HAPPI GWAS: Holistic Analysis with Pre- and Post-Integration GWAS
HAPPI GWAS: Holistic Analysis with Pre- and Post-Integration GWAS
复制标题
HAPPI GWAS：集成前后 GWAS 的整体分析
DOI：
10.1093/bioinformatics/btaa589
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发表时间：
2020
期刊：
影响因子：
5.8
通讯作者：
Angelovici, Ruthie
中科院分区：
文献类型：
--
作者：
Slaten, Marianne L;Chan, Yen On;Shrestha, Vivek;Lipka, Alexander E;Angelovici, Ruthie
动机来自大群体的先进的公开可用的测序数据已经使信息丰富的全基因组关联研究（GWAS）能够将SNP与感兴趣的表型性状相关联。许多能够执行GWAS的公开可用的工具已经被开发以响应增加的需求。然而，这些工具缺乏一个全面的管道，包括GWAS前的分析，如离群值删除，数据转换和最佳线性无偏预测或最佳线性无偏估计的计算。此外，后GWAS分析，如单块分析和候选基因鉴定，是lacked.ResultsHere，我们提出整体分析与前和后整合（HAPPI）GWAS，一个开源的GWAS工具，能够执行前GWAS，使用命令行界面在自动化管道中进行GWAS和GWAS后分析。可用性和实现HAPPI GWAS是用R编写的，适用于任何Unix-类似于操作系统，可在GitHub（https：//github.com/Angelovici-Lab/HAPPI.GWAS.git）上获得。补充信息补充数据可在Bioinformatics online获得。
MotivationAdvanced publicly available sequencing data from large populations have enabled informative genome-wide association studies (GWAS) that associate SNPs with phenotypic traits of interest. Many publicly available tools able to perform GWAS have been developed in response to increased demand. However, these tools lack a comprehensive pipeline that includes both pre-GWAS analysis, such as outlier removal, data transformation and calculation of Best Linear Unbiased Predictions or Best Linear Unbiased Estimates. In addition, post-GWAS analysis, such as haploblock analysis and candidate gene identification, is lacking.ResultsHere, we present Holistic Analysis with Pre- and Post-Integration (HAPPI) GWAS, an open-source GWAS tool able to perform pre-GWAS, GWAS and post-GWAS analysis in an automated pipeline using the command-line interface.Availability and implementationHAPPI GWAS is written in R for any Unix-like operating systems and is available on GitHub (https://github.com/Angelovici-Lab/HAPPI.GWAS.git).Supplementary informationSupplementary data are available atBioinformaticsonline.