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

Multiple-trait mapping of quantitative trait loci after selective genotyping using logistic regression

J M Henshall1, M E Goddard

  • 1Animal Genetics and Breeding Unit, University of New England, Armidale, New South Wales 2351, Australia. jhenshal@metz.une.edu.au

Genetics
|February 2, 1999
PubMed
Summary

This study introduces logistic regression for quantitative trait loci (QTL) analysis, offering unbiased estimation of QTL effects from selectively genotyped, multiple-trait data. This method enhances accuracy in genetic mapping studies.

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

Genetic and genomic analyses of the effect of heat stress, measured as temperature-humidity index, on cow fertility in Australian Holstein cattle.

Journal of dairy science·2026
Same author

Genomic regions affecting perinatal and early life survival in dairy calves.

Journal of dairy science·2025
Same author

Genetic variation in novel calf traits using a farmer-centered, co-design approach to data collection.

Journal of dairy science·2025
Same author

Use of termination events and mortality data recorded during the lactation as a proxy to predict the genetics of resilience and health of dairy cattle.

Journal of dairy science·2025
Same author

Genetic evaluation for stillbirth and preweaning mortality in Australian dairy cattle.

Journal of dairy science·2024
Same author

Archetypal clustering reveals physiological mechanisms linking milk yield and fertility in dairy cattle.

Journal of dairy science·2024

Area of Science:

  • Quantitative genetics
  • Statistical genomics
  • Animal breeding

Background:

  • Quantitative trait loci (QTL) mapping often involves analyzing multiple traits and selective genotyping of extreme individuals.
  • Standard analysis methods can produce biased estimates of QTL effects when dealing with selectively genotyped, multiple-trait data.

Purpose of the Study:

  • To describe and evaluate the use of logistic regression for estimating QTL effects in the presence of phenotypic selection.
  • To demonstrate how logistic regression can overcome bias in QTL effect estimation from selectively genotyped data.

Main Methods:

  • Logistic regression was employed, treating animal genotype as the dependent variable and phenotype as the independent variable.
  • This approach was validated using a simulated half-sib experimental design.

Related Experiment Videos

  • The method's equivalence to maximum-likelihood analysis under normality assumptions was noted, enabling use with standard statistical packages.

    • Main Results:

    • Logistic regression analysis provided unbiased estimates for both the effect and chromosomal position of QTL.
    • The analysis confirmed that using multiple traits in conjunction with logistic regression increases the statistical power for QTL detection.
    • Phenotypic selection did not introduce bias into the QTL effect estimates when using this logistic regression framework.

    Conclusions:

    • Logistic regression offers a robust statistical method for QTL mapping with selectively genotyped, multiple-trait data.
    • This approach effectively mitigates bias introduced by selection on phenotypes, improving the reliability of genetic analyses.
    • The method enhances the power of QTL detection, making it a valuable tool for genetic research and breeding programs.