DOI: 10.5061/dryad.1rn8pk0tf
title: Hormonal pleiotropy structures genetic covariance

description: Quantitative genetic theory proposes that phenotypic evolution is shaped
 by G, the matrix of genetic variances and covariances among traits. In
 species with separate sexes, the evolution of sexual dimorphism is also
 shaped by B, the matrix of between-sex genetic variances and covariances.
 Despite considerable focus on estimating these matrices, their underlying
 biological mechanisms are largely speculative. We experimentally tested
 the hypothesis that G and B are structured by hormonal pleiotropy, which
 occurs when one hormone influences multiple phenotypes. Using juvenile
 brown anole lizards (Anolis sagrei) bred in a paternal half-sibling
 design, we elevated the steroid hormone testosterone with slow-release
 implants while administering empty implants to siblings as a control. We
 quantified effects of this manipulation on the genetic architecture of a
 suite of sexually dimorphic traits, including body size (males are larger
 than females) and the area, hue, saturation, and brightness of the dewlap
 (a colorful ornament that is larger in males than in females).
 Testosterone masculinized females by increasing body size and dewlap area,
 hue, and saturation, while reducing dewlap brightness. Control females and
 males differed significantly in G, but treatment of females with
 testosterone rendered G statistically indistinguishable from males.
 Whereas B was characterized by low between-sex genetic correlations when
 estimated between control females and control males, these same
 correlations increased significantly when estimated between testosterone
 females and either control or testosterone males. The full G matrix
 (including B) for testosterone females and either control or testosterone
 males was significantly less permissive of sexually dimorphic evolution
 than was G estimated between control females and control males, suggesting
 that natural sex differences in testosterone help decouple genetic
 variance between the sexes. Our results confirm that hormonal pleiotropy
 structures genetic covariance, implying that hormones play an important
 yet overlooked role in mediating evolutionary responses to selection.
description: We experimentally tested the hypothesis that <b>G</b>
 and<b> B </b>are structured by hormonal pleiotropy, which
 occurs when one hormone influences multiple phenotypes. We bred anoles in
 a paternal half-sibling design with two dams per sire
 (<i>n</i> = 120 dams, 60 sires). We raised F<sub>3
 </sub>progeny to 3 months of age and then administered one of two
 treatments: (1) a slow-release implant containing 100 μg testosterone, or
 (2) an empty implant as a control. To balance treatments within maternal
 families, we haphazardly determined whether the first offspring of each
 sex would receive a testosterone or a control implant for a given family,
 then alternated between treatment groups for all subsequent progeny of
 each sex. At 8 months of age, we measured each individual for snout-vent
 length (length between the tip of the snout and the cloaca, measured to
 the nearest mm) and photographed its dewlap to measure area, hue,
 saturation, and brightness. We uploaded images into ImageJ and set the
 scale of measurement using the 5-mm grids of the graph paper. We measured
 area (mm<sup>2</sup>) by outlining the dewlap from its
 anterior projection from chin to its posterior attachment to the venter
 using the “polygon” tool. To quantify the color of the dewlap, we used the
 “oval” tool to define a circle in the center of the dewlap, with diameter
 of the circle equal to 1/3 the width of the dewlap, providing a consistent
 measure of the “center” of each dewlap despite variation in its absolute
 size. We used the “color histogram” function to extract the mean red,
 green, and blue values for the selected area, then transformed these
 values into hue (primary color reflected, measured on a 360° color wheel),
 saturation (purity of the hue, 0% = achromatic, 100% = pure color), and
 brightness (relative to maximum possible for color of the same hue and
 saturation, 0% = black, 100% = white-tint-pure color) using the rgb2hsv
 function of the package gDevices within R3.6.2 (R Core Team 2019). To
 quantify sexual dimorphism before treatment we measured these same traits
 following the same procedure on a subset of individuals at three months of
 age. We estimated genetic covariance matricies (G) for these five traits
 using the program WOMBAT, and a pedigree describing the relationships
 among F<sub>1</sub> grandparents, F<sub>2</sub>
 parents, and F<sub>3</sub> experimental progeny. We estimated
 separate within-sex <b>G</b> matrices for each of the four
 experimental groups (control females, control males,
 testosterone females, testosterone males). For pairs of male
 and female treatments, we estimated full <b>G</b> matrices
 including both within-sex matrices
 (<b>G<sub>F</sub></b>,<b>
 G<sub>M</sub></b>) and the between-sex matrix
 (<b>B</b>). We standardized the genetic covariance matrices to
 the phenotypic variance. We performed analysis on both standardized and
 unstandardized matricies. 
description: We provide all the files necessary to preform the analysis
 presented in the manuscript, as well as the data files and pedigree used
 to estimate the genetic covariance matrices. Readme files detailing the
 types of data given in each file and how to use them are provided.
 Further, code used for the analysis presented in the manuscript is
 provided at https://github.com/ty-wittman/evo_qg_analysis_r_code.