10.5061/DRYAD.K6M50
Careau, Vincent
Deakin University
Wolak, Matthew E.
University of Aberdeen
Carter, Patrick A.
Washington State University
Garland, Theodore
University of California, Riverside
Data from: Evolution of the additive genetic variance–covariance matrix
under continuous directional selection on a complex behavioural phenotype
Dryad
dataset
2015
wheel running
Mus musculus
Bayesian animal model
genetic covariance tensor
G-matrix
Artificial selection
2015-10-23T17:12:07Z
2015-10-23T17:12:07Z
en
https://doi.org/10.1098/rspb.2015.1119
9524470 bytes
1
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Given the pace at which human-induced environmental changes occur, a
pressing challenge is to determine the speed with which selection can
drive evolutionary change. A key determinant of adaptive response to
multivariate phenotypic selection is the additive genetic
variance–covariance matrix (G). Yet knowledge of G in a population
experiencing new or altered selection is not sufficient to predict
selection response because G itself evolves in ways that are poorly
understood. We experimentally evaluated changes in G when closely related
behavioural traits experience continuous directional selection. We applied
the genetic covariance tensor approach to a large dataset (n = 17 328
individuals) from a replicated, 31-generation artificial selection
experiment that bred mice for voluntary wheel running on days 5 and 6 of a
6-day test. Selection on this subset of G induced proportional changes
across the matrix for all 6 days of running behaviour within the first
four generations. The changes in G induced by selection resulted in a
fourfold slower-than-predicted rate of response to selection. Thus,
selection exacerbated constraints within G and limited future adaptive
response, a phenomenon that could have profound consequences for
populations facing rapid environmental change.
Careau_et_al_G-matrix_DATA_DRYADCareau_et_al_G-matrix_PEDIGREE_DRYADCareau_et_al_G-matrix_CODE_TENSOR_SIMULATIONTensorExampleMod_pop1aTensorExampleMod_pop1bTensorExampleMod_pop1cTensorExampleMod_pop2aTensorExampleMod_pop1dTensorExampleMod_pop2bTensorExampleMod_pop2cTensorExampleMod_pop2d