Consequences of Single-Locus and Tightly Linked Genomic Architectures for Evolutionary Responses to Environmental Change

Abstract

<jats:title>Abstract</jats:title> <jats:p>Genetic and genomic architectures of traits under selection are key factors influencing evolutionary responses. Yet, knowledge of their impacts has been limited by a widespread assumption that most traits are controlled by unlinked polygenic architectures. Recent advances in genome sequencing and eco-evolutionary modelling are unlocking the potential for integrating genomic information into predictions of population responses to environmental change. Using eco-evolutionary simulations, we demonstrate that hypothetical single-locus control of a life history trait produces highly variable and unpredictable harvesting-induced evolution relative to the classically applied multi-locus model. Single-locus control of complex traits is thought to be uncommon, yet blocks of linked genes, such as those associated with some types of structural genomic variation, have emerged as taxonomically widespread phenomena. Inheritance of linked architectures resembles that of single loci, thus enabling single-locus-like modeling of polygenic adaptation. Yet, the number of loci, their effect sizes, and the degree of linkage among them all occur along a continuum. We review how linked architectures are often associated, directly or indirectly, with traits expected to be under selection from anthropogenic stressors and are likely to play a large role in adaptation to environmental disturbance. We suggest using single-locus models to explore evolutionary extremes and uncertainties when the trait architecture is unknown, refining parameters as genomic information becomes available, and explicitly incorporating linkage among loci when possible. By overestimating the complexity (e.g., number of independent loci) of the genomic architecture of traits under selection, we risk underestimating the complexity (e.g., nonlinearity) of their evolutionary dynamics.</jats:p>

A Roadmap for Understanding the Evolutionary Significance of Structural Genomic Variation

“…SVs are frequently associated with various stages of diversification, including local adaptation [76], pre-mating isolation [7], and speciation [9,54]. Blocks of differentiation are predicted to be favoured under adaptation with gene flow [48] and are expected to alter the evolutionary trajectory of polygenic traits under selection as they resemble single loci of large effect, rather than many loci of small effect [77].…”

Maturation in Atlantic salmon (Salmo salar, Salmonidae): a synthesis of ecological, genetic, and molecular processes

“…The effect of genetic changes associated with fishing, i.e., fishing-induced evolution (Conover, Arnott, Walsh, & Munch, 2005;Reznick & Ghalambor, 2005), on salmon sea age may be more difficult to predict and interpret than has been assumed based on phenotypic and ecological data (Kuparinen & Hutchings, 2017. The results of Hutchings (2017, 2019) and Oomen et al (2020) demonstrate how the outcomes of size-selective fishing can depend on the genetic architecture behind the trait being selected for, and highlight the unpredictability of the evolutionary outcomes of different fishing strategies.…”

“…For example, the annual value of recreational fishing trips to a single remote Atlantic salmon river system have been estimated to range from € 2.6 to 3.7 million (Pokki et al, 2018). Moreover, information concerning the timing of maturation is needed for developing accurate models for sustainable recreational and commercial fisheries (Kuparinen & Hutchings, 2017Kuparinen & Merilä, 2007;Oomen et al, 2020) and for predicting evolutionary change as a result of exploitation and climate change (de Roos, Boukal, & Persson, 2006).…”

“…Next steps should aim to incorporate knowledge of estimated effect sizes for known candidate genes in such models. Additionally, it may be valuable to consider how linkage disequilibrium patterns within the genome may modify the effects that fishing has on maturation (see Oomen et al, 2020). Integrating new information of genes involved in maturation will help enhance and inform management strategies aimed at maintaining variability in the timing of maturation in salmon populations.…”

Evolution of anticipatory effects mediated by epigenetic changes

“…Previous models have mainly investigated the evolution of maternal or anticipatory effects using quantitative genetic framework, where the assumption is that traits are determined by infinitely many small effect loci. While this is likely to be a reasonable assumption for many phenotypes, there are certainly examples of large genetic contributions of single loci to the phenotype for a number of different traits, and if genetic architecture is based on a few large effect loci, this can change the observed evolutionary dynamics (Oomen et al, 2020). There could be constrains on the evolution of anticipatory effects if, for example, mutational effects sizes or the number of loci are important factors the evolution of the genetic traits that govern the induction of anticipatory effects.…”