Institute of Statistical Science, Academia Sinica.
Brief Bioinform. 2019 Jan 18;20(1):1-14. doi: 10.1093/bib/bbx068.
Combining statistical significances (P-values) from a set of single-locus association tests in genome-wide association studies is a proof-of-principle method for identifying disease-associated genomic segments, functional genes and biological pathways. We review P-value combinations for genome-wide association studies and introduce an integrated analysis tool, Omnibus P-value Association Tests (OPATs), which provides popular analysis methods of P-value combinations. The software OPATs programmed in R and R graphical user interface features a user-friendly interface. In addition to analysis modules for data quality control and single-locus association tests, OPATs provides three types of set-based association test: window-, gene- and biopathway-based association tests. P-value combinations with or without threshold and rank truncation are provided. The significance of a set-based association test is evaluated by using resampling procedures. Performance of the set-based association tests in OPATs has been evaluated by simulation studies and real data analyses. These set-based association tests help boost the statistical power, alleviate the multiple-testing problem, reduce the impact of genetic heterogeneity, increase the replication efficiency of association tests and facilitate the interpretation of association signals by streamlining the testing procedures and integrating the genetic effects of multiple variants in genomic regions of biological relevance. In summary, P-value combinations facilitate the identification of marker sets associated with disease susceptibility and uncover missing heritability in association studies, thereby establishing a foundation for the genetic dissection of complex diseases and traits. OPATs provides an easy-to-use and statistically powerful analysis tool for P-value combinations. OPATs, examples, and user guide can be downloaded from http://www.stat.sinica.edu.tw/hsinchou/genetics/association/OPATs.htm.
组合全基因组关联研究中单个关联测试的统计显著性(P 值)是识别与疾病相关的基因组片段、功能基因和生物途径的一种原理验证方法。我们回顾了全基因组关联研究中的 P 值组合,并介绍了一种集成分析工具——综合 P 值关联测试(OPATs),它提供了流行的 P 值组合分析方法。软件 OPATs 是用 R 语言编写的,具有图形用户界面,具有用户友好的界面。除了数据质量控制和单基因座关联测试的分析模块外,OPATs 还提供了三种基于集合的关联测试:窗口、基因和生物途径关联测试。提供了有或没有阈值和排名截断的 P 值组合。使用重采样程序评估基于集合的关联测试的显著性。通过模拟研究和真实数据分析评估了 OPATs 中基于集合的关联测试的性能。这些基于集合的关联测试有助于提高统计功效、缓解多重检验问题、减少遗传异质性的影响、提高关联测试的复制效率,并通过简化测试程序和整合生物相关基因组区域中多个变异的遗传效应来促进关联信号的解释。总之,P 值组合有助于识别与疾病易感性相关的标记集,并揭示关联研究中的缺失遗传力,从而为复杂疾病和性状的遗传剖析奠定基础。OPATs 为 P 值组合提供了一个易于使用且具有统计功效的分析工具。可以从 http://www.stat.sinica.edu.tw/hsinchou/genetics/association/OPATs.htm 下载 OPATs、示例和用户指南。