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An Efficient Resampling Method for Assessing Genome-Wide Statistical Significance in Mapping Quantitative Trait Loci

机译:评估数量性状位点的全基因组统计意义的有效重采样方法

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摘要

Assessing genome-wide statistical significance is an important and difficult problem in multipoint linkage analysis. Due to multiple tests on the same genome, the usual pointwise significance level based on the chi-square approximation is inappropriate. Permutation is widely used to determine genome-wide significance. Theoretical approximations are available for simple experimental crosses. In this article, we propose a resampling procedure to assess the significance of genome-wide QTL mapping for experimental crosses. The proposed method is computationally much less intensive than the permutation procedure (in the order of 102 or higher) and is applicable to complex breeding designs and sophisticated genetic models that cannot be handled by the permutation and theoretical methods. The usefulness of the proposed method is demonstrated through simulation studies and an application to a Drosophila backcross.
机译:在多点连锁分析中,评估全基因组统计显着性是一个重要而困难的问题。由于对同一基因组进行了多次测试,因此基于卡方近似的通常逐点显着性水平是不合适的。排列被广泛用于确定全基因组范围的重要性。理论近似值可用于简单的实验交叉。在本文中,我们提出了一种重采样程序来评估全基因组QTL作图对实验杂交的重要性。所提出的方法在计算上比置换过程要少得多(大约10 2 或更高),并且适用于无法通过置换和理论方法处理的复杂育种设计和复杂的遗传模型。通过仿真研究和在果蝇回交中的应用证明了该方法的有效性。

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