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使用随机化检验来检测多个上位性数量性状基因座。

Use of randomization testing to detect multiple epistatic QTLs.

作者信息

Carlborg Orjan, Andersson Leif

机构信息

Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, BMC, Box 597, S-751 24 Uppsala, Sweden.

出版信息

Genet Res. 2002 Apr;79(2):175-84. doi: 10.1017/s001667230200558x.

Abstract

Here, we describe a randomization testing strategy for mapping interacting quantitative trait loci (QTLs). In a forward selection strategy, non-interacting QTLs and simultaneously mapped interacting QTL pairs are added to a total genetic model. Simultaneous mapping of epistatic QTLs increases the power of the mapping strategy by allowing detection of interacting QTL pairs where none of the QTL can be detected by their marginal additive and dominance effects. Randomization testing is used to derive empirical significance thresholds for every model selection step in the procedure. A simulation study was used to evaluate the statistical properties of the proposed randomization tests and for which types of epistasis simultaneous mapping of epistatic QTLs adds power. Least squares regression was used for QTL parameter estimation but any other QTL mapping method can be used. A genetic algorithm was used to search for interacting QTL pairs, which makes the proposed strategy feasible for single processor computers. We believe that this method will facilitate the evaluation of the importance at epistatic interaction among QTLs controlling multifactorial traits and disorders.

摘要

在此,我们描述了一种用于定位相互作用数量性状基因座(QTL)的随机化测试策略。在正向选择策略中,非相互作用的QTL以及同时定位的相互作用QTL对被添加到一个总体遗传模型中。上位性QTL的同时定位通过允许检测那些其单个QTL无法通过其边际加性和显性效应检测到的相互作用QTL对,提高了定位策略的功效。随机化测试用于为该过程中的每个模型选择步骤得出经验显著性阈值。进行了一项模拟研究,以评估所提出的随机化测试的统计特性,以及上位性QTL的同时定位对哪些类型的上位性增加了功效。使用最小二乘回归进行QTL参数估计,但也可以使用任何其他QTL定位方法。使用遗传算法搜索相互作用的QTL对,这使得所提出的策略对于单处理器计算机是可行的。我们相信,这种方法将有助于评估控制多因素性状和疾病的QTL之间上位性相互作用的重要性。

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