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

13.2K
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...
13.2K
Genome Annotation and Assembly03:36

Genome Annotation and Assembly

18.8K
The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
18.8K
Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

5.7K
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...
5.7K

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

FREM1 promotes anlotinib resistance in non-small cell lung cancer through regulation of extracellular matrix organization.

Discover oncology·2026
Same author

Disentangling adiposity-related and non-adiposity-related genetic pathways for type 2 diabetes.

medRxiv : the preprint server for health sciences·2026
Same author

Towards the Efficient Inference by Incorporating Automated Computational Phenotypes under Covariate Shift.

Proceedings of machine learning research·2026
Same author

Deep learning-enabled temporal sequencing of metasurface for rewritable and customizable electromagnetic illusions.

National science review·2026
Same author

Identifying circulating protein targets for common factors underlying schizophrenia, depression, and bipolar disorder.

medRxiv : the preprint server for health sciences·2026
Same author

An Accurate Genetic Colocalisation Method for the HLA Locus.

HLA·2026

相关实验视频

Updated: Jun 11, 2025

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

机器学习辅助全基因组关联研究的有效推断.

Jiacheng Miao1, Yixuan Wu1, Zhongxuan Sun1

  • 1Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA.

Nature genetics
|September 30, 2024
PubMed
概括

机器学习 (ML) 辅助的全基因组关联研究 (GWAS) 有错误阳性风险. 一个新的框架,后预测GWAS (POP-GWAS),确保复杂的特征遗传学研究的有效统计推断.

更多相关视频

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

12.9K
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

22.8K

相关实验视频

Last Updated: Jun 11, 2025

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

12.9K
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

22.8K

科学领域:

  • 人类遗传学 人类遗传学
  • 统计遗传学 统计遗传学
  • 生物信息学是一种生物信息学.

背景情况:

  • 机器学习 (ML) 在人类遗传学中越来越多地用于复杂的特征分析.
  • 用ML辅助的全基因组关联研究 (GWAS) 归因于表型,但面临有效性问题.
  • 现有的ML辅助GWAS方法带有虚假阳性关联的风险.

研究的目的:

  • 为了评估ML辅助的GWAS协会的有效性.
  • 引入一个强大的统计框架,即后预测GWAS (POP-GWAS),用于分析ML计算的结果.
  • 确保复杂特征遗传学的有效和强大的统计推断.

主要方法:

  • 开发了后预测GWAS (POP-GWAS) 的统计框架.
  • 针对ML计算的结果,重新设计了GWAS方法.
  • 仅需要GWAS总结统计作为输入,无论ML归算质量或算法如何.

主要成果:

  • 在当前ML辅助的GWAS中发现了假阳性关联的普遍风险.
  • 成功使用POP-GWAS对14个骨部位的骨矿物质密度的GWAS.
  • 发现了89个与骨矿物质密度相关的新型遗传位置,揭示了骨部位特定的遗传结构.

结论:

  • POP-GWAS为ML辅助的GWAS提供了一个统计严格的解决方案.
  • 该框架确保了有效的推断,解决了先前ML计算的GWAS方法的局限性.
  • POP-GWAS是一个强大的工具,用于未来的复杂特征遗传学研究,利用ML.