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

Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

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...
Gene-Environment Interactions01:20

Gene-Environment Interactions

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...
Background and Environment Affect Phenotype02:27

Background and Environment Affect Phenotype

Although the genetic makeup of an organism plays a major role in determining the phenotype, there are also several environmental factors, such as temperature, oxygen availability, presence of mutagens, that can alter an organism’s phenotype.
An example of how genetic background affects phenotype can be seen in horses. The Extension gene in horses is responsible for their coat color. A wild-type gene (EE) produces black pigment in the coat, while a mutant gene (ee) produces red pigment. A...
Epistasis Analysis01:09

Epistasis Analysis

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

Mechanistic Models: Compartment Models in Individual and Population Analysis

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 squares (OLS)...
Heritability01:06

Heritability

Heritability is a statistical concept that measures the degree to which genetic differences among individuals contribute to trait variations within a population. It is a fundamental idea in genetics, often prone to misinterpretation. Heritability is expressed as a percentage, reflecting the proportion of variation in a specific trait across a population that can be linked to genetic differences. However, it's important to understand that heritability does not determine how "genetic" a trait is,...

You might also read

Related Articles

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

Sort by
Same author

Genetics of major depressive disorder in a homogeneous population with uniform phenotyping.

Molecular psychiatry·2026
Same author

Causation Between Smoking Quantity and Depressive Symptoms in Young Adults: Evidence From Novel Cross-Lagged Twin Models.

medRxiv : the preprint server for health sciences·2025
Same author

The Power to Resolve Cultural Transmission and Sibling Interaction Using Polygenic Scores.

Behavior genetics·2025
Same author

Genetic and environmental contributions to variation in plasma phosphorylated tau 217.

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

Measurement Error and Power in Family-Based Extensions to Mendelian Randomization.

Behavior genetics·2025
Same author

Intergenerational transmission of complex traits and the offspring methylome.

Molecular psychiatry·2025

Related Experiment Video

Updated: May 26, 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

Detecting specific genotype by environment interactions using marginal maximum likelihood estimation in the classical

Dylan Molenaar1, Sophie van der Sluis, Dorret I Boomsma

  • 1Department of Psychology, University of Amsterdam, Weesperplein 4, Amsterdam, The Netherlands. D.Molenaar@uva.nl

Behavior Genetics
|December 8, 2011
PubMed
Summary

This study enhances genotype-environment (G×E) interaction analysis by extending existing methods to include DZ twin data and model additional interactions. Simulations and real-world data on cognitive abilities demonstrate the improved power and distinction of these new approaches.

More Related Videos

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients
07:34

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

Related Experiment Videos

Last Updated: May 26, 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

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients
07:34

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

Area of Science:

  • Behavioral Genetics
  • Quantitative Genetics
  • Psychology

Background:

  • Genotype-environment (G×E) interactions are crucial for understanding phenotypic variation in traits like cognition and personality.
  • Previous methods, such as the Jinks and Fulker (1970) and van der Sluis et al. (2006) approaches, have limitations, especially with unmeasured environments.
  • Existing G×E analyses often rely on observed environmental variables and univariate models.

Purpose of the Study:

  • To identify and address challenges in the investigation of genotype-environment (G×E) interactions.
  • To propose and evaluate extensions to existing heteroscedasticity-based G×E analysis methods.
  • To enhance the power and specificity of G×E interaction detection, including distinguishing between different interaction types (A×E, A×C, C×E).

Main Methods:

  • Proposed extensions include incorporating DZ twin data, modeling additive genetic by unmeasured environment (A×E) and additive genetic by common environment (A×C) interactions, and extending univariate to multivariate approaches.
  • Simulations were conducted to assess the power of univariate methods in detecting various G×E interactions under different conditions.
  • A multivariate extension of the proposed model was applied to an empirical dataset concerning cognitive abilities.

Main Results:

  • The study identified four key challenges in current G×E interaction research.
  • Simulations demonstrated the effectiveness of the extended methods in detecting G×E interactions and differentiating between A×E, A×C, and C×E interactions.
  • The multivariate application to cognitive data showcased the practical utility of the enhanced analytical framework.

Conclusions:

  • The proposed extensions significantly improve the investigation of genotype-environment (G×E) interactions, offering greater power and precision.
  • The refined methods allow for a more nuanced understanding of how genetic and environmental factors interact to shape complex traits.
  • This work provides a robust framework for future research in behavioral and quantitative genetics.