Consequences of single-locus and tightly linked genomic architectures for evolutionary responses to environmental change

Abstract

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.

Dissecting the loci underlying maturation timing in Atlantic salmon using haplotype and multi-SNP based association methods

“…A large body of work has shown the relevance of genetic architecture in determining evolutionary responses [60][61][62][63][64][65][66][67][68]. Recent works highlight the relevance of the genetic architecture underlying fitness traits when predicting a population's response to environmental changes [69] and selective pressures such a fishing [70]. Future work elucidating how such mixed genetic architectures affect predicted evolution of traits, compared to that of omnigenic or polygenic architectures, will be valuable.…”

Rapid Parallel Adaptation to Anthropogenic Heavy Metal Pollution

“…As in S. uniflora, those species that evolved to survive in environments with natural sources of high abiotic stress, but which do not compete well in low-abiotic stress/high-biotic competition environments, may be particularly well suited to cope with the ongoing human modification of the planet. Alongside evidence of widespread local adaptation to different environmental conditions in other species (Fournier-Level et al 2011;Papadopulos et al 2014), our findings indicate that while it may be possible to predict which species will adapt to specific environments, the underlying genetic basis to that adaptation may be considerably more variable than is currently understood from the limited number of well-studied examples (Oomen et al 2020). In order to be accurate, predictions of evolutionary responses to environmental change from genomic data will need to account for the possibility that multiple genetic architectures can produce similar phenotypic responses.…”

Ecology drives the degree of convergence in the gene expression of extremophile fishes

“…To test whether population pairs in more ecologically similar habitats exhibit higher degrees of convergence, we measured aspects of water quality in situ (temperature, pH, specific conductivity, dissolved oxygen, and H2S concentration) and downloaded 19 geospatial variables that describe climatic variation for each sulfide spring (28). Principal component (PC) analysis indicated that different springs primarily segregated along the first PC axis (Figure 2C), which was correlated with temperature, pH, and H2S concentration, as well as bioclimatic variables 1,4,6,7,9,11,12,13,14,16,17,18, and 19 (correlations: |0.20| < r < |0.28|; Table S1). In general, sulfide springs in Mexico (negative scores along PC axis 1) exhibited higher temperatures and H2S concentrations, lower pH, less seasonality, and more precipitation compared to the springs in Florida and the Dominican Republic (positive scores along PC axis 1).…”

“…In fact, unique, lineage-specific evolutionary outcomes in response to apparently similar selective regimes can arise through a multitude of genetic, functional, and ecological mechanisms (11). For example, differences in genetic architecture can bias responses to selection (12), functional redundancy can cause equivalent fitness by alternative means (13), and cryptic ecological differences among lineages can select for different phenotypic optima (14). Still, attempts to explain the degree of convergent and lineage-specific ( i.e ., nonconvergent) evolutionary patterns remain scarce.…”