Genomic patterns of pleiotropy and the evolution of complexity

Reconciling theories of dominance with the relative rates of adaptive substitution on sex chromosomes and autosomes

The dominance of beneficial mutations is a key evolutionary parameter affecting the rate and genetic basis of adaptation, yet it is notoriously difficult to estimate. A leading method to infer it is to compare the relative rates of adaptive substitution for X-linked and autosomal genes, which-according to a classic model by Charlesworth et al. (1987)-is a simple function of the dominance of new beneficial mutations. Recent evidence that rates of adaptive substitution are faster for X-linked genes implies, accordingly, that beneficial mutations are usually recessive. However, this conclusion is incompatible with leading theories of dominance, which predict that beneficial mutations tend to be dominant or overdominant with respect to fitness. To address this incompatibility, we use Fisher's geometric model to predict the distribution of fitness effects of new mutations and the relative rates of positively selected substitution on the X and autosomes. Previous predictions of faster-X theory emerge as a special case of our model in which the phenotypic effects of mutations are small relative to the distance to the phenotypic optimum. But as mutational effects become large relative to the optimum, we observe an elevated tempo of positively selected substitutions on the X relative to the autosomes across a broader range of dominance conditions, including those predicted by theories of dominance. Our results imply that, contrary to previous models, dominant and overdominant beneficial mutations can plausibly generate patterns of faster-X adaptation. We discuss resulting implications for genomic studies of adaptation and inferences of dominance.

Epistasis and pleiotropy-induced variation for plant breeding

Epistasis refers to nonallelic interaction between genes that cause bias in estimates of genetic parameters for a phenotype with interactions of two or more genes affecting the same trait. Partitioning of epistatic effects allows true estimation of the genetic parameters affecting phenotypes. Multigenic variation plays a central role in the evolution of complex characteristics, among which pleiotropy, where a single gene affects several phenotypic characters, has a large influence. While pleiotropic interactions provide functional specificity, they increase the challenge of gene discovery and functional analysis. Overcoming pleiotropy-based phenotypic trade-offs offers potential for assisting breeding for complex traits. Modelling higher order nonallelic epistatic interaction, pleiotropy and non-pleiotropy-induced variation, and genotype × environment interaction in genomic selection may provide new paths to increase the productivity and stress tolerance for next generation of crop cultivars. Advances in statistical models, software and algorithm developments, and genomic research have facilitated dissecting the nature and extent of pleiotropy and epistasis. We overview emerging approaches to exploit positive (and avoid negative) epistatic and pleiotropic interactions in a plant breeding context, including developing avenues of artificial intelligence, novel exploitation of large-scale genomics and phenomics data, and involvement of genes with minor effects to analyse epistatic interactions and pleiotropic quantitative trait loci, including missing heritability.

The genetic architecture of repeated local adaptation to climate in distantly related plants

Closely related species often use the same genes to adapt to similar environments. However, we know little about why such genes possess increased adaptive potential and whether this is conserved across deeper evolutionary lineages. Adaptation to climate presents a natural laboratory to test these ideas, as even distantly related species must contend with similar stresses. Here, we re-analyse genomic data from thousands of individuals from 25 plant species as diverged as lodgepole pine and Arabidopsis (~300 Myr). We test for genetic repeatability based on within-species associations between allele frequencies in genes and variation in 21 climate variables. Our results demonstrate significant statistical evidence for genetic repeatability across deep time that is not expected under randomness, identifying a suite of 108 gene families (orthogroups) and gene functions that repeatedly drive local adaptation to climate. This set includes many orthogroups with well-known functions in abiotic stress response. Using gene co-expression networks to quantify pleiotropy, we find that orthogroups with stronger evidence for repeatability exhibit greater network centrality and broader expression across tissues (higher pleiotropy), contrary to the 'cost of complexity' theory. These gene families may be important in helping wild and crop species cope with future climate change, representing important candidates for future study.

Impacts of pleiotropy and migration on repeated genetic adaptation

Observations of genetically repeated evolution (repeatability) in complex organisms are incongruent with the Fisher-Orr model, which implies that repeated use of the same gene should be rare when mutations are pleiotropic (i.e. affect multiple traits). When spatially divergent selection occurs in the presence of migration, mutations of large effect are more strongly favored, and hence, repeatability is more likely, but it is unclear whether this observation is limited by pleiotropy. Here, we explore this question using individual-based simulations of a two-patch model incorporating multiple quantitative traits governed by mutations with pleiotropic effects. We explore the relationship between fitness trade-offs and repeatability by varying the alignment between mutation effect and spatial variation in trait optima. While repeatability decreases with increasing trait dimensionality, trade-offs in mutation effects on traits do not strongly limit the contribution of a locus of large effect to repeated adaptation, particularly under increased migration. These results suggest that repeatability will be more pronounced for local rather than global adaptation. Whereas pleiotropy limits repeatability in a single-population model, when there is local adaptation with gene flow, repeatability can occur if some loci are able to produce alleles of large effect, even when there are pleiotropic trade-offs.

Genetic regulators of a resource polyphenism interact to couple predatory morphology and behaviour

Phenotypic plasticity often requires the coordinated response of multiple traits observed individually as morphological, physiological or behavioural. The integration, and hence functionality, of this response may be influenced by whether and how these component traits share a genetic basis. In the case of polyphenism, or discrete plasticity, at least part of the environmental response is categorical, offering a simple readout for determining whether and to what degree individual components of a plastic response can be decoupled. Here, we use the nematode Pristionchus pacificus, which has a resource polyphenism allowing it to be a facultative predator of other nematodes, to understand the genetic integration of polyphenism. The behavioural and morphological consequences of perturbations to the polyphenism's genetic regulatory network show that both predatory activity and ability are strongly influenced by morphology, different axes of morphological variation are associated with different aspects of predatory behaviour, and rearing environment can decouple predatory morphology from behaviour. Further, we found that interactions between some polyphenism-modifying genes synergistically affect predatory behaviour. Our results show that the component traits of an integrated polyphenic response can be decoupled and, in principle, selected upon individually, and they suggest that multiple routes to functionally comparable phenotypes are possible.

Parthenogenetic Stick Insects Exhibit Signatures of Preservation in the Molecular Architecture of Male Reproduction

After the loss of a trait, theory predicts that the molecular machinery underlying its phenotypic expression should decay. Yet, empirical evidence is contrasting. Here, we test the hypotheses that (i) the molecular ground plan of a lost trait could persist due to pleiotropic effects on other traits and (ii) that gene co-expression network architecture could constrain individual gene expression. Our testing ground has been the Bacillus stick insect species complex, which contains close relatives that are either bisexual or parthenogenetic. After the identification of genes expressed in male reproductive tissues in a bisexual species, we investigated their gene co-expression network structure in two parthenogenetic species. We found that gene co-expression within the male gonads was partially preserved in parthenogens. Furthermore, parthenogens did not show relaxed selection on genes upregulated in male gonads in the bisexual species. As these genes were mostly expressed in female gonads, this preservation could be driven by pleiotropic interactions and an ongoing role in female reproduction. Connectivity within the network also played a key role, with highly connected-and more pleiotropic-genes within male gonad also having a gonad-biased expression in parthenogens. Our findings provide novel insight into the mechanisms which could underlie the production of rare males in parthenogenetic lineages; more generally, they provide an example of the cryptic persistence of a lost trait molecular architecture, driven by gene pleiotropy on other traits and within their co-expression network.

