Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Hardy-Weinberg Principle01:49

Hardy-Weinberg Principle

72.1K
Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.
72.1K
Heritability01:06

Heritability

196
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"...
196
Dihybrid Crosses01:18

Dihybrid Crosses

74.8K
Overview
74.8K
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

3.0K
When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
3.0K
Chi-square Analysis02:46

Chi-square Analysis

38.2K
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...
38.2K
Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

4.2K
On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
4.2K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Crop breeding by design: integrating big data for future food security.

National science review·2026
Same author

An Expectation and Maximization Algorithm for Multivariate Genome-wide Association Studies (EMmvGWAS).

Genetics·2026
Same author

Genome-wide association studies and QTL mapping for traits deviating from normal distribution.

National science review·2026
Same author

GS-Impute: A neural network framework for accurate imputation of low-density markers in across-population genomic selection.

Plant communications·2026
Same author

Estimating recombination fraction via Pearson correlation.

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

A novel polypeptide encoded by circSPIRE1 promotes prostate cancer proliferation and migration by restraining the ubiquitin-dependent degradation of LRP5.

Journal of experimental & clinical cancer research : CR·2025

相关实验视频

Updated: Jun 24, 2025

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

11.0K

估计由定量性质位点所贡献的遗传变异:去除麻烦参数.

Shizhong Xu1

  • 1Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA.

Genetics
|June 11, 2024
PubMed
概括

这项研究解决了Beavis效应,该效应在全基因组关联研究 (GWAS) 中高估了定量特征位点 (QTL) 遗传性. 新的公式纠正了具有多个遗传效应的QTLs的这些偏差,提高了遗传研究的准确性.

科学领域:

  • 遗传学 遗传学 是一个
  • 生物统计学 生物统计学
  • 量化遗传学 量化遗传学

背景情况:

  • 绘制定量特征位置 (QTL) 和全基因组关联研究 (GWAS) 的目标是识别和定位影响特征的基因组区域.
  • 估计由QTL (遗传性) 解释的表型变异至关重要,但往往偏向向上,特别是在小样本大小中的小QTL,这种现象被称为Beavis效应.
  • 纠正Beavis效应的现有方法仅限于添加基因模型,不包括具有多重效应的QTL,如主导.

研究的目的:

  • 开发明确的公式来估计具有多种遗传效应的QTLs的变异性和遗传性.
  • 引入一种使用消灭器矩阵去除干扰参数的方法.
  • 调查和纠正复杂遗传模型估计的QTL变异中的Beavis效应偏差.

主要方法:

  • 开发明确的公式来估计在存在多个遗传效应 (例如,主导) 的情况下的QTL变异性和遗传性.
  • 应用一个消灭器矩阵来有效地从差异估计中去除干扰参数.
  • 在QTL差异估计中的Beavis效应诱导偏差的实证调查和纠正.

主要成果:

  • 成功推导出用于估计多重效应的QTL差异和遗传性的公式.
  • 展示一种方法来消除干扰参数,提高估计精度.
关键词:
贝维斯效应 (Beavis Effect) 是一个波效应.在GWAS中,GWAS就是GWAS.QTL 的遗传性 QTL 的遗传性在 QTL 映射中使用 QTL 映射.消灭器矩阵是一个消灭器矩阵.这是一个混合模型混合模型.

更多相关视频

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

3.6K
Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
11:35

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Published on: August 21, 2016

13.0K

相关实验视频

Last Updated: Jun 24, 2025

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

11.0K
Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
08:27

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

3.6K
Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA
11:35

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Published on: August 21, 2016

13.0K
  • 量化和纠正估计的QTL变异中由于Beavis效应而导致的上升偏差.
  • 结论:

    • 开发的方法为复杂的遗传结构提供了QTL变异和遗传性的准确估计,解决了以前方法的局限性.
    • 该研究成功地纠正了涉及多种遗传效应的QTL分析中的Beavis效应.
    • 该方法通过在混合种群中分析1000粒重量 (KGW) 属性来验证,展示了其实际适用性.