Global epistasis and the emergence of function in microbial consortia.

The many functions of microbial communities emerge from a complex web of interactions between organisms and their environment. This poses a significant obstacle to engineering microbial consortia, hindering our ability to harness the potential of microorganisms for biotechnological applications. In this study, we demonstrate that the collective effect of ecological interactions between microbes in a community can be captured by simple statistical models that predict how adding a new species to a community will affect its function. These predictive models mirror the patterns of global epistasis reported in genetics, and they can be quantitatively interpreted in terms of pairwise interactions between community members. Our results illuminate an unexplored path to quantitatively predicting the function of microbial consortia from their composition, paving the way to optimizing desirable community properties and bringing the tasks of predicting biological function at the genetic, organismal, and ecological scales under the same quantitative formalism.

Screening Human Embryos for Polygenic Traits Has Limited Utility.

The increasing proportion of variance in human complex traits explained by polygenic scores, along with progress in preimplantation genetic diagnosis, suggests the possibility of screening embryos for traits such as height or cognitive ability. However, the expected outcomes of embryo screening are unclear, which undermines discussion of associated ethical concerns. Here, we use theory, simulations, and real data to evaluate the potential gain of embryo screening, defined as the difference in trait value between the top-scoring embryo and the average embryo. The gain increases very slowly with the number of embryos but more rapidly with the variance explained by the score. Given current technology, the average gain due to screening would be ≈2.5 cm for height and ≈2.5 IQ points for cognitive ability. These mean values are accompanied by wide prediction intervals, and indeed, in large nuclear families, the majority of children top-scoring for height are not the tallest.

Mapping Causal Variants with Single-Nucleotide Resolution Reveals Biochemical Drivers of Phenotypic Change.

Understanding the sequence determinants that give rise to diversity among individuals and species is the central challenge of genetics. However, despite ever greater numbers of sequenced genomes, most genome-wide association studies cannot distinguish causal variants from linked passenger mutations spanning many genes. We report that this inherent challenge can be overcome in model organisms. By pushing the advantages of inbred crossing to its practical limit in Saccharomyces cerevisiae, we improved the statistical resolution of linkage analysis to single nucleotides. This "super-resolution" approach allowed us to map 370 causal variants across 26 quantitative traits. Missense, synonymous, and cis-regulatory mutations collectively gave rise to phenotypic diversity, providing mechanistic insight into the basis of evolutionary divergence. Our data also systematically unmasked complex genetic architectures, revealing that multiple closely linked driver mutations frequently act on the same quantitative trait. Single-nucleotide mapping thus complements traditional deletion and overexpression screening paradigms and opens new frontiers in quantitative genetics.

Leveraging Single-Cell Populations to Uncover the Genetic Basis of Complex Traits.

The ease and throughput of single-cell genomics have steadily improved, and its current trajectory suggests that surveying single-cell populations will become routine. We discuss the merger of quantitative genetics with single-cell genomics and emphasize how this synergizes with advantages intrinsic to plants. Single-cell population genomics provides increased detection resolution when mapping variants that control molecular traits, including gene expression or chromatin accessibility. Additionally, single-cell population genomics reveals the cell types in which variants act and, when combined with organism-level phenotype measurements, unveils which cellular contexts impact higher-order traits. Emerging technologies, notably multiomics, can facilitate the measurement of both genetic changes and genomic traits in single cells, enabling single-cell genetic experiments. The implementation of single-cell genetics will advance the investigation of the genetic architecture of complex molecular traits and provide new experimental paradigms to study eukaryotic genetics.

What Has a Century of Quantitative Genetics Taught Us About Nature's Genetic Tool Kit?

The complexity of heredity has been appreciated for decades: Many traits are controlled not by a single genetic locus but instead by polymorphisms throughout the genome. The importance of complex traits in biology and medicine has motivated diverse approaches to understanding their detailed genetic bases. Here, we focus on recent systematic studies, many in budding yeast, which have revealed that large numbers of all kinds of molecular variation, from noncoding to synonymous variants, can make significant contributions to phenotype. Variants can affect different traits in opposing directions, and their contributions can be modified by both the environment and the epigenetic state of the cell. The integration of prospective (synthesizing and analyzing variants) and retrospective (examining standing variation) approaches promises to reveal how natural selection shapes quantitative traits. Only by comprehensively understanding nature's genetic tool kit can we predict how phenotypes arise from the complex ensembles of genetic variants in living organisms.

