Bivariate GWAS reveals pleiotropic regions among feed efficiency and beef quality-related traits in Nelore cattle.

Feed-efficient cattle selection is among the most leading solutions to reduce cost for beef cattle production. However, technical difficulties in measuring feed efficiency traits had limited the application in livestock. Here, we performed a Bivariate Genome-Wide Association Study (Bi-GWAS) and presented candidate biological mechanisms underlying the association between feed efficiency and meat quality traits in a half-sibling design with 353 Nelore steers derived from 34 unrelated sires. A total of 13 Quantitative Trait Loci (QTL) were found explaining part of the phenotypic variations. An important transcription factor of adipogenesis in cattle, the TAL1 (rs133408775) gene located on BTA3 was associated with intramuscular fat and average daily gain (IMF-ADG), and a region located on BTA20, close to CD180 and MAST4 genes, both related to fat accumulation. We observed a low positive genetic correlation between IMF-ADG (r = 0.30 ± 0.0686), indicating that it may respond to selection in the same direction. Our findings contributed to clarifying the pleiotropic modulation of the complex traits, indicating new QTLs for bovine genetic improvement.

Developmental constraints enforce altruism and avert the tragedy of the commons in a social microbe.

Organisms often cooperate through the production of freely available public goods. This can greatly benefit the group but is vulnerable to the "tragedy of the commons" if individuals lack the motivation to make the necessary investment into public goods production. Relatedness to groupmates can motivate individual investment because group success ultimately benefits their genes' own self-interests. However, systems often lack mechanisms that can reliably ensure that relatedness is high enough to promote cooperation. Consequently, groups face a persistent threat from the tragedy unless they have a mechanism to enforce investment when relatedness fails to provide adequate motivation. To understand the real threat posed by the tragedy and whether groups can avert its impact, we determine how the social amoeba Dictyostelium discoideum responds as relatedness decreases to levels that should induce the tragedy. We find that, while investment in public goods declines as overall within-group relatedness declines, groups avert the expected catastrophic collapse of the commons by continuing to invest, even when relatedness should be too low to incentivize any contribution. We show that this is due to a developmental buffering system that generates enforcement because insufficient cooperation perturbs the balance of a negative feedback system controlling multicellular development. This developmental constraint enforces investment under the conditions expected to be most tragic, allowing groups to avert a collapse in cooperation. These results help explain how mechanisms that suppress selfishness and enforce cooperation can arise inadvertently as a by-product of constraints imposed by selection on different traits.

The genetic architecture underlying prey-dependent performance in a microbial predator.

Natural selection should favour generalist predators that outperform specialists across all prey types. Two genetic solutions could explain why intraspecific variation in predatory performance is, nonetheless, widespread: mutations beneficial on one prey type are costly on another (antagonistic pleiotropy), or mutational effects are prey-specific, which weakens selection, allowing variation to persist (relaxed selection). To understand the relative importance of these alternatives, we characterised natural variation in predatory performance in the microbial predator Dictyostelium discoideum. We found widespread nontransitive differences among strains in predatory success across different bacterial prey, which can facilitate stain coexistence in multi-prey environments. To understand the genetic basis, we developed methods for high throughput experimental evolution on different prey (REMI-seq). Most mutations (~77%) had prey-specific effects, with very few (~4%) showing antagonistic pleiotropy. This highlights the potential for prey-specific effects to dilute selection, which would inhibit the purging of variation and prevent the emergence of an optimal generalist predator.

Runaway evolution from male-male competition.

Wondrously elaborate weapons and displays that appear to be counter to ecological optima are widespread features of male contests for mates across the animal kingdom. To understand how such diverse traits evolve, here we develop a quantitative genetic model of sexual selection for a male signaling trait that mediates aggression in male-male contests and show that an honest indicator of aggression can generate selection on itself by altering the social environment. This can cause selection to accelerate as the trait is elaborated, leading to runaway evolution. Thus, an evolving source of selection provided by the social environment is the fundamental unifying feature of runaway sexual selection driven by either male-male competition or female mate choice. However, a key difference is that runaway driven by male-male competition requires signal honesty. Our model identifies simple conditions that provide clear, testable predictions for empirical studies using standard quantitative genetic methods.

Evolutionary robustness of killer meiotic drives.

