Data from: Evolution of the additive genetic variance–covariance matrix under continuous directional selection on a complex behavioural phenotype
Data files
Oct 23, 2015 version files 9.52 MB
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Careau_et_al_G-matrix_CODE_TENSOR_SIMULATION.txt
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Careau_et_al_G-matrix_DATA_DRYAD.csv
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Careau_et_al_G-matrix_PEDIGREE_DRYAD.csv
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README_for_Careau_et_al_G-matrix_DATA_DRYAD.txt
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TensorExampleMod_pop1a.RData
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TensorExampleMod_pop1b.RData
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TensorExampleMod_pop1c.RData
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TensorExampleMod_pop1d.RData
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TensorExampleMod_pop2a.RData
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TensorExampleMod_pop2b.RData
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TensorExampleMod_pop2c.RData
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TensorExampleMod_pop2d.RData
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Abstract
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.