Brief Communication: The Predictable Network Topology of Evolutionary Genomic Constraint

Large-scale comparative genomics studies offer valuable resources for understanding both functional and evolutionary rate constraints. It is suggested that constraint aligns with the topology of genomic networks, increasing toward the center, with intermediate nodes combining relaxed constraint with higher contributions to the phenotype due to pleiotropy. However, this pattern has yet to be demonstrated in vertebrates. This study shows that constraint intensifies toward the network's center in placental mammals. Genes with rate changes associated with emergence of hibernation cluster mostly toward intermediate positions, with higher constraint in faster-evolving genes, which is indicative of a "sweet spot" for adaptation. If this trend holds universally, network node metrics could predict high-constraint regions even in clades lacking empirical constraint data.

Eyeless cave-dwelling Leptonetela spiders still rely on light

Subterranean animals living in perpetual darkness may maintain photoresponse. However, the evolutionary processes behind the conflict between eye loss and maintenance of the photoresponse remain largely unknown. We used Leptonetela spiders to investigate the driving forces behind the maintenance of the photoresponse in cave-dwelling spiders. Our behavioral experiments showed that all eyeless/reduced-eyed cave-dwelling species retained photophobic response and that they had substantially decreased survival at cave entrances due to weak drought resistance. The transcriptomic analysis demonstrated that nearly all phototransduction pathway genes were present and that all tested phototransduction pathway genes were subjected to strong functional constraints in cave-dwelling species. Our results suggest that cave-dwelling eyeless spiders still use light and that light detection likely plays a role in avoiding the cave entrance habitat. This study confirms that some eyeless subterranean animals have retained their photosensitivity due to natural selection and provides a case of mismatch between phenotype and genotype or physiological function in a long-term evolutionary process.

Natural variation in the SVP contributes to the pleiotropic adaption of Arabidopsis thaliana across contrasted habitats

Coordinated plant adaptation involves the interplay of multiple traits driven by habitat-specific selection pressures. Pleiotropic effects, wherein genetic variants of a single gene control multiple traits, can expedite such adaptations. Until present, only a limited number of genes have been reported to exhibit pleiotropy. Here, we create a recombinant inbred line (RIL) population derived from two Arabidopsis thaliana (A. thaliana) ecotypes originating from divergent habitats. Using this RIL population, we identify an allelic variation in a MADS-box transcription factor, SHORT VEGETATIVE PHASE (SVP), which exerts a pleiotropic effect on leaf size and drought-versus-humidity tolerance. Further investigation reveals that a natural null variant of the SVP protein disrupts its normal regulatory interactions with target genes, including GRF3, CYP707A1/3, and AtBG1, leading to increased leaf size, enhanced tolerance to humid conditions, and changes in flowering time of humid conditions in A. thaliana. Remarkably, polymorphic variations in this gene have been traced back to early A. thaliana populations, providing a genetic foundation and plasticity for subsequent colonization of diverse habitats by influencing multiple traits. These findings advance our understanding of how plants rapidly adapt to changing environments by virtue of the pleiotropic effects of individual genes on multiple trait alterations.

Patterns and evolutionary consequences of pleiotropy

Pleiotropy refers to the phenomenon of one gene or one mutation affecting multiple phenotypic traits. While the concept of pleiotropy is as old as Mendelian genetics, functional genomics has finally allowed the first glimpses of the extent of pleiotropy for a large fraction of genes in a genome. After describing conceptual and operational difficulties in quantifying pleiotropy and the pros and cons of various methods for measuring pleiotropy, I review empirical data on pleiotropy, which generally show an L-shaped distribution of the degree of pleiotropy (i.e., the number of traits affected) with most genes having low pleiotropy. I then review the current understanding of the molecular basis of pleiotropy. The rest of the review discusses evolutionary consequences of pleiotropy, focusing on advances in topics including the cost of complexity, regulatory vs. coding evolution, environmental pleiotropy and adaptation, evolution of ageing and other seemingly harmful traits, and evolutionary resolution of pleiotropy.

Integration of eQTL and machine learning to dissect causal genes with pleiotropic effects in genetic regulation networks of seed cotton yield

The dissection of a gene regulatory network (GRN) that complements the genome-wide association study (GWAS) locus and the crosstalk underlying multiple agronomical traits remains a major challenge. In this study, we generate 558 transcriptional profiles of lint-bearing ovules at one day post-anthesis from a selective core cotton germplasm, from which 12,207 expression quantitative trait loci (eQTLs) are identified. Sixty-six known phenotypic GWAS loci are colocalized with 1,090 eQTLs, forming 38 functional GRNs associated predominantly with seed yield. Of the eGenes, 34 exhibit pleiotropic effects. Combining the eQTLs within the seed yield GRNs significantly increases the portion of narrow-sense heritability. The extreme gradient boosting (XGBoost) machine learning approach is applied to predict seed cotton yield phenotypes on the basis of gene expression. Top-ranking eGenes (NF-YB3, FLA2, and GRDP1) derived with pleiotropic effects on yield traits are validated, along with their potential roles by correlation analysis, domestication selection analysis, and transgenic plants.

Genic constraint against nonsynonymous variation across the mouse genome

BACKGROUND

Selective constraint, the depletion of variation due to negative selection, provides insights into the functional impact of variants and disease mechanisms. However, its characterization in mice, the most commonly used mammalian model, remains limited. This study aims to quantify mouse gene constraint using a new metric called the nonsynonymous observed expected ratio (NOER) and investigate its relationship with gene function.

RESULTS

NOER was calculated using whole-genome sequencing data from wild mouse populations (Mus musculus sp and Mus spretus). Positive correlations were observed between mouse gene constraint and the number of associated knockout phenotypes, indicating stronger constraint on pleiotropic genes. Furthermore, mouse gene constraint showed a positive correlation with the number of pathogenic variant sites in their human orthologues, supporting the relevance of mouse models in studying human disease variants.

CONCLUSIONS

NOER provides a resource for assessing the fitness consequences of genetic variants in mouse genes and understanding the relationship between gene constraint and function. The study's findings highlight the importance of pleiotropy in selective constraint and support the utility of mouse models in investigating human disease variants. Further research with larger sample sizes can refine constraint estimates in mice and enable more comprehensive comparisons of constraint between mouse and human orthologues.

