Despite the many triumphs of comparative biology during the past few decades, the field has remained strangely divorced from evolutionary genetics. In particular, comparative methods have failed to incorporate multivariate process models of microevolution that include genetic constraint in the form of the G matrix. Here we explore the insights that might be gained by such an analysis. A neutral model of evolution by genetic drift that depends on effective population size and the G matrix predicts a probability distribution for divergence of population trait means on a phylogeny. Use of a maximum likelihood (ML) framework then allows us to compare independent direct estimates of G with the ML estimates based on the observed pattern of trait divergence among taxa. We assess the departure from neutrality, and thus the role of different types of selection and other forces, in a stepwise hypothesis-testing procedure based on parameters for the size, shape, and orientation of G. We illustrate our approach with a test case of data on vertebral number evolution in garter snakes.
KeywordsG matrix; genetic line of least resistance; maximum likelihood; selective line of least resistance; Thamnophis; vertebral number A large literature on the comparative analysis of evolving traits has blossomed during the past 25 years (Felsenstein 1985;Harvey and Pagel 1991;Lynch 1991;Martins and Garland 1991;Martins 1994Martins , 2000Rohlf 2001;Steppan 2004;Carvalho et al. 2005). Interest in tracing trait evolution on independently estimated phylogenies has been responsible for putting a new face on comparative biology. New methodologies have allowed investigators to assay statistical associations between trait values and putative selective pressures (e.g., Darst et al. 2005;Ord and Martins 2006;Strmberg 2006) and to test alternative models of evolutionary process (Hansen 1997;Butler and King 2004). Despite success on these and other fronts, the new comparative biology has been strangely divorced from evolutionary genetics. Process models have been adopted from evolutionary genetics to provide the substructure for statistical inference (Martins and Hansen 1997), but estimates of genetic parameters usually play no role in comparative biology. The divorce between comparative biology and evolutionary genetics is especially vivid in the case of continuously distributed phenotypic traits. For these kinds of traits, which are often polygenic, quantitative genetic models promise deep understanding of