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Related Concept Videos

Genetic Variation01:25

Genetic Variation

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Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
Genes exist in different versions called alleles,...
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Heritability01:06

Heritability

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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"...
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Background and Environment Affect Phenotype02:27

Background and Environment Affect Phenotype

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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...
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One-Way ANOVA01:18

One-Way ANOVA

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One-way ANOVA analyzes more than three samples categorized by one factor. For example, it can compare the average mileage of sports bikes. Here, the data is categorized by one factor - the company. However, one-way ANOVA cannot be used to simultaneously compare the sample mean of three or more samples categorized by two factors. An example of two factors would be sports bikes from different companies driven in different terrains, such as a desert or snowy landscape. Here, two-way ANOVA is used...
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Gene-Environment Interactions01:20

Gene-Environment Interactions

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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...
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Statistical Methods to Analyze Parametric Data: ANOVA01:12

Statistical Methods to Analyze Parametric Data: ANOVA

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Analysis of Variance, or ANOVA, is a powerful statistical technique used to analyze parametric data, primarily in research and experimental studies. It's designed to compare the means of two or more groups, assisting researchers in identifying any significant differences between these group means. There are two main types of ANOVA based on the complexity of the analysis: one-way and two-way.
One-way ANOVA is applied when a single independent variable or factor is scrutinized. It compares...
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Multi-locus Variable-number Tandem-repeat Analysis of the Fish-pathogenic Bacterium Yersinia ruckeri by Multiplex PCR and Capillary Electrophoresis
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Information Based Diagnostic for Genetic Variance Parameter Estimation in Multi-Environment Trials.

Chris Lisle1, Alison Smith1, Carole L Birrell2

  • 1Centre for Biometrics and Data Science for Sustainable Primary Industries, School of Mathematics and Applied Statistics, National Institute for Applied Statistics Research Australia, University of Wollongong, Wollongong, NSW, Australia.

Frontiers in Plant Science
|December 24, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new diagnostic for plant breeding trials, using the φ-optimality criterion. It improves the estimation of genetic variance parameters compared to traditional variety connectivity measures.

Keywords:
linear mixed modelsmulti-environment trialssimulation studyvariety connectivity

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Area of Science:

  • Agricultural Science
  • Biometrics
  • Genetics

Background:

  • Multi-environment trials (METs) are crucial for evaluating plant varieties across diverse conditions.
  • Accurate estimation of variety by environment (VE) effects is essential for successful plant breeding.
  • Traditional methods relied on 'variety connectivity' to assess the reliability of genetic variance parameter estimation.

Purpose of the Study:

  • To link plant breeding trial objectives with model-based experimental design.
  • To propose and evaluate the φ-optimality criterion as a diagnostic for estimating genetic variance parameters in METs.
  • To compare the efficacy of φ-optimality with traditional connectivity measures.

Main Methods:

  • Utilized the φ-optimality criterion for diagnostic analysis in METs.
  • Applied residual maximum likelihood (REML) for genetic variance parameter estimation.
  • Validated the diagnostic using a real dataset with pedigree information and two simulation studies.

Main Results:

  • The φ-optimality criterion effectively captures information for REML estimation of genetic variance parameters.
  • Demonstrated superior performance of φ-optimality over connectivity measures in forecasting uncertainty.
  • Simulation studies confirmed the diagnostic's reliability and predictive power.

Conclusions:

  • The φ-optimality criterion offers a more reliable diagnostic for assessing the quality of METs data.
  • This approach enhances the accuracy of genetic variance parameter estimation and VE effect predictions.
  • Improved diagnostics can lead to more efficient and effective plant breeding programs.