Determining the effective dimensionality of the genetic variance-covariance matrix
Determining the effective dimensionality of the genetic variance-covariance matrix
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DOI：
10.1534/genetics.105.054627
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发表时间：
2006-06-01
期刊：
影响因子：
3.3
通讯作者：
Blows, MW
中科院分区：
文献类型：
--
作者：
Hine, E;Blows, MW
确定 G 的维数为了解多元性状的遗传基础提供了一个重要的视角。自从引入费舍尔几何模型以来，一组功能相关的表型性状背后的遗传独立性状的数量已被认为是影响选择反应的重要因素。在这里，我们展示了如何使用 AMEMIYA（1985）引入的多元一般线性模型确定效应空间维数的方法来建立 G 的有效维数。我们使用半同胞实验估计了锯齿果蝇八种表皮碳氢化合物的 G，将此方法与其他两种可用方法（因子分析建模和引导法）进行了比较。在我们的示例中，通过 Amemiya 的方法和父系水平协方差结构的因子分析模型，仅两个潜在遗传维度即可充分代表八种信息素性状。相比之下，自举法确定了具有显着遗传方差的四个维度。模拟研究表明，虽然雨宫方法的性能对功率限制更敏感，但在正确识别中等到高遗传力水平的原始遗传维度方面，它的表现与因子分析模型一样好甚至更好。在所有情况下，引导方法始终高估了维数，并且在子空间恢复方面的表现不如雨宫方法。
Determining the dimensionality of G provides an important perspective on the genetic basis of a multivariate suite of traits. Since the introduction of Fisher's geometric model, the number of genetically independent traits underlying a set of functionally related phenotypic traits has been recognized as an important factor influencing the response to selection. Here, we show how the effective dimensionality of G can be established, using a method for the determination of the dimensionality of the effect space from a multivariate general linear model introduced by AMEMIYA (1985). We compare this approach with two other available methods, factor-analytic modeling and bootstrapping, using a half-sib experiment that estimated G for eight cuticular hydrocarbons of Drosophila serrata. In our example, eight pheromone traits were shown to be adequately represented by only two underlying genetic dimensions by Amemiya's approach and factor-analytic modeling of the covariance structure at the sire level. In, contrast, bootstrapping identified four dimensions with significant genetic variance. A simulation study indicated that while the performance of Amemiya's method was more sensitive to power constraints, it performed as well or better than factor-analytic modeling in correctly identifying the original genetic dimensions at moderate to high levels of heritability. The bootstrap approach consistently overestimated the number of dimensions in all cases and performed less well than Amemiya's method at subspace recovery.