EvolQG - An R package for evolutionary quantitative genetics

Abstract

We present an open source package for performing evolutionary quantitative genetics analyses in the R environment for statistical computing. Evolutionary theory shows that evolution depends critically on the available variation in a given population. When dealing with many quantitative traits this variation is expressed in the form of a covariance matrix, particularly the additive genetic covariance matrix or sometimes the phenotypic matrix, when the genetic matrix is unavailable and there is evidence the phenotypic matrix is sufficiently similar to the genetic matrix. Given this mathematical representation of available variation, the EvolQG package provides functions for calculation of relevant evolutionary statistics; estimation of sampling error; corrections for this error; matrix comparison via correlations, distances and matrix decomposition; analysis of modularity patterns; and functions for testing evolutionary hypotheses on taxa diversification.

A multiple peak adaptive landscape based on feeding strategies and roosting ecology shaped the evolution of cranial covariance structure and morphological differentiation in phyllostomid bats

“…This equation was implemented in the R package evolqg (Melo et al. ) using the MultiMahalanobis function. The morphological distance matrix was then used to investigate the relationship between species multivariate average phenotype and morphological integration patterns (Marroig and Cheverud ; Ackermann ; Oliveira et al.…”

Fluctuations in Evolutionary Integration Allow for Big Brains and Disparate Faces

“…These response vectors index unconditional evolvability (ability of a clade to evolve in the direction of selection), respondability (how rapidly a clade can respond to directional selection), conditional evolvability (ability of a clade to evolve in the direction of selection while under stabilizing selection), constraints (the effect of PC1 on the response to selection) and autonomy (the proportion of evolvability that remains after conditioning on other traits). All simulations were run in the R package EvolQG 62. Instead of emphasizing mean values of evolvability indexes, we emphasize the maximum values of the various indexes for ease of interpretation4.…”

Constraints on geographic variation in fiddler crabs (Ocypodidae: Uca) from the western Atlantic

“…Because very little genetic and developmental work has been carried out in fiddler crabs, we are largely ignorant of what produces covariation among phenotypic traits in these species. The high similarity we found between P‐matrices of different populations (Table S2) suggests a common underlying genetic structure because such similarity is not likely to arise from environmental structures that compensate for radically different G‐matrices (Melo et al ., ). At the same time, the little work that has been carried out on the development of fiddler crabs suggests that plasticity may be an important factor in determining fiddler crab phenotypes, including carapace shape (Hampton et al ., ; Wieman et al ., ), and therefore merits consideration.…”

“…This also indicates high similarity between P‐matrices of different populations, which in turn suggests high similarity between the P‐matrix and the unknown G‐matrix. This is because such similarity between populations is more likely to arise from a common genetic structure rather than from environmental structures that compensate for radically different G‐matrices (Melo et al ., ). Given the large sample sizes required for adequately characterizing the G‐matrix, it has been argued that the P‐matrix may often be a better estimate of true genetic architecture when otherwise only imprecise estimates of the G‐matrix are available (Cheverud, ; Roff, ; Reusch & Blanckenhorn, ).…”