Heritability within groups is uninformative about differences among groups: Cases from behavioral, evolutionary, and statistical genetics.

Without the ability to control or randomize environments (or genotypes), it is difficult to determine the degree to which observed phenotypic differences between two groups of individuals are due to genetic vs. environmental differences. However, some have suggested that these concerns may be limited to pathological cases, and methods have appeared that seem to give-directly or indirectly-some support to claims that aggregate heritable variation within groups can be related to heritable variation among groups. We consider three families of approaches: the "between-group heritability" sometimes invoked in behavior genetics, the statistic [Formula: see text] used in empirical work in evolutionary quantitative genetics, and methods based on variation in ancestry in an admixed population, used in anthropological and statistical genetics. We take up these examples to show mathematically that information on within-group genetic and phenotypic information in the aggregate cannot separate among-group differences into genetic and environmental components, and we provide simulation results that support our claims. We discuss these results in terms of the long-running debate on this topic.

Microevolutionary change in wild stickleback: Using integrative time-series data to infer responses to selection.

A central goal in evolutionary biology is to understand how different evolutionary processes cause trait change in wild populations. However, quantifying evolutionary change in the wild requires linking trait change to shifts in allele frequencies at causal loci. Nevertheless, datasets that allow for such tests are extremely rare and existing theoretical approaches poorly account for the evolutionary dynamics that likely occur in ecological settings. Using a decade-long integrative phenome-to-genome time-series dataset on wild threespine stickleback (Gasterosteus aculeatus), we identified how different modes of selection (directional, episodic, and balancing) drive microevolutionary change in correlated traits over time. Most strikingly, we show that feeding traits changed by as much 25% across 10 generations which was driven by changes in the genetic architecture (i.e., in both genomic breeding values and allele frequencies at genetic loci for feeding traits). Importantly, allele frequencies at genetic loci related to feeding traits changed at a rate greater than expected under drift, suggesting that the observed change was a result of directional selection. Allele frequency dynamics of loci related to swimming traits appeared to be under fluctuating selection evident in periodic population crashes in this system. Our results show that microevolutionary change in a wild population is characterized by different modes of selection acting simultaneously on different traits, which likely has important consequences for the evolution of correlated traits. Our study provides one of the most thorough descriptions to date of how microevolutionary processes result in trait change in a natural population.

On the Road to Breeding 4.0: Unraveling the Good, the Bad, and the Boring of Crop Quantitative Genomics.

Understanding the quantitative genetics of crops has been and will continue to be central to maintaining and improving global food security. We outline four stages that plant breeding either has already achieved or will probably soon achieve. Top-of-the-line breeding programs are currently in Breeding 3.0, where inexpensive, genome-wide data coupled with powerful algorithms allow us to start breeding on predicted instead of measured phenotypes. We focus on three major questions that must be answered to move from current Breeding 3.0 practices to Breeding 4.0: ( a) How do we adapt crops to better fit agricultural environments? ( b) What is the nature of the diversity upon which breeding can act? ( c) How do we deal with deleterious variants? Answering these questions and then translating them to actual gains for farmers will be a significant part of achieving global food security in the twenty-first century.

Discovery of a rapidly evolving yeast defense factor, KTD1, against the secreted killer toxin K28.

Secreted protein toxins are widely used weapons in conflicts between organisms. Elucidating how organisms genetically adapt to defend themselves against these toxins is fundamental to understanding the coevolutionary dynamics of competing organisms. Within yeast communities, "killer" toxins are secreted to kill nearby sensitive yeast, providing a fitness advantage in competitive growth environments. Natural yeast isolates vary in their sensitivity to these toxins, but to date, no polymorphic genetic factors contributing to defense have been identified. We investigated the variation in resistance to the killer toxin K28 across diverse natural isolates of the Saccharomyces cerevisiae population. Using large-scale linkage mapping, we discovered a novel defense factor, which we named KTD1. We identified many KTD1 alleles, which provided different levels of K28 resistance. KTD1 is a member of the DUP240 gene family of unknown function, which is rapidly evolving in a region spanning its two encoded transmembrane helices. We found that this domain is critical to KTD1's protective ability. Our findings implicate KTD1 as a key polymorphic factor in the defense against K28 toxin.

Bivariate GWA mapping reveals associations between aliphatic glucosinolates and plant responses to thrips and heat stress.

