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

Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

893
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
893
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

316
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
316
Multiple Allele Traits01:49

Multiple Allele Traits

14.7K
14.7K
Multiple Allele Traits01:49

Multiple Allele Traits

38.5K
The Concept of Multiple Allelism
38.5K
What is Population Genetics?01:25

What is Population Genetics?

65.3K
A population is composed of members of the same species that simultaneously live and interact in the same area. When individuals in a population breed, they pass down their genes to their offspring. Many of these genes are polymorphic, meaning that they occur in multiple variants. Such variations of a gene are referred to as alleles. The collective set of all the alleles within a population is known as the gene pool.
65.3K
Multiple Regression01:25

Multiple Regression

4.2K
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...
4.2K

You might also read

Related Articles

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

Sort by
Same author

Primary-Level Meta-Analysis of Diversity Outbred Mice Identifies a Fasting Plasma Trimethylamine N-Oxide (TMAO) Locus Modified by Sex and Diet.

bioRxiv : the preprint server for biology·2026
Same author

ENPP1 blockade with a humanized monoclonal antibody enhances renal repair after acute kidney injury.

Cell stem cell·2026
Same author

Heterogeneous nuclear ribonucleoprotein A1 (hnRNPA1) maintains muscle progenitor identity by stabilizing the Ppp1r1b-lncRNA-PRC2 complex.

Nucleic acids research·2026
Same author

A liver-heart endocrine axis revealed by systems genetics and mediated by hepatocyte growth factor activator.

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

p21<sup>+</sup>TREM2<sup>+</sup> senescent macrophages fuel inflammaging and metabolic dysfunction-associated steatotic liver disease.

Nature aging·2026
Same author

Novel candidate genes for vestibular function identified through GWAS in the hybrid mouse diversity panel.

BMC genomics·2026
Same journal

Inherited long telomeres induce a genome-wide transcriptional response in budding yeast.

Genetics·2026
Same journal

Adaptive Dynamics of Quantitative Traits in a Steadily Changing Environment.

Genetics·2026
Same journal

Functional Landscape of Zebrafish Gonadotropins and Receptors: A Comprehensive Genetic Analysis.

Genetics·2026
Same journal

Synergistic actions of Nup43 and Myosin VI drive actin cone assembly during Drosophila spermiogenesis.

Genetics·2026
Same journal

Identification of two Cryptococcus neoformans heme transporters involved in Fhb1-mediated nitrosative stress protection in a fission yeast model.

Genetics·2026
Same journal

Analysis of a hypomorphic mei-P26 mutation reveals coordination between developmental programming of germ cells and meiotic chromosome dynamics.

Genetics·2026
See all related articles

Related Experiment Video

Updated: Mar 13, 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

6.8K

Efficient and Accurate Multiple-Phenotype Regression Method for High Dimensional Data Considering Population

Jong Wha J Joo1, Eun Yong Kang2, Elin Org3

  • 1Bioinformatics Interdepartmental Ph.D. Program, University of California, Los Angeles, California.

Genetics
|October 23, 2016
PubMed
Summary
This summary is machine-generated.

We developed GAMMA, a new method for analyzing multiple traits simultaneously. It accurately identifies genetic variants associated with complex traits while correcting for population structure, improving upon existing methods.

Keywords:
mixed modelsmultivariate analysispopulation structure

More Related Videos

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.9K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.4K

Related Experiment Videos

Last Updated: Mar 13, 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

6.8K
Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.9K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.4K

Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genomics

Background:

  • Traditional genome-wide association studies (GWAS) analyze one phenotype at a time, potentially missing complex biological network insights.
  • Existing multivariate methods for analyzing multiple phenotypes do not account for population structure, leading to false positives.
  • There is a need for methods that can simultaneously analyze multiple phenotypes and correct for population structure in genetic studies.

Purpose of the Study:

  • To introduce a novel methodology, Generalized Analysis of Molecular Variance for Mixed-Model Analysis (GAMMA), for multivariate genetic analysis.
  • To demonstrate GAMMA's capability in simultaneously analyzing multiple phenotypes while correcting for population structure.
  • To evaluate GAMMA's performance against existing methods using simulated and real biological data.

Main Methods:

  • Developed GAMMA, a generalized analysis of molecular variance for mixed-model analysis.
  • Utilized simulated genetic data with known true genetic effects and population structure for validation.
  • Applied GAMMA to genetic studies of yeast and mouse gut microbiome data.

Main Results:

  • GAMMA accurately identified true genetic effects in simulations without introducing false positives from population structure.
  • GAMMA outperformed existing methods in simulations, which either missed true effects or produced numerous false positives.
  • Application to yeast and gut microbiome data revealed several variants likely possessing true biological mechanisms.

Conclusions:

  • GAMMA offers a powerful approach for multivariate genetic analysis, effectively handling population structure.
  • The method enhances the power to detect genetic variants associated with complex traits by analyzing multiple phenotypes simultaneously.
  • GAMMA represents a significant improvement over existing methods for identifying biologically relevant genetic variants.