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

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

15.3K
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
15.3K
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

17.9K
A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
17.9K
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

18.6K
Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...
18.6K

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

Genome-wide analysis of Ariadne genes associated with drought and salt stress tolerance in wheat (Triticum aestivum L.).

BMC plant biology·2026
Same author

Spider Silk-Like Nanodomain Spacings Enable Mechanically Robust and Healable Elastomers with Record-High Puncture Resistance.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

Comprehensive analysis of HB-PHD transcription factors associated with drought and salt stresses in wheat.

BMC genomics·2026
Same author

Frontiers and emerging trends in research on metabolic syndrome and metabolic dysfunction-associated fatty liver disease: a bibliometric analysis (2005-2024).

Frontiers in endocrinology·2026
Same author

Bidirectional association between breast cancer and cardiovascular disease: Longitudinal analysis of UK Biobank data.

Atherosclerosis·2026
Same author

Modular Systems Engineering Enables Streamlined Development of <i>Escherichia coli</i> MG1655 Strains for Neutral Core Human Milk Oligosaccharide Synthesis.

Journal of agricultural and food chemistry·2026

相关实验视频

Updated: Jan 13, 2026

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

4.8K

GRE:与小麦产量相关的显著SNP识别框架利用GWAS-随机森林联合特征选择和可解释的机器学习基因组选择算法

Mei Song1, Shanghui Zhang1, Shijie Qiu1

  • 1School of Mathematics and Statistics, Ludong University, Yantai 264025, China.

Genes
|October 29, 2025
PubMed
概括

基因组选择 (GS) 模型使用新的框架 (GRE) 进行了改进,该框架结合了GWAS和随机森林,以准确预测小麦产量. 这种方法提高了育种效率,并有助于识别可持续农业的关键遗传标记.

关键词:
在GWAS中,GWAS就是GWAS.这就是 SHAP SHAP 的意思.可以解释的机器学习基因组选择 基因组选择随机的森林随机的森林小麦收益率小麦收益率

更多相关视频

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
05:01

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

3.7K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.9K

相关实验视频

Last Updated: Jan 13, 2026

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

4.8K
A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
05:01

A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information

Published on: July 1, 2020

3.7K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.9K

科学领域:

  • 农业科学 农业科学
  • 遗传学 遗传学是一种遗传学.
  • 生物信息学是一种生物信息学.

背景情况:

  • 全球小麦生产面临环境退化和耕地减少的挑战,需要进行育种创新以提高产量.
  • 基因组选择 (GS) 通过增加遗传收益来提高小麦育种的效率,但受到高维基因组数据的阻碍.
  • 精确预测基因组估计育种值 (GEBV) 对于有效的育种计划至关重要.

研究的目的:

  • 开发一种可解释的机器学习框架 (GRE),用于小麦的基因组选择.
  • 将GWAS的生物学意义与RF的预测能力相结合,用于改进的GS模型.
  • 提高小麦产量特征预测的准确性和可解释性.

主要方法:

  • 拟议的GRE框架将GWAS和随机森林 (RF) 结合起来用于SNP选择和分析.
  • 评估了六个GS算法,包括GBLUP和五个机器学习模型,使用预测准确性 (PCC) 和错误指标.
  • 使用Shapley添加式解释 (SHAP) 进行模型解释,揭示SNP对小麦产量的影响.

主要成果:

  • 在XGBoost和ElasticNet模型中,使用GRE.0.5识别的383个SNP实现了高预测准确度 (PCC>0.864) 和稳定性 (SD<0.005).
  • SHAP分析有效地解释了显著SNP对小麦产量特征的主要影响和相互作用.
  • 该研究确定了最佳的SNP子集和机器学习算法,以实现高效的小麦育种.

结论:

  • GRE框架为推进小麦育种中的基因组选择提供了一个强大的,可解释的工具.
  • 这种方法支持智能育种芯片设计和重要特征基因的挖掘.
  • 这些发现有助于转化GS技术,以实现可持续的全球农业生产力.