A meiotic driver is a selfish genetic element that interferes with the process of meiosis to promote its own transmission. The most common mechanism of interference is gamete killing, where the meiotic driver kills gametes that do not contain it. A killer meiotic driver is predicted to spread rapidly through a population at the expense of other genes in the rest of the genome. The rapid spread of a killer meiotic driver is expected to be chased by the rapid spread of a suppressor that returns fair meiosis. Paradoxically, while this might imply that meiotic drivers should be evolutionarily transient, numerous ancient killer meiotic drivers have been discovered that have persisted for millions of years. To understand the rationale that could potentially explain such evolutionary robustness, we explore different possible mechanisms of killer meiotic drive and the different possible associated mechanisms of suppression. We use a framework that considers how the different stages of meiosis result in different structured interactions among cells with different genotypes in various combinations. Across possible interactions, we show that there are three genotypically distinct drive mechanisms that create alternative selective conditions for the spread of different types of suppressors. We show that killer meiotic drivers are more evolutionarily robust if they operate among sister cells (after meiosis I and before meiosis II) than at any other point during meiosis. The different drive mechanisms we identify make testable predictions that could explain why some killer meiotic drivers are transient while others are ancient.

Mutant resources for functional genomics in Dictyostelium discoideum using REMI-seq technology.

BACKGROUND: Genomes can be sequenced with relative ease, but ascribing gene function remains a major challenge. Genetically tractable model systems are crucial to meet this challenge. One powerful model is the social amoeba Dictyostelium discoideum, a eukaryotic microbe widely used to study diverse questions in the cell, developmental and evolutionary biology. RESULTS: We describe REMI-seq, an adaptation of Tn-seq, which allows high throughput, en masse, and quantitative identification of the genomic site of insertion of a drug resistance marker after restriction enzyme-mediated integration. We use REMI-seq to develop tools which greatly enhance the efficiency with which the sequence, transcriptome or proteome variation can be linked to phenotype in D. discoideum. These comprise (1) a near genome-wide resource of individual mutants and (2) a defined pool of 'barcoded' mutants to allow large-scale parallel phenotypic analyses. These resources are freely available and easily accessible through the REMI-seq website that also provides comprehensive guidance and pipelines for data analysis. We demonstrate that integrating these resources allows novel regulators of cell migration, phagocytosis and macropinocytosis to be rapidly identified. CONCLUSIONS: We present methods and resources, generated using REMI-seq, for high throughput gene function analysis in a key model system.

Inferring Adaptive Codon Preference to Understand Sources of Selection Shaping Codon Usage Bias.

Alternative synonymous codons are often used at unequal frequencies. Classically, studies of such codon usage bias (CUB) attempted to separate the impact of neutral from selective forces by assuming that deviations from a predicted neutral equilibrium capture selection. However, GC-biased gene conversion (gBGC) can also cause deviation from a neutral null. Alternatively, selection has been inferred from CUB in highly expressed genes, but the accuracy of this approach has not been extensively tested, and gBGC can interfere with such extrapolations (e.g., if expression and gene conversion rates covary). It is therefore critical to examine deviations from a mutational null in a species with no gBGC. To achieve this goal, we implement such an analysis in the highly AT rich genome of Dictyostelium discoideum, where we find no evidence of gBGC. We infer neutral CUB under mutational equilibrium to quantify "adaptive codon preference," a nontautologous genome wide quantitative measure of the relative selection strength driving CUB. We observe signatures of purifying selection consistent with selection favoring adaptive codon preference. Preferred codons are not GC rich, underscoring the independence from gBGC. Expression-associated "preference" largely matches adaptive codon preference but does not wholly capture the influence of selection shaping patterns across all genes, suggesting selective constraints associated specifically with high expression. We observe patterns consistent with effects on mRNA translation and stability shaping adaptive codon preference. Thus, our approach to quantifying adaptive codon preference provides a framework for inferring the sources of selection that shape CUB across different contexts within the genome.

Greenbeard Genes: Theory and Reality.

Greenbeard genes were proposed as a cartoonish thought experiment to explain why altruism can be a selfish strategy from the perspective of genes. The likelihood of finding a real greenbeard gene in nature was thought to be remote because they were believed to require a set of improbable properties. Yet, despite this expectation, there is an ongoing explosion in claimed discoveries of greenbeard genes. Bringing together the latest theory and experimental findings, we argue that there is a need to dispose of the cartoon presentation of a greenbeard to refocus their burgeoning empirical study on the more fundamental concept that the thought experiment was designed to illustrate.