Detecting natural selection in trait-trait coevolution

No phenotypic trait evolves independently of all other traits, but the cause of trait-trait coevolution is poorly understood. While the coevolution could arise simply from pleiotropic mutations that simultaneously affect the traits concerned, it could also result from multivariate natural selection favoring certain trait relationships. To gain a general mechanistic understanding of trait-trait coevolution, we examine the evolution of 220 cell morphology traits across 16 natural strains of the yeast Saccharomyces cerevisiae and the evolution of 24 wing morphology traits across 110 fly species of the family Drosophilidae, along with the variations of these traits among gene deletion or mutation accumulation lines (a.k.a. mutants). For numerous trait pairs, the phenotypic correlation among evolutionary lineages differs significantly from that among mutants. Specifically, we find hundreds of cases where the evolutionary correlation between traits is strengthened or reversed relative to the mutational correlation, which, according to our population genetic simulation, is likely caused by multivariate selection. Furthermore, we detect selection for enhanced modularity of the yeast traits analyzed. Together, these results demonstrate that trait-trait coevolution is shaped by natural selection and suggest that the pleiotropic structure of mutation is not optimal. Because the morphological traits analyzed here are chosen largely because of their measurability and thereby are not expected to be biased with regard to natural selection, our conclusion is likely general.

Patterns of transcription factor binding and epigenome at promoters allow interpretable predictability of multiple functions of non-coding and coding genes

Understanding the biological roles of all genes only through experimental methods is challenging. A computational approach with reliable interpretability is needed to infer the function of genes, particularly for non-coding RNAs. We have analyzed genomic features that are present across both coding and non-coding genes like transcription factor (TF) and cofactor ChIP-seq (823), histone modifications ChIP-seq (n = 621), cap analysis gene expression (CAGE) tags (n = 255), and DNase hypersensitivity profiles (n = 255) to predict ontology-based functions of genes. Our approach for gene function prediction was reliable (>90% balanced accuracy) for 486 gene-sets. PubMed abstract mining and CRISPR screens supported the inferred association of genes with biological functions, for which our method had high accuracy. Further analysis revealed that TF-binding patterns at promoters have high predictive strength for multiple functions. TF-binding patterns at the promoter add an unexplored dimension of explainable regulatory aspects of genes and their functions. Therefore, we performed a comprehensive analysis for the functional-specificity of TF-binding patterns at promoters and used them for clustering functions to reveal many latent groups of gene-sets involved in common major cellular processes. We also showed how our approach could be used to infer the functions of non-coding genes using the CRISPR screens of coding genes, which were validated using a long non-coding RNA CRISPR screen. Thus our results demonstrated the generality of our approach by using gene-sets from CRISPR screens. Overall, our approach opens an avenue for predicting the involvement of non-coding genes in various functions.

Elucidating the patterns of pleiotropy and its biological relevance in maize

Pleiotropy-when a single gene controls two or more seemingly unrelated traits-has been shown to impact genes with effects on flowering time, leaf architecture, and inflorescence morphology in maize. However, the genome-wide impact of biological pleiotropy across all maize phenotypes is largely unknown. Here, we investigate the extent to which biological pleiotropy impacts phenotypes within maize using GWAS summary statistics reanalyzed from previously published metabolite, field, and expression phenotypes across the Nested Association Mapping population and Goodman Association Panel. Through phenotypic saturation of 120,597 traits, we obtain over 480 million significant quantitative trait nucleotides. We estimate that only 1.56-32.3% of intervals show some degree of pleiotropy. We then assess the relationship between pleiotropy and various biological features such as gene expression, chromatin accessibility, sequence conservation, and enrichment for gene ontology terms. We find very little relationship between pleiotropy and these variables when compared to permuted pleiotropy. We hypothesize that biological pleiotropy of common alleles is not widespread in maize and is highly impacted by nuisance terms such as population structure and linkage disequilibrium. Natural selection on large standing natural variation in maize populations may target wide and large effect variants, leaving the prevalence of detectable pleiotropy relatively low.

Carry-over effects and fitness trade-offs in marine life histories: The costs of complexity for adaptation

Most marine organisms have complex life histories, where the individual stages of a life cycle are often morphologically and ecologically distinct. Nevertheless, life-history stages share a single genome and are linked phenotypically (by "carry-over effects"). These commonalities across the life history couple the evolutionary dynamics of different stages and provide an arena for evolutionary constraints. The degree to which genetic and phenotypic links among stages hamper adaptation in any one stage remains unclear and yet adaptation is essential if marine organisms will adapt to future climates. Here, we use an extension of Fisher's geometric model to explore how both carry-over effects and genetic links among life-history stages affect the emergence of pleiotropic trade-offs between fitness components of different stages. We subsequently explore the evolutionary trajectories of adaptation of each stage to its optimum using a simple model of stage-specific viability selection with nonoverlapping generations. We show that fitness trade-offs between stages are likely to be common and that such trade-offs naturally emerge through either divergent selection or mutation. We also find that evolutionary conflicts among stages should escalate during adaptation, but carry-over effects can ameliorate this conflict. Carry-over effects also tip the evolutionary balance in favor of better survival in earlier life-history stages at the expense of poorer survival in later stages. This effect arises in our discrete-generation framework and is, therefore, unrelated to age-related declines in the efficacy of selection that arise in models with overlapping generations. Our results imply a vast scope for conflicting selection between life-history stages, with pervasive evolutionary constraints emerging from initially modest selection differences between stages. Organisms with complex life histories should also be more constrained in their capacity to adapt to global change than those with simple life histories.

Incompatibility and Interchangeability in Molecular Evolution

There is remarkable variation in the rate at which genetic incompatibilities in molecular interactions accumulate. In some cases, minor changes-even single-nucleotide substitutions-create major incompatibilities when hybridization forces new variants to function in a novel genetic background from an isolated population. In other cases, genes or even entire functional pathways can be horizontally transferred between anciently divergent evolutionary lineages that span the tree of life with little evidence of incompatibilities. In this review, we explore whether there are general principles that can explain why certain genes are prone to incompatibilities while others maintain interchangeability. We summarize evidence pointing to four genetic features that may contribute to greater resistance to functional replacement: (1) function in multisubunit enzyme complexes and protein-protein interactions, (2) sensitivity to changes in gene dosage, (3) rapid rate of sequence evolution, and (4) overall importance to cell viability, which creates sensitivity to small perturbations in molecular function. We discuss the relative levels of support for these different hypotheses and lay out future directions that may help explain the striking contrasts in patterns of incompatibility and interchangeability throughout the history of molecular evolution.

Leveraging pleiotropy for joint analysis of genome-wide association studies with per trait interpretations

We introduce pleiotropic association test (PAT) for joint analysis of multiple traits using genome-wide association study (GWAS) summary statistics. The method utilizes the decomposition of phenotypic covariation into genetic and environmental components to create a likelihood ratio test statistic for each genetic variant. Though PAT does not directly interpret which trait(s) drive the association, a per trait interpretation of the omnibus p-value is provided through an extension to the meta-analysis framework, m-values. In simulations, we show PAT controls the false positive rate, increases statistical power, and is robust to model misspecifications of genetic effect. Additionally, simulations comparing PAT to three multi-trait methods, HIPO, MTAG, and ASSET, show PAT identified 15.3% more omnibus associations over the next best method. When these associations were interpreted on a per trait level using m-values, PAT had 37.5% more true per trait interpretations with a 0.92% false positive assignment rate. When analyzing four traits from the UK Biobank, PAT discovered 22,095 novel variants. Through the m-values interpretation framework, the number of per trait associations for two traits were almost tripled and were nearly doubled for another trait relative to the original single trait GWAS.

