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

Empirical threshold values for quantitative trait mapping

G A Churchill1, R W Doerge

  • 1Biometrics Unit, Cornell University, Ithaca, New York 14853.

Genetics
|November 1, 1994
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

Systems genetics uncover new loci containing functional gene candidates in <i>Mycobacterium tuberculosis</i>-infected Diversity Outbred mice.

bioRxiv : the preprint server for biology·2024
Same author

Ghost QTL and hotspots in experimental crosses: novel approach for modeling polygenic effects.

Genetics·2021
Same author

A weighted FDR procedure under discrete and heterogeneous null distributions.

Biometrical journal. Biometrische Zeitschrift·2020
Same author

Cholesterol Sulfotransferase SULT2B1b Modulates Sensitivity to Death Receptor Ligand TNFα in Castration-Resistant Prostate Cancer.

Molecular cancer research : MCR·2019
Same author

Genetic background influences susceptibility to chemotherapy-induced hematotoxicity.

The pharmacogenomics journal·2017
Same author

Novel Resampling Improves Statistical Power for Multiple-Trait QTL Mapping.

G3 (Bethesda, Md.)·2017
Same journal

Adaptive Dynamics of Quantitative Traits in a Steadily Changing Environment.

Genetics·2026
Same journal

Functional Landscape of Zebrafish Gonadotropins and Receptors: A Comprehensive Genetic Analysis.

Genetics·2026
Same journal

Synergistic actions of Nup43 and Myosin VI drive actin cone assembly during Drosophila spermiogenesis.

Genetics·2026
Same journal

Identification of two Cryptococcus neoformans heme transporters involved in Fhb1-mediated nitrosative stress protection in a fission yeast model.

Genetics·2026
Same journal

Analysis of a hypomorphic mei-P26 mutation reveals coordination between developmental programming of germ cells and meiotic chromosome dynamics.

Genetics·2026
Same journal

Neural and Genetic Mechanisms Regulating Copulation Latency in Male Drosophila melanogaster.

Genetics·2026
See all related articles

This study introduces a permutation test method to determine appropriate significance thresholds for quantitative trait loci (QTL) detection in genetic mapping. This approach tailors threshold values to specific experimental data, improving QTL analysis accuracy.

Area of Science:

  • Genetics and Genomics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Identifying genes controlling quantitative traits is crucial in genetic mapping.
  • Quantitative trait loci (QTL) mapping methods are widely used to locate these genes.
  • Determining appropriate significance thresholds for QTL effects is a common challenge.

Purpose of the Study:

  • To address the issue of setting appropriate significance thresholds in QTL mapping.
  • To describe an empirical method for estimating data-tailored threshold values.
  • To demonstrate the utility of permutation tests for QTL significance threshold determination.

Main Methods:

  • An empirical method based on permutation tests was developed.
  • The method estimates significance threshold values tailored to specific experimental data.

Related Experiment Videos

  • The approach was tested using real data from F(2) and recombinant inbred plant populations and simulated backcross data.

    • Main Results:

    • The permutation test method provides a data-driven approach to setting QTL significance thresholds.
    • Demonstrated effectiveness on diverse plant population datasets (F(2), RIL).
    • Simulated data showed marker density influences threshold values.

    Conclusions:

    • The described permutation test method offers a robust way to establish significance thresholds for QTL detection.
    • This empirical approach enhances the reliability of identifying significant QTL effects.
    • Understanding the impact of marker density is important for optimizing QTL mapping studies.