Cultivated beet (Beta vulgaris L. ssp. vulgaris) originated from sea beet (B. vulgaris ssp. maritima (L.) Arcang), a wild beet species widely distributed along the coasts of the Mediterranean Sea and Atlantic Ocean, as well as northern Africa. Understanding the evolution of sea beet will facilitate its efficient use in sugarbeet improvement. We used SNPs (single nucleotide polymorphisms) covering the whole genome to analyze 599 sea beet accessions collected from the north Atlantic Ocean and Mediterranean Sea coasts. All B. maritima accessions can be grouped into eight clusters with each corresponding to a specific geographic region. Clusters 2, 3 and 4 with accessions mainly collected from Mediterranean coasts are genetically close to each other as well as to Cluster 6 that contained mainly cultivated beet. Other clusters were relatively distinct from cultivated beets with Clusters 1 and 5 containing accessions from north Atlantic Ocean coasts, Clusters 7 and Cluster 8 mainly have accessions from northern Egypt and southern Europe, and northwest Morocco, respectively. Distribution of B. maritima subpopulations aligns well with the direction of marine currents that was considered a main dynamic force in spreading B. maritima during evolution. Estimation of genetic diversity indices supported the formation of B. maritima subpopulations due to local genetic drift, historic migration, and limited gene flow. Our results indicated that B. maritima originated from southern Europe and then spread to other regions through marine currents to form subpopulations. This research provides vital information for conserving, collecting, and utilizing wild sea beet to sustain sugarbeet improvement.

The evolution of a population by means of genetic drift and natural selection operating on a gene regulatory network (GRN) of an individual has not been scrutinized in depth. Thus, the relative importance of various evolutionary forces and processes on shaping genetic variability in GRNs is understudied. In this study, we implemented a simulation framework, called EvoNET, that simulates forward-in-time the evolution of GRNs in a population. The fitness effect of mutations is not constant, rather fitness of each individual is evaluated on the phenotypic level, by measuring its distance from an optimal phenotype. Each individual goes through a maturation period, where its GRN may reach an equilibrium, thus deciding its phenotype. Afterwards, individuals compete to produce the next generation. We examine properties of the GRN evolution, such as robustness against the deleterious effect of mutations and the role of genetic drift. We are able to confirm previous hypotheses regarding the effect of mutations and we provide new insights on the interplay between random genetic drift and natural selection.

The major drawback to the implementation of genomic selection in a breeding program lies in long-term decrease in additive genetic variance, which is a trade-off for rapid genetic improvement in short term. Balancing increase in genetic gain with retention of additive genetic variance necessitates careful optimization of this trade-off. In this study, we proposed an integrated index selection approach within the genomic inferred cross-selection (GCS) framework to maximize genetic gain across multiple traits. With this method, we identified optimal crosses that simultaneously maximize progeny performance and maintain genetic variance for multiple traits. Using a stochastic simulated recurrent breeding program over a 40-years period, we evaluated different GCS methods along with other factors, such as the number of parents, crosses, and progeny per cross, that influence genetic gain in a pulse crop breeding program. Across all breeding scenarios, the posterior mean variance consistently enhances genetic gain when compared to other methods, such as the usefulness criterion, optimal haploid value, mean genomic estimated breeding value, and mean index selection value of the superior parents. In addition, we provide a detailed strategy to optimize the number of parents, crosses, and progeny per cross that can potentially maximize short- and long-term genetic gain in a public breeding program.

The functions of proteins generally depend on their assembly into complexes. During evolution, some complexes have transitioned from homomers encoded by a single gene to heteromers encoded by duplicate genes. This transition could occur without adaptive evolution through intermolecular compensatory mutations. Here, we experimentally duplicated and evolved a homodimeric enzyme to determine whether and how this could happen. We identified hundreds of deleterious mutations that inactivate individual homodimers but produce functional enzymes when coexpressed as duplicated proteins that heterodimerize. The structure of one such heteromer reveals how both losses of function are buffered through the introduction of asymmetry in the complex that allows them to subfunctionalize. Constructive neutral evolution can thus occur by gene duplication followed by only one deleterious mutation per duplicate.