In Silico Pleiotropy Analysis in KEGG Signaling Networks Using a Boolean Network Model

Pleiotropy, which refers to the ability of different mutations on the same gene to cause different pathological effects in human genetic diseases, is important in understanding system-level biological diseases. Although some biological experiments have been proposed, still little is known about pleiotropy on gene–gene dynamics, since most previous studies have been based on correlation analysis. Therefore, a new perspective is needed to investigate pleiotropy in terms of gene–gene dynamical characteristics. To quantify pleiotropy in terms of network dynamics, we propose a measure called in silico Pleiotropic Scores (sPS), which represents how much a gene is affected against a pair of different types of mutations on a Boolean network model. We found that our model can identify more candidate pleiotropic genes that are not known to be pleiotropic than the experimental database. In addition, we found that many types of functionally important genes tend to have higher sPS values than other genes; in other words, they are more pleiotropic. We investigated the relations of sPS with the structural properties in the signaling network and found that there are highly positive relations to degree, feedback loops, and centrality measures. This implies that the structural characteristics are principles to identify new pleiotropic genes. Finally, we found some biological evidence showing that sPS analysis is relevant to the real pleiotropic data and can be considered a novel candidate for pleiotropic gene research. Taken together, our results can be used to understand the dynamics pleiotropic characteristics in complex biological systems in terms of gene–phenotype relations.

Maintenance of quantitative genetic variance in complex, multi-trait phenotypes: The contribution of rare, large effect variants in two Drosophila species

The interaction of evolutionary processes to determine quantitative genetic variation has implications for contemporary and future phenotypic evolution, as well as for our ability to detect causal genetic variants. While theoretical studies have provided robust predictions to discriminate among competing models, empirical assessment of these has been limited. In particular, theory highlights the importance of pleiotropy in resolving observations of selection and mutation, but empirical investigations have typically been limited to few traits. Here, we applied high-dimensional Bayesian Sparse Factor Genetic modeling to gene expression datasets in 2 species, Drosophila melanogaster and Drosophila serrata, to explore the distributions of genetic variance across high-dimensional phenotypic space. Surprisingly, most of the heritable trait covariation was due to few lines (genotypes) with extreme [>3 interquartile ranges (IQR) from the median] values. Intriguingly, while genotypes extreme for a multivariate factor also tended to have a higher proportion of individual traits that were extreme, we also observed genotypes that were extreme for multivariate factors but not for any individual trait. We observed other consistent differences between heritable multivariate factors with outlier lines vs those factors without extreme values, including differences in gene functions. We use these observations to identify further data required to advance our understanding of the evolutionary dynamics and nature of standing genetic variation for quantitative traits.

When the "satisficing" is the new "fittest": how a proscriptive definition of adaptation can change our view of cognition and culture

Since Darwin's theory of evolution, adaptationism is frequently invoked to explain cognition and cultural processes. Adaptationism can be described as a prescriptive view, as phenotypes that do not optimize fitness should not be selected by natural selection. From an epistemological perspective, the principle of a prescriptive definition of adaptation seems incompatible with recent advances in epigenetics, evolutionary developmental biology, ethology, and genomics. From these challenges, a proscriptive view of adaptation has emerged, postulating that phenotypes that are not deleterious will be evolutionary maintained. In this epistemological investigation, we examine how the shift from adaptationism to a proscriptive view changes our view of cognition and culture. We argue that, while adaptationism leads to cognitivism and a view of culture as strategies to optimize overall fitness, the proscriptive definition favors embodied theories of cognition and a view of culture as the cumulative diffusion of behaviors allowed by the constraints of reproduction.

Genome-wide association meta-analysis of 88,250 individuals highlights pleiotropic mechanisms of five ocular diseases in UK Biobank

BACKGROUND

Ocular diseases may exhibit common clinical symptoms and epidemiological comorbidity. However, the extent of pleiotropic mechanisms across ocular diseases remains unclear. We aim to examine shared genetic etiology in age-related macular degeneration (AMD), diabetic retinopathy (DR), glaucoma, retinal detachment (RD), and myopia.

METHODS

We analyzed genome-wide association analyses for the five ocular diseases in 43,877 cases and 44,373 controls of European ancestry from UK Biobank, estimated their genetic relationships (LDSC, GNOVA, and Genomic SEM), and identified pleiotropic loci (ASSET and METASOFT).

FINDINGS

The genetic correlation of common SNPs revealed a meaningful genetic structure within these diseases, identifying genetic correlations between AMD, DR, and glaucoma. Cross-trait meta-analysis identified 23 pleiotropic loci associated with at least two ocular diseases and 14 loci unique to individual disorders (non-pleiotropic). We found that the genes associated with these shared genetic loci are involved in neuron differentiation (P = 8.80 × 10^-6) and eye development systems (P = 3.86 × 10^-5), and single cell RNA sequencing data reveals their heightened gene expression from multipotent progenitors to other differentiated retinal cells during retina developmental process.

INTERPRETATION

These results highlighted the potential common genetic architectures among these ocular diseases and can deepen the understanding of the molecular mechanisms underlying the related diseases.

FUNDING

The National Natural Science Foundation of China (61871294), Zhejiang Provincial Natural Science Foundation of China (LR19C060001), and the Scientific Research Foundation for Talents of Wenzhou Medical University (QTJ18023).

Genetics of adaptation

The rediscovery of Mendel’s work showing that the heredity of phenotypes is controlled by discrete genes was followed by the reconciliation of Mendelian genetics with evolution by natural selection in the middle of the last century with the Modern Synthesis. In the past two decades, dramatic advances in genomic methods have facilitated the identification of the loci, genes, and even individual mutations that underlie phenotypic variants that are the putative targets of natural selection. Moreover, these methods have also changed how we can study adaptation by flipping the problem around, allowing us to first examine what loci show evidence of having been under selection, and then connecting these genetic variants to phenotypic variation. As a result, we now have an expanding list of actual genetic changes that underlie potentially adaptive phenotypic variation. Here, we synthesize how considering the effects of these adaptive loci in the context of cellular environments, genomes, organisms, and populations has provided new insights to the genetic architecture of adaptation.

Untangling the relationship between developmental and evolutionary integration

Patterns of integration and modularity among organismal traits are prevalent across the tree of life, and at multiple scales of biological organization. Over the past several decades, researchers have studied these patterns at the developmental, and evolutionary levels. While their work has identified the potential drivers of these patterns at different scales, there appears to be a lack of consensus on the relationship between developmental and evolutionary integration. Here, we review and summarize key studies and build a framework to describe the conceptual relationship between these patterns across organismal scales and illustrate how, and why some of these studies may have yielded seemingly conflicting outcomes. We find that among studies that analyze patterns of integration and modularity using morphological data, the lack of consensus may stem in part from the difficulty of fully disentangling the developmental and functional causes of integration. Nonetheless, in some empirical systems, patterns of evolutionary modularity have been found to coincide with expectations based on developmental processes, suggesting that in some circumstances, developmental modularity may translate to evolutionary modularity. We also advance an extension to Hallgrímsson et al.'s palimpsest model to describe how patterns of trait modularity may shift across different evolutionary scales. Finally, we also propose some directions for future research which will hopefully be useful for investigators interested in these issues.

