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首页> 外文期刊>Genetical Research >Detecting dominant QTL with variance component analysis in simulated pedigrees
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Detecting dominant QTL with variance component analysis in simulated pedigrees

机译：通过模拟谱系中的方差成分分析检测优势QTL

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摘要

Dominance is an important source of variation in complex traits. Here, we have carried out the first thorough investigation of quantitative trait locus (QTL) detection using variance component (VC) models extended to incorporate both additive and dominant QTL effects. Simulation results showed that the empirical distribution of the test statistic when testing for dominant QTL effects did not behave in accordance with existing theoretical expectations and varied with pedigree structure. Extensive simulations were carried out to assess accuracy of estimates, type 1 error and statistical power in two-generation human-, poultry- and pig-type pedigrees each with 1900 progeny in small-, medium- and large-sized families, respectively. The distribution of the likelihood-ratio test statistic was heavily dependent on family structure, with empirical thresholds lower for human pedigrees. Power to detect QTL was high (0.84-1.0) in pig and poultry scenarios for dominance effects accounting for >7% of phenotypic variance but much lower (0.42) in human-type pedigrees. Maternal or common environment effects can be partially confounded with dominance and must be fitted in the QTL model. Including dominance in the QTL model did not affect power to detect additive QTL effects. Also, detection of spurious dominance QTL effects only occurred when maternal effects were not included in the QTL model. When dominance effects were present in the data but not in the analysis model, this resulted in spurious detection of additive QTL or inflated estimates of additive QTL effects. The study demonstrates that dominance can be included routinely in QTL analysis of general pedigrees; however, optimal power is dependent on selection of the appropriate thresholds for pedigree structure.
机译：优势是复杂性状变异的重要来源。在这里，我们已经进行了第一个彻底的定量性状基因位点（QTL）检测研究，使用方差分量（VC）模型扩展了模型，使其结合了加性和显性QTL效应。仿真结果表明，当测试主要QTL效应时，测试统计量的经验分布不符合现有的理论预期，并且随谱系结构而变化。进行了广泛的模拟，以评估在两代人，家禽和猪型谱系中分别具有1900个后代的小型，中型和大型家族的估计值，1型错误和统计功效。似然比检验统计量的分布在很大程度上取决于家庭结构，而对于人类谱系来说，经验阈值较低。在猪和家禽中，对QTL的检出能力较高（0.84-1.0），占优势的表型差异> 7％，而在人型谱系中则低得多（0.42）。母体或常见的环境影响可以与主导地位部分地混淆，并且必须适合QTL模型。在QTL模型中包括优势并不会影响检测加性QTL效应的能力。另外，仅当母体效应未包括在QTL模型中时，才进行伪优势QTL效应的检测。当数据中存在优势效应而不是分析模型中的优势效应时，这会导致对附加QTL的虚假检测或对附加QTL效应的虚增估计。研究表明，可以在常规血统的QTL分析中常规地包括优势。但是，最佳能力取决于血统结构的适当阈值的选择。

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