Department of Botany and Plant Sciences, University of California, Riverside, CA, 92521, USA.
Heredity (Edinb). 2019 Sep;123(3):287-306. doi: 10.1038/s41437-019-0205-3. Epub 2019 Mar 11.
Power calculation prior to a genetic experiment can help investigators choose the optimal sample size to detect a quantitative trait locus (QTL). Without the guidance of power analysis, an experiment may be underpowered or overpowered. Either way will result in wasted resource. QTL mapping and genome-wide association studies (GWAS) are often conducted using a linear mixed model (LMM) with controls of population structure and polygenic background using markers of the whole genome. Power analysis for such a mixed model is often conducted via Monte Carlo simulations. In this study, we derived a non-centrality parameter for the Wald test statistic for association, which allows analytical power analysis. We show that large samples are not necessary to detect a biologically meaningful QTL, say explaining 5% of the phenotypic variance. Several R functions are provided so that users can perform power analysis to determine the minimum sample size required to detect a given QTL with a certain statistical power or calculate the statistical power with given sample size and known values of other population parameters.
在进行遗传实验之前进行功效计算可以帮助研究人员选择最佳的样本量来检测数量性状基因座（QTL）。如果没有功效分析的指导，实验可能会出现功效不足或功效过剩。这两种情况都会导致资源浪费。QTL 作图和全基因组关联研究（GWAS）通常使用带有群体结构和多基因背景控制的线性混合模型（LMM）进行，使用整个基因组的标记。这种混合模型的功效分析通常通过蒙特卡罗模拟进行。在这项研究中，我们推导出了 Wald 检验统计量的非中心参数，用于关联分析，从而可以进行分析性功效分析。我们表明，不需要大样本量来检测具有生物学意义的 QTL，例如解释 5%的表型方差。提供了几个 R 函数，以便用户可以进行功效分析，以确定检测给定 QTL 所需的最小样本量，以达到一定的统计功效，或者计算给定样本量和其他群体参数的已知值的统计功效。
