Cheng Riyan, Doerge R W, Borevitz Justin
Research School of Biology, The Australian National University, Acton, Australian Capital Territory 2601, Australia, ARC Center of Excellence in Plant Energy Biology, The Australian National University, Acton, ACT 2601, Australia
Department of Statistics, Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA 15213.
G3 (Bethesda). 2017 Mar 10;7(3):813-822. doi: 10.1534/g3.116.037531.
Multiple-trait analysis typically employs models that associate a quantitative trait locus (QTL) with all of the traits. As a result, statistical power for QTL detection may not be optimal if the QTL contributes to the phenotypic variation in only a small proportion of the traits. Excluding QTL effects that contribute little to the test statistic can improve statistical power. In this article, we show that an optimal power can be achieved when the number of QTL effects is best estimated, and that a stringent criterion for QTL effect selection may improve power when the number of QTL effects is small but can reduce power otherwise. We investigate strategies for excluding trivial QTL effects, and propose a method that improves statistical power when the number of QTL effects is relatively small, and fairly maintains the power when the number of QTL effects is large. The proposed method first uses resampling techniques to determine the number of nontrivial QTL effects, and then selects QTL effects by the backward elimination procedure for significance test. We also propose a method for testing QTL-trait associations that are desired for biological interpretation in applications. We validate our methods using simulations and transcript data.
多性状分析通常采用将数量性状基因座（QTL）与所有性状相关联的模型。因此，如果某个QTL仅对一小部分性状的表型变异有贡献，那么检测该QTL的统计功效可能并非最优。排除对检验统计量贡献不大的QTL效应可以提高统计功效。在本文中，我们表明当QTL效应的数量得到最佳估计时，可以实现最优功效，并且当QTL效应数量较少时，严格的QTL效应选择标准可能会提高功效，但在其他情况下可能会降低功效。我们研究了排除微不足道的QTL效应的策略，并提出了一种方法，当QTL效应数量相对较少时，该方法可提高统计功效，而当QTL效应数量较多时，能相当程度地保持功效。所提出的方法首先使用重抽样技术来确定非平凡QTL效应的数量，然后通过向后淘汰程序选择QTL效应进行显著性检验。我们还提出了一种用于检验应用中生物学解释所需的QTL - 性状关联的方法。我们使用模拟和转录数据验证了我们的方法。
