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

899
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...
899
Genetic Screens02:46

Genetic Screens

5.5K
Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which...
5.5K
Gene-Environment Interactions01:20

Gene-Environment Interactions

955
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...
955
Pedigree Analysis01:35

Pedigree Analysis

88.5K
Overview
88.5K
Human Genetics01:28

Human Genetics

1.3K
Human genetics provides a profound framework for understanding the interplay between genetic predispositions and human psychology. At the heart of this discipline lies the study of how genes influence physical traits, behaviors, and susceptibility to diseases. Each person carries a unique genetic code that subtly or significantly shapes their psychological and behavioral landscape.
The complex relationship between genetics and psychology is observable through common biological components such...
1.3K
Heritability01:06

Heritability

518
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"...
518

You might also read

Related Articles

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

Sort by
Same author

Sharing Data With Shared Benefits: Artificial Intelligence Perspective.

Journal of medical Internet research·2023
Same author

Granger Causality among Graphs and Application to Functional Brain Connectivity in Autism Spectrum Disorder.

Entropy (Basel, Switzerland)·2021
Same author

Heritability and Sex-Specific Genetic Effects of Self-Reported Physical Activity in a Brazilian Highly Admixed Population.

Human heredity·2020
Same author

Variance-Preserving Estimation of Intensity Values Obtained From Omics Experiments.

Frontiers in genetics·2019
Same author

Assessing functional status after intensive care unit stay: the Barthel Index and the Katz Index.

International journal for quality in health care : journal of the International Society for Quality in Health Care·2018
Same author

Genetic analysis of age-at-onset for cardiovascular risk factors in a Brazilian family study.

Human heredity·2009

Related Experiment Video

Updated: Dec 23, 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

6.7K

Learning genetic and environmental graphical models from family data.

Adèle H Ribeiro1, Júlia Maria Pavan Soler2

  • 1Department of Computer Science, Institute of Mathematics and Statistics, University of São Paulo (IME-USP), São Paulo, Brazil.

Statistics in Medicine
|April 30, 2020
PubMed
Summary

This study introduces new methods to analyze genetic and environmental influences on complex traits using probabilistic graphical models (PGMs) in family data. These methods effectively separate genetic and environmental factors for better understanding disease associations.

Keywords:
covariance matrix decompositionfamily datapolygenic mixed modelstructure learningtest for zero partial correlation

More Related Videos

Gene-environment Interaction Models to Unmask Susceptibility Mechanisms in Parkinson's Disease
08:09

Gene-environment Interaction Models to Unmask Susceptibility Mechanisms in Parkinson's Disease

Published on: January 7, 2014

7.8K
Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.6K

Related Experiment Videos

Last Updated: Dec 23, 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

6.7K
Gene-environment Interaction Models to Unmask Susceptibility Mechanisms in Parkinson's Disease
08:09

Gene-environment Interaction Models to Unmask Susceptibility Mechanisms in Parkinson's Disease

Published on: January 7, 2014

7.8K
Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.6K

Area of Science:

  • Genetics and Bioinformatics
  • Statistical Modeling
  • Computational Biology

Background:

  • Understanding variable associations, influenced by genetic and environmental factors, is crucial for biomedical research, particularly for complex diseases.
  • Probabilistic graphical models (PGMs) are established tools for representing variable relationships, but standard methods struggle with correlated family data.

Purpose of the Study:

  • To develop and evaluate methods for learning decomposed PGMs from observational Gaussian family data.
  • To differentiate the influence of genetic (between-family) and environmental (within-family) factors on variable associations.

Main Methods:

  • Proposed novel conditional independence tests based on univariate polygenic linear mixed models to account for familial dependence.
  • Extended existing structure learning algorithms (IC/PC, RFCI) for Gaussian family data to learn decomposed PGMs.
  • Utilized simulation studies and the Genetic Analysis Workshop 13 dataset for evaluation.

Main Results:

  • The proposed methods successfully assess the significance of partial correlations attributed to genetic and environmental factors separately.
  • Extended algorithms effectively learn PGMs decomposed into genetic and environmental components.
  • Demonstrated the utility of the methods on a real-world simulated dataset.

Conclusions:

  • The developed methods provide a robust framework for dissecting genetic and environmental contributions to complex traits using PGMs.
  • This approach enhances the understanding of disease etiology by separating familial and environmental influences within a unified model.