Detection of SNP epistasis effects of quantitative traits using an extended Kempthorne model
Detection of SNP epistasis effects of quantitative traits using an extended Kempthorne model
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DOI：
10.1152/physiolgenomics.00096.2006
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发表时间：
2006-12-13
影响因子：
4.6
通讯作者：
Da, Yang
中科院分区：
文献类型：
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作者：
Mao, Yongcai;London, Nicole R.;Da, Yang
上位性效应（基因互作）已被越来越多地认为是复杂性状的重要遗传因素。大量单核苷酸多态性（SNPs）的存在为利用候选基因分析筛选影响复杂性状的DNA变异提供了机遇和挑战。本文考虑了两个候选基因SNPs在Hardy-Weinberg不平衡（HWD）和连锁不平衡（LD）下的4种上位性效应：加性×加性、加性×显性、显性×加性和显性×显性。选择Kempthorne遗传模型是因为它对上位性效应有吸引力的遗传解释。本研究中的方法包括扩展Kempthorne的35个个体遗传效应的定义，允许HWD和LD，35个扩展的个体遗传效应的遗传对比，以定义4个上位性效应，和一个线性模型方法来检验上位性效应。公式预测统计功率（作为对比遗传力，样本量，和I型错误的函数）和样本量（作为对比遗传力，I型错误，和II型错误的函数）检测每个上位效应，理论预测与模拟研究一致。使用模拟评估在不存在所有或三种上位性效应的情况下估计每种上位性效应的准确性和假阳性率。上位性测试的方法可以是一个有用的工具，以了解确切的上位性模式，组装全基因组SNP到上位性网络，并组装所有SNP的影响表型使用成对上位性测试。
Epistasis effects (gene interactions) have been increasingly recognized as important genetic factors underlying complex traits. The existence of a large number of single nucleotide polymorphisms (SNPs) provides opportunities and challenges to screen DNA variations affecting complex traits using a candidate gene analysis. In this article, four types of epistasis effects of two candidate gene SNPs with Hardy-Weinberg disequilibrium (HWD) and linkage disequilibrium (LD) are considered: additive x additive, additive x dominance, dominance x additive, and dominance x dominance. The Kempthorne genetic model was chosen for its appealing genetic interpretations of the epistasis effects. The method in this study consists of extension of Kempthorne's definitions of 35 individual genetic effects to allow HWD and LD, genetic contrasts of the 35 extended individual genetic effects to define the 4 epistasis effects, and a linear model method for testing epistasis effects. Formulas to predict statistical power (as a function of contrast heritability, sample size, and type I error) and sample size (as a function of contrast heritability, type I error, and type II error) for detecting each epistasis effect were derived, and the theoretical predictions agreed well with simulation studies. The accuracy in estimating each epistasis effect and rates of false positives in the absence of all or three epistasis effects were evaluated using simulations. The method for epistasis testing can be a useful tool to understand the exact mode of epistasis, to assemble genome-wide SNPs into an epistasis network, and to assemble all SNP effects affecting a phenotype using pairwise epistasis tests.