eQTpLot:一个用户友好的 R 软件包,用于可视化 eQTL 和 GWAS 信号之间的共定位。

IF 4 3区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Theodore G Drivas, Anastasia Lucas, Marylyn D Ritchie
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引用次数: 0

摘要

背景:基因组研究越来越多地将表达定量性状位点(eQTL)信息整合到其分析管道中,但很少有工具可用于可视化eQTL与GWAS结果之间的共定位。现有工具的分析选项有限,而且没有将 eQTL 和 GWAS 信息整合到一个图板中,因此难以实现共定位的可视化:eQTpLot 将标准的 GWAS 和顺式 eQTL 统计摘要以及可选的成对 LD 信息作为输入,生成一系列图表,直观显示给定基因-性状对的 eQTL 和 GWAS 信号之间的共定位、相关性和富集性。利用 eQTpLot,研究人员可以轻松生成一系列可定制的图谱,清楚地说明给定基因-性状对的情况:1)GWAS 和 eQTL 信号之间的共定位;2)GWAS 和 eQTL p 值之间的相关性;3)eQTL 在性状显著变异中的富集;4)相关位点的 LD 景观;5)eQTL 信号的效应方向与共定位 GWAS 峰效应方向之间的关系。结论:eQTpLot 提供了一种独特的、用户友好的、直观的方法来可视化 eQTL 和 GWAS 信号的共定位,并结合了其他 eQTL 可视化软件所没有的新功能。我们相信,eQTpLot 将成为研究人员寻求方便、可定制的 eQTL 和 GWAS 数据共定位可视化的有用工具。可用性和实施:eQTpLot R 软件包和教程可从 https://github.com/RitchieLab/eQTpLot 获取。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

eQTpLot: a user-friendly R package for the visualization of colocalization between eQTL and GWAS signals.

eQTpLot: a user-friendly R package for the visualization of colocalization between eQTL and GWAS signals.

eQTpLot: a user-friendly R package for the visualization of colocalization between eQTL and GWAS signals.

eQTpLot: a user-friendly R package for the visualization of colocalization between eQTL and GWAS signals.

Background: Genomic studies increasingly integrate expression quantitative trait loci (eQTL) information into their analysis pipelines, but few tools exist for the visualization of colocalization between eQTL and GWAS results. Those tools that do exist are limited in their analysis options, and do not integrate eQTL and GWAS information into a single figure panel, making the visualization of colocalization difficult.

Results: To address this issue, we developed the intuitive and user-friendly R package eQTpLot. eQTpLot takes as input standard GWAS and cis-eQTL summary statistics, and optional pairwise LD information, to generate a series of plots visualizing colocalization, correlation, and enrichment between eQTL and GWAS signals for a given gene-trait pair. With eQTpLot, investigators can easily generate a series of customizable plots clearly illustrating, for a given gene-trait pair: 1) colocalization between GWAS and eQTL signals, 2) correlation between GWAS and eQTL p-values, 3) enrichment of eQTLs among trait-significant variants, 4) the LD landscape of the locus in question, and 5) the relationship between the direction of effect of eQTL signals and the direction of effect of colocalizing GWAS peaks. These clear and comprehensive plots provide a unique view of eQTL-GWAS colocalization, allowing for a more complete understanding of the interaction between gene expression and trait associations.

Conclusions: eQTpLot provides a unique, user-friendly, and intuitive means of visualizing eQTL and GWAS signal colocalization, incorporating novel features not found in other eQTL visualization software. We believe eQTpLot will prove a useful tool for investigators seeking a convenient and customizable visualization of eQTL and GWAS data colocalization.

Availability and implementation: the eQTpLot R package and tutorial are available at https://github.com/RitchieLab/eQTpLot.

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来源期刊
Biodata Mining
Biodata Mining MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
7.90
自引率
0.00%
发文量
28
审稿时长
23 weeks
期刊介绍: BioData Mining is an open access, open peer-reviewed journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data. Topical areas include, but are not limited to: -Development, evaluation, and application of novel data mining and machine learning algorithms. -Adaptation, evaluation, and application of traditional data mining and machine learning algorithms. -Open-source software for the application of data mining and machine learning algorithms. -Design, development and integration of databases, software and web services for the storage, management, retrieval, and analysis of data from large scale studies. -Pre-processing, post-processing, modeling, and interpretation of data mining and machine learning results for biological interpretation and knowledge discovery.
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