Abstract
We present an open source package for performing evolutionary quantitative geneticsanalyses in the R environment for statistical computing. Evolutionary theoryshows that evolution depends critically on the available variation in a given population.When dealing with many quantitative traits this variation is expressed in theform of a covariance matrix, particularly the additive genetic covariance matrix orsometimes the phenotypic matrix, when the genetic matrix is unavailable. Given thismathematical representation of available variation, the EvolQG package providesfunctions for calculation of relevant evolutionary statistics, estimation of samplingerror, corrections for this error, matrix comparison via correlations and distances,and functions for testing evolutionary hypotheses on taxa diversification.
Highlights
Quantitative genetics deals with the evolution and inheritance of continuous traits, like body size, bone lengths, gene expressions or any other inheritable characteristic that can be measured on a continuous scale, or which can be transformed to a continuous scale
Summary We have described a suite of functions dedicated to analyzing multivariate data sets within an evolutionary quantitative genetics framework
These functions focus on the central role that covariance and correlation matrices play in this framework; we provide functions that perform both descriptive statistics and hypothesis testing related to such matrices within an evolutionary context
Summary
Quantitative genetics deals with the evolution and inheritance of continuous traits, like body size, bone lengths, gene expressions or any other inheritable characteristic that can be measured on a continuous scale, or which can be transformed to a continuous scale. The covariance (or correlation) matrix for each sample is compared to the observed matrix, and the mean of these comparisons is an estimate of the repeatability[27] This method has the advantage of being easy to apply to matrices coming from linear models with many controlled effects, and not requiring the original data. We can use this B-matrix as the Σ parameter in a multivariate normal distribution and sample n populations from this distribution Using this sample of random populations, we can assess the amount of divergence expected by drift, estimated as the norm of the difference vectors between ancestral (or reference) and simulated population means.
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