Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD 20850, USA.
Department of Biostatistics, Bloomberg School of Public Health, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA.
Bioinformatics. 2018 May 1;34(9):1506-1513. doi: 10.1093/bioinformatics/btx770.
Genome-wide association studies are now shifting focus from analysis of common to rare variants. As power for association testing for individual rare variants may often be low, various aggregate level association tests have been proposed to detect genetic loci. Typically, power calculations for such tests require specification of large number of parameters, including effect sizes and allele frequencies of individual variants, making them difficult to use in practice. We propose to approximate power to a varying degree of accuracy using a smaller number of key parameters, including the total genetic variance explained by multiple variants within a locus.
We perform extensive simulation studies to assess the accuracy of the proposed approximations in realistic settings. Using these simplified power calculations, we develop an analytic framework to obtain bounds on genetic architecture of an underlying trait given results from genome-wide association studies with rare variants. Finally, we provide insights into the required quality of annotation/functional information for identification of likely causal variants to make meaningful improvement in power.
A shiny application that allows a variety of Power Analysis of GEnetic AssociatioN Tests (PAGEANT), in R is made publicly available at https://andrewhaoyu.shinyapps.io/PAGEANT/.
Supplementary data are available at Bioinformatics online.
全基因组关联研究现在将焦点从常见变体分析转移到罕见变体分析。由于个体罕见变异关联测试的功效可能通常较低，因此已经提出了各种聚合水平的关联测试来检测遗传位点。通常，此类测试的功效计算需要指定大量参数，包括个体变异的效应大小和等位基因频率，这使得它们在实践中难以使用。我们建议使用较少的关键参数在不同程度上近似功效，包括一个基因座内多个变体解释的总遗传方差。
我们进行了广泛的模拟研究，以评估在现实环境中提出的近似值的准确性。使用这些简化的功效计算，我们开发了一个分析框架，根据罕见变异全基因组关联研究的结果，获得潜在性状遗传结构的界限。最后，我们深入了解了识别可能的因果变异所需的注释/功能信息的质量，以在功效方面做出有意义的改进。
一个名为 Power Analysis of GEnetic AssociatioN Tests (PAGEANT) 的 R 语言 shiny 应用程序已在 https://andrewhaoyu.shinyapps.io/PAGEANT/ 上公开提供。
补充数据可在 Bioinformatics 在线获得。