Natural variation in Drosophila shows weak pleiotropic effects

Background

Pleiotropy describes the phenomenon in which a gene affects multiple phenotypes. The extent of pleiotropy is still disputed, mainly because of issues of inadequate power of analyses. A further challenge is that empirical tests of pleiotropy are restricted to a small subset of all possible phenotypes. To overcome these limitations, we propose a new measurement of pleiotropy that integrates across many phenotypes and multiple generations to improve power.

Results

We infer pleiotropy from the fitness cost imposed by frequency changes of pleiotropic loci. Mixing Drosophila simulans populations, which adapted independently to the same new environment using different sets of genes, we show that the adaptive frequency changes have been accompanied by measurable fitness costs.

Conclusions

Unlike previous studies characterizing the molecular basis of pleiotropy, we show that many loci, each of weak effect, contribute to genome-wide pleiotropy. We propose that the costs of pleiotropy are reduced by the modular architecture of gene expression, which facilitates adaptive gene expression changes with low impact on other functions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-022-02680-4.

Sparse dictionary learning recovers pleiotropy from human cell fitness screens

In high-throughput functional genomic screens, each gene product is commonly assumed to exhibit a singular biological function within a defined protein complex or pathway. In practice, a single gene perturbation may induce multiple cascading functional outcomes, a genetic principle known as pleiotropy. Here, we model pleiotropy in fitness screen collections by representing each gene perturbation as the sum of multiple perturbations of biological functions, each harboring independent fitness effects inferred empirically from the data. Our approach (Webster) recovered pleiotropic functions for DNA damage proteins from genotoxic fitness screens, untangled distinct signaling pathways upstream of shared effector proteins from cancer cell fitness screens, and predicted the stoichiometry of an unknown protein complex subunit from fitness data alone. Modeling compound sensitivity profiles in terms of genetic functions recovered compound mechanisms of action. Our approach establishes a sparse approximation mechanism for unraveling complex genetic architectures underlying high-dimensional gene perturbation readouts.

Evolutionary rewiring of regulatory networks contributes to phenotypic differences between human and mouse orthologous genes

Mouse models have been engineered to reveal the biological mechanisms of human diseases based on an assumption. The assumption is that orthologous genes underlie conserved phenotypes across species. However, genetically modified mouse orthologs of human genes do not often recapitulate human disease phenotypes which might be due to the molecular evolution of phenotypic differences across species from the time of the last common ancestor. Here, we systematically investigated the evolutionary divergence of regulatory relationships between transcription factors (TFs) and target genes in functional modules, and found that the rewiring of gene regulatory networks (GRNs) contributes to the phenotypic discrepancies that occur between humans and mice. We confirmed that the rewired regulatory networks of orthologous genes contain a higher proportion of species-specific regulatory elements. Additionally, we verified that the divergence of target gene expression levels, which was triggered by network rewiring, could lead to phenotypic differences. Taken together, a careful consideration of evolutionary divergence in regulatory networks could be a novel strategy to understand the failure or success of mouse models to mimic human diseases. To help interpret mouse phenotypes in human disease studies, we provide quantitative comparisons of gene expression profiles on our website (http://sbi.postech.ac.kr/w/RN).

Pleiotropy facilitates parallel adaptation in sticklebacks

Highly pleiotropic genes are predicted to be used less often during adaptation, as mutations in these loci are more likely to have negative fitness consequences. Following this logic, we tested whether pleiotropy impacts the probability that a locus will be used repeatedly in adaptation. We used two proxies to estimate pleiotropy: number of phenotypic traits affected by a given genomic region and gene connectivity. We first surveyed 16 independent stream‐lake and three independent benthic‐limnetic ecotype pairs of threespine stickleback to estimate genome‐wide patterns in parallel genomic differentiation. Our analysis revealed parallel divergence across the genome; 30%–37% of outlier regions were shared between at least two independent pairs in either the stream‐lake or benthic‐limnetic comparisons. We then tested whether parallel genomic regions are less pleiotropic than nonparallel regions. Counter to our a priori prediction, parallel genomic regions contained genes with significantly more pleiotropy; that is, influencing a greater number of traits and more highly connected. The increased pleiotropy of parallel regions could not be explained by other genomic factors, as there was no significant difference in mean gene count, mutation or recombination rates between parallel and nonparallel regions. Interestingly, although nonparallel regions contained genes that were less connected and influenced fewer mapped traits on average than parallel regions, they also tended to contain the genes that were predicted to be the most pleiotropic. Taken together, our findings are consistent with the idea that pleiotropy only becomes constraining at high levels and that low or intermediate levels of pleiotropy may be beneficial for adaptation.

The relative impact of evolving pleiotropy and mutational correlation on trait divergence

Both pleiotropic connectivity and mutational correlations can restrict the decoupling of traits under divergent selection, but it is unknown which is more important in trait evolution. To address this question, we create a model that permits within-population variation in both pleiotropic connectivity and mutational correlation, and compare their relative importance to trait evolution. Specifically, we developed an individual-based stochastic model where mutations can affect whether a locus affects a trait and the extent of mutational correlations in a population. We find that traits can decouple whether there is evolution in pleiotropic connectivity or mutational correlation, but when both can evolve, then evolution in pleiotropic connectivity is more likely to allow for decoupling to occur. The most common genotype found in this case is characterized by having one locus that maintains connectivity to all traits and another that loses connectivity to the traits under stabilizing selection (subfunctionalization). This genotype is favored because it allows the subfunctionalized locus to accumulate greater effect size alleles, contributing to increasingly divergent trait values in the traits under divergent selection without changing the trait values of the other traits (genetic modularization). These results provide evidence that partial subfunctionalization of pleiotropic loci may be a common mechanism of trait decoupling under regimes of corridor selection.

Multi-Cellular Immunological Interactions Associated With COVID-19 Infections

To rapidly prognosticate and generate hypotheses on pathogenesis, leukocyte multi-cellularity was evaluated in SARS-CoV-2 infected patients treated in India or the United States (152 individuals, 384 temporal observations). Within hospital (<90-day) death or discharge were retrospectively predicted based on the admission complete blood cell counts (CBC). Two methods were applied: (i) a "reductionist" one, which analyzes each cell type separately, and (ii) a "non-reductionist" method, which estimates multi-cellularity. The second approach uses a proprietary software package that detects distinct data patterns generated by complex and hypothetical indicators and reveals each data pattern's immunological content and associated outcome(s). In the Indian population, the analysis of isolated cell types did not separate survivors from non-survivors. In contrast, multi-cellular data patterns differentiated six groups of patients, including, in two groups, 95.5% of all survivors. Some data structures revealed one data point-wide line of observations, which informed at a personalized level and identified 97.8% of all non-survivors. Discovery was also fostered: some non-survivors were characterized by low monocyte/lymphocyte ratio levels. When both populations were analyzed with the non-reductionist method, they displayed results that suggested survivors and non-survivors differed immunologically as early as hospitalization day 1.

