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

14.7K
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...
14.7K
Polygenic Traits01:18

Polygenic Traits

67.0K
When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
67.0K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

333
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
333
Epistasis Analysis01:09

Epistasis Analysis

5.4K
Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
5.4K
Gene-Environment Interactions01:20

Gene-Environment Interactions

685
Gene expression is a dynamic process that is significantly influenced by environmental factors. This interaction underlies the complex nature of biological development and the phenotypic differences observed among individuals, even among those with identical genetic makeups. Factors such as radiation, temperature, behavior, nutrition, and stress play pivotal roles in determining how genes are expressed. The concept of the reaction range is central to understanding this interaction. It posits...
685
Multiple Regression01:25

Multiple Regression

3.3K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.3K

You might also read

Related Articles

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

Sort by
Same author

Cyclophilin-a as a key paracrine factor replicating hPVSC therapeutic effects in a chemotherapy-induced ovarian damage model.

Scientific reports·2026
Same author

Evaluation of the Preclinical and Clinical Obesity Framework for Risk Stratification of Major Adverse Liver Outcomes.

Mayo Clinic proceedings·2026
Same author

A Novel <i>Glycyrrhiza korshinskyi</i> Cultivar 'Wongam' Alleviates Benign Prostatic Hyperplasia <i>via</i> G1/S Checkpoint Arrest and Apoptosis: Insights from Network Pharmacology and Experimental Validation.

The world journal of men's health·2026
Same author

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

Scientific reports·2026
Same author

Polyvinylpyrrolidone-Polycarbosilane Core-Shell Fibrous Membrane as an Advanced Material for Triboelectric Nanogenerators.

ACS applied materials & interfaces·2026
Same author

Extracellular vesicles conjugated with c(RGDyk) peptide targeting integrin αVβ3 repair optic nerve injury through YAP/TAZ and Smad2/3 signaling.

Stem cells translational medicine·2026

Related Experiment Video

Updated: Oct 18, 2025

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

4.4K

Spatial rank-based multifactor dimensionality reduction to detect gene-gene interactions for multivariate phenotypes.

Mira Park1, Hoe-Bin Jeong2, Jong-Hyun Lee2

  • 1Department of Preventive Medicine, Eulji University, Daejeon, 34824, Republic of Korea.

BMC Bioinformatics
|October 5, 2021
PubMed
Summary

We developed a robust multivariate rank-based MDR (MR-MDR) method for detecting gene-gene interactions in genome-wide association studies. This new approach effectively handles multiple continuous phenotypes and is less sensitive to skewed data and outliers.

Keywords:
Fuzzy clusteringGene–gene interactionMultifactor dimensionality reductionSpatial rank statistic

More Related Videos

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.6K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.9K

Related Experiment Videos

Last Updated: Oct 18, 2025

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

4.4K
Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts
08:51

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

Published on: September 20, 2024

1.6K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.9K

Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical genetics

Background:

  • Genome-wide association studies (GWAS) aim to identify gene-gene interactions for complex diseases.
  • Multifactor dimensionality reduction (MDR) is a common method for detecting gene-gene interactions.
  • Existing multivariate MDR methods are limited, especially for multiple related phenotypes, and current statistics are sensitive to data distribution issues.

Purpose of the Study:

  • To propose a robust multivariate rank-based MDR (MR-MDR) method for analyzing multiple continuous phenotypes.
  • To overcome the limitations of existing methods that are sensitive to skewed distributions and outliers.
  • To provide a more reliable approach for detecting gene-gene interactions in GWAS.

Main Methods:

  • The MR-MDR method utilizes nonparametric statistics, specifically spatial signs and ranks.
  • It employs fuzzy k-means clustering to group multi-locus genotypes.
  • A spatial rank-sum statistic is used as an evaluation measure, with tenfold cross-validation to prevent overfitting.

Main Results:

  • MR-MDR demonstrated outstanding performance with skewed distributions in simulation studies.
  • It showed comparable power to existing methods for symmetric distributions.
  • The method was successfully applied to a Korean GWAS dataset, identifying genetic interactions related to kidney function phenotypes.

Conclusions:

  • MR-MDR is a robust and useful multivariate non-parametric approach for gene-gene interaction analysis.
  • It is effective across various phenotype distributions, correlations, and sample sizes.
  • The method offers improved inference in GWAS, particularly for complex disease genetics.