposed Bayesian approach offers a robust method for QTL estimation.
    • This framework enhances the accuracy of locating genetic regions influencing traits.
    • The findings are particularly valuable for comparative mapping and genetic research.