Jove
Visualize
联系我们

相关概念视频

Frequency-dependent Selection01:21

Frequency-dependent Selection

24.1K
When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
24.1K
What is Natural Selection?01:32

What is Natural Selection?

129.2K
Natural selection is an evolutionary process in which individuals with survival-promoting traits reproduce at higher rates. These favorable traits become more common within a population or species. Naturally selected traits initially arise via random genetic mutations. In order for selection to occur, there must be variation within a population, the trait controlling the variation must be heritable, and there must be an evolutionary advantage for variation in the trait.
129.2K
Multiple Allele Traits01:49

Multiple Allele Traits

38.1K
The Concept of Multiple Allelism
38.1K
Limits to Natural Selection01:38

Limits to Natural Selection

35.0K
Organisms that are well-adapted to their environment are more likely to survive and reproduce. However, natural selection does not lead to perfectly adapted organisms. Several factors constrain natural selection.
35.0K
Natural Selection and Adaptation01:15

Natural Selection and Adaptation

1.4K
Natural selection, a fundamental concept in evolutionary biology, is the mechanism by which evolution is driven, favoring organisms that are best adapted to their environments. This process enhances their chances of survival and reproduction. Adaptation, a key outcome of this process, involves genetic modifications that optimize an organism's functionality under specific environmental challenges, such as extreme cold or thinner air at high altitudes.
Beyond physical adaptations,...
1.4K
Time-Series Graph00:54

Time-Series Graph

5.2K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
5.2K

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

On the Edge of Empire: Paleogenomic Insights into Roman Dacia.

bioRxiv : the preprint server for biology·2026
Same author

The evolutionary history and unique genetic diversity of Indigenous Americans.

Nature·2026
Same author

Temporal shifts in polygenic traits track major epidemics in Western Eurasia.

bioRxiv : the preprint server for biology·2026
Same author

The Genomic Legacy of the Norman Conquest in Rural England.

bioRxiv : the preprint server for biology·2026
Same author

A large mass grave from the Early Iron Age indicates selective violence towards women and children in the Carpathian Basin.

Nature human behaviour·2026
Same author

A 5500-year-old <i>Treponema pallidum</i> genome from Sabana de Bogotá, Colombia.

Science (New York, N.Y.)·2026
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

相关实验视频

Updated: Feb 4, 2026

Optimized Bone Sampling Protocols for the Retrieval of Ancient DNA from Archaeological Remains
06:18

Optimized Bone Sampling Protocols for the Retrieval of Ancient DNA from Archaeological Remains

Published on: November 30, 2021

5.1K

评估古代DNA采样策略用于人类自然选择的推理,使用等位基因频率时间序列数据.

Lucas Anchieri1,2, Carlos Eduardo G Amorim3, Samuel Neuenschwander1,4

  • 1Department of Computational Biology, University of Lausanne, Lausanne 1015, Switzerland.

Genome biology and evolution
|February 2, 2026
PubMed
概括
此摘要是机器生成的。

在古代人类DNA (aDNA) 中检测自然选择是可行的,尽管数据稀疏,但强选择 (s ≥0.02) 和~100个样本是可行的. ApproxWF表现最好,虚假阳性很低,用于强大的选择推理的功率很高.

关键词:
一个DNADNA的DNA古遗传学是一种古遗传学.人口遗传学 人口遗传学选择的选择选择的选择.模拟器模拟器模拟器模拟器时间序列时间序列

更多相关视频

Assessment of DNA Contamination in RNA Samples Based on Ribosomal DNA
13:16

Assessment of DNA Contamination in RNA Samples Based on Ribosomal DNA

Published on: January 22, 2018

22.3K
An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
10:17

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

23.4K

相关实验视频

Last Updated: Feb 4, 2026

Optimized Bone Sampling Protocols for the Retrieval of Ancient DNA from Archaeological Remains
06:18

Optimized Bone Sampling Protocols for the Retrieval of Ancient DNA from Archaeological Remains

Published on: November 30, 2021

5.1K
Assessment of DNA Contamination in RNA Samples Based on Ribosomal DNA
13:16

Assessment of DNA Contamination in RNA Samples Based on Ribosomal DNA

Published on: January 22, 2018

22.3K
An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
10:17

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

23.4K

科学领域:

  • 人口遗传学 人口遗传学
  • 古遗传学是古遗传学的一部分.
  • 进化生物学 进化生物学

背景情况:

  • 古代人类的基因组数据使得使用时间序列进行自然选择估计.
  • 现有的方法是用大样本大小验证的,而不是稀疏的古代DNA (aDNA) 数据.
  • aDNA数据带来了诸如高缺失率和不同时间点的样本大小等挑战.

研究的目的:

  • 使用aDNA类时间序列数据集的基准选择推断方法.
  • 用稀疏的数据和小样本大小来评估方法性能.
  • 确定检测古人类群体中选择的可行性.

主要方法:

  • 使用DNA类时间序列数据集进行了广泛的模拟.
  • 基准测试使用了四种方法:ApproxWF,BMWS,Slattice和Sr.
  • 测试各种采样方案和选择系数 (s).

主要成果:

  • 选择检测是可能的强选择 (s ≥ 0.02) 的~100个样本,假设恒定的有效人口大小 (Ne).
  • 与其他方法相比,ApproxWF在模拟中表现出卓越的性能.
  • 观察到低虚假阳性率 (<6%) 和高功率 (>90%) 检测强选择.
  • 跨时间点均的数据采样提高了估计准确度.

结论:

  • 从稀疏的DNA数据推断自然选择是可以实现的,特别是在强选择方面.
  • 建议使用ApproxWF来分析DNA时间序列数据.
  • 未来的研究应该专注于数据在时间上的均分布,以提高准确性.