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

Parallel computing in interval mapping of quantitative trait loci.

O Carlborg1, L Andersson-Eklund, L Andersson

  • 1Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Uppsala Biomedical Center, Box 597, S-751 24 Uppsala, Sweden. orjan.carlborg@hgen.slu.se

The Journal of Heredity
|January 5, 2002
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

Full pedigree quantitative trait locus analysis in commercial pigs using variance components.

Journal of animal science·2003
Same author

Melanocortin 1-receptor (MC1R) mutations are associated with plumage colour in chicken.

Animal genetics·2003
Same author

The twofold difference in adult size between the red junglefowl and White Leghorn chickens is largely explained by a limited number of QTLs.

Animal genetics·2003
Same author

Pneumoperitoneum versus abdominal wall lift: effects on central haemodynamics and intrathoracic pressure during laparoscopic cholecystectomy.

Acta anaesthesiologica Scandinavica·2003
Same author

Identification of quantitative trait loci for production traits in commercial pig populations.

Genetics·2003
Same author

Development and evaluation of a flow-cytometric assay of specific cell-mediated immune response in activated whole blood for the detection of cell-mediated immunity against varicella-zoster virus.

Journal of immunological methods·2003
Same journal

Chromosome-scale genome assembly of the rice stink bug (Hemiptera: Oebalus pugnax) illuminates genome structure and gene family evolution in Pentatomidae.

The Journal of heredity·2026
Same journal

Rapid Derivation of Cloning-Competent Cells from Peripheral Blood Advances Conservation Biobanking.

The Journal of heredity·2026
Same journal

Genomic vulnerability to climate change of a poorly dispersing and threatened fish, the southern pygmy perch (Nannoperca australis).

The Journal of heredity·2026
Same journal

The complete genomes of two hybridizing field crickets, Gryllus pennsylvanicus and Gryllus firmus.

The Journal of heredity·2026
Same journal

Chromosome-level genome assembly and annotation of the transverse lady beetle Coccinella transversoguttata.

The Journal of heredity·2026
Same journal

A Chromosome-Level Assembly of the Pine Processionary Moth (Thaumetopoea pityocampa) genome.

The Journal of heredity·2026
See all related articles

This study introduces a parallel algorithm to speed up quantitative trait loci (QTL) mapping and permutation testing. The new method significantly reduces computation time, making complex genetic analyses more accessible.

Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Linear regression is computationally efficient for single quantitative trait loci (QTL) mapping.
  • Simultaneous multi-QTL analysis, permutation testing, and large datasets increase computational demands.
  • Existing methods face challenges with computational intensity for complex genetic studies.

Purpose of the Study:

  • To present an easily implemented parallel algorithm for reducing computational time in QTL mapping.
  • To enhance the efficiency of permutation testing for significance thresholds.
  • To enable routine use of permutations for complex QTL models and multiple traits.

Main Methods:

  • Development and implementation of a parallel algorithm for QTL analysis.

Related Experiment Videos

  • Utilizing multiple processors to distribute computational load.
  • Application of the algorithm to QTL mapping and permutation testing.

    • Main Results:

    • The parallel algorithm significantly decreased analysis time, achieving less than 15% of single-processor time with 18 processors.
    • Demonstrated methods for improving computational efficiency through even workload distribution.
    • Showcased how data distribution strategies facilitate the use of more processors.

    Conclusions:

    • Parallel computing drastically reduces computation time for QTL mapping and permutation testing.
    • This approach makes empirical significance threshold determination feasible for complex multi-QTL models.
    • The algorithm enhances the feasibility of computationally intensive QTL analysis methods.