Exploring the molecular regulation of vernalization-induced flowering synchrony in Arabidopsis

Many plant populations exhibit synchronous flowering, which can be advantageous in plant reproduction. However, molecular mechanisms underlying flowering synchrony remain poorly understood. We studied the role of known vernalization-response and flower-promoting pathways in facilitating synchronized flowering in Arabidopsis thaliana. Using the vernalization-responsive Col-FRI genotype, we experimentally varied germination dates and daylength among individuals to test flowering synchrony in field and controlled environments. We assessed the activity of flowering regulation pathways by measuring gene expression across leaves produced at different time points during development and through a mutant analysis. We observed flowering synchrony across germination cohorts in both environments and discovered a previously unknown process where flower-promoting and repressing signals are differentially regulated between leaves that developed under different environmental conditions. We hypothesized this mechanism may underlie synchronization. However, our experiments demonstrated that signals originating from sources other than leaves must also play a pivotal role in synchronizing flowering time, especially in germination cohorts with prolonged growth before vernalization. Our results suggest flowering synchrony is promoted by a plant-wide integration of flowering signals across leaves and among organs. To summarize our findings, we propose a new conceptual model of vernalization-induced flowering synchrony and provide suggestions for future research in this field.

Allele-specific expression reveals multiple paths to highland adaptation in maize

Maize is a staple food of smallholder farmers living in highland regions up to 4,000 meters above sea level worldwide. Mexican and South American highlands are two major highland maize growing regions, and population genetic data suggests the maize’s adaptation to these regions occurred largely independently, providing a case study for convergent evolution. To better understand the mechanistic basis of highland adaptation, we crossed maize landraces from 108 highland and lowland sites of Mexico and South America with the inbred line B73 to produce F1 hybrids and grew them in both highland and lowland sites in Mexico. We identified thousands of genes with divergent expression between highland and lowland populations. Hundreds of these genes show patterns of convergent evolution between Mexico and South America. To dissect the genetic architecture of the divergent gene expression, we developed a novel allele-specific expression analysis pipeline to detect genes with divergent functional cis-regulatory variation between highland and lowland populations. We identified hundreds of genes with divergent cis-regulation between highland and lowland landrace alleles, with 20 in common between regions, further suggesting convergence in the genes underlying highland adaptation. Further analyses suggest multiple mechanisms contribute to this convergence in gene regulation. Although the vast majority of evolutionary changes associated with highland adaptation were region-specific, our findings highlight an important role for convergence at the gene expression and gene regulation levels as well.

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.

Demonstration of local adaptation in maize landraces by reciprocal transplantation

Populations are locally adapted when they exhibit higher fitness than foreign populations in their native habitat. Maize landrace adaptations to highland and lowland conditions are of interest to researchers and breeders. To determine the prevalence and strength of local adaptation in maize landraces, we performed a reciprocal transplant experiment across an elevational gradient in Mexico. We grew 120 landraces, grouped into four populations (Mexican Highland, Mexican Lowland, South American Highland, South American Lowland), in Mexican highland and lowland common gardens and collected phenotypes relevant to fitness and known highland‐adaptive traits such as anthocyanin pigmentation and macrohair density. 67k DArTseq markers were generated from field specimens to allow comparisons between phenotypic patterns and population genetic structure. We found phenotypic patterns consistent with local adaptation, though these patterns differ between the Mexican and South American populations. Quantitative trait differentiation (Q_ST) was greater than neutral allele frequency differentiation (F_ST) for many traits, signaling directional selection between pairs of populations. All populations exhibited higher fitness metric values when grown at their native elevation, and Mexican landraces had higher fitness than South American landraces when grown in these Mexican sites. As environmental distance between landraces’ native collection sites and common garden sites increased, fitness values dropped, suggesting landraces are adapted to environmental conditions at their natal sites. Correlations between fitness and anthocyanin pigmentation and macrohair traits were stronger in the highland site than the lowland site, supporting their status as highland‐adaptive. These results give substance to the long‐held presumption of local adaptation of New World maize landraces to elevation and other environmental variables across North and South America.

Modeling allelic diversity of multiparent mapping populations affects detection of quantitative trait loci

The search for quantitative trait loci that explain complex traits such as yield and drought tolerance has been ongoing in all crops. Methods such as biparental quantitative trait loci mapping and genome-wide association studies each have their own advantages and limitations. Multiparent advanced generation intercross populations contain more recombination events and genetic diversity than biparental mapping populations and are better able to estimate effect sizes of rare alleles than association mapping populations. Here, we discuss the results of using a multiparent advanced generation intercross population of doubled haploid maize lines created from 16 diverse founders to perform quantitative trait loci mapping. We compare 3 models that assume bi-allelic, founder, and ancestral haplotype allelic states for quantitative trait loci. The 3 methods have differing power to detect quantitative trait loci for a variety of agronomic traits. Although the founder approach finds the most quantitative trait loci, all methods are able to find unique quantitative trait loci, suggesting that each model has advantages for traits with different genetic architectures. A closer look at a well-characterized flowering time quantitative trait loci, qDTA8, which contains vgt1, highlights the strengths and weaknesses of each method and suggests a potential epistatic interaction. Overall, our results reinforce the importance of considering different approaches to analyzing genotypic datasets, and shows the limitations of binary SNP data for identifying multiallelic quantitative trait loci.

Analysis of genotype-by-environment interactions in a maize mapping population

Genotype-by-environment interactions are a significant challenge for crop breeding as well as being important for understanding the genetic basis of environmental adaptation. In this study, we analyzed genotype-by-environment interactions in a maize multiparent advanced generation intercross population grown across 5 environments. We found that genotype-by-environment interactions contributed as much as genotypic effects to the variation in some agronomically important traits. To understand how genetic correlations between traits change across environments, we estimated the genetic variance–covariance matrix in each environment. Changes in genetic covariances between traits across environments were common, even among traits that show low genotype-by-environment variance. We also performed a genome-wide association study