Studying phenotypic evolution using multivariate quantitative genetics

Abstract

Quantitative genetics provides a powerful framework for studying phenotypic evolution and the evolution of adaptive genetic variation. Central to the approach is G, the matrix of additive genetic variances and covariances. G summarizes the genetic basis of the traits and can be used to predict the phenotypic response to multivariate selection or to drift. Recent analytical and computational advances have improved both the power and the accessibility of the necessary multivariate statistics. It is now possible to study the relationships between G and other evolutionary parameters, such as those describing the mutational input, the shape and orientation of the adaptive landscape, and the phenotypic divergence among populations. At the same time, we are moving towards a greater understanding of how the genetic variation summarized by G evolves. Computer simulations of the evolution of G, innovations in matrix comparison methods, and rapid development of powerful molecular genetic tools have all opened the way for dissecting the interaction between allelic variation and evolutionary process. Here I discuss some current uses of G, problems with the application of these approaches, and identify avenues for future research.

Phenotypic convergence along a gradient of predation risk

“…Under convergence (or divergence), the phenotypic variation facing selection pressures is dramatically reduced (increased) and, in the case of divergence, may facilitate ecological speciation. These patterns of plasticity-induced variation thus reflect past selection pressures and the opportunities for further evolution, and may contribute to our understanding of mechanisms controlling ecological differentiation [11,29,30].…”

“…Evaluating a multivariate description of plasticity along a fine-scale gradient in individuals from multiple populations allows one to develop a comprehensive, context-dependent description of the response to risk (via well-characterized reaction norms) and begins to characterize the response as a strategy rather than a single trait (e.g. [10,11]). …”

Quantitative trait locus analysis of hatch timing, weight, length and growth rate in coho salmon, Oncorhynchus kisutch

“…It has become increasingly common to use multivariate approaches to the breeder's equation to describe evolution in natural populations (McGuigan, 2006;Garant et al, 2008). In this type of approach, trait interactions are described by the G matrix, a matrix of the genetic variances and covariances between traits (McGuigan, 2006).…”

“…It has become increasingly common to use multivariate approaches to the breeder's equation to describe evolution in natural populations (McGuigan, 2006;Garant et al, 2008). In this type of approach, trait interactions are described by the G matrix, a matrix of the genetic variances and covariances between traits (McGuigan, 2006). Although very useful for describing evolution, this multivariate statistical approach assumes that all genetic change is due to additive variance and covariance, and that all underlying QTL are of small effect (Phillips and McGuigan, 2006;Roff, 2007a).…”

“…Evolutionary response is determined by phenotypic variation, by the strength and direction of selection, by the underlying genetic variation within a population and by the genetic covariance between traits (Falconer and Mackay, 1996). Genetic correlations limit the number of trait combinations that are advantageous and thus constrain the evolution of correlated phenotypes (McGuigan, 2006). Statistical quantitative techniques have often been used to examine the genetic variance and covariance of fitness traits (Lynch and Walsh, 1998).…”