We present a geometry-based interpretation of the f-statistics framework, commonly used in population genetics to estimate phylogenetic relationships from genomic data. The focus is on the determination of the mixing coefficients in population admixture events subject to post-admixture drift. The interpretation takes advantage of the high dimension of the dataset and analyzes the problem as a dimensional reduction issue. We show that it is possible to think of the f-statistics technique as an implicit transformation of the genomic data from a phase space into a subspace where the mapped data structure is more similar to the ancestral admixture configuration. The two-way mixing coefficient is, as a matter of fact, carried out implicitly in this subspace. In addition, we propose the admixture test to be evaluated in the subspace because the comparison with the conventional one provides an important assessment of the admixture model. The overarching geometric framework provides slightly more general formulas than the f-formalism by using a different rationale as a starting point. Explicitly addressed are two-way and three-way admixtures. The mixture proportions are provided by suitable linear fits, in two or three dimensions, that can be easily visualized. The difficulties encountered with introgression and gene flow are also addressed. The developments and findings are illustrated with numerical simulations and real-world cases.

When biological populations expand into new territory, the evolutionary outcomes can be strongly influenced by genetic drift, the random fluctuations in allele frequencies. Meanwhile, spatial variability in the environment can also significantly influence the competition between subpopulations vying for space. Little is known about the interplay of these intrinsic and extrinsic sources of noise in population dynamics: When does environmental heterogeneity dominate over genetic drift or vice versa, and what distinguishes their population genetics signatures? Here, in the context of neutral evolution, we examine the interplay between a population\'s intrinsic, demographic noise and an extrinsic, quenched random noise provided by a heterogeneous environment. Using a multispecies Eden model, we simulate a population expanding over a landscape with random variations in local growth rates and measure how this variability affects genealogical tree structure, and thus genetic diversity. We find that, for strong heterogeneity, the genetic makeup of the expansion front is to a great extent predetermined by the set of fastest paths through the environment. The landscape-dependent statistics of these optimal paths then supersede those of the population\'s intrinsic noise as the main determinant of evolutionary dynamics. Remarkably, the statistics for coalescence of genealogical lineages, derived from those deterministic paths, strongly resemble the statistics emerging from demographic noise alone in uniform landscapes. This cautions interpretations of coalescence statistics and raises new challenges for inferring past population dynamics.

OBJECTIVE: Globally, barriers triggered by climatic changes have caused habitat fragmentation and population allopatric divergence. Across North America, oscillations during the Quaternary have played important roles in the distribution of wildlife. Notably, diverse plant species from the Baja California Peninsula in western North America, isolated during the Pleistocene glacial-interglacial cycles, exhibit strong genetic structure and highly concordant divergent lineages across their ranges. A representative plant genus of the peninsula is Yucca, with Y. valida having the widest range. Although a dominant species, it has an extensive distribution discontinuity between 26° N and 27° N, suggesting restricted gene flow. Moreover, historical distribution models indicate the absence of an area with suitable conditions for the species during the Last Interglacial, making it an interesting model for studying genetic divergence.
METHODS: We assembled 4411 SNPs from 147 plants of Y. valida throughout its range to examine its phylogeography to identify the number of genetic lineages, quantify their genetic differentiation, reconstruct their demographic history and estimate the age of the species.
RESULTS: Three allopatric lineages were identified based on the SNPs. Our analyses support that genetic drift is the driver of genetic differentiation among these lineages. We estimated an age of less than 1 million years for the common ancestor of Y. valida and its sister species.
CONCLUSIONS: Habitat fragmentation caused by climatic changes, low dispersal, and an extensive geographical range gap acted as cumulative mechanisms leading to allopatric divergence in Y. valida.

