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

Log-linear models for gene mapping with affected sib pair data.

Yang Yang1, Jürg Ott

  • 1Laboratory of Statistical Genetics, Rockefeller University, New York, NY 10021, USA. yyang@linkage.rockfeller.edu

Human Heredity
|November 19, 2002
PubMed
Summary

This study introduces log-linear models for analyzing multiple genetic susceptibility loci and their interactions in complex traits. The new models improve the accuracy and power of mapping disease-related genes using affected sib pair data.

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

Sex-Specific Polygenic Risk Scores and Replication in a Model-Free Analysis of Schizophrenia Data.

Genes·2025
Same author

Digenic Analysis Finds Highly Interactive Genetic Variants Underlying Polygenic Traits.

Medical research archives·2024
Same author

A multi-threaded approach to genotype pattern mining for detecting digenic disease genes.

Frontiers in genetics·2023
Same author

Overview of frequent pattern mining.

Genomics & informatics·2023
Same author

Comorbidity of Novel <i>CRHR2</i> Gene Variants in Type 2 Diabetes and Depression.

International journal of molecular sciences·2022
Same author

Implication of Melanocortin Receptor Genes in the Familial Comorbidity of Type 2 Diabetes and Depression.

International journal of molecular sciences·2022

Area of Science:

  • Genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • Complex traits are influenced by multiple interacting genetic loci.
  • Current linkage and association analyses often focus on single loci, limiting discovery.
  • Non-Mendelian inheritance patterns can indicate proximity to disease loci.

Purpose of the Study:

  • To develop statistical models for joint analysis of multiple marker loci and their interactions.
  • To improve the mapping of susceptibility loci for complex heritable traits.
  • To enhance the analysis of affected sib pair data by considering genetic interactions.

Main Methods:

  • Development of log-linear models for the joint distribution of Identical by Descent (IBD) values.
  • Application of an independence log-linear model and a neighboring interaction model.

Related Experiment Videos

  • Utilizing likelihood methods on simulated data with one or two susceptibility loci.

    • Main Results:

    • The neighboring interaction log-linear model demonstrated higher efficiency compared to the independence model.
    • Incorporating locus interactions in two-locus analysis increased power and accuracy for trait locus mapping.
    • Simulated data analysis confirmed the effectiveness of the proposed models.

    Conclusions:

    • Log-linear models provide a powerful framework for joint analysis of multiple genetic loci and interactions.
    • The neighboring interaction model offers improved efficiency for mapping complex trait loci.
    • This approach enhances the ability to identify multiple interacting genes contributing to heritable diseases.