Effects of causal networks on the structure and stability of resource allocation trait correlations
Effects of causal networks on the structure and stability of resource allocation trait correlations
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DOI：
10.1016/j.jtbi.2011.09.034
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发表时间：
2012-01-21
影响因子：
2
通讯作者：
Remington, David L.
中科院分区：
文献类型：
--
作者：
Gove, Robert P.;Chen, William;Remington, David L.
发现遗传变异影响表型的机制是理解生命史进化不可或缺的。描述发展层次中性状之间因果关系的模型为理解生活史性状之间经常观察到的相关性提供了功能基础。在本文中，我们评估了一个发展网络模型的生活史性状的基础上，多年生草本植物拟南芥lyrata，评估表型，遗传和环境的协方差矩阵下不同的情况下获得的数量性状位点（QTL）的影响，在模拟杂交，测试的功效结构方程模型，以确定多性状QTL效应的正确基础，并比较模型预测与田间数据。我们发现，性状网络约束的表型协方差模式在不同程度上，这取决于哪些性状直接受到QTL。遗传和环境协方差矩阵强相关，只有当直接QTL效应分布在许多性状。包括所有模拟性状的结构方程模型正确地识别了直接受QTL影响的性状，但启发式搜索算法发现了几个网络结构，而不是正确的网络结构，这些网络结构也与数据密切相关。当模拟QTL影响已知亲本群体之间存在差异的性状时，田间研究F-2数据中性状子集之间的估计相关性与模型预测密切相关。我们的研究结果表明，因果性状网络模型可以统一数量遗传理论的几个方面与遗传和表型协方差模式的经验观察，并将性状网络的遗传分析提供了承诺，阐明生活史进化的机制。(C)2011爱思唯尔有限公司保留所有权利。
Discovering the mechanisms by which genetic variation influences phenotypes is integral to understanding life-history evolution. Models describing causal relationships among traits in a developmental hierarchy provide a functional basis for understanding the correlations often observed among life-history traits. In this paper, we evaluate a developmental network model of life-history traits based on the perennial herb Arabidopsis lyrata, evaluate phenotypic, genetic, and environmental covariance matrices obtained under different scenarios of quantitative trait locus (QTL) effects in simulated crosses, test the efficacy of structural equation modeling to identify the correct basis for multiple-trait QTL effects, and compare model predictions with field data. We found that the trait network constrained the phenotypic covariance patterns to varying degrees, depending on which traits were directly affected by QTLs. Genetic and environmental covariance matrices were strongly correlated only when direct QTL effects were spread over many traits. Structural equation models that included all simulated traits correctly identified traits directly affected by QTLs, but heuristic search algorithms found several network structures other than the correct one that also fit the data closely. Estimated correlations among a subset of traits in F-2 data from field studies corresponded closely to model predictions when simulated QTLs affected traits known to differ between the parental populations. Our results show that causal trait network models can unify several aspects of quantitative genetic theory with empirical observations on genetic and phenotypic covariance patterns, and that incorporating trait networks into genetic analysis offers promise for elucidating mechanisms of life history evolution. (C) 2011 Elsevier Ltd. All rights reserved.