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Multivariate genetic architecture of the Anolis dewlap reveals both shared and sex-specific features of a sexually dimorphic ornament.

Robert M CoxR A CostelloB E CamberJoel W McGlothlin
Published in: Journal of evolutionary biology (2017)
Darwin viewed the ornamentation of females as an indirect consequence of sexual selection on males and the transmission of male phenotypes to females via the 'laws of inheritance'. Although a number of studies have supported this view by demonstrating substantial between-sex genetic covariance for ornament expression, the majority of this work has focused on avian plumage. Moreover, few studies have considered the genetic basis of ornaments from a multivariate perspective, which may be crucial for understanding the evolution of sex differences in general, and of complex ornaments in particular. Here, we provide a multivariate, quantitative-genetic analysis of a sexually dimorphic ornament that has figured prominently in studies of sexual selection: the brightly coloured dewlap of Anolis lizards. Using data from a paternal half-sibling breeding experiment in brown anoles (Anolis sagrei), we show that multiple aspects of dewlap size and colour exhibit significant heritability and a genetic variance-covariance structure (G) that is broadly similar in males (Gm ) and females (Gf ). Whereas sexually monomorphic aspects of the dewlap, such as hue, exhibit significant between-sex genetic correlations (rmf ), sexually dimorphic features, such as area and brightness, exhibit reduced rmf values that do not differ from zero. Using a modified random skewers analysis, we show that the between-sex genetic variance-covariance matrix (B) should not strongly constrain the independent responses of males and females to sexually antagonistic selection. Our microevolutionary analysis is in broad agreement with macroevolutionary perspectives indicating considerable scope for the independent evolution of coloration and ornamentation in males and females.
Keyphrases
  • genome wide
  • copy number
  • mental health
  • data analysis
  • poor prognosis
  • gene expression
  • big data
  • long non coding rna
  • binding protein