Evolution of genetic networks for human creativity

The genetic basis for the emergence of creativity in modern humans remains a mystery despite sequencing the genomes of chimpanzees and Neanderthals, our closest hominid relatives. Data-driven methods allowed us to uncover networks of genes distinguishing the three major systems of modern human personality and adaptability: emotional reactivity, self-control, and self-awareness. Now we have identified which of these genes are present in chimpanzees and Neanderthals. We replicated our findings in separate analyses of three high-coverage genomes of Neanderthals. We found that Neanderthals had nearly the same genes for emotional reactivity as chimpanzees, and they were intermediate between modern humans and chimpanzees in their numbers of genes for both self-control and self-awareness. 95% of the 267 genes we found only in modern humans were not protein-coding, including many long-non-coding RNAs in the self-awareness network. These genes may have arisen by positive selection for the characteristics of human well-being and behavioral modernity, including creativity, prosocial behavior, and healthy longevity. The genes that cluster in association with those found only in modern humans are over-expressed in brain regions involved in human self-awareness and creativity, including late-myelinating and phylogenetically recent regions of neocortex for autobiographical memory in frontal, parietal, and temporal regions, as well as related components of cortico-thalamo-ponto-cerebellar-cortical and cortico-striato-cortical loops. We conclude that modern humans have more than 200 unique non-protein-coding genes regulating co-expression of many more protein-coding genes in coordinated networks that underlie their capacities for self-awareness, creativity, prosocial behavior, and healthy longevity, which are not found in chimpanzees or Neanderthals.

Allen Orr and the genetics of adaptation

Over most of the 20th century, evolutionary biologists predominantly subscribed to a strong form of "micro-mutationism," in which adaptive phenotypic divergence arises from allele frequency changes at many loci, each with a small effect on the phenotype. To be sure, there were well-known examples of large-effect alleles contributing to adaptation, yet such cases were generally regarded as atypical and unrepresentative of evolutionary change in general. In 1998, Allen Orr published a landmark theoretical paper in Evolution, which showed that both small- and large-effect mutations are likely to contribute to "adaptive walks" of a population to an optimum. Coupled with a growing set of empirical examples of large-effect alleles contributing to divergence (e.g., from QTL studies), Orr's paper provided a mathematical formalism that converted many evolutionary biologists from micro-mutationism to a more pluralistic perspective on the genetic basis of evolutionary change. We revisit the theoretical insights emerging from Orr's paper within the historical context leading up to 1998, and track the influence of this paper on the field of evolutionary biology through an examination of its citations over the last two decades and an analysis of the extensive body of theoretical and empirical research that Orr's pioneering paper inspired.

Pleiotropy data resource as a primer for investigating co-morbidities/multi-morbidities and their role in disease

Most current biomedical and protein research focuses only on a small proportion of genes, which results in a lost opportunity to identify new gene-disease associations and explore new opportunities for therapeutic intervention. The International Mouse Phenotyping Consortium (IMPC) focuses on elucidating gene function at scale for poorly characterized and/or under-studied genes. A key component of the IMPC initiative is the implementation of a broad phenotyping pipeline, which is facilitating the discovery of pleiotropy. Characterizing pleiotropy is essential to identify gene-disease associations, and it is of particular importance when elucidating the genetic causes of syndromic disorders. Here we show how the IMPC is effectively uncovering pleiotropy and how the new mouse models and gene function hypotheses generated by the IMPC are increasing our understanding of the mammalian genome, forming the basis of new research and identifying new gene-disease associations.

Biologically grounded scientific methods: The challenges ahead for combating epidemics

The protracted COVID 19 pandemic may indicate failures of scientific methodologies. Hoping to facilitate the evaluation and/or update of methods relevant in Biomedicine, several aspects of scientific processes are here explored. First, the background is reviewed. In particular, eight topics are analyzed: (i) the history of Higher Education models in reference to the pursuit of science and the type of student cognition pursued, (ii) whether explanatory or actionable knowledge is emphasized depending on the well- or ill-defined nature of problems, (iii) the role of complexity and dynamics, (iv) how differences between Biology and other fields influence methodologies, (v) whether theory, hypotheses or data drive scientific research, (vi) whether Biology is reducible to one or a few factors, (vii) the fact that data, to become actionable knowledge, require structuring, and (viii) the need of inter-/trans-disciplinary knowledge integration. To illustrate how these topics interact, a second section describes four temporal stages of scientific methods: conceptualization, operationalization, validation and evaluation. They refer to the transition from abstract (non-measurable) concepts (such as 'health') to the selection of concrete (measurable) operations (such as 'quantification of ́anti-virus specific antibody titers'). Conceptualization is the process that selects concepts worth investigating, which continues as operationalization when data-producing variables viewed to reflect critical features of the concepts are chosen. Because the operations selected are not necessarily valid, informative, and may fail to solve problems, validations and evaluations are critical stages, which require inter/trans-disciplinary knowledge integration. It is suggested that data structuring can substantially improve scientific methodologies applicable in Biology, provided that other aspects here mentioned are also considered. The creation of independent bodies meant to evaluate biologically oriented scientific methods is recommended.

Highly pleiotropic variants of human traits are enriched in genomic regions with strong background selection

Recent studies have shown the ubiquity of pleiotropy for variants affecting human complex traits. These studies also show that rare variants tend to be less pleiotropic than common ones, suggesting that purifying natural selection acts against highly pleiotropic variants of large effect. Here, we investigate the mean frequency, effect size and recombination rate associated with pleiotropic variants, and focus particularly on whether highly pleiotropic variants are enriched in regions with putative strong background selection. We evaluate variants for 41 human traits using data from the NHGRI-EBI GWAS Catalog, as well as data from other three studies. Our results show that variants involving a higher degree of pleiotropy tend to be more common, have larger mean effect sizes, and contribute more to heritability than variants with a lower degree of pleiotropy. This is consistent with the fact that variants of large effect and frequency are more likely detected by GWAS. Using data from four different studies, we also show that more pleiotropic variants are enriched in genome regions with stronger background selection than less pleiotropic variants, suggesting that highly pleiotropic variants are subjected to strong purifying selection. From the above results, we hypothesized that a number of highly pleiotropic variants of low effect/frequency may pass undetected by GWAS.

Mapping the adaptive landscape of a major agricultural pathogen reveals evolutionary constraints across heterogeneous environments

The adaptive potential of pathogens in novel or heterogeneous environments underpins the risk of disease epidemics. Antagonistic pleiotropy or differential resource allocation among life-history traits can constrain pathogen adaptation. However, we lack understanding of how the genetic architecture of individual traits can generate trade-offs. Here, we report a large-scale study based on 145 global strains of the fungal wheat pathogen Zymoseptoria tritici from four continents. We measured 50 life-history traits, including virulence and reproduction on 12 different wheat hosts and growth responses to several abiotic stressors. To elucidate the genetic basis of adaptation, we used genome-wide association mapping coupled with genetic correlation analyses. We show that most traits are governed by polygenic architectures and are highly heritable suggesting that adaptation proceeds mainly through allele frequency shifts at many loci. We identified negative genetic correlations among traits related to host colonization and survival in stressful environments. Such genetic constraints indicate that pleiotropic effects could limit the pathogen's ability to cause host damage. In contrast, adaptation to abiotic stress factors was likely facilitated by synergistic pleiotropy. Our study illustrates how comprehensive mapping of life-history trait architectures across diverse environments allows to predict evolutionary trajectories of pathogens confronted with environmental perturbations.

