Function-valued traits in evolution.

Function-valued traits in evolution.
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DOI:
10.1098/rsif.2012.1032
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发表时间:
2013-05-06
期刊:
Journal of the Royal Society, Interface
影响因子:
--
通讯作者:
Knight CG
Knight CG
中科院分区:
其他
文献类型:
--
作者:
Hadjipantelis PZ;Jones NS;Moriarty J;Springate DA;Knight CG

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许多生物学特征的进化兴趣不是标量变量,而是连续函数。给定进化产生的功能值性状的数据集,我们开发了一种实用的统计方法来推断祖先功能值性状,并估计生成进化过程。我们这样做是通过结合降维和系统发育高斯过程回归,一个非参数的程序,明确占已知的系统发育关系。我们测试的性能的方法模拟,从随机演化模型生成的函数值数据。该方法适用于假设,只有在其尖端的分类群的遗传,和功能值性状是已知的。我们的方法是强大的,适用于广泛的功能值的数据,也提供了一个遗传意识的方法来估计功能值的性状的自相关。
Many biological characteristics of evolutionary interest are not scalar variables but continuous functions. Given a dataset of function-valued traits generated by evolution, we develop a practical, statistical approach to infer ancestral function-valued traits, and estimate the generative evolutionary process. We do this by combining dimension reduction and phylogenetic Gaussian process regression, a non-parametric procedure that explicitly accounts for known phylogenetic relationships. We test the performance of methods on simulated, function-valued data generated from a stochastic evolutionary model. The methods are applied assuming that only the phylogeny, and the function-valued traits of taxa at its tips are known. Our method is robust and applicable to a wide range of function-valued data, and also offers a phylogenetically aware method for estimating the autocorrelation of function-valued traits.
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