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

Linkage analysis of longitudinal data.

Young Ju Suh1, Taesung Park, Soo Yeon Cheong

  • 1Department of Statistics, Seoul National University, Seoul, South Korea. ysprite@hotmail.com

BMC Genetics
|February 21, 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

Parametric hypothesis testing for pathway based hierarchical structural component models.

Genes & genomics·2026
Same author

Analysis of severity in COVID-19 patients by using longitudinal immune profiles.

iScience·2026
Same author

Enhancing polygenic risk prediction by modeling quantile-specific genetic effects.

Scientific reports·2026
Same author

Optoelectronic Synaptic Transistors Based on Colloidal CdSe Nanowires for Energy-Efficient Neuromorphic Computing.

ACS applied materials & interfaces·2026
Same author

Rapid improvement of itch with nemolizumab in atopic dermatitis and prurigo nodularis phase 3 studies.

Journal of the European Academy of Dermatology and Venereology : JEADV·2025
Same author

Impact of ACEI/ARB use on COVID-19 mortality in patients with ischaemic heart disease: insights from South Korean National health insurance service data.

BMC infectious diseases·2025
Same journal

Geographic distribution of sex chromosome polymorphism in Anastrepha fraterculus sp. 1 from Argentina.

BMC genetics·2020
Same journal

Development and characterization of a pupal-colour based genetic sexing strain of Anastrepha fraterculus sp. 1 (Diptera: Tephritidae).

BMC genetics·2020
Same journal

Improvement on the genetic engineering of an invasive agricultural pest insect, the cherry vinegar fly, Drosophila suzukii.

BMC genetics·2020
Same journal

Precise single base substitution in the shibire gene by CRISPR/Cas9-mediated homology directed repair in Bactrocera tryoni.

BMC genetics·2020
Same journal

Climate stress resistance in male Queensland fruit fly varies among populations of diverse geographic origins and changes during domestication.

BMC genetics·2020
Same journal

Genetic structure and symbiotic profile of worldwide natural populations of the Mediterranean fruit fly, Ceratitis capitata.

BMC genetics·2020
See all related articles

This study introduces a flexible statistical model for analyzing longitudinal genetic data. The new mixed model effectively detects linkage for traits like systolic blood pressure, outperforming simpler models.

Area of Science:

  • Genetics
  • Biostatistics
  • Statistical Genetics

Background:

  • Proposes a novel statistical mixed model for linkage analysis of longitudinal data.
  • Extends the Haseman and Elston model to incorporate multiple random effects.
  • Accounts for correlations among siblings within families, including those with shared parents.

Purpose of the Study:

  • To develop and evaluate a statistical model for linkage analysis in longitudinal genetic studies.
  • To assess the model's performance in detecting linkage for quantitative traits using simulated data.

Main Methods:

  • Utilized a mixed-effects statistical model incorporating several random effects.
  • Applied the model to the Genetic Analysis Workshop 13 simulated dataset.
  • Compared the proposed model against an independence model and other random effects models.

Related Experiment Videos

Main Results:

  • The proposed model demonstrated good power for linkage detection in longitudinal systolic blood pressure data.
  • Random effects models, including the proposed one, slightly outperformed the independence model.
  • Both evaluated random effects models exhibited comparable performance.

Conclusions:

  • The developed statistical models are effective and flexible for linkage detection with longitudinal genetic data.
  • The models can accommodate diverse correlation structures within families.
  • Further research into models with more generalized covariance structures is recommended.