Conserved pleiotropy of an ancient plant homeobox gene uncovered by cis-regulatory dissection

Divergence of gene function is a hallmark of evolution, but assessing functional divergence over deep time is not trivial. The few alleles available for cross-species studies often fail to expose the entire functional spectrum of genes, potentially obscuring deeply conserved pleiotropic roles. Here, we explore the functional divergence of WUSCHEL HOMEOBOX9 (WOX9), suggested to have species-specific roles in embryo and inflorescence development. Using a cis-regulatory editing drive system, we generate a comprehensive allelic series in tomato, which revealed hidden pleiotropic roles for WOX9. Analysis of accessible chromatin and conserved cis-regulatory sequences identifies the regions responsible for this pleiotropic activity, the functions of which are conserved in groundcherry, a tomato relative. Mimicking these alleles in Arabidopsis, distantly related to tomato and groundcherry, reveals new inflorescence phenotypes, exposing a deeply conserved pleiotropy. We suggest that targeted cis-regulatory mutations can uncover conserved gene functions and reduce undesirable effects in crop improvement.

Quercus species divergence is driven by natural selection on evolutionarily less integrated traits

Functional traits are organismal attributes that can respond to environmental cues, thereby providing important ecological functions. In addition, an organism's potential for adaptation is defined by the patterns of covariation among groups of functionally related traits. Whether an organism is evolutionarily constrained or has the potential for adaptation is based on the phenotypic integration or modularity of these traits. Here, we revisited leaf morphology in two European sympatric white oaks (Quercus petraea (Matt.) Liebl. and Quercus robur L.), sampling 2098 individuals, across much of their geographical distribution ranges. At the phenotypic level, leaf morphology traditionally encompasses discriminant attributes among different oak species. Here, we estimated in situ heritability, genetic correlation, and integration across such attributes. Also, we performed Selection Response Decomposition to test these traits for potential differences in oak species' evolutionary responses. Based on the uncovered functional units of traits (modules) in our study, the morphological module "leaf size gradient" was highlighted among functionally integrated traits. Equally, this module was defined in both oaks as being under "global regulation" in vegetative bud establishment and development. Lamina basal shape and intercalary veins' number were not, or, less integrated within the initially defined leaf functional unit, suggesting more than one module within the leaf traits' ensemble. Since these traits generally show the greatest species discriminatory power, they potentially underwent effective differential response to selection among oaks. Indeed, the selection of these traits could have driven the ecological preferences between the two sympatric oaks growing under different microclimates.

How the Toxin got its Toxicity

Venom systems are functional and ecological traits, typically used by one organism to subdue or deter another. A predominant subset of their constituent molecules—“toxins”—share this ecological function and are therefore molecules that mediate interactions between organisms. Such molecules have been referred to as “exochemicals.” There has been debate within the field of toxinology concerning the evolutionary pathways leading to the “recruitment” of a gene product for a toxic role within venom. We review these discussions and the evidence interpreted in support of alternate pathways, along with many of the most popular models describing the origin of novel molecular functions in general. We note that such functions may arise with or without gene duplication occurring and are often the consequence of a gene product encountering a novel “environment,” i.e., a range of novel partners for molecular interaction. After stressing the distinction between “activity” and “function,” we describe in detail the results of a recent study which reconstructed the evolutionary history of a multigene family that has been recruited as a toxin and argue that these results indicate that a pluralistic approach to understanding the origin of novel functions is advantageous. This leads us to recommend that an expansive approach be taken to the definition of “neofunctionalization”—simply the origins of a novel molecular function by any process—and “recruitment”—the “weaponization” of a molecule via the acquisition of a toxic function in venom, by any process. Recruitment does not occur at the molecular level or even at the level of gene expression, but only when a confluence of factors results in the ecological deployment of a physiologically active molecule as a toxin. Subsequent to recruitment, the evolutionary regime of a gene family may shift into a more dynamic form of “birth-and-death.” Thus, recruitment leads to a form of “downwards causation,” in which a change at the ecological level at which whole organisms interact leads to a change in patterns of evolution at the genomic level.

Stressful development: integrating endoderm development, stress, and longevity

In addition to performing digestion and nutrient absorption, the intestine serves as one of the first barriers to the external environment, crucial for protecting the host from environmental toxins, pathogenic invaders, and other stress inducers. The gene regulatory network (GRN) governing embryonic development of the endoderm and subsequent differentiation and maintenance of the intestine has been well-documented in C. elegans. A key regulatory input that initiates activation of the embryonic GRN for endoderm and mesoderm in this animal is the maternally provided SKN-1 transcription factor, an ortholog of the vertebrate Nrf1 and 2, which, like C. elegans SKN-1, perform conserved regulatory roles in mediating a variety of stress responses across metazoan phylogeny. Other key regulatory factors in early gut development also participate in stress response as well as in innate immunity and aging and longevity. In this review, we discuss the intersection between genetic nodes that mediate endoderm/intestine differentiation and regulation of stress and homeostasis. We also consider how direct signaling from the intestine to the germline, in some cases involving SKN-1, facilitates heritable epigenetic changes, allowing transmission of adaptive stress responses across multiple generations. These connections between regulation of endoderm/intestine development and stress response mechanisms suggest that varying selective pressure exerted on the stress response pathways may influence the architecture of the endoderm GRN, thereby leading to genetic and epigenetic variation in early embryonic GRN regulatory events.

Hidden paths to endless forms most wonderful: Complexity of bacterial motility shapes diversification of latent phenotypes

BACKGROUND

Evolution in one selective environment often latently generates phenotypic change that is manifested only later in different environments, but the complexity of behavior important to fitness in the original environment might influence the character of such latent-phenotype evolution. Using Myxococcus xanthus, a bacterium possessing two motility systems differing in effectiveness on hard vs. soft surfaces, we test (i) whether and how evolution while swarming on one surface-the selective surface-latently alters motility on the alternative surface type and (ii) whether patterns of such latent-phenotype evolution depend on the complexity of ancestral motility, specific ancestral motility genotypes and/or the selective surface of evolution. We analysze an experiment in which populations established from three ancestral genotypes-one with both motility systems intact and two others with one system debilitated-evolved while swarming across either hard or soft agar in six evolutionary treatments. We then compare motility-phenotype patterns across selective vs. alternative surface types.

RESULTS

Latent motility evolution was pervasive but varied in character as a function of the presence of one or two functional motility systems and, for some individual-treatment comparisons, the specific ancestral genotype and/or selective surface. Swarming rates on alternative vs. selective surfaces were positively correlated generally among populations with one functional motility system but not among those with two. This suggests that opportunities for pleiotropy and epistasis generated by increased genetic complexity underlying behavior can alter the character of latent-phenotype evolution. No tradeoff between motility performance across surface types was detected in the dual-system treatments, even after adaptation on a surface on which one motility system dominates strongly over the other in driving movement, but latent-phenotype evolution was instead idiosyncratic in these treatments. We further find that the magnitude of stochastic diversification at alternative-surface swarming among replicate populations greatly exceeded diversification of selective-surface swarming within some treatments and varied across treatments.

