DOI: 10.5061/dryad.5gr8r
title: Data from: The evolution of phenotypic integration: how directional selection reshapes covariation in mice

description: ‭Variation is the basis for evolution, and understanding how variation can
 evolve is a central question in biology. In complex phenotypes,
 covariation plays an even more important role, as genetic associations
 between traits can bias and alter evolutionary change. Covariation can be
 shaped by complex interactions between loci, and this genetic architecture
 can also change during evolution. In this article, we analyzed mouse lines
 experimentally selected for changes in size to address the question of how
 multivariate covariation changes under directional selection, as well as
 to identify the consequences of these changes to evolution. Selected lines
 showed a clear restructuring of covariation in their cranium and, instead
 of depleting their size variation, these lines increased their magnitude
 of integration and the proportion of variation associated with the
 direction of selection. This result is compatible with recent theoretical
 works on the evolution of covariation that take the complexities of
 genetic architecture into account. This result also contradicts the
 traditional view of the effects of selection on available covariation and
 suggests a much more complex view of how populations respond to selection.
description: All_control_t_posterior_GComplete set of posterior distribution of 1000 variance and covariance G-matrices from the BSFG model for control t line in .csv format.All_downwards_s_posterior_GComplete set of posterior distribution of 1000 variance and covariance G-matrices from the BSFG model for downwards s line in .csv format.All_downwards_h_posterior_GComplete set of posterior distribution of 1000 variance and covariance G-matrices from the BSFG model for downwards h line in .csv format.All_upwards_s'_posterior_GComplete set of posterior distribution of 1000 variance and covariance G-matrices from the BSFG model for upwards s' line in .csv format.All_upwards_h'_posterior_GComplete set of posterior distribution of 1000 variance and covariance G-matrices from the BSFG model for upwards h' line in .csv format.All_control_t_posterior_PComplete set of posterior distribution of 1000 variance and covariance P-matrices from the BSFG model for control t line in .csv format.All_downwards_s_posterior_PComplete set of posterior distribution of 1000 variance and covariance P-matrices from the BSFG model for downwards s line in .csv format.All_downwards_h_posterior_PComplete set of posterior distribution of 1000 variance and covariance P-matrices from the BSFG model for downwards h line in .csv format.All_upwards_s'_posterior_PComplete set of posterior distribution of 1000 variance and covariance P-matrices from the BSFG model for upwards s' line in .csv format.All_upwards_h'_posterior_PComplete set of posterior distribution of 1000 variance and covariance P-matrices from the BSFG model for upwards h' line in .csv format.CI_control_t_distribution_GConfidence intervals and mean of the complete set of posterior distribuition of 1000 variance and covariance G-matrices for control t line in .csv format.CI_downwards_s_distribution_GConfidence intervals and mean of the complete set of posterior distribuition of 1000 variance and covariance G-matrices for downwards s line in .csv format.CI_downwards_h_distribution_GConfidence intervals and mean of the complete set of posterior distribuition of 1000 variance and covariance G-matrices for downwards h line in .csv format.CI_upwards_s'_distribution_GConfidence intervals and mean of the complete set of posterior distribuition of 1000 variance and covariance G-matrices for upwards s' line in .csv format.CI_upwards_h'_distribution_GConfidence intervals and mean of the complete set of posterior distribuition of 1000 variance and covariance G-matrices for upwards h' line in .csv format.CI_control_t_distribution_PConfidence intervals and mean of the complete set of posterior distribuition of 1000 variance and covariance P-matrices for control t line in .csv format.CI_downwards_s_distribution_PConfidence intervals and mean of the complete set of posterior distribuition of 1000 variance and covariance P-matrices for downwards s line in .csv format.CI_downwards_h_distribution_PConfidence intervals and mean of the complete set of posterior distribuition of 1000 variance and covariance P-matrices for downwards h line in .csv format.CI_upwards_s'_distribution_PConfidence intervals and mean of the complete set of posterior distribuition of 1000 variance and covariance P-matrices for upwards s' line in .csv format.CI_upwards_h'_distribution_PConfidence intervals and mean of the complete set of posterior distribuition of 1000 variance and covariance P-matrices for upwards h' line in .csv format.ratones_df_rawTable containing individual weight at 49 days and all cranial measurements in .csv format. Description of data: ID: catalog number, SEX: sex (Male or Female), GER: generation of the artificial selection experiment, LIN: experimental line (control t, downwards s, downwards h, upwards s' and upwards h'), MADRE: Pedigree Dam, PADRE: Pedigree Sire, AGE: age at sacrifice, P49: weight at 49 days of age, TAKE: paired individual replicas, selection: direction of selection (downwards, upwards or control), IS_PM-BA_OPI: set of 35 cranial euclidean distances (represented in Figure S4).pedigreePedigree data in .csv format.