Individual Cryptic Scaling Relationships and the Evolution of Animal Form.

Artificial selection offers a powerful tool for the exploration of how selection and development shape the evolution of morphological scaling relationships. An emerging approach models the expression and evolution of morphological scaling relationships as a function of variation among individuals in the developmental mechanisms that regulate trait growth. These models posit the existence of genotype-specific morphological scaling relationships that are unseen or "cryptic." Within-population allelic variation at growth-regulating loci determines how these individual cryptic scaling relationships are distributed, and exposure to environmental factors that affect growth determines the size phenotype expressed by each individual on their cryptic, genotype-specific scaling relationship. These models reveal that evolution of the intercept and slope of the population-level static allometry is determined, often in counterintuitive ways, largely by the shape of the distribution of these underlying individual-level scaling relationships. Here we review this modeling framework and present the wing-body size individual cryptic scaling relationships from a population of Drosophila melanogaster. To determine how these models might inform interpretation of published work on scaling relationship evolution, we review studies where artificial selection was applied to alter the parameters of population-level static allometries. Finally, motivated by our review, we outline areas in need of empirical work and describe a research program to address these topics; the approach includes describing the distribution of individual cryptic scaling relationships across populations and environments, empirical testing of the model's predictions, and determining the effects of environmental heterogeneity on realized trait distributions and how this affects allometry evolution.

Conditional expression explains molecular evolution of social genes in a microbe.

Conflict is thought to play a critical role in the evolution of social interactions by promoting diversity or driving accelerated evolution. However, despite our sophisticated understanding of how conflict shapes social traits, we have limited knowledge of how it impacts molecular evolution across the underlying social genes. Here we address this problem by analyzing the genome-wide impact of social interactions using genome sequences from 67 Dictyostelium discoideum strains. We find that social genes tend to exhibit enhanced polymorphism and accelerated evolution. However, these patterns are not consistent with conflict driven processes, but instead reflect relaxed purifying selection. This pattern is most likely explained by the conditional nature of social interactions, whereby selection on genes expressed only in social interactions is diluted by generations of inactivity. This dilution of selection by inactivity enhances the role of drift, leading to increased polymorphism and accelerated evolution, which we call the Red King process.

Genomic Perspective on Multivariate Variation, Pleiotropy, and Evolution.

Multivariate quantitative genetics provides a powerful framework for understanding patterns and processes of phenotypic evolution. Quantitative genetics parameters, like trait heritability or the G-matrix for sets of traits, can be used to predict evolutionary response or to understand the evolutionary history of a population. These population-level approaches have proven to be extremely successful, but the underlying genetics of multivariate variation and evolutionary change typically remain a black box. Establishing a deeper empirical understanding of how individual genetic effects lead to genetic (co)variation is then crucial to our understanding of the evolutionary process. To delve into this black box, we exploit an experimental population of mice composed from lineages derived by artificial selection. We develop an approach to estimate the multivariate effect of loci and characterize these vectors of effects in terms of their magnitude and alignment with the direction of evolutionary divergence. Using these estimates, we reconstruct the traits in the ancestral populations and quantify how much of the divergence is due to genetic effects. Finally, we also use these vectors to decompose patterns of genetic covariation and examine the relationship between these components and the corresponding distribution of pleiotropic effects. We find that additive effects are much larger than dominance effects and are more closely aligned with the direction of selection and divergence, with larger effects being more aligned than smaller effects. Pleiotropic effects are highly variable but are, on average, modular. These results are consistent with pleiotropy being partly shaped by selection while reflecting underlying developmental constraints.

Evolutionary Quantitative Genetics of Genomic Imprinting.

