General Methods for Evolutionary Quantitative Genetic Inference from Generalized Mixed Models.
General Methods for Evolutionary Quantitative Genetic Inference from Generalized Mixed Models.
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DOI：
10.1534/genetics.115.186536
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发表时间：
2016-11
期刊：
影响因子：
3.3
通讯作者：
Morrissey M
中科院分区：
文献类型：
--
作者：
de Villemereuil P;Schielzeth H;Nakagawa S;Morrissey M
进化数量遗传参数的推断和解释方法，以及选择反应的预测方法，最适合于正态分布的性状。许多与进化有关的特征，包括许多生活史和行为特征，都具有固有的非正态分布。广义线性混合模型（GLMM）框架已成为一个广泛使用的工具，估计非正态性状的数量遗传参数。然而，尽管GLP 10在统计上方便的潜在尺度上提供推断，但通常期望在测量性状的尺度上表达定量遗传参数。拟合的GLP-S的参数，尽管是在一个潜在的规模上，完全确定所有的潜在利益的规模上的性状表示的数量。我们提供了用于推导每个此类量的表达式，包括群体均值、表型（协）方差、方差分量（包括加性遗传（协）方差）以及遗传力等参数。我们证明了固定效应对这些参数有很大的影响，并展示了如何通过对固定效应进行平均或积分来处理。这些表达式需要在潜在值的分布上对由链接函数确定的量进行积分。在一般情况下，所需的积分必须解决数值，但有效的方法是可用的，我们提供了一个R包，QGglmm的实现。我们表明，已知的公式，如性状的遗传力与二项分布和泊松分布的特殊情况下，我们的表达式。此外，我们展示了如何拟合GLMM可以纳入现有的方法来预测进化轨迹。我们证明了进化预测的方法模拟和应用我们的方法从野生纯种脊椎动物种群的数据的准确性。
Methods for inference and interpretation of evolutionary quantitative genetic parameters, and for prediction of the response to selection, are best developed for traits with normal distributions. Many traits of evolutionary interest, including many life history and behavioral traits, have inherently nonnormal distributions. The generalized linear mixed model (GLMM) framework has become a widely used tool for estimating quantitative genetic parameters for nonnormal traits. However, whereas GLMMs provide inference on a statistically convenient latent scale, it is often desirable to express quantitative genetic parameters on the scale upon which traits are measured. The parameters of fitted GLMMs, despite being on a latent scale, fully determine all quantities of potential interest on the scale on which traits are expressed. We provide expressions for deriving each of such quantities, including population means, phenotypic (co)variances, variance components including additive genetic (co)variances, and parameters such as heritability. We demonstrate that fixed effects have a strong impact on those parameters and show how to deal with this by averaging or integrating over fixed effects. The expressions require integration of quantities determined by the link function, over distributions of latent values. In general cases, the required integrals must be solved numerically, but efficient methods are available and we provide an implementation in an R package, QGglmm. We show that known formulas for quantities such as heritability of traits with binomial and Poisson distributions are special cases of our expressions. Additionally, we show how fitted GLMM can be incorporated into existing methods for predicting evolutionary trajectories. We demonstrate the accuracy of the resulting method for evolutionary prediction by simulation and apply our approach to data from a wild pedigreed vertebrate population.
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影响因子：
3.3
作者：
LANDE, R;ARNOLD, SJ
通讯作者：
ARNOLD, SJ
影响因子：
3.3
作者：
LANDE, R
通讯作者：
LANDE, R
影响因子：
3.3
作者：
LANDE, R
通讯作者：
LANDE, R
影响因子：
2.9
作者：
BULMER, MG
通讯作者：
BULMER, MG
影响因子：
2.6
作者：
Ayres, D. R.;Pereira, R. J.;Albuquerque, L. G.
通讯作者：
Albuquerque, L. G.