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相关概念视频

Background and Environment Affect Phenotype02:27

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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.
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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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Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
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在果中进行综合多环境基因组预测.

Michaela Jung1,2, Carles Quesada-Traver2, Morgane Roth3

  • 1Fruit Breeding, Agroscope, Mueller-Thurgau-Strasse 29, 8820 Waedenswil, Switzerland.

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概括

多环境基因组预测模型改善了不同气候的果基因型的选择. 统计和深度学习方法提高预测能力,帮助适应环境变化.

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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.
  • 植物育种 植物育种

背景情况:

  • 基因组预测模型整合了基因和环境数据来进行基因型选择.
  • 多环境基因组预测对于作物适应各种条件至关重要.
  • 在果育种中的应用受到复杂的数据集和模型的限制.

研究的目的:

  • 应用先进的统计和深度学习模型用于果多环境基因组预测.
  • 评估基因组和环境组数据的整合,以预测11个果特征.
  • 评估基因型与环境相互作用和非添加效应的影响.

主要方法:

  • 使用了包含基因型与环境相互作用 (GxE) 的统计模型.
  • 采用深度学习方法进行基因组预测.
  • 综合基因组 (添加剂,非添加剂) 和环境数据.
  • 将模型与基准进行比较 (G-BLUP).

主要成果:

  • 使用GxE的统计模型对9个特征的预测能力提高了高达0.08%.
  • 替代的核心 (高斯,深) 有效地取代了G-BLUP.
  • 深度学习模型在三个寡原性特征中实现了最高的预测能力 (高达0.10的改进).
  • 与基准值相比,非添加剂和环境效应的影响最小.

结论:

  • 统计模型有效地捕捉了果中的基因型与环境相互作用.
  • 深度学习模型有效地整合了多样化的基因组和环境数据.
  • 这项研究促进了气候适应性果品种选择的多环境基因组预测.