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首页> 外文期刊>Molecular ecology >Structure and stability of genetic variance-covariance matrices: A Bayesian sparse factor analysis of transcriptional variation in the three-spined stickleback
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Structure and stability of genetic variance-covariance matrices: A Bayesian sparse factor analysis of transcriptional variation in the three-spined stickleback

机译：遗传方差 - 协方差矩阵的结构与稳定性：三翼岩袋中转录变异的贝叶斯稀疏因子分析

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摘要

The genetic variance-covariance matrix (G) is a quantity of central importance in evolutionary biology due to its influence on the rate and direction of multivariate evolution. However, the predictive power of empirically estimated G-matrices is limited for two reasons. First, phenotypes are high-dimensional, whereas traditional statistical methods are tuned to estimate and analyse low-dimensional matrices. Second, the stability of G to environmental effects and over time remains poorly understood. Using Bayesian sparse factor analysis (BSFG) designed to estimate high-dimensional G-matrices, we analysed levels variation and covariation in 10,527 expressed genes in a large (n=563) half-sib breeding design of three-spined sticklebacks subject to two temperature treatments. We found significant differences in the structure of G between the treatments: heritabilities and evolvabilities were higher in the warm than in the low-temperature treatment, suggesting more and faster opportunity to evolve in warm (stressful) conditions. Furthermore, comparison of G and its phenotypic equivalent P revealed the latter is a poor substitute of the former. Most strikingly, the results suggest that the expected impact of G on evolvabilityas well as the similarity among G-matricesmay depend strongly on the number of traits included into analyses. In our results, the inclusion of only few traits in the analyses leads to underestimation in the differences between the G-matrices and their predicted impacts on evolution. While the results highlight the challenges involved in estimating G, they also illustrate that by enabling the estimation of large G-matrices, the BSFG method can improve predicted evolutionary responses to selection.
机译：由于其对多变量演化的速率和方向的影响，遗传方差 - 协方差基质（G）是进化生物学中的核心重要性。然而，经验估计的G矩阵的预测力量是有限的两个原因。首先，表型是高维的，而传统的统计方法被调整以估计和分析低维矩阵。其次，G对环境影响的稳定性和随着时间的推移仍然很差。使用贝叶斯稀疏因子分析（BSFG）旨在估计高维G矩阵，我们在大（n = 563）半SIB繁殖设计中分析了10,527个表达基因的水平变化和协变量，其三个脊柱汗背面受到两个温度治疗。我们发现治疗之间G结构的显着差异：遗传性和进化在温暖中比低温处理更高，表明在温暖（压力）条件下进化的更多和更快的机会。此外，G及其表型等同P的比较显示后者是前者的替代品不佳。最令人惊讶的是，结果表明G对Evolvibility的预期影响以及G族基质中的相似性强烈依赖于分析中的分析的特征的数量。在我们的结果中，在分析中仅包含少量特征导致G矩阵与其对进化的预测影响之间的差异低估。虽然结果突出显示估计G所涉及的挑战，但它们还示出了通过使大G矩阵的估计来说，BSFG方法可以改善预测对选择的进化响应。

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