CONCLUSION

Collectively, our results suggest that increases in the genetic and mechanistic complexity of behavior can increase the complexity of latent-phenotype evolution outcomes and illustrate that diversification manifested during evolution in one environment can be augmented greatly by diversification of latent phenotypes manifested later.

Natural variation at FLM splicing has pleiotropic effects modulating ecological strategies in Arabidopsis thaliana

Investigating the evolution of complex phenotypes and the underlying molecular bases of their variation is critical to understand how organisms adapt to their environment. Applying classical quantitative genetics on a segregating population derived from a Can-0xCol-0 cross, we identify the MADS-box transcription factor FLOWERING LOCUS M (FLM) as a player of the phenotypic variation in plant growth and color. We show that allelic variation at FLM modulates plant growth strategy along the leaf economics spectrum, a trade-off between resource acquisition and resource conservation, observable across thousands of plant species. Functional differences at FLM rely on a single intronic substitution, disturbing transcript splicing and leading to the accumulation of non-functional FLM transcripts. Associations between this substitution and phenotypic and climatic data across Arabidopsis natural populations, show how noncoding genetic variation at a single gene might be adaptive through pleiotropic effects.

FLOWERING LOCUS M (FLM) is known as a repressor of Arabidopsis flowering. Here, the authors show that a single intronic substitution of FLM modulates leaf color and plant growth strategy along the leaf economics spectrum, as well as plays a role in plant adaptation.

Fly wing evolution explained by a neutral model with mutational pleiotropy

To what extent the speed of mutational production of phenotypic variation determines the rate of long-term phenotypic evolution is a central question. Houle et al. recently addressed this question by studying the mutational variances, additive genetic variances, and macroevolution of locations of vein intersections on fly wings, reporting very slow phenotypic evolution relative to the rates of mutational input, high phylogenetic signals, and a strong, linear relationship between the mutational variance of a trait and its rate of evolution. Houle et al. found no existing model of phenotypic evolution to be consistent with all these observations, and proposed the improbable scenario of equal influence of mutational pleiotropy on all traits. Here, we demonstrate that the purported linear relationship between mutational variance and evolutionary divergence is artifactual. We further show that the data are explainable by a simple model in which the wing traits are effectively neutral at least within a range of phenotypic values but their evolutionary rates are differentially reduced because mutations affecting these traits are purged owing to their different pleiotropic effects on other traits that are under stabilizing selection. Thus, the evolutionary patterns of fly wing morphologies are explainable under the existing theoretical framework of phenotypic evolution.

Adaptation via pleiotropy and linkage: Association mapping reveals a complex genetic architecture within the stickleback Eda locus

Genomic mapping of the loci associated with phenotypic evolution has revealed genomic "hotspots," or regions of the genome that control multiple phenotypic traits. This clustering of loci has important implications for the speed and maintenance of adaptation and could be due to pleiotropic effects of a single mutation or tight genetic linkage of multiple causative mutations affecting different traits. The threespine stickleback (Gasterosteus aculeatus) is a powerful model for the study of adaptive evolution because the marine ecotype has repeatedly adapted to freshwater environments across the northern hemisphere in the last 12,000 years. Freshwater ecotypes have repeatedly fixed a 16 kilobase haplotype on chromosome IV that contains Ectodysplasin (Eda), a gene known to affect multiple traits, including defensive armor plates, lateral line sensory hair cells, and schooling behavior. Many additional traits have previously been mapped to a larger region of chromosome IV that encompasses the Eda freshwater haplotype. To identify which of these traits specifically map to this adaptive haplotype, we made crosses of rare marine fish heterozygous for the freshwater haplotype in an otherwise marine genetic background. Further, we performed fine-scale association mapping in a fully interbreeding, polymorphic population of freshwater stickleback to disentangle the effects of pleiotropy and linkage on the phenotypes affected by this haplotype. Although we find evidence that linked mutations have small effects on a few phenotypes, a small 1.4-kb region within the first intron of Eda has large effects on three phenotypic traits: lateral plate count, and both the number and patterning of the posterior lateral line neuromasts. Thus, the Eda haplotype is a hotspot of adaptation in stickleback due to both a small, pleiotropic region affecting multiple traits as well as multiple linked mutations affecting additional traits.

Extent and context dependence of pleiotropy revealed by high-throughput single-cell phenotyping

Pleiotropy-when a single mutation affects multiple traits-is a controversial topic with far-reaching implications. Pleiotropy plays a central role in debates about how complex traits evolve and whether biological systems are modular or are organized such that every gene has the potential to affect many traits. Pleiotropy is also critical to initiatives in evolutionary medicine that seek to trap infectious microbes or tumors by selecting for mutations that encourage growth in some conditions at the expense of others. Research in these fields, and others, would benefit from understanding the extent to which pleiotropy reflects inherent relationships among phenotypes that correlate no matter the perturbation (vertical pleiotropy). Alternatively, pleiotropy may result from genetic changes that impose correlations between otherwise independent traits (horizontal pleiotropy). We distinguish these possibilities by using clonal populations of yeast cells to quantify the inherent relationships between single-cell morphological features. Then, we demonstrate how often these relationships underlie vertical pleiotropy and how often these relationships are modified by genetic variants (quantitative trait loci [QTL]) acting via horizontal pleiotropy. Our comprehensive screen measures thousands of pairwise trait correlations across hundreds of thousands of yeast cells and reveals ample evidence of both vertical and horizontal pleiotropy. Additionally, we observe that the correlations between traits can change with the environment, genetic background, and cell-cycle position. These changing dependencies suggest a nuanced view of pleiotropy: biological systems demonstrate limited pleiotropy in any given context, but across contexts (e.g., across diverse environments and genetic backgrounds) each genetic change has the potential to influence a larger number of traits. Our method suggests that exploiting pleiotropy for applications in evolutionary medicine would benefit from focusing on traits with correlations that are less dependent on context.

Decoupling gene functions from knockout effects by evolutionary analyses

Genic functions have long been confounded by pleiotropic mutational effects. To understand such genetic effects, we examine HAP4, a well-studied transcription factor in Saccharomyces cerevisiae that functions by forming a tetramer with HAP2, HAP3 and HAP5. Deletion of HAP4 results in highly pleiotropic gene expression responses, some of which are clustered in related cellular processes (clustered effects) while most are distributed randomly across diverse cellular processes (distributed effects). Strikingly, the distributed effects that account for much of HAP4 pleiotropy tend to be non-heritable in a population, suggesting they have few evolutionary consequences. Indeed, these effects are poorly conserved in closely related yeasts. We further show substantial overlaps of clustered effects, but not distributed effects, among the four genes encoding the HAP2/3/4/5 tetramer. This pattern holds for other biochemically characterized yeast protein complexes or metabolic pathways. Examination of a set of cell morphological traits of the deletion lines yields consistent results. Hence, only some deletion effects of a gene support related biochemical understandings with the rest being often pleiotropic and evolutionarily decoupled from the gene's normal functions. This study suggests a new framework for reverse genetic analysis.