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.

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.

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.

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.

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.

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.

Nectar and floral morphology differ in evolutionary potential in novel pollination environments.

Plants can evolve rapidly after pollinator changes, but the response of different floral traits to novel selection can vary. Floral morphology is often expected to show high integration to maintain pollination accuracy, while nectar traits can be more environmentally sensitive. The relative role of genetic correlations and phenotypic plasticity (PP) in floral evolution remains unclear, particularly for nectar traits, and can be studied in the context of recent pollinator changes. Digitalis purpurea shows rapid recent evolution of corolla morphology but not nectar traits following a range expansion with hummingbirds added as pollinators. We use this species to compare PP, heritability, evolvability and integration of floral morphology and nectar in a common garden. Morphological traits showed higher heritability than nectar traits, and the proximal section of the corolla, which regulates access to nectar and underwent rapid change in introduced populations, presented lower integration than the rest of the floral phenotype. Nectar was more plastic than morphology, driven by highly plastic sugar concentration. Nectar production rate showed high potential to respond to selection. These results explain the differential rapid evolution of floral traits previously observed in this species and show how intrafloral modularity determines variable evolutionary potential in morphological and nectar traits.

Genetic basis and expression of ventral colour in polymorphic common lizards.

Colour is an important visual cue that can correlate with sex, behaviour, life history or ecological strategies, and has evolved divergently and convergently across animal lineages. Its genetic basis in non-model organisms is rarely known, but such information is vital for determining the drivers and mechanisms of colour evolution. Leveraging genetic admixture in a rare contact zone between oviparous and viviparous common lizards (Zootoca vivipara), we show that females (N = 558) of the two otherwise morphologically indistinguishable reproductive modes differ in their ventral colouration (from pale to vibrant yellow) and intensity of melanic patterning. We find no association between female colouration and reproductive investment, and no evidence for selection on colour. Using a combination of genetic mapping and transcriptomic evidence, we identified two candidate genes associated with ventral colour differentiation, DGAT2 and PMEL. These are genes known to be involved in carotenoid metabolism and melanin synthesis respectively. Ventral melanic spots were associated with two genomic regions, including a SNP close to protein tyrosine phosphatase (PTP) genes. Using genome re-sequencing data, our results show that fixed coding mutations in the candidate genes cannot account for differences in colouration. Taken together, our findings show that the evolution of ventral colouration and its associations across common lizard lineages is variable. A potential genetic mechanism explaining the flexibility of ventral colouration may be that colouration in common lizards, but also across squamates, is predominantly driven by regulatory genetic variation.

Genetic architecture of inbreeding depression may explain its persistence in a population of wild red deer.

Inbreeding depression is of major concern in declining populations, but relatively little is known about its genetic architecture in wild populations, such as the degree to which it is composed of large or small effect loci and their distribution throughout the genome. Here, we combine fitness and genomic data from a wild population of red deer to investigate the genomic distribution of inbreeding effects. Based on the runs of homozygosity (ROH)-based inbreeding coefficient, FROH, we use chromosome-specific inbreeding coefficients (FROHChr) to explore whether the effect of inbreeding varies between chromosomes. Under the assumption that within an individual the probability of being identical-by-descent is equal across all chromosomes, we used a multi-membership model to estimate the deviation of FROHChr from the average inbreeding effect. This novel approach ensures effect sizes are not overestimated whilst maximising the power of our available dataset of >3000 individuals genotyped on >35,000 autosomal SNPs. We find that most chromosomes confer a minor reduction in fitness-related traits, which when these effects are summed, results in the observed inbreeding depression in birth weight, survival and lifetime breeding success. However, no chromosomes had a significant detrimental effect compared to the overall effect of inbreeding, indicating no major effect loci. We conclude that in this population, inbreeding depression is likely the result of multiple mildly or moderately deleterious mutations spread across all chromosomes, which are difficult to detect with statistical confidence. Such mutations will be inefficiently purged, which may explain the persistence of inbreeding depression in this population.

Identification of major QTLs for drought tolerance in soybean, together with a novel candidate gene, GmUAA6.

Drought tolerance is a complex trait in soybean that is controlled by polygenetic quantitative trait loci (QTLs). In this study, wilting score, days-to-wilting, leaf relative water content, and leaf relative conductivity were used to identify QTLs associated with drought tolerance in recombinant inbred lines derived from a cross between a drought-sensitive variety, Lin, and a drought-tolerant variety, Meng. A total of 33 drought-tolerance QTLs were detected. Of these 17 were major QTLs. In addition, 15 were novel drought-tolerance QTLs. The most predominant QTL was on chromosome 11. This was detected in at least three environments. The overlapped mapping interval of the four measured traits was 0.2 cM in genetic distance (about 220 kb in physical length). Glyma.11g143500 (designated as GmUAA6), which encodes a UDP-N-acetylglucosamine transporter, was identified as the most likely candidate gene. The allele of GmUAA6 from Lin (GmUAA6Lin) was associated with improved soybean drought tolerance. Overexpression of GmUAA6Lin in Arabidopsis and soybean hairy roots enhanced drought tolerance. Furthermore, a 3-bp insertion/deletion (InDel) in the coding sequence of GmUAA6 explained up to 49.9% of the phenotypic variation in drought tolerance-related traits, suggesting that this InDel might be used in future marker-assisted selection of drought-tolerant lines in soybean breeding programs.