Genomic imprinting shapes the genotype-phenotype relationship by creating an asymmetry between the influences of paternally and maternally inherited gene copies. Consequently, imprinting can impact heritable and nonheritable variation, resemblance of relatives, and evolutionary dynamics. Although previous analyses have identified some of the quantitative genetic consequences of imprinting, we lack a framework that cleanly separates the influence of imprinting from other components of variation, particularly dominance. Here we apply a simple orthogonal genetic model to evaluate the roles of genetic (additive and dominance) and epigenetic (imprinting) effects. Imprinting increases the resemblance of relatives who share the expressed allele, and therefore increases variance among families of full or half-siblings. However, only part of this increased variance is heritable and contributes to selection responses. When selection is within, or among, families sharing only a single parent (half-siblings), which is common in selective breeding programs, imprinting can alter overall responses. Selection is more efficient when it acts among families sharing the expressed parent, or within families sharing the parent with lower expression. Imprinting also affects responses to sex-specific selection. When selection is on the sex whose gene copy has lower expression, the response is diminished or delayed the next generation, although the long-term response is unaffected. Our findings have significant implications for understanding patterns of variation, interpretation of short-term selection responses, and the efficacy of selective breeding programs, demonstrating the importance of considering the independent influence of genomic imprinting in quantitative genetics.

Strategic investment explains patterns of cooperation and cheating in a microbe.

Contributing to cooperation is typically costly, while its rewards are often available to all members of a social group. So why should individuals be willing to pay these costs, especially if they could cheat by exploiting the investments of others? Kin selection theory broadly predicts that individuals should invest more into cooperation if their relatedness to group members is high (assuming they can discriminate kin from nonkin). To better understand how relatedness affects cooperation, we derived the ?Collective Investment" game, which provides quantitative predictions for patterns of strategic investment depending on the level of relatedness. We then tested these predictions by experimentally manipulating relatedness (genotype frequencies) in mixed cooperative aggregations of the social amoeba Dictyostelium discoideum, which builds a stalk to facilitate spore dispersal. Measurements of stalk investment by natural strains correspond to the predicted patterns of relatedness-dependent strategic investment, wherein investment by a strain increases with its relatedness to the group. Furthermore, if overall group relatedness is relatively low (i.e., no strain is at high frequency in a group) strains face a scenario akin to the "Prisoner's Dilemma" and suffer from insufficient collective investment. We find that strains employ relatedness-dependent segregation to avoid these pernicious conditions. These findings demonstrate that simple organisms like D. discoideum are not restricted to being ?cheaters" or ?cooperators" but instead measure their relatedness to their group and strategically modulate their investment into cooperation accordingly. Consequently, all individuals will sometimes appear to cooperate and sometimes cheat due to the dynamics of strategic investing.

A polychromatic 'greenbeard' locus determines patterns of cooperation in a social amoeba.

Cheaters disrupt cooperation by reaping the benefits without paying their fair share of associated costs. Cheater impact can be diminished if cooperators display a tag ('greenbeard') and recognise and preferentially direct cooperation towards other tag carriers. Despite its popular appeal, the feasibility of such greenbeards has been questioned because the complex patterns of partner-specific cooperative behaviours seen in nature require greenbeards to come in different colours. Here we show that a locus ('Tgr') of a social amoeba represents a polychromatic greenbeard. Patterns of natural Tgr locus sequence polymorphisms predict partner-specific patterns of cooperation by underlying variation in partner-specific protein-protein binding strength and recognition specificity. Finally, Tgr locus polymorphisms increase fitness because they help avoid potential costs of cooperating with incompatible partners. These results suggest that a polychromatic greenbeard can provide a key mechanism for the evolutionary maintenance of cooperation.

The coadaptation theory for genomic imprinting.

Imprinted genes are peculiar in that expression of the two copies differs depending on whether the copy was maternally or paternally inherited. The discovery of this striking pattern of gene expression inspired myriad evolutionary theories, the most successful of which identify scenarios that create an asymmetry between the maternally and paternally inherited gene copies that favors silencing of one of the copies. Most notably, imprinting can evolve when gene dosage affects kin interactions (typically involving conflict) or when silencing enhances coadaptation by coordinating traits expressed by interacting kin. Although we have a well-established theory for the former process (the "Kinship Theory"), the coadaptation process has only been explored for the specific case of interactions between mothers and offspring. Here, we fill this critical gap in our understanding by developing a general "Coadaptation Theory" that explains how imprinting can evolve to coordinate interactions between all types of relatives. Using a simple model in which fitness of an individual is determined by an interaction between its own phenotype (and hence genotype) and that of its social partner(s), we find that when the relatedness of interactants differs through their maternally versus paternally inherited gene copies, then selection favors expression of the allele through which relatedness is higher. The predictions of this Coadaptation Theory potentially apply whenever a gene underlies traits that mediate the outcome of conspecific interactions, regardless of their mechanism or the type of organism, and therefore provide a potential explanation for enigmatic patterns of imprinting, including those underlying adult traits. By providing simple testable predictions that often directly contrast with those derived from alternative theories, our model should play an important role in consolidating our understanding of the evolution of imprinting across genes and species, which will ultimately provide crucial insights into imprinted gene function and dysfunction.

