GWAS 摘要统计的金矿:对方法和工具的系统回顾。

IF 4 3区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Biodata Mining Pub Date : 2024-09-05 DOI:10.1186/s13040-024-00385-x
Panagiota I Kontou, Pantelis G Bagos
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引用次数: 0

摘要

全基因组关联研究(GWAS)彻底改变了我们对复杂性状和疾病遗传结构的认识。全基因组关联研究摘要统计已成为各种遗传分析(包括荟萃分析、精细图谱绘制和风险预测)的基本工具。然而,GWAS 统计摘要的数量越来越多,用于分析的软件工具也多种多样,这使得研究人员在选择最适合其特定需求的工具时面临挑战。本系统综述旨在全面概述目前可用于 GWAS 摘要统计分析的软件工具和数据库。我们进行了全面的文献检索,以确定相关的软件工具和数据库。我们按照工具和数据库的功能进行了分类,包括数据管理、质量控制、单性状分析和多性状分析。我们还根据工具和数据库的功能、局限性和易用性对其进行了比较。我们的研究共发现了 305 种专用于 GWAS 摘要统计的功能软件工具和数据库,每种工具和数据库都有其独特的优势和局限性。我们对每种工具和数据库的主要特点进行了描述,包括其输入/输出格式、数据类型和计算要求。我们还讨论了每种工具在不同研究方案中的整体可用性和适用性。对于有兴趣使用 GWAS 摘要统计来研究复杂性状和疾病遗传基础的研究人员来说,这篇综合综述将成为宝贵的资源。通过对现有工具和数据库的详细概述,我们旨在促进对工具的知情选择,并最大限度地提高 GWAS 概要统计分析的有效性。
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The goldmine of GWAS summary statistics: a systematic review of methods and tools.

Genome-wide association studies (GWAS) have revolutionized our understanding of the genetic architecture of complex traits and diseases. GWAS summary statistics have become essential tools for various genetic analyses, including meta-analysis, fine-mapping, and risk prediction. However, the increasing number of GWAS summary statistics and the diversity of software tools available for their analysis can make it challenging for researchers to select the most appropriate tools for their specific needs. This systematic review aims to provide a comprehensive overview of the currently available software tools and databases for GWAS summary statistics analysis. We conducted a comprehensive literature search to identify relevant software tools and databases. We categorized the tools and databases by their functionality, including data management, quality control, single-trait analysis, and multiple-trait analysis. We also compared the tools and databases based on their features, limitations, and user-friendliness. Our review identified a total of 305 functioning software tools and databases dedicated to GWAS summary statistics, each with unique strengths and limitations. We provide descriptions of the key features of each tool and database, including their input/output formats, data types, and computational requirements. We also discuss the overall usability and applicability of each tool for different research scenarios. This comprehensive review will serve as a valuable resource for researchers who are interested in using GWAS summary statistics to investigate the genetic basis of complex traits and diseases. By providing a detailed overview of the available tools and databases, we aim to facilitate informed tool selection and maximize the effectiveness of GWAS summary statistics analysis.

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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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