Using Genetic Markers to Directly Estimate Male Selection Gradients

Abstract

Abstract. We present an analysis of Raphanus raphanistrum and simulations illustrating the utility of directly estimating male phenotypic selection gradients using genetic markers. The method offers a much more refined characterization of selection than attempting to assign paternity to individual progeny. Our analysis of R. raphanistrum reveals selection on remarkably fine features of floral morphology, including anther exsertion, that were opaque to previous approaches. The new results also undermine a previous conclusion that selection on wild radish floral morphology acts primarily through female fitness. Simulation results show that selection gradients on the order of ␤ ϭ 0.1-0.2 can be readily detected with allozyme markers in moderate-sized (Ͻ 200 paternal individuals) populations. Highly polymorphic (e.g., microsatellite) markers will likely detect fine scale selection (␤ Ͻ 0.1) in larger populations (Ն 400 individuals). Increased progeny sample size, by sampling either additional maternal families or more progeny per maternal parent, partly compensates for low exclusion probability. Increasing the number of possible fathers without changing progeny sample size decreases the ability to detect selection, especially at lower exclusion probabilities. Sampling only some male genotypes reduces the power to detect selection and biases (underestimates) the magnitude of the selection gradient estimate.

Patterns of phenotypic correlations among morphological traits across plants and animals

“…Most wild radish floral correlations are low to moderate (0.28 -0.59), with the exception of the three correlations among the short and long filaments (the filaments mainly determine the length of the stamens) and the corolla tube ( [22]; table 2). We have evidence for stabilizing selection through lifetime male seed siring success on the difference in lengths between the long filament and corolla tube (anther exsertion; [24]) and on the difference in lengths between the two filaments [25]. Stabilizing selection on the difference between two traits is evidence for correlational selection to increase the correlation between the two traits [26,27].…”

Selection on floral traits through male fertility in a natural plant population

“…The current analyses based on paternal probability models and maximum likelihood methods revealed many more significant paternal selection gradients in addition, indicating that the low number of significant paternal selection gradients found by van Kleunen and Ritland (2004) are mainly a consequence of the low power of the analyses. Similarly, in a reanalysis of data on floral traits of Raphanus raphanistrum that had previously been analysed with standard regression methods by Conner et al (1996), Morgan and Conner (2001) also found more significant paternal selection gradients when they used the maximum-likelihood approach. This supports our result indicating that the maximum-likelihood approach is much more powerful than simple regression of male fertility estimates on traits of interest.…”

“…Because estimates of paternity are more accurate when all potential males in a population are sampled, other studies on paternal selection were restricted to artificial (Morgan and Conner 2001;Elle and Meagher 2000;Conner et al 2003) or relatively small (<500 flowering plants) natural (Smouse et al 1999;Vassiliadis et al 2002;Wright and Meagher 2004) populations. A simulation study by Morgan and Conner (2001) showed that sampling only a fraction of the potential males in a population reduces the power to detect significant selection and underestimates the magnitude of the selection gradients.…”

“…A simulation study by Morgan and Conner (2001) showed that sampling only a fraction of the potential males in a population reduces the power to detect significant selection and underestimates the magnitude of the selection gradients. Similarly, the simulations showed that the use of markers with low exclusion probability also reduces the power to detect significant selection and underestimates selection gradients.…”

“…This is especially true when genetic discrimination is low (Roeder et al 1989). A potentially more powerful approach is estimation of paternal selection gradients directly from the molecular genetic data and the trait values by using probability models that account for the genetic composition of offspring in progeny arrays, and estimating the selection coefficients using maximum-likelihood methods (Adams and Birkes 1991;Burczyk et al 1996Burczyk et al , 2002Smouse et al 1999;Morgan and Conner 2001).…”