Advanced 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.

中文翻译：

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HAPPI GWAS：使用集成前和集成后GWAS进行整体分析。

来自大量人群的先进的公开可用测序数据已启用了信息丰富的全基因组关联研究（GWAS），该研究将SNP与感兴趣的表型特征相关联。为了响应不断增长的需求，开发了许多能够执行GWAS的公开可用工具。但是，这些工具缺乏包括GWAS前分析（例如异常值去除，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）在内的全面管道。此外，缺少GWAS后分析，例如单倍体分析和候选基因鉴定。