Although plants harbor a huge phytochemical diversity, only a fraction of plant metabolites is functionally characterized. In this work, we aimed to identify the genetic basis of metabolite functions during harsh environmental conditions in Arabidopsis thaliana. With machine learning algorithms we predicted stress-specific metabolomes for 23 (a)biotic stress phenotypes of 300 natural Arabidopsis accessions. The prediction models identified several aliphatic glucosinolates (GLSs) and their breakdown products to be implicated in responses to heat stress in siliques and herbivory by Western flower thrips, Frankliniella occidentalis. Bivariate GWA mapping of the metabolome predictions and their respective (a)biotic stress phenotype revealed genetic associations with MAM, AOP, and GS-OH, all three involved in aliphatic GSL biosynthesis. We, therefore, investigated thrips herbivory on AOP, MAM, and GS-OH loss-of-function and/or overexpression lines. Arabidopsis accessions with a combination of MAM2 and AOP3, leading to 3-hydroxypropyl dominance, suffered less from thrips feeding damage. The requirement of MAM2 for this effect could, however, not be confirmed with an introgression line of ecotypes Cvi and Ler, most likely due to other, unknown susceptibility factors in the Ler background. However, AOP2 and GS-OH, adding alkenyl or hydroxy-butenyl groups, respectively, did not have major effects on thrips feeding. Overall, this study illustrates the complex implications of aliphatic GSL diversity in plant responses to heat stress and a cell-content-feeding herbivore.

The Genetic Architecture of Recombination Rates is Polygenic and Differs Between the Sexes in Wild House Sparrows (Passer domesticus).

Meiotic recombination through chromosomal crossing-over is a fundamental feature of sex and an important driver of genomic diversity. It ensures proper disjunction, allows increased selection responses, and prevents mutation accumulation; however, it is also mutagenic and can break up favorable haplotypes. This cost-benefit dynamic is likely to vary depending on mechanistic and evolutionary contexts, and indeed, recombination rates show huge variation in nature. Identifying the genetic architecture of this variation is key to understanding its causes and consequences. Here, we investigate individual recombination rate variation in wild house sparrows (Passer domesticus). We integrate genomic and pedigree data to identify autosomal crossover counts (ACCs) and intrachromosomal allelic shuffling (r¯intra) in 13,056 gametes transmitted from 2,653 individuals to their offspring. Females had 1.37 times higher ACC, and 1.55 times higher r¯intra than males. ACC and r¯intra were heritable in females and males (ACC h2 = 0.23 and 0.11; r¯intra  h2 = 0.12 and 0.14), but cross-sex additive genetic correlations were low (rA = 0.29 and 0.32 for ACC and r¯intra). Conditional bivariate analyses showed that all measures remained heritable after accounting for genetic values in the opposite sex, indicating that sex-specific ACC and r¯intra can evolve somewhat independently. Genome-wide models showed that ACC and r¯intra are polygenic and driven by many small-effect loci, many of which are likely to act in trans as global recombination modifiers. Our findings show that recombination rates of females and males can have different evolutionary potential in wild birds, providing a compelling mechanism for the evolution of sexual dimorphism in recombination.

The "New Synthesis".

When Mendel's work was rediscovered in 1900, and extended to establish classical genetics, it was initially seen in opposition to Darwin's theory of evolution by natural selection on continuous variation, as represented by the biometric research program that was the foundation of quantitative genetics. As Fisher, Haldane, and Wright established a century ago, Mendelian inheritance is exactly what is needed for natural selection to work efficiently. Yet, the synthesis remains unfinished. We do not understand why sexual reproduction and a fair meiosis predominate in eukaryotes, or how far these are responsible for their diversity and complexity. Moreover, although quantitative geneticists have long known that adaptive variation is highly polygenic, and that this is essential for efficient selection, this is only now becoming appreciated by molecular biologists-and we still do not have a good framework for understanding polygenic variation or diffuse function.

A method to predict the response to directional selection using a Kalman filter.

