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

Multiple Allele Traits01:49

Multiple Allele Traits

15.2K
15.2K
Multiple Allele Traits01:49

Multiple Allele Traits

39.2K
The Concept of Multiple Allelism
39.2K
Multiple Regression01:25

Multiple Regression

4.4K
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...
4.4K
Correlation and Regression00:53

Correlation and Regression

4.4K
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
4.4K
Calculating and Interpreting the Linear Correlation Coefficient01:11

Calculating and Interpreting the Linear Correlation Coefficient

8.6K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable, x, and the dependent variable, y. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
8.6K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

508
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
508

You might also read

Related Articles

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

Sort by
Same author

Genetic and transcriptomic determinants of disseminated coccidioidomycosis identify a founder variant in <i>NLRX1</i> and ancestry-specific rare variants in immune response genes.

medRxiv : the preprint server for health sciences·2026
Same author

Automated implementation of the SwabSeq COVID-19 diagnostic assay on the opentrons flex liquid-handling robot.

Diagnostic microbiology and infectious disease·2026
Same author

Single-cell profiling of DNA methylation in autism spectrum disorder prefrontal cortex reveals distinct regulatory and aging signatures.

Cell genomics·2026
Same author

Systematic evaluation of 24 extraction and library preparation combinations for metagenomic sequencing of SARS-CoV-2 in saliva.

bioRxiv : the preprint server for biology·2026
Same author

A Single-Cell and Spatial 3D Multi-omic Atlas of Developing Human Basal Ganglia and Inhibitory Neurons.

bioRxiv : the preprint server for biology·2026
Same author

A personal health large language model for sleep and fitness coaching.

Nature medicine·2025

Related Experiment Video

Updated: Apr 16, 2026

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.9K

Efficient multiple-trait association and estimation of genetic correlation using the matrix-variate linear mixed

Nicholas A Furlotte1, Eleazar Eskin2

  • 1*Department of Computer Science, University of California, Los Angeles, California 90095.

Genetics
|March 1, 2015
PubMed
Summary

We developed a faster method for multiple-trait association mapping using a matrix-variate linear mixed model. This approach makes genome-wide analysis of multiple traits computationally feasible for large populations.

Keywords:
association studiesgenetic correlationmultivariate analysis

More Related Videos

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.8K
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

5.1K

Related Experiment Videos

Last Updated: Apr 16, 2026

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.9K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.8K
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

5.1K

Area of Science:

  • Genetics
  • Statistical genetics
  • Bioinformatics

Background:

  • Multiple-trait association mapping identifies genetic variants influencing several traits simultaneously.
  • Current methods use linear mixed models but are computationally intensive for large datasets.
  • Estimating genetic correlation provides insights into the genetic architecture of multiple traits.

Purpose of the Study:

  • To introduce a computationally efficient matrix-variate linear mixed model for multiple-trait association mapping.
  • To enable genome-wide association studies for multiple traits in large populations.
  • To analyze gene expression data and understand gene coexpression.

Main Methods:

  • Reformulated multiple-trait association mapping using a matrix-variate linear mixed model.
  • Employed a data transformation to accelerate maximum-likelihood inference.
  • Applied the method to a human cohort and gene expression data.

Main Results:

  • Achieved over a 10-fold speedup in computational time compared to existing methods.
  • Demonstrated the feasibility of genome-wide multiple-trait association studies in large cohorts.
  • Decomposed gene coexpression into genetic and environmental components, revealing insights into coexpressed genes.

Conclusions:

  • The matrix-variate linear mixed model significantly enhances the computational efficiency of multiple-trait association mapping.
  • This advancement facilitates large-scale genome-wide analyses and the study of complex genetic architectures.
  • The method provides valuable insights into the genetic underpinnings of gene coexpression.