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

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

153
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...
153
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

179
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
179
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

921
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
921
Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

767
Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
The primary methodologies used in behavior genetics include family studies, twin studies, and adoption studies, each providing unique...
767
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

6.6K
Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
6.6K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

375
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...
375

You might also read

Related Articles

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

Sort by
Same author

Social Media Usage and Its Association With the Social Media Addiction Questionnaire Scale Among Early Adolescents.

JAACAP open·2026
Same authorSame journal

Tracing the Right Path: Determination of Large Pedigree Segmentation and Relatedness.

Behavior genetics·2026
Same author

umx version 4.5: Extending Twin and Path-Based SEM in R with CLPM, MR-DoC, Definition Variables, Ωnyx Integration, and Censored Distributions.

Twin research and human genetics : the official journal of the International Society for Twin Studies·2026
Same author

Extending reliability to intensive longitudinal data with the Kalman filter.

The British journal of mathematical and statistical psychology·2026
Same author

Social Determinants of Health and Pediatric Long COVID in the US.

JAMA pediatrics·2026
Same author

Clinical Manifestations.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025

Related Experiment Video

Updated: Nov 21, 2025

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

6.6K

Combining Structural-Equation Modeling with Genomic-Relatedness-Matrix Restricted Maximum Likelihood in OpenMx.

Robert M Kirkpatrick1,2, Joshua N Pritikin3, Michael D Hunter4

  • 1Virginia Commonwealth University, Richmond, USA. robert.kirkpatrick@vcuhealth.org.

Behavior Genetics
|January 13, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces mxGREML, a new feature for OpenMx, enabling the fitting of traditional biometrical structural equation models (SEMs) using modern genomic data. This bridges pregenomic behavioral genetics with current genome-wide association studies.

Keywords:
GenomicsSoftwareStatistical methodsStructural equation modeling

More Related Videos

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

10.4K
Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

7.1K

Related Experiment Videos

Last Updated: Nov 21, 2025

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

6.6K
Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

10.4K
Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

7.1K

Area of Science:

  • Behavioral Genetics
  • Quantitative Genetics
  • Genomics

Background:

  • Biometrical structural equation models (SEMs) have a long history in behavioral genetics using twin, family, and adoption studies.
  • Genomic-relatedness-matrix restricted maximum-likelihood (GREML) estimates genetic components using observed genetic resemblance in unrelated individuals with genome-wide genotypes.

Purpose of the Study:

  • To introduce a new feature, "mxGREML", within the OpenMx package.
  • To enable the fitting of biometrical SEMs from pregenomic studies using contemporary genomic data.
  • To extend the capabilities of GREML beyond heritability and genetic correlations to SEMs for multitrait genomic samples.

Main Methods:

  • Integration of GREML methodology into the OpenMx package.
  • Development of "mxGREML" to fit biometrical SEMs on genome-wide genotype data.
  • Illustrative example provided to demonstrate the feature's application.

Main Results:

  • The mxGREML feature in OpenMx now supports fitting biometrical SEMs in genomic study designs.
  • The new functionality allows for the analysis of multitrait samples of genotyped participants using established SEM frameworks.
  • The study details the functionality and provides a practical example of its use.

Conclusions:

  • mxGREML bridges traditional biometrical SEMs with modern genomic data analysis.
  • The feature enhances OpenMx's utility for researchers in behavioral and quantitative genetics.
  • Future development plans are outlined, addressing current limitations and potential extensions.