Manipulating evolutionary forces imposed by hosts on pathogens like genetic drift and selection could avoid the emergence of virulent pathogens. For instance, increasing genetic drift could decrease the risk of pathogen adaptation through the random fixation of deleterious mutations or the elimination of favorable ones in the pathogen population. However, no experimental proof of this approach is available for a plant-pathogen system. We studied the impact of pepper (Capsicum annuum) lines carrying the same major resistance gene but contrasted genetic backgrounds on the evolution of Potato virus Y (PVY). The pepper lines were chosen for the contrasted levels of genetic drift (inversely related to Ne, the effective population size) they exert on PVY populations, as well as for their contrasted resistance efficiency (inversely related to the initial replicative fitness, Wi, of PVY in these lines). Experimental evolution was performed by serially passaging 64 PVY populations every month on six contrasted pepper lines during seven months. These PVY populations exhibited highly divergent evolutionary trajectories, ranging from viral extinctions to replicative fitness gains. The sequencing of the PVY VPg cistron, where adaptive mutations are likely to occur, allowed linking these replicative fitness gains to parallel adaptive nonsynonymous mutations. Evolutionary trajectories were well explained by the genetic drift imposed by the host. More specifically, Ne, Wi and their synergistic interaction played a major role in the fate of PVY populations. When Ne was low (i.e. strong genetic drift), the final PVY replicative fitness remained close to the initial replicative fitness, whereas when Ne was high (i.e. low genetic drift), the final PVY replicative fitness was high independently of the replicative fitness of the initially inoculated virus. We show that combining a high resistance efficiency (low Wi) and a strong genetic drift (low Ne) is the best solution to increase resistance durability, that is, to avoid virus adaptation on the long term.

Adaptive differentiation of traits and underlying loci can occur at a small geographical scale if natural selection is stronger than countervailing gene flow and drift. We investigated this hypothesis using coupled quantitative genetic and genomic approaches for a wind-pollinated tree species, Quercus rubra, along the steep, narrow gradient of the Lake Superior coast that encompasses four USDA Hardiness Zones within 100 km. For the quantitative genetic component of this study, we examined phenotypic differentiation among eight populations in a common garden, measuring seed mass, germination, height, stem diameter, leaf number, specific leaf area and survival. For the genomic component, we quantified genetic differentiation for 26 populations from the same region using RAD-seq. Because hybridisation with Quercus ellipsoidalis occurs in other parts of the species\' range, we included two populations of this congener for comparison. In the common garden study, we found a strong signal of population differentiation that was significantly associated with at least one climate factor for nine of 10 measured traits. In contrast, we found no evidence of genomic differentiation among populations based on FST or any other measures. However, both distance-based and genotype-environment association analyses identified loci showing the signature of selection, with one locus in common across five analyses. This locus was associated with the minimum temperature of the coldest month, a factor that defines the climate zones and was also significant in the common garden analyses. In addition, we documented introgression from Q. ellipsoidalis into Q. rubra, with rates of introgression correlated with the climate gradient. In sum, this study reveals signatures of selection at the quantitative trait and genomic level consistent with climate adaptation, a pattern that is more often documented at a much broader geographical scale, especially in long-lived wind-pollinated species.

Adaptive evolution can facilitate species\' range expansions across environmentally heterogeneous landscapes. However, serial founder effects can limit the efficacy of selection, and the evolution of increased dispersal during range expansions may result in gene flow swamping local adaptation. Here, we study how genetic drift, gene flow and selection interact during the cane toad\'s (Rhinella marina) invasion across the heterogeneous landscape of Australia. Following its introduction in 1935, the cane toad colonised eastern Australia and established several stable range edges. The ongoing, more rapid range expansion in north-central Australia has occurred concomitant with an evolved increase in dispersal capacity. Using reduced representation genomic data of Australian cane toads from the expansion front and from two areas of their established range, we test the hypothesis that high gene flow constrains local adaptation at the expansion front relative to established areas. Genetic analyses indicate the three study areas are genetically distinct but show similar levels of allelic richness, heterozygosity and inbreeding. Markedly higher gene flow or recency of colonisation at the expansion front have likely hindered local adaptation at the time of sampling, as indicated by reduced slopes of genetic-environment associations (GEAs) estimated using a novel application of geographically weighted regression that accounts for allele surfing; GEA slopes are significantly steeper in established parts of the range. Our work bolsters evidence supporting adaptation of invasive species post-introduction and adds novel evidence for differing strengths of evolutionary forces among geographic areas with different invasion histories.