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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: Jan 7, 2026

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

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

Published on: November 3, 2010

使用哈普洛型聚合基因特异性表达数据改进了对具有大影响的罕见遗传变异的鉴定.

Kaushik Ram Ganapathy^1,2, Martin Broly^3,4, Sarah Silverstein^5,6,7

¹Dept. of Integrative Structural and Computational Biology, Scripps Research, La Jolla, CA, USA.

medRxiv : the preprint server for health sciences

|December 25, 2025

概括

ANEVA-h通过利用原型级别的等位基因特异性表达 (ASE) 数据来改善罕见变异的解释. 这种方法增强了对遗传调节变异的检测,减少噪音,并增加了在不同种群中的基因发现.

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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

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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

相关实验视频

Last Updated: Jan 7, 2026

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

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

Published on: November 3, 2010

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

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Published on: June 23, 2012

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

科学领域:

基因组学就是基因组学.
统计遗传学统计遗传学

背景情况:

基因特异表达 (ASE) 异常点检测识别了调控变异,但在低数基因中面临数据稀疏性和噪声方面的挑战.
基因组分相可以将ASE信号沿着单元型聚合在一起,减轻稀疏性和噪音.
现有的统计工具缺乏可靠的方法,用于在罕见变异解释中利用单元型水平的ASE数据.

研究的目的:

引入ANEVA-h,这是一个新的统计工具,用于从杂型级别的ASE数据中量化基因表达的遗传变异.
通过分析人口水平的哈普洛型ASE数据,使监管效应的更准确和更全面的检测成为可能.
促进将哈普洛型级别的ASE异常值测试整合到罕见变异解释管道中.

主要方法:

开发和应用ANEVA-h用于分析哈普罗型级别的ASE数据.
与兼容剂量异常值测试的整合.
适用于GTEx项目数据和临床队列 (神经肌肉和先天性心脏病).
对全球多样化的人口进行分析,以评估祖先的影响.

主要成果:

与现有方法相比,ANEVA-h显示可测试基因的增加超过2倍.
减少了虚假的异常值调用,并改善了对罕见,高影响变体的丰富.
在临床队列中增强基因优先级,识别其他工具遗漏的候选诊断.
对参考和测试种群的祖先背景影响的表征.

结论:

ANEVA-h通过利用哈普罗型级别的ASE数据显著改善了基因调节变异的检测.
该工具增强了临床环境中的基因优先级和诊断能力.
ANEVA-h为推进罕见变异解释管道提供了必要的工具和数据,特别是在多样化的群体中.