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相关概念视频

Types of Hypothesis Testing01:11

Types of Hypothesis Testing

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There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p...
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Punnett Squares01:00

Punnett Squares

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Overview
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Trihybrid Crosses02:27

Trihybrid Crosses

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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...
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Chi-square Analysis02:46

Chi-square Analysis

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The chi-square test is a statistical hypothesis test. It is used to check whether there is a significant difference between an expected value and an observed value. In the context of genetics, it enables us to either accept or reject a hypothesis, based on how much the observed values deviate from the expected values.
The chi-square test was developed by Pearson in 1990.
The first step of performing a Chi-square analysis is to establish a null hypothesis, which assumes that there is no real...
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Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

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A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
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Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
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Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
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稀少的测试设计,以优化甘种群的预测能力.

Julian Garcia-Abadillo1,2, Paul Adunola3, Fernando Silva Aguilar4

  • 1Centro de Biotecnología y Genómica de Plantas, Universidad Politécnica de Madrid, Madrid, Spain.

Frontiers in plant science
|August 7, 2024
PubMed
概括
此摘要是机器生成的。

使用稀疏的测试设计,甘育种可以更高效. 基因组预测模型准确地预测了具有较少表型基因型的产量特征,优化了多环境试验 (MET).

关键词:
基因组预测 全科医生全科医生基因组选择 GS GS 基因组选择优化的优化优化优化.稀疏的测试设计设计.甘育种 甘育种 甘育种

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High-throughput Screening of Recalcitrance Variations in Lignocellulosic Biomass: Total Lignin, Lignin Monomers, and Enzymatic Sugar Release
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科学领域:

  • 植物育种与遗传学
  • 农业科学 农业科学
  • 生物能源作物研究 生物能源作物研究

背景情况:

  • 甘产量取决于糖含量和总重量,这对于糖和生物能源至关重要.
  • 基因型与环境 (G×E) 相互作用显著影响这些复杂的遗传特征.
  • 通过多环境试验 (METs) 进行准确的基因型稳定性评估至关重要,但由于成本和材料的限制,通常是不切实际的.

研究的目的:

  • 引入和评估用于甘育种的稀疏测试设计.
  • 利用基因组预测模型来预测未观察到的基因型-环境组合.
  • 优化MET中表型化策略的成本效益.

主要方法:

  • 应用基因组预测模型,包括环境,基因型,基因组标记和G×E相互作用.
  • 在六个环境中利用了186种甘基因型的数据集 (共1116种表型).
  • 测试的校准设置大小从6.5%到16.7%的总表型来预测未观察到的组合.

主要成果:

  • 在训练套件中,在环境中实现了最小的或没有共同的基因型,从而实现了对糖糖积累 (SA) 和每公甘 (TCH) 的最大预测准确性.
  • 稀疏的设计,很少有 (3) 至没有常见的基因型,最大限度地提高了测试的独特基因型的数量.
  • 减少校准的表型记录对预测能力的影响最小;每个环境中的12个不重叠的基因型 (共72个) 提供了最佳的成本效益.

结论:

  • 稀缺的测试设计与基因组预测相结合,为甘中传统的MET提供了可行且具有成本效益的替代方案.
  • 优化跨环境的基因型分配是最大限度地提高产量特征预测准确性的关键.
  • 这种方法可以显著降低表型化负担,而不会影响繁殖效率.