U-PASS: unified power analysis and forensics for qualitative traits in genetic association studies
U-PASS: unified power analysis and forensics for qualitative traits in genetic association studies
复制标题
U-PASS：遗传关联研究中定性特征的统一功效分析和取证
DOI：
10.1093/bioinformatics/btz637
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发表时间：
2019
期刊：
影响因子：
5.8
通讯作者：
Schwartz, ed., Russell
中科院分区：
文献类型：
--
作者：
Gao, Zheng;Terhorst, Jonathan;Van Hout, Cristopher V.;Stoev, Stilian;Schwartz, ed., Russell
摘要尽管现有的计算器在遗传关联研究中的统计功效分析的可用性，还没有一个模型不变和测试独立的工具，允许规划前瞻性研究和系统回顾报告的结果。在这项工作中，我们开发了一个基于Web的应用程序U-PASS（关联研究的统一功率分析），实现了一个统一的框架，用于分析二元定性性状的常见关联测试。该应用程序量化了常见关联检验的共享渐近功效极限，并可视化了风险等位基因频率和比值比之间的基本统计权衡。该应用程序还解决了有限样本中基于渐近的功效计算的适用性，并为基于单SNP的关联测试提供了指导方针。除了设计前瞻性研究，U-PASS还能让研究人员回顾性地评估先前报告的关联的统计有效性。可用性和实施U-PASS是一个开源的R Shiny应用程序。一个实时实例托管在https://power.stat.lsa.umich.edu上。来源可在https://github.com/Pill-GZ/U-PASS.Supplementary上获得信息补充数据可在Bioinformaticsonline上获得。
SummaryDespite the availability of existing calculators for statistical power analysis in genetic association studies, there has not been a model-invariant and test-independent tool that allows for both planning of prospective studies and systematic review of reported findings. In this work, we develop a web-based application U-PASS (Unified Power analysis of ASsociation Studies), implementing a unified framework for the analysis of common association tests for binary qualitative traits. The application quantifies the shared asymptotic power limits of the common association tests, and visualizes the fundamental statistical trade-off between risk allele frequency and odds ratio. The application also addresses the applicability of asymptotics-based power calculations in finite samples, and provides guidelines for single-SNP-based association tests. In addition to designing prospective studies, U-PASS enables researchers to retrospectively assess the statistical validity of previously reported associations.Availability and implementationU-PASS is an open-source R Shiny application. A live instance is hosted at https://power.stat.lsa.umich.edu. Source is available on https://github.com/Pill-GZ/U-PASS.Supplementary informationSupplementary data are available atBioinformaticsonline.
影响因子：
30.8
作者：
Skol, AD;Scott, LJ;Boehnke, M
通讯作者：
Boehnke, M
影响因子：
3.5
作者：
Givisay Dominguez-Cruz, Miriam;de Lourdes Munoz, Maria;Diaz-Badillo, Alvaro
通讯作者：
Diaz-Badillo, Alvaro