Evolutionary genetics of maternal effects.

Maternal genetic effects (MGEs), where genes expressed by mothers affect the phenotype of their offspring, are important sources of phenotypic diversity in a myriad of organisms. We use a single-locus model to examine how MGEs contribute patterns of heritable and nonheritable variation and influence evolutionary dynamics in randomly mating and inbreeding populations. We elucidate the influence of MGEs by examining the offspring genotype-phenotype relationship, which determines how MGEs affect evolutionary dynamics in response to selection on offspring phenotypes. This approach reveals important results that are not apparent from classic quantitative genetic treatments of MGEs. We show that additive and dominance MGEs make different contributions to evolutionary dynamics and patterns of variation, which are differentially affected by inbreeding. Dominance MGEs make the offspring genotype-phenotype relationship frequency dependent, resulting in the appearance of negative frequency-dependent selection, while additive MGEs contribute a component of parent-of-origin dependent variation. Inbreeding amplifies the contribution of MGEs to the additive genetic variance and, therefore enhances their evolutionary response. Considering evolutionary dynamics of allele frequency change on an adaptive landscape, we show that this landscape differs from the mean fitness surface, and therefore, under some condition, fitness peaks can exist but not be "available" to the evolving population.

Fitness Trade-offs Result in the Illusion of Social Success.

Cooperation is ubiquitous across the tree of life, from simple microbes to the complex social systems of animals. Individuals cooperate by engaging in costly behaviors that can be exploited by other individuals who benefit by avoiding these associated costs. Thus, if successful exploitation of social partners during cooperative interactions increases relative fitness, then we expect selection to lead to the emergence of a single optimal winning strategy in which individuals maximize their gain from cooperation while minimizing their associated costs. Such social "cheating" appears to be widespread in nature, including in several microbial systems, but despite the fitness advantages favoring social cheating, populations tend to harbor significant variation in social success rather than a single optimal winning strategy. Using the social amoeba Dictyostelium discoideum, we provide a possible explanation for the coexistence of such variation. We find that genotypes typically designated as "cheaters" because they produce a disproportionate number of spores in chimeric fruiting bodies do not actually gain higher fitness as a result of this apparent advantage because they produce smaller, less viable spores than putative "losers." As a consequence of this trade-off between spore number and viability, genotypes with different spore production strategies, which give the appearance of differential social success, ultimately have similar realized fitness. These findings highlight the limitations of using single fitness proxies in evolutionary studies and suggest that interpreting social trait variation in terms of strategies like cheating or cooperating may be misleading unless these behaviors are considered in the context of the true multidimensional nature of fitness.

Coadaptation between mother and offspring: why not?

A Formal Comment has challenged the interpretation of a study into an imprinted gene, maintaining that conflict, rather than mother-offspring co-adaptation, provides a better mechanistic explanation. Here authors of the original Research Article reply.

Evolutionary rates for multivariate traits: the role of selection and genetic variation.

A fundamental question in evolutionary biology is the relative importance of selection and genetic architecture in determining evolutionary rates. Adaptive evolution can be described by the multivariate breeders' equation (Δz(-)=Gß), which predicts evolutionary change for a suite of phenotypic traits (Δz(-)) as a product of directional selection acting on them (ß) and the genetic variance-covariance matrix for those traits (G ). Despite being empirically challenging to estimate, there are enough published estimates of G and ß to allow for synthesis of general patterns across species. We use published estimates to test the hypotheses that there are systematic differences in the rate of evolution among trait types, and that these differences are, in part, due to genetic architecture. We find some evidence that sexually selected traits exhibit faster rates of evolution compared with life-history or morphological traits. This difference does not appear to be related to stronger selection on sexually selected traits. Using numerous proposed approaches to quantifying the shape, size and structure of G, we examine how these parameters relate to one another, and how they vary among taxonomic and trait groupings. Despite considerable variation, they do not explain the observed differences in evolutionary rates.