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

Epistasis Analysis01:09

Epistasis Analysis

6.3K
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...
6.3K
Factorial Design02:01

Factorial Design

16.0K
Factorial Analysis is an experimental design that applies Analysis of Variance (ANOVA) statistical procedures to examine a change in a dependent variable due to more than one independent variable, also known as factors. Changes in worker productivity can be reasoned, for example, to be influenced by salary and other conditions, such as skill level. One way to test this hypothesis is by categorizing salary into three levels (low, moderate, and high) and skills sets into two levels (entry level...
16.0K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

629
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...
629
One-Way ANOVA01:18

One-Way ANOVA

15.2K
One-way ANOVA analyzes more than three samples categorized by one factor. For example, it can compare the average mileage of sports bikes. Here, the data is categorized by one factor - the company. However, one-way ANOVA cannot be used to simultaneously compare the sample mean of three or more samples categorized by two factors. An example of two factors would be sports bikes from different companies driven in different terrains, such as a desert or snowy landscape. Here, two-way ANOVA is used...
15.2K
Dimensional Analysis01:27

Dimensional Analysis

856
Dimensional analysis is a valuable technique in fluid mechanics for simplifying complex problems by reducing them into dimensionless groups. These groups capture the essential relationships between the variables involved, allowing researchers and engineers to analyze fluid flow without dealing with each variable individually. This approach reduces the number of independent variables, allowing for easier analysis and better understanding of physical phenomena.
In fluid mechanics, dimensional...
856
Dimensional Analysis03:40

Dimensional Analysis

68.5K
Dimensional analysis, also known as the factor label method, is a versatile approach for mathematical operations. The main principle behind this approach is: the units of quantities must be subjected to the same mathematical operations as their associated numbers. This method can be applied to computations ranging from simple unit conversions to more complex and multi-step calculations involving several different quantities and their units.
Conversion Factors and Dimensional Analysis
The unit...
68.5K

You might also read

Related Articles

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

Sort by
Same author

Ensuring Fairness in Detecting Mild Cognitive Impairment with MRI.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2025
Same author

Enhancing clinical outcome predictions through effective sample size evaluation in graph-based digital twin modeling.

BioData mining·2025
Same author

Perceptual and technical barriers in sharing and formatting metadata accompanying omics studies.

Cell genomics·2025
Same author

Erratum: A latent transfer learning method for estimating hospital-specific post-acute healthcare demands following SARS-CoV-2 infection.

Patterns (New York, N.Y.)·2025
Same author

AI as an accelerator for defining new problems that transcends boundaries.

BioData mining·2025
Same author

Preoperative anemia is an unsuspecting driver of machine learning prediction of adverse outcomes after lumbar spinal fusion.

The spine journal : official journal of the North American Spine Society·2025
Same journal

Mapping the 3D Chromosome Organization of a Biosynthetic Gene Cluster by Capture Hi-C (CHi-C).

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Mapping the 3D Chromosome Organization of Streptomyces by Hi-C.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

CUT&Tag Epigenomic Profiling of Biosynthetic Gene Clusters in Arabidopsis thaliana.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Rhizobium rhizogenes-Mediated Hairy Root Transformation Protocol for Lotus japonicus and Other Legumes.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Characterization of Bioactive Saponins from Sea Cucumbers.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Methods for Functional Validation of Terpenoid Metabolic Clusters in Nicotiana benthamiana and Aspergillus oryzae.

Methods in molecular biology (Clifton, N.J.)·2026
See all related articles

Related Experiment Video

Updated: Apr 20, 2026

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

2.4K

Epistasis analysis using multifactor dimensionality reduction.

Jason H Moore1, Peter C Andrews

  • 1Department of Genetics, Geisel School of Medicine, DHMC, One Medical Center Dr., HB 7937, Lebanon, NH, 03756, USA, Jason.H.Moore@Dartmouth.edu.

Methods in Molecular Biology (Clifton, N.J.)
|November 19, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces Multifactor Dimensionality Reduction (MDR), a method and software for identifying gene interactions (epistasis) in genetic studies. It aids in understanding complex human diseases by analyzing genetic associations.

More Related Videos

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

4.1K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

3.1K

Related Experiment Videos

Last Updated: Apr 20, 2026

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

2.4K
Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

4.1K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

3.1K

Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Epistasis, or gene-gene interaction, is crucial for understanding complex human diseases.
  • Traditional genetic association studies often struggle to detect these interactions.
  • Identifying epistatic effects is essential for a comprehensive genetic analysis.

Purpose of the Study:

  • Introduce the Multifactor Dimensionality Reduction (MDR) methodology.
  • Present the open-source MDR software package for epistasis detection.
  • Facilitate the characterization of gene-gene interactions in genetic association studies.

Main Methods:

  • Multifactor Dimensionality Reduction (MDR) approach.
  • Application of MDR software for analyzing genetic data.
  • Detection and characterization of epistasis in genetic association studies.

Main Results:

  • The MDR methodology effectively detects and characterizes epistasis.
  • The MDR software package provides key functions for genetic analysis.
  • Published studies demonstrate the utility of MDR in complex human diseases.

Conclusions:

  • MDR is a valuable tool for uncovering gene-gene interactions.
  • The open-source MDR software facilitates epistasis analysis.
  • MDR contributes to a deeper understanding of the genetic basis of complex diseases.