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相关概念视频

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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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...
270
Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

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

Mechanistic Models: Compartment Models in Individual and Population Analysis

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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...
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Epistasis Analysis01:09

Epistasis Analysis

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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Updated: Jun 17, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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一个模拟基因型与环境相互作用的框架,使用乘法模型进行模拟.

J Bančič1, G Gorjanc2, D J Tolhurst3

  • 1The Roslin Institute and Royal (Dick) School of Veterinary Studies, The University of Edinburgh, Easter Bush, Midlothian, UK. jbancic@ed.ac.uk.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
|August 6, 2024
PubMed
概括
此摘要是机器生成的。

一个新的模拟框架模拟了基因型与环境相互作用 (GEI) 以实现现实的植物育种模拟. 该工具增强了多环境试验 (MET) 数据生成,并改进了基因组选择策略.

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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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相关实验视频

Last Updated: Jun 17, 2025

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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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科学领域:

  • 农业科学 农业科学
  • 遗传学 是一个遗传学.
  • 生物技术是生物技术.

背景情况:

  • 基因型与环境的相互作用 (GEI) 在植物育种中至关重要.
  • 目前的模拟经常无法捕捉到GEI的全部复杂性.
  • 有效的育种计划需要现实的GEI建模.

研究的目的:

  • 开发一个可扩展的框架来模拟GEI使用乘法模型.
  • 创建现实的多环境试验 (MET) 数据集.
  • 改进植物育种计划的模拟.

主要方法:

  • 利用乘法模型来模拟GEI.
  • 开发了对差异的解释和预期精度的测量.
  • 实施了R包中的框架FieldSimR.
  • 生成的MET数据集具有不同的GEI级别.

主要成果:

  • 预测准确度随着GEI的减少或MET环境的增加而增加.
  • 基因组选择显示了比表型选择高出50-70%的性能.
  • 该框架成功生成了现实的MET数据集和模拟的育种计划.

结论:

  • 乘法模型框架为植物育种中GEI模拟提供了一个强大的方法.
  • 现场SimR包为优化育种方法提供了一个有价值的工具.
  • 改进的模拟导致更有效的育种策略和提高作物产量.