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

Gene-Environment Interactions01:20

Gene-Environment Interactions

669
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...
669
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

14.7K
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
14.7K
Background and Environment Affect Phenotype02:27

Background and Environment Affect Phenotype

6.8K
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...
6.8K
Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

642
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...
642
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

875
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
875

You might also read

Related Articles

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

Sort by
Same author

GLP-1 Receptor Agonists and Age-related Macular Degeneration Risk in Diabetes or Non-diabetic Obesity: A Retrospective Cohort Study.

The American journal of medicine·2026
Same author

Association between preoperative serum albumin and long-term all-cause mortality after percutaneous vertebroplasty for vertebral compression fracture.

PloS one·2026
Same author

Placental toxic metal concentrations and preterm birth: Modification by social stressors.

Reproductive toxicology (Elmsford, N.Y.)·2026
Same author

Simulation-based machine learning for real-time assessment of side-branch hemodynamics in coronary bifurcation lesions.

The international journal of high performance computing applications·2026
Same author

Effectiveness of maternal influenza vaccine to infants: A population-based cohort study.

Pediatrics and neonatology·2026
Same author

Additive Association of <i>ABCG2</i> rs4148155 and <i>SLC22A12</i> rs75786299 Polymorphisms with Hyperuricemia, Gout, and Nephrolithiasis: A Hospital-Based, Case-Control Study.

Kidney diseases (Basel, Switzerland)·2026

Related Experiment Video

Updated: Oct 12, 2025

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays
14:06

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Published on: November 12, 2012

46.6K

SEAGLE: A Scalable Exact Algorithm for Large-Scale Set-Based Gene-Environment Interaction Tests in Biobank Data.

Jocelyn T Chi1, Ilse C F Ipsen2, Tzu-Hung Hsiao3

  • 1Department of Statistics, North Carolina State University, Raleigh, NC, United States.

Frontiers in Genetics
|November 19, 2021
PubMed
Summary
This summary is machine-generated.

SEAGLE is a new computational tool for analyzing gene-environment interactions (GxE) in large biobank datasets. It efficiently performs set-based GxE variance component tests, enabling new discoveries in complex diseases.

Keywords:
GxE collapsing test for biobank dataGxE test for large-scale sequencing datagene-based GxE test for biobank datagene-environment kernel testgene-environment variance component testregional-based gene-environment testscalable GEI test

More Related Videos

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

4.4K
A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

18.0K

Related Experiment Videos

Last Updated: Oct 12, 2025

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays
14:06

Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

Published on: November 12, 2012

46.6K
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

4.4K
A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

18.0K

Area of Science:

  • Genetics
  • Computational Biology
  • Bioinformatics

Background:

  • Biobank data enables large-scale gene-environment interaction (GxE) studies for complex diseases.
  • Set-based GxE variance component (VC) tests are crucial for analyzing joint effects of multiple variants.
  • Large sample sizes in biobanks present computational challenges for GxE assessment.

Purpose of the Study:

  • To present SEAGLE, a Scalable Exact Algorithm for Large-scale set-based GxE tests.
  • To enable computationally efficient GxE VC tests for biobank-scale data.
  • To facilitate the study of gene-environment interactions on continuous traits.

Main Methods:

  • SEAGLE utilizes modern matrix computations for efficient calculation of GxE VC test statistics and p-values.
  • The algorithm does not require additional assumptions or approximations.
  • It is designed for large sample sizes (e.g., 10^5) and can be implemented on standard laptops.

Main Results:

  • SEAGLE provides a computationally efficient solution for set-based GxE VC tests on biobank-scale data.
  • Demonstrated performance through extensive simulations.
  • Successfully applied to genome-wide gene-based GxE analysis on Taiwan Biobank data.

Conclusions:

  • SEAGLE overcomes computational challenges in large-scale GxE studies.
  • Enables efficient analysis of gene-environment interactions on continuous traits.
  • Facilitates exploration of GxE on complex diseases using biobank data.