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 Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism
Multiple Allele Traits01:49

Multiple Allele Traits

The Concept of Multiple Allelism

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Retraction Note: Information entropy and relative entropy models for analyzing structural robustness in airway networks.

Scientific reports·2026
Same author

Beyond Mendel: a call to revisit the genotype-phenotype map through new experimental paradigms.

Genetics·2026
Same author

Advanced imaging strategies in cardiac organoids: bridging the gap between structural complexity and functional analysis.

Cardiovascular diabetology·2026
Same author

Information entropy and relative entropy models for analyzing structural robustness in airway networks.

Scientific reports·2025
Same author

Major alleles of CDCA7 shape CG methylation in Arabidopsis thaliana.

Nature plants·2025
Same author

Potential synthetic associations created by epistasis.

Genome biology·2025

Related Experiment Video

Updated: Jul 5, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Genome-wide association mapping using mixed-models: application to GAW15 Problem 3.

Keyan Zhao1, Magnus Nordborg, Paul Marjoram

  • 1Molecular and Computational Biology Program, University of Southern California 1050 Childs Way Room 201B, Los Angeles, California 90089, USA. kzhao@usc.edu

BMC Proceedings
|May 10, 2008
PubMed
Summary

Mixed-models effectively identified genetic signals in simulated data by accounting for relatedness. However, the false-positive rate was not reduced due to simulation methods lacking population stratification effects.

More Related Videos

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration
04:41

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Published on: January 9, 2020

Related Experiment Videos

Last Updated: Jul 5, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration
04:41

Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration

Published on: January 9, 2020

Area of Science:

  • Genetics
  • Statistical Genetics
  • Bioinformatics

Background:

  • Population structure and cryptic relatedness can inflate false-positive rates in genetic association studies.
  • Mixed-effects models are valuable for analyzing complex family structures and varying degrees of relatedness.
  • Efficient computational methods are crucial for applying these models to genome-wide data.

Purpose of the Study:

  • To apply mixed-effects models to the Genetic Analysis Workshop 15, Problem 3 simulated data.
  • To assess the ability of mixed-models to detect genetic signals.
  • To evaluate the impact of mixed-models on the false-positive rate.

Main Methods:

  • Utilized mixed-effects models for statistical analysis.
  • Applied the method to simulated genetic data from Genetic Analysis Workshop 15, Problem 3.
  • Focused on modeling varying degrees of individual relatedness.

Main Results:

  • Mixed-models successfully identified genetic signals within the provided simulated dataset.
  • The false-positive rate was not significantly reduced.
  • The simulation design did not incorporate population stratification, a key factor for p-value inflation.

Conclusions:

  • Mixed-effects models are effective for signal detection in genetic data with complex relatedness structures.
  • The lack of reduction in false positives was attributed to the simulation's limitations, not the model's performance.
  • Further research should consider simulation designs that better reflect real-world population complexities.