How does the strength of selection influence genetic correlations ?

Abstract

Genetic correlations between traits can strongly impact evolutionary responses to selection, and may thus impose constraints on adaptation. Theoretical and empirical work has made it clear that, without strong linkage, genetic correlations at evolutionary equilibrium result from an interplay of correlated pleiotropic effects of mutations, and correlational selection favoring combinations of trait values. However, it is not entirely clear how the strength of stabilizing selection influences this compromise between mutation and selection effects on genetic correlations. Here, we show that the answer to this question crucially depends on the intensity of genetic drift. In large, effectively infinite populations, genetic correlations are unaffected by the strength of selection, regardless of whether the genetic architecture involves common small-effect mutations (Gaussian regime), or rare large-effect mutations (House-of-Cards regime). In contrast in finite populations, the strength of selection does affect genetic correlations, by shifting the balance from drift-dominated to selection-dominated evolutionary dynamics. The transition between these domains depends on mutation parameters to some extent, but with a similar dependence of genetic correlation on the strength of selection. Our results are particularly relevant for understanding how senescence shapes patterns of genetic correlations across ages, and genetic constraints on adaptation during colonization of novel habitats.

Physiological traits and their relationships vary along an elevational gradient within and among Fijian bee species

“…Further work is needed to determine whether the observed phenotypic correlation is underpinned by the same or different alleles and to examine evolutionary responses of linked traits that are important for species survival with climate change. Variation in the strength of trait relationships could have implications for the way in which species evolve with further anthropogenic climate change [55]. As organisms are composed of multiple interacting traits and adaptation is a multidimensional problem, we believe it is critical for research to explore how climate tolerance traits are linked and evolve across landscapes together as we move further into the Anthropocene.…”

G-matrix stability in clinally diverging populations of an annual weed

“…Additionally, G among populations may differ if a population is small and subject to substantial genetic drift or inbreeding (Lande 1979, Phillips et al 2001, Jones et al 2004, Doroszuk et al 2008), has experienced recent population bottlenecks (Shaw et al 1995, Roff 2000), or is under strong selection due to novel conditions (Turelli 1988, Roff 2000, Conner et al 2011, Uesugi et al 2017), which are all features often associated with range-edge populations. Recent theory by Chantepie and Chevin (2020) suggests that in finite populations subject to drift, genetic correlations will more closely reflect mutational (co)variances as effective population sizes become smaller. Chantepie and Chevin (2020) note that colonizing populations (which may be similar to range-edge populations) often experience reduced Ne and strong selection, and thus G will be more reflective of mutational covariances, and stronger genetic constraints.…”

“…Recent theory by Chantepie and Chevin (2020) suggests that in finite populations subject to drift, genetic correlations will more closely reflect mutational (co)variances as effective population sizes become smaller. Chantepie and Chevin (2020) note that colonizing populations (which may be similar to range-edge populations) often experience reduced Ne and strong selection, and thus G will be more reflective of mutational covariances, and stronger genetic constraints. Taken together, we might predict less overall genetic variance (Johnson & Barton 1995), or different patterns of genetic covariance, which may in turn lead to evolutionary constraint in range-edge populations.…”

How development affects evolution

“…Calls for synthesis between developmental and evolutionary biology have also asked for consideration of the mechanistic basis of phenotype construction, non-linear genotype-phenotype maps, gene-gene and gene-environment interactions, non-normal distributions, far-from-equilibrium evolutionary dynamics, dynamic fitness landscapes, evolution and the nature of the G -matrix, evolvability and epigenetics, and a variety of other complexities (Pigliucci and Schlichting, 1997; Pigliucci, 2007). Mathematical frameworks and specific mathematical models integrating some of these complexities have become available in recent decades (e.g., Dieckmann and Law, 1996;Caswell, 2001;Hansen and Wagner, 2001;Day and Bonduriansky, 2011;Mullon and Lehmann, 2018; Lande, 2019; Chantepie and Chevin, 2020; Engen and Sæther, 2021). Additionally, theoretical research has often used individual-based simulations integrating some these complexities (e.g., Salazar-Ciudad and Marín-Riera, 2013; Watson et al ., 2013; Jones et al ., 2014a;Miloco and Salazar-Ciudad, 2022), although we note it is of particular interest to obtain mathematical frameworks with the aim of further deepening insights.…”