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Genome-wide Association Studies-GWAS

Genome-wide Association Studies-GWAS

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...

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Updated: Jul 6, 2025

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

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

Published on: July 27, 2021

使用GWAS总结统计数据进行多特征分析的子集扫描.

Rui Cao¹, Evan Olawsky¹, Edward McFowland²

¹Division of Biostatistics and Health Data Science, School of Public Health, University of Minnesota, Minneapolis, MN 55414, United States.

Bioinformatics (Oxford, England)

|January 8, 2024

概括

此摘要是机器生成的。

通过识别相关的特征和测试遗传关联,TraitScan增强了多特征分析,优于现有的方法. 这种强大的算法有助于使用大型生物银行数据集发现复杂疾病病因.

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Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

相关实验视频

Last Updated: Jul 6, 2025

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

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

Published on: July 27, 2021

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

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Published on: June 21, 2018

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

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Published on: March 12, 2021

科学领域:

遗传学和生物信息学遗传学和生物信息学
统计基因组学统计基因组学
计算生物学计算生物学

背景情况:

多特征分析提供了比单特征方法更大的统计能力.
现有的方法通常处理有限的特征,并优先考虑权力,而不是特征识别,需要领域专业知识.
发现类型的特征对于理解具有模糊病因的复杂疾病至关重要.

研究的目的:

开发一个强大而快速的算法,TraitScan,用于从众多特征中识别潜在的类特征.
测试遗传变异和选定的特征之间的关联,包括个人级和总结级GWAS数据.
在大规模生物银行时代,实现有效的多特征分析.

主要方法:

开发了TraitScan,一种能够处理数十到数千个特征的算法.
实现了TraitScan,以处理个人层面和总结层面的全基因组协会研究 (GWAS) 数据.
通过广泛的模拟评估了TraitScan的性能,并将其应用于英国生物库数据.

主要成果:

与现有方法相比,TraitScan在测试功率和特征选择方面都表现出优异的性能,在低至适度稀疏的情况下.
英国生物银行对尤文肉瘤的应用确定了进一步调查的有希望的特征.
扩展的特征扫描用于分析多基因风险得分和遗传指定的基因表达.

结论:

TraitScan为多特征分析提供了更有效的方法,特别是对于大型数据集.
该算法有助于发现新的特征关联,并有助于理解复杂的遗传架构.
TraitScan可以作为R包,促进其在遗传研究中的使用.