Predicting evolution remains challenging. The field of quantitative genetics provides predictions for the response to directional selection through the breeder's equation, but these predictions can have errors. The sources of these errors include omission of traits under selection, inaccurate estimates of genetic variance, and nonlinearities in the relationship between genetic and phenotypic variation. Previous research showed that the expected value of these prediction errors is often not zero, so predictions are systematically biased. Here, we propose that this bias, rather than being a nuisance, can be used to improve the predictions. We use this to develop a method to predict evolution, which is built on three key innovations. First, the method predicts change as the breeder's equation plus a bias term. Second, the method combines information from the breeder's equation and from the record of past changes in the mean to predict change using a Kalman filter. Third, the parameters of the filter are fitted in each generation using a learning algorithm on the record of past changes. We compare the method to the breeder's equation in two artificial selection experiments, one using the wing of the fruit fly and another using simulations that include a complex mapping of genotypes to phenotypes. The proposed method outperforms the breeder's equation, particularly when traits under selection are omitted from the analysis, when data are noisy, and when additive genetic variance is estimated inaccurately or not estimated at all. The proposed method is easy to apply, requiring only the trait means over past generations.

From QTL to gene: C. elegans facilitates discoveries of the genetic mechanisms underlying natural variation.

Although many studies have examined quantitative trait variation across many species, only a small number of genes and thereby molecular mechanisms have been discovered. Without these data, we can only speculate about evolutionary processes that underlie trait variation. Here, we review how quantitative and molecular genetics in the nematode Caenorhabditis elegans led to the discovery and validation of 37 quantitative trait genes over the past 15 years. Using these data, we can start to make inferences about evolution from these quantitative trait genes, including the roles that coding versus noncoding variation, gene family expansion, common versus rare variants, pleiotropy, and epistasis play in trait variation across this species.

The genetics of plant-plant interactions and their cascading effects on agroecosystem - from model plants to application.

Plants are typically surrounded by neighboring individuals in agricultural fields or natural environments. In such circumstances, plant-plant interactions (PPI) are ubiquitous and represent not only important evolutionary forces but also have consequences on (agro-)ecosystem functioning, such as increased productivity and resistance. However, many mechanisms underlying these PPIs remain poorly understood. Recently, new genomic technologies and tools, such as genome-wide association studies, have facilitated genetic approaches to study PPIs, particularly among conspecific individuals. Here, we highlight emerging issues and advances in the field by focusing on three different aspects. First, we overview the current status of genetic mapping studies on PPIs and pinpoint that these studies open a new opportunity that is relevant to agriculture and breeding. Second, we introduce two proof-of-concept studies in Arabidopsis thaliana, in which genetic differences among plants improved the functioning of genotype mixtures. Both studies were able to predict effective mixtures with different experimental designs applied to different outcomes (productivity increase vs. herbivory mitigation). Third, we discuss implications from these case studies about how PPIs have cascading effects that propagate to higher levels of biological organization, such as populations or communities. At the population level, plant yield or resistance can be optimized through breeding that achieves reduced competition or push-pull protection systems, respectively. At the community level, these population-level changes may have further beneficial effects on plant-associated organisms. Overall, we suggest that the increasing availability of genomic resources will improve our understanding of PPIs and thereby contribute to the management of crops or semi-natural ecosystems.

Comparing the Predicted versus Realized Rate of Adaptation of Chamaecrista fasciculata to Climate Change.

AbstractFisher's fundamental theorem of natural selection (FTNS) can be used in a quantitative genetics framework to predict the rate of adaptation in populations. Here, we estimated the capacity for a wild population of the annual legume Chamaecrista fasciculata to adapt to future environments and compared predicted and realized rates of adaptation. We planted pedigreed seeds from one population into three prairie reconstructions along an east-to-west decreasing precipitation gradient. The FTNS predicted adaptation at all sites, but we found a response to selection that was smaller at the home and westernmost sites and maladaptive at the middle site because of changes in the selective environment between generations. However, mean fitness of the progeny generation at the home and westernmost sites exceeded population replacement, which suggests that the environment was sufficiently favorable to promote population persistence. More studies employing the FTNS are needed to clarify the degree to which predictions of the rate of adaptation are realized and its utility in the conservation of populations at risk of extinction from climate change.

Predictable and Divergent Change in the Multivariate P Matrix during Parallel Adaptation.

AbstractAdaptation to replicated environmental conditions can be remarkably predictable, suggesting that parallel evolution may be a common feature of adaptive radiation. An open question, however, is how phenotypic variation itself evolves during repeated adaptation. Here, we use a dataset of morphological measurements from 35 populations of threespine stickleback, consisting of 16 parapatric lake-stream pairs and three marine populations, to understand how phenotypic variation has evolved during transitions from marine to freshwater environments and during subsequent diversification across the lake-stream boundary. We find statistical support for divergent phenotypic covariance (P) across populations, with most diversification of P occurring among freshwater populations. Despite a close correspondence between within-population phenotypic variation and among-population divergence, we find that variation in P is unrelated to total variation in population means across the set of populations. For lake-stream pairs, we find that theoretical predictions for microevolutionary change can explain more than 30% of divergence in P matrices across the habitat boundary. Together, our results indicate that divergence in variance structure occurs primarily in dimensions of trait space with low phenotypic integration, correlated with disparate lake and stream environments. Our findings illustrate how conserved and divergent features of multivariate variation can underlie adaptive radiation.

Unraveling Adaptive Evolutionary Divergence at Microgeographic Scales.

AbstractStriking examples of local adaptation at fine geographic scales are increasingly being documented in natural populations. However, the relative contributions made by natural selection, phenotype-dependent dispersal (when individuals disperse with respect to a habitat preference), and mate preference in generating and maintaining microgeographic adaptation and divergence are not well studied. Here, we develop quantitative genetics models and individual-based simulations (IBSs) to uncover the evolutionary forces that possibly drive microgeographic divergence. We also perform Bayesian estimation of the parameters in our IBS using empirical data on habitat-specific variation in bill morphology in the island scrub-jay (Aphelocoma insularis) to apply our models to a natural system. We find that natural selection and phenotype-dependent dispersal can generate the patterns of divergence we observe in the island scrub-jay. However, mate preference for a mate with similar bill morphology, even though observed in the species, does not play a significant role in driving divergence. Our modeling approach provides insights into phenotypic evolution occurring over small spatial scales relative to dispersal ranges, suggesting that adaptive divergence at microgeographic scales may be common across a wider range of taxa than previously thought. Our quantitative genetic models help to inform future theoretical and empirical work to determine how selection, habitat preference, and mate preference contribute to local adaptation and microgeographic divergence.

The Role of (Co)variation in Shaping the Response to Selection in New World Leaf-Nosed Bats.

AbstractUnderstanding and predicting the evolutionary responses of complex morphological traits to selection remains a major challenge in evolutionary biology. Because traits are genetically correlated, selection on a particular trait produces both direct effects on the distribution of that trait and indirect effects on other traits in the population. The correlations between traits can strongly impact evolutionary responses to selection and may thus impose constraints on adaptation. Here, we used museum specimens and comparative quantitative genetic approaches to investigate whether the covariation among cranial traits facilitated or constrained the response to selection during the major dietary transitions in one of the world's most ecologically diverse mammalian families-the phyllostomid bats. We reconstructed the set of net selection gradients that would have acted on each cranial trait during the major transitions to feeding specializations and decomposed the selection responses into their direct and indirect components. We found that for all transitions, most traits capturing craniofacial length evolved toward adaptive directions owing to direct selection. Additionally, we showed instances of dietary transitions in which the complex interaction between the patterns of covariation among traits and the strength and direction of selection either constrained or facilitated evolution. Our work highlights the importance of considering the within-species covariation estimates to quantify evolvability and to disentangle the relative contribution of variational constraints versus selective causes for observed patterns.

Weak response to selection on stigma-anther distance in a primarily selfing population of yellow monkeyflower.

Stebbins hypothesized that selfing lineages are evolutionary dead ends because they lack adaptive potential. While selfing populations often possess limited nucleotide variability compared with closely related outcrossers, reductions in the genetic variability of quantitative characters remain unclear, especially for key traits determining selfing rates. Yellow monkeyflower (Mimulus guttatus) populations generally outcross and maintain extensive quantitative genetic variation in floral traits. Here, we study the Joy Road population (Bodega Bay, CA, USA) of M. guttatus, where individuals exhibit stigma-anther distances (SAD) typical of primarily selfing monkeyflowers. We show that this population is closely related to nearby conspecifics on the Pacific Coast with a modest 33% reduction in genome-wide variation compared with a more highly outcrossing population. A five-generation artificial selection experiment challenged the hypothesis that the Joy Road population harbours comparatively low evolutionary potential in stigma-anther distance, a critical determinant of selfing rate in Mimulus. Artificial selection generated a weak phenotypic response, with low realized heritabilities (0.020-0.028) falling 84% below those measured for floral characters in more highly outcrossing M. guttatus. These results demonstrate substantial declines in evolutionary potential with a transition toward selfing. Whether these findings explain infrequent reversals to outcrossing or general limits on adaptation in selfers requires further investigation.