Division of Plant Sciences, Research School of Biology, The Australian National University, Canberra, Australian Capital Territory 0200, Australia.
Genetics. 2013 Nov;195(3):683-91. doi: 10.1534/genetics.113.155937. Epub 2013 Aug 26.
A major consideration in multitrait analysis is which traits should be jointly analyzed. As a common strategy, multitrait analysis is performed either on pairs of traits or on all of traits. To fully exploit the power of multitrait analysis, we propose variable selection to choose a subset of informative traits for multitrait quantitative trait locus (QTL) mapping. The proposed method is very useful for achieving optimal statistical power for QTL identification and for disclosing the most relevant traits. It is also a practical strategy to effectively take advantage of multitrait analysis when the number of traits under consideration is too large, making the usual multivariate analysis of all traits challenging. We study the impact of selection bias and the usage of permutation tests in the context of variable selection and develop a powerful implementation procedure of variable selection for genome scanning. We demonstrate the proposed method and selection procedure in a backcross population, using both simulated and real data. The extension to other experimental mapping populations is straightforward.
多特质分析的一个主要考虑因素是应该联合分析哪些特质。作为一种常见的策略，多特质分析可以在两个特质之间进行，也可以在所有特质之间进行。为了充分利用多特质分析的能力，我们提出了变量选择，以选择一组信息丰富的特质用于多特质数量性状基因座（QTL）映射。该方法对于实现 QTL 鉴定的最佳统计能力和揭示最相关的特质非常有用。当考虑的特质数量过多，使得通常对所有特质进行多元分析具有挑战性时，这也是一种有效利用多特质分析的实用策略。我们研究了选择偏差和置换检验在变量选择中的影响，并为基因组扫描开发了一种强大的变量选择实现程序。我们使用模拟和真实数据在回交群体中演示了所提出的方法和选择程序。该方法很容易扩展到其他实验作图群体。
