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

5.9K
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...
5.9K
Gene-Environment Interactions01:20

Gene-Environment Interactions

1.5K
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...
1.5K
Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

1.5K
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...
1.5K
Trihybrid Crosses02:27

Trihybrid Crosses

24.6K
Trihybrid Crosses
Some of Mendel’s crosses examined three pairs of contrasting characteristics. Such a cross is called a trihybrid cross. A trihybrid cross is a combination of three individual monohybrid crosses. For example, plant height (tall vs. short), seed shape (round vs. wrinkled), and seed color (yellow vs. green).
The F1 generation plants of a trihybrid cross are heterozygous for all three traits and produce eight gametes. Upon self-fertilization, these gametes have an equal...
24.6K
Two-Way ANOVA01:17

Two-Way ANOVA

2.5K
The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the...
2.5K
Multiple Regression01:25

Multiple Regression

3.4K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
3.4K

You might also read

Related Articles

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

Sort by
Same journal

Tailoring AI and ML models for genotype-by-environment prediction leveraging environmental covariates: A European rye example.

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

StCYP90C1 modulates plant architecture via regulating brassinosteroid biosynthesis in potato.

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

A combination of QTL mapping and genome‑wide association study revealed the key gene for the growth period traits in soybean.

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

Mapping and marker development of the rye-derived Hessian fly resistance gene H25 in wheat.

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

Soybean MYB transcription factors GmMYB73 and GmMYB85 regulate lipid biosynthesis through lncRNA-mediated mechanisms.

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

Unraveling the complexity of the oilseed rape genome: from sequencing to gene discovery for trait improvement.

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

Related Experiment Video

Updated: May 6, 2026

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

9.2K

Factor regression for interpreting genotype-environment interaction in bread-wheat trials.

C P Baril1

  • 1Research Station for Plant Genetics, INRA-CNRS, University of South Paris, Ferme du Moulon, F-91190, Gif-sur-Yvette, France.

TAG. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik
|November 9, 2013
PubMed
Summary

French wheat breeding uses factor regression to predict yield. Three key traits—1,000-kernel weight, lodging susceptibility, and spike length—explain 91% of genotype-environment interaction, improving yield estimation accuracy.

More Related Videos

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
05:01

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

4.7K
Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes
06:41

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Published on: March 28, 2025

1.8K

Related Experiment Videos

Last Updated: May 6, 2026

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

9.2K
A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
05:01

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

4.7K
Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes
06:41

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Published on: March 28, 2025

1.8K

Area of Science:

  • Agricultural Science
  • Plant Breeding
  • Genetics

Background:

  • Wheat breeding programs rely on multilocation trials for high-yielding, adapted varieties.
  • Accurate yield estimation is challenged by genotype-environment (GE) interactions.

Purpose of the Study:

  • To partition GE interaction into interpretable components.
  • To identify key agronomic traits influencing wheat yield prediction.

Main Methods:

  • Factor regression analysis was applied to yield data from 34 wheat genotypes across four environments.
  • Twelve auxiliary agronomic traits were used as covariates to analyze GE interaction.

Main Results:

  • The factor regression model explained 91% of the GE interaction.
  • Thousand-kernel weight, lodging susceptibility, and spike length were the primary drivers of GE interaction.

Conclusions:

  • Easily measurable traits like 1,000-kernel weight, lodging susceptibility, and spike length can effectively predict wheat yield.
  • Factor regression offers a practical tool for enhancing wheat breeding program efficiency.