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

Background and Environment Affect Phenotype02:27

Background and Environment Affect Phenotype

6.5K
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...
6.5K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

33
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...
33
Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

346
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...
346
Punnett Squares01:00

Punnett Squares

114.1K
Overview
114.1K
Gene-Environment Interactions01:20

Gene-Environment Interactions

270
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...
270
Hardy-Weinberg Principle01:49

Hardy-Weinberg Principle

72.0K
Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.
72.0K

You might also read

Related Articles

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

Sort by
Same author

Barley HvBODYGUARD1 controls cuticular specialisations regulated by SHINE transcription factors.

The New phytologist·2026
Same author

Evaluation and improvement of two European soybean VCU networks.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik·2026
Same author

Association mapping of wheat distinctness, uniformity, and stability traits identifies evidence of <i>TaDof-B</i> copy number variation associated with stem pith thickness.

Frontiers in plant science·2026
Same author

Integrating genomic prediction into crop DUS testing: new approaches in support of reference collection management and distinctness assessment.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik·2026
Same author

Genomic Erosion in the Assessment of Species' Extinction Risk and Recovery Potential.

The Journal of heredity·2026
Same author

Genome-wide association study (GWAS) identifies genetic loci controlling Distinctness, Uniformity, and Stability (DUS) traits in wheat.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik·2026

Related Experiment Video

Updated: Jun 16, 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

6.3K

Modeling QTL-by-environment interactions for multi-parent populations.

Wenhao Li1, Martin P Boer1, Ronny V L Joosen2

  • 1Biometris, Wageningen University and Research Center, Wageningen, Netherlands.

Frontiers in Plant Science
|August 15, 2024
PubMed
Summary

This study introduces a new statistical method for analyzing quantitative trait loci (QTLs) in multi-parent populations across multiple environments. The approach effectively detects both consistent and environment-specific QTLs, improving genetic and breeding studies.

Keywords:
MAGICNAMQTL-by-environment interactiondiallelmaizemulti-parent populationmultienvironment trialwheat

More Related Videos

QTL Mapping and CRISPR/Cas9 Editing to Identify a Drug Resistance Gene in Toxoplasma gondii
11:37

QTL Mapping and CRISPR/Cas9 Editing to Identify a Drug Resistance Gene in Toxoplasma gondii

Published on: June 22, 2017

16.3K
Modeling Fetal Alcohol Spectrum Disorders in Zebrafish to Characterize the Impact of an Adverse Embryonic Environment on Adult Social Behavior
04:50

Modeling Fetal Alcohol Spectrum Disorders in Zebrafish to Characterize the Impact of an Adverse Embryonic Environment on Adult Social Behavior

Published on: February 9, 2024

314

Related Experiment Videos

Last Updated: Jun 16, 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

6.3K
QTL Mapping and CRISPR/Cas9 Editing to Identify a Drug Resistance Gene in Toxoplasma gondii
11:37

QTL Mapping and CRISPR/Cas9 Editing to Identify a Drug Resistance Gene in Toxoplasma gondii

Published on: June 22, 2017

16.3K
Modeling Fetal Alcohol Spectrum Disorders in Zebrafish to Characterize the Impact of an Adverse Embryonic Environment on Adult Social Behavior
04:50

Modeling Fetal Alcohol Spectrum Disorders in Zebrafish to Characterize the Impact of an Adverse Embryonic Environment on Adult Social Behavior

Published on: February 9, 2024

314

Area of Science:

  • Quantitative genetics
  • Plant and animal breeding
  • Statistical genomics

Background:

  • Multi-parent populations (MPPs) offer genetic diversity and controlled structures for genetic studies.
  • Existing quantitative trait loci (QTL) mapping methods primarily focus on single environments, neglecting QTL-by-environment interactions (QEIs).
  • There is a need for robust methods to analyze QTLs in multi-environment trials (METs) and model QEIs.

Purpose of the Study:

  • To develop and present mixed-model approaches for detecting and modeling consistent versus environment-dependent QTLs (QEIs) in MPPs.
  • To provide a flexible framework applicable to various MPP designs and MET data.
  • To improve the accuracy and scope of QTL mapping in complex breeding programs.

Main Methods:

  • Utilized mixed models with normally distributed QTL effects, incorporating variances for consistency and environment/family dependence.
  • Employed identity-by-descent (IBD) probabilities derived from parental origins in design matrices.
  • Integrated polygenic effects to account for background genetic variation.

Main Results:

  • Successfully detected and modeled both consistent and environment-dependent QTLs across diverse MPP datasets (diallel, NAM, MAGIC) from METs.
  • Demonstrated the method's ability to handle complex genetic architectures and environmental influences.
  • Achieved favorable comparisons with existing, specialized QTL mapping methods.

Conclusions:

  • The proposed mixed-model approach offers a powerful and versatile tool for QTL and QEI analysis in MPPs under MET conditions.
  • This method enhances the understanding of genotype-by-environment interactions, crucial for developing robust crop varieties and livestock.
  • The approach is broadly applicable and provides a significant advancement over single-environment QTL mapping.