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PheWAS-ME:一个用于交互式探索 PheWAS 中多种疾病模式的网络应用程序。

PheWAS-ME: a web-app for interactive exploration of multimorbidity patterns in PheWAS.

机构信息

Department of Biostatistics, Vanderbilt University, Nashville, TN 37203, USA.

Department of Medical Administration, Vanderbilt University School of Medicine, Nashville, TN 37232, USA.

出版信息

Bioinformatics. 2021 Jul 19;37(12):1778-1780. doi: 10.1093/bioinformatics/btaa870.


DOI:10.1093/bioinformatics/btaa870
PMID:33051675
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8487628/
Abstract

SUMMARY: Electronic health records (EHRs) linked with a DNA biobank provide unprecedented opportunities for biomedical research in precision medicine. The Phenome-wide association study (PheWAS) is a widely used technique for the evaluation of relationships between genetic variants and a large collection of clinical phenotypes recorded in EHRs. PheWAS analyses are typically presented as static tables and charts of summary statistics obtained from statistical tests of association between a genetic variant and individual phenotypes. Comorbidities are common and typically lead to complex, multivariate gene-disease association signals that are challenging to interpret. Discovering and interrogating multimorbidity patterns and their influence in PheWAS is difficult and time-consuming. We present PheWAS-ME: an interactive dashboard to visualize individual-level genotype and phenotype data side-by-side with PheWAS analysis results, allowing researchers to explore multimorbidity patterns and their associations with a genetic variant of interest. We expect this application to enrich PheWAS analyses by illuminating clinical multimorbidity patterns present in the data. AVAILABILITY AND IMPLEMENTATION: A demo PheWAS-ME application is publicly available at https://prod.tbilab.org/phewas_me/. Sample datasets are provided for exploration with the option to upload custom PheWAS results and corresponding individual-level data. Online versions of the appendices are available at https://prod.tbilab.org/phewas_me_info/. The source code is available as an R package on GitHub (https://github.com/tbilab/multimorbidity_explorer). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

摘要

摘要:电子健康记录 (EHR) 与 DNA 生物库相连,为精准医学中的生物医学研究提供了前所未有的机会。表型-全基因组关联研究 (PheWAS) 是一种广泛用于评估遗传变异与 EHR 中记录的大量临床表型之间关系的技术。PheWAS 分析通常以静态表格和图表的形式呈现,这些表格和图表汇总了统计测试中获得的关联遗传变异和个体表型的摘要统计数据。合并症很常见,通常会导致复杂的多变量基因-疾病关联信号,难以解释。发现和探究 PheWAS 中的合并症模式及其影响是困难且耗时的。我们提出了 PheWAS-ME:一个交互式仪表板,可并排显示个体水平的基因型和表型数据以及 PheWAS 分析结果,使研究人员能够探索合并症模式及其与感兴趣的遗传变异的关联。我们期望通过阐明数据中存在的临床合并症模式,丰富 PheWAS 分析。

可用性和实施情况:一个演示版的 PheWAS-ME 应用程序可在 https://prod.tbilab.org/phewas_me/ 上公开获取。提供了示例数据集供探索,还可以选择上传自定义的 PheWAS 结果和相应的个体水平数据。附录的在线版本可在 https://prod.tbilab.org/phewas_me_info/ 上获取。源代码可在 GitHub 上作为 R 包获取(https://github.com/tbilab/multimorbidity_explorer)。

补充信息:补充数据可在生物信息学在线获取。

相似文献

[1]
PheWAS-ME: a web-app for interactive exploration of multimorbidity patterns in PheWAS.

Bioinformatics. 2021-7-19

[2]
PheMIME: An Interactive Web App and Knowledge Base for Phenome-Wide, Multi-Institutional Multimorbidity Analysis.

medRxiv. 2023-7-30

[3]
PheMIME: an interactive web app and knowledge base for phenome-wide, multi-institutional multimorbidity analysis.

J Am Med Inform Assoc. 2024-11-1

[4]
Interactive network-based clustering and investigation of multimorbidity association matrices with associationSubgraphs.

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[5]
INTEGRATING CLINICAL LABORATORY MEASURES AND ICD-9 CODE DIAGNOSES IN PHENOME-WIDE ASSOCIATION STUDIES.

Pac Symp Biocomput. 2016

[6]
SAIGEgds-an efficient statistical tool for large-scale PheWAS with mixed models.

Bioinformatics. 2021-5-5

[7]
PheWAS analysis on large-scale biobank data with PheTK.

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[8]
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Bioinformatics. 2018-9-1

[9]
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Gigascience. 2022-2-15

[10]
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Neuroinformatics. 2021-4

引用本文的文献

[1]
pyPheWAS Explorer: a visualization tool for exploratory analysis of phenome-disease associations.

JAMIA Open. 2023-4-3

[2]
Interactive network-based clustering and investigation of multimorbidity association matrices with associationSubgraphs.

Bioinformatics. 2023-1-1

[3]
mGWAS-Explorer: Linking SNPs, Genes, Metabolites, and Diseases for Functional Insights.

Metabolites. 2022-6-7

[4]
NETMAGE: A human disease phenotype map generator for the network-based visualization of phenome-wide association study results.

Gigascience. 2022-2-15

[5]
Human and Machine Intelligence Together Drive Drug Repurposing in Rare Diseases.

Front Genet. 2021-7-28

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