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Use of genomic models to study genetic control of environmental variance

机译：利用基因组模型研究环境变异的遗传控制

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Vast amount of genetic marker information is being used to obtain insight into the genetic architecture of complex traits, for locating genomic regions (quantitative trait loci (QTL)) affecting disease and for enhancing the accuracy of prediction of genetic values in selection programmes. The genomic model commonly found in the literature, with marker effects affecting mean only, is extended to investigate putative effects at the level of the environmental variance. Two classes of models are proposed and their behaviour, studied using simulated data, indicates that they are capable of detecting genetic variation at the level of mean and variance. Implementation is via Markov chain Monte Carlo (McMC) algorithms. The models are compared in terms of a measure of global fit, in their ability to detect QTL effects and in terms of their predictive power. The models are subsequently fitted to back fat thickness data in pigs. The analysis of back fat thickness shows that the data support genomic models with effects on the mean but not on the variance. The relative sizes of experiment necessary to detect effects on mean and variance is discussed and an extension of the McMC algorithm is proposed.
机译：大量的遗传标记信息被用于深入了解复杂性状的遗传结构，用于定位影响疾病的基因组区域（定量性状基因座（QTL）），并提高选择程序中遗传值预测的准确性。文献中普遍存在的基因组模型仅具有影响均值的标志物效应，因此被扩展用于研究环境方差水平的假定效应。提出了两类模型，并使用模拟数据研究了它们的行为，表明它们能够检测均值和方差水平的遗传变异。通过马尔可夫链蒙特卡洛（McMC）算法实现。比较这些模型的方法是：整体拟合程度，检测QTL效果的能力以及预测能力。随后将模型拟合到猪的背部脂肪厚度数据。对背部脂肪厚度的分析表明，该数据支持基因组模型，该模型对平均值具有影响，但对方差没有影响。讨论了检测均值和方差影响所需的实验的相对大小，并提出了McMC算法的扩展。

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