Data from: Predicting multivariate responses of sexual dimorphism to direct and indirect selection
Houle, David; Cheng, Changde (2020), Data from: Predicting multivariate responses of sexual dimorphism to direct and indirect selection, Dryad, Dataset, https://doi.org/10.5061/dryad.2280gb5pb
Sexual dimorphism is often assumed to result from balancing the strength of antagonistic selection in favor of dimorphism against the degree of constraint imposed by the shared genome of the sexes, reflected in the B matrix of genetic inter-sexual covariances. To investigate the totality of forces shaping dimorphism, we reparameterized the Lande equation to predict changes in trait averages and trait differences between the sexes. As genetic constraints on the evolution of dimorphism in response to antagonistic selection become larger, dimorphism will tend to respond more rapidly to concordant selection (which favors the same direction of change in male and female traits) than to antagonistic selection. When we apply this theory to four empirical estimates of B in Drosophila melanogaster, the indirect responses of dimorphism to concordant selection are of comparable or larger magnitude than the direct responses of dimorphism to antagonistic selection in two suites of traits with typical levels of inter-sex correlation. Antagonistic selection is more important in two suites of traits where the inter-sex correlations are unusually low. This suggests that the evolution of sexual dimorphism may sometimes be dominated by concordant selection, rather than antagonistic selection.
This submission include data set includes sexually differentiated G-matrix estimates, and SAS code to estimate the genetic variation within and between sexually antagonistic and concordant subspaces, and calculate estimates of the direct and indirect respondabilities to selection in each subspace.
The G-matrix estimates are drawn from Drosophila melanogaster populations presented in three papers (Houle and Cheng 2020; Sztepanacz and Houle 2019 and Ingleby et al. 2014): The Houle and Cheng (2020) and Sztepanacz and Houle (2019) data include 1,000 estimates of G-matrices consistent with the underlying data estimated using the REML-MVN method. The Ingleby et al. (2014) data includes just the best estimates of G given in that paper's Tables 3 and 5. No estimates of sampling variation are given.
Houle, D., and C. Cheng. 2020. Predicted evolution of multivariate sexual dimorphism of gene expression in Drosophila melanogaster. bioRxiv doi:
Sztepanacz, J. L., and D. Houle. 2019. Cross-sex genetic covariances limit the evolvability of wing-shape within and among species of Drosophila. Evolution 73:1617-1633.
Ingleby, F. C., P. Innocenti, H. D. Rundle, and E. H. Morrow. 2014. Between-sex genetic covariance constrains the evolution of sexual dimorphism in Drosophila melanogaster. Journal of Evolutionary Biology 27:1721-1732.
To analyze these data as presented, you must have the SAS system software (e.g.SAS 2016) installed. Once you have unpacked the ZIP file, change the path within the SAS files to point to the directory where you have unpacked the data, and run the programs, which have .SAS extensions. Some data are in .csv files, but most are in SAS data sets. If you do not have SAS, you can still use conversion utilities in other software, such as R, to read that data.
SAS Institute, Inc. 2016.The SAS System for Windows, Release 9.4.SAS Institute, Cary, NC.
National Science Foundation, Award: 1556774