Catch Me if You Can: Adaptation from Standing Genetic Variation to a Moving Phenotypic Optimum

Abstract

Adaptation lies at the heart of Darwinian evolution. Accordingly, numerous studies have tried to provide a formal framework for the description of the adaptive process. Of these, two complementary modeling approaches have emerged: While so-called adaptive-walk models consider adaptation from the successive fixation of de novo mutations only, quantitative genetic models assume that adaptation proceeds exclusively from preexisting standing genetic variation. The latter approach, however, has focused on short-term evolution of population means and variances rather than on the statistical properties of adaptive substitutions. Our aim is to combine these two approaches by describing the ecological and genetic factors that determine the genetic basis of adaptation from standing genetic variation in terms of the effect-size distribution of individual alleles. Specifically, we consider the evolution of a quantitative trait to a gradually changing environment. By means of analytical approximations, we derive the distribution of adaptive substitutions from standing genetic variation, that is, the distribution of the phenotypic effects of those alleles from the standing variation that become fixed during adaptation. Our results are checked against individual-based simulations. We find that, compared to adaptation from de novo mutations, (i) adaptation from standing variation proceeds by the fixation of more alleles of small effect and (ii) populations that adapt from standing genetic variation can traverse larger distances in phenotype space and, thus, have a higher potential for adaptation if the rate of environmental change is fast rather than slow.

Factors influencing the effect size distribution of adaptive substitutions

“…This is not intended to be an exhaustive list, but rather a broad overview of some of the theories discussed in the text. [3,22] moving optimum similar to [3] except initially close to moving optimum; new mutation and standing genetic variation distribution has more intermediate effect sizes; faster moving optimum results in larger effect sizes; slower moving optimum results in smaller effect sizes [21,22,59,60] standing genetic variation variation maintained in a single population in mutation -selection -drift balance before a change in environment smaller-effect alleles have higher probability of fixation than new mutations of same effect size [20,22] small effective population size small-effect mutations effectively neutral small-effect mutations lost to drift; intermediate effect size mutations have highest probability of fixation [2] migration divergent selection in the face of gene flow increased migration leads to larger-effect mutations and/or linked small-effect mutations [89] rspb.royalsocietypublishing.org Proc. R. Soc.…”

“…An alternative is adaptation from standing genetic variation (hereafter SGV) [19,20,22,[62][63][64][65][66], which in principle could be applied to all the selective scenarios above. The source of SGV in this body of theory is typically modelled as neutral or deleterious alleles underlying fitness maintained in mutation-selection-drift equilibrium [20,22, but see 67]. At some point, the environment changes such that previously deleterious and/or neutral alleles become favourable, and provide the raw material for adaptation to the new phenotypic optimum.…”

“…a mutational hotspot or a large mutational target size, such that many individual mutations might produce the same phenotype [65]. Matuszewski et al [22] predict the distribution of effect sizes from SGV for a moving optimum (see above) for very large values of Q (2.5-10). We suggest that there is a critical need for theoretical predictions about the distributions of effect sizes, and even the likelihood of adaptation from SGV versus new mutation, for the range of values of Q initially considered (0.4-0.004) by Hermisson & Pennings [20].…”

“…Moreover, the theoretical expectations put forth by Fisher, Kimura and Orr do not adequately capture the full range of biological scenarios that are relevant to understanding the distribution of effect sizes [20][21][22]. Although Orr's [3] proposal was a paradigm shift for the field of evolutionary genetics, as he acknowledged, his model represents a small subset of the possible alternatives.…”

Genetic characterization of red-colored heartwood genotypes of Chinese fir using simple sequence repeat (SSR) markers

“…Abundant genetic variation allows the development of broad forest varieties adapted to multiple environmental conditions and enhances the genetic gain for several useful traits (Hoffmann and Sgrò, 2011;Matuszewski et al, 2015;Lopes et al, 2015). The biological factors inherent in a particular species, such as gene flow, divergence between populations, and reduction in diversity, can be elucidated based on genetic diversity and population structure, measured using molecular markers (Frankham et al, 2002).…”