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

A statistical framework for quantitative trait mapping.

S Sen1, G A Churchill

  • 1The Jackson Laboratory, Bar Harbor, Maine 04609, USA.

Genetics
|September 19, 2001
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

Genetic background influences susceptibility to chemotherapy-induced hematotoxicity.

The pharmacogenomics journal·2017
Same author

Identification of quantitative trait loci associated with acylsugar accumulation using intraspecific populations of the wild tomato, Lycopersicon pennellii.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik·2014
Same author

High-precision genetic mapping of behavioral traits in the diversity outbred mouse population.

Genes, brain, and behavior·2013
Same author

Discovery of blood transcriptomic markers for depression in animal models and pilot validation in subjects with early-onset major depression.

Translational psychiatry·2012
Same author

Gene expression patterns in the hippocampus and amygdala of endogenous depression and chronic stress models.

Molecular psychiatry·2010
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

We present a statistical framework for genetic analysis of quantitative trait data in inbred line crosses. This method simplifies complex genetic analyses by separating genotype and phenotype data, enabling efficient quantitative trait locus (QTL) mapping.

Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Quantitative trait locus (QTL) analysis in inbred line crosses is crucial for understanding genetic contributions to complex traits.
  • Existing methods can be computationally intensive and challenging to adapt for complex genetic architectures.

Purpose of the Study:

  • To develop a general statistical framework for the genetic analysis of quantitative trait data in inbred line crosses.
  • To simplify QTL mapping by conditioning on unobserved QTL genotypes, splitting the problem into independent genotype and phenotype components.

Main Methods:

  • A Bayesian quantitative trait locus (QTL) analysis approach using a Monte Carlo algorithm.
  • Simulation of complete genotype information across a genome-wide grid, weighted by phenotype data.

Related Experiment Videos

  • Approximation of statistical inference quantities for QTL locations and effect sizes.

    • Main Results:

    • The framework effectively separates the relationship between QTL and phenotype from QTL genomic location.
    • The Monte Carlo algorithm facilitates Bayesian QTL analysis by simulating and weighting genotypes.
    • The approach allows for efficient model comparison by only recomputing weights for new models.

    Conclusions:

    • The proposed framework offers a flexible and computationally manageable approach for genetic analysis of quantitative traits.
    • It accommodates complex scenarios including multiple interacting QTL, non-normal phenotypes, and missing data.
    • A software tool is available to implement this advanced QTL analysis methodology.