Evolutionary Quantitative Genetics

Abstract

Evolutionary quantitative genetics is the study of how complex traits evolve over time. While this field builds on traditional concepts from quantitative genetics widely used by applied breeders and human geneticists (in particular, the inheritance of complex traits), its unique feature is in examining the role of natural selection in changing the population distribution of a complex trait over time. Our review focuses on this role of selection, starting with response under the standard infinitesimal model, in which trait variation is determined by a very large number of loci, each of small effect. We then turn to issues of measuring fitness (and hence natural selection) on both univariate and multivariate traits. We conclude by examining models that treat fitness itself as a complex trait.

Selective Sweep at a Quantitative Trait Locus in the Presence of Background Genetic Variation

“…Pleiotropy can alter predictions regarding selection at the focal locus based on a single character (Otto 2004), and genetic covariances in the background can lead to changes of the mean genetic value of the focal trait as an indirect consequence of selection on other traits (Lande 1979). Multivariate approaches are now a standard tool in evolutionary genetics (Walsh 2007) and have been applied recently to relate the phenotypic effect of pleiotropic mutations to their fitness effects, in the absence of background genetic variation (Martin and Lenormand 2006a). To understand how selection affects pleiotropic mutations in the presence of other polymorphic loci, our approach may be generalized by treating the focal mutation as a vector of effects on several traits, in the presence of a background genetic variance-covariance G-matrix, as in Agrawal et al (2001).…”

“…Therefore it is intended for sexual organisms of reasonably low population sizes. At its most extreme, this view leads to the infinitesimal model designed by Fisher (1918), in which many loci of small effect contribute to a trait, such that the genetic values are normally distributed, an approach that has led to many fascinating developments in evolutionary quantitative genetics (Walsh 2007). In practice, the periodic selection and quantitative genetics views are probably two extremes of a continuum, and their main interest is that they provide fairly good approximations of more complex real genetic systems under certain conditions.…”

Joint Analysis of Binomial and Continuous Traits with a Recursive Model: A Case Study Using Mortality and Litter Size of Pigs

“…Several models for mortality are considered and the best fits are obtained by postulating linear and cubic relationships between the logit of the probability of mortality and litter size, for Landrace and Yorkshire, respectively. An interpretation of how the presence of genetic variation affects the probability of mortality in the population is provided and we discuss and quantify the prospects of selecting for reduced mortality, without affecting litter size.M IXED linear models (Henderson 1984) are broadly used in livestock and plant breeding and play an important role in evolutionary and theoretical quantitative genetics (Lande 1979;Cheverud 1984;Walsh 2003). The classical approach for a multiple-trait analysis is to use models posing that the nature of the correlation between response variables (phenotypes) is due to linear associations between unobservables, such as additive genetic values or nongenetic sources, like permanent or temporary environmental effects.…”

Analysis of Litter Size and Average Litter Weight in Pigs Using a Recursive Model

“…In Yorkshire, the same criterion favors a model with recursion at the level of specific environmental effects only, or, in terms of the SMM, the association between traits is shown to be exclusively due to an environmental (negative) correlation. It is argued that the choice between a SMM or a RMM should be guided by the availability of software, by ease of interpretation, or by the need to test a particular theory or hypothesis that may best be formulated under one parameterization and not the other.M IXED linear models (Henderson 1984) are broadly used to predict breeding values and to estimate variance components for traits of interest in livestock and plant breeding and play an important role in evolutionary and theoretical quantitative genetics (Lande 1979;Cheverud 1984;Walsh 2003). In genetic improvement programs, the objective of selection includes typically several correlated traits.…”

“…M IXED linear models (Henderson 1984) are broadly used to predict breeding values and to estimate variance components for traits of interest in livestock and plant breeding and play an important role in evolutionary and theoretical quantitative genetics (Lande 1979;Cheverud 1984;Walsh 2003). In genetic improvement programs, the objective of selection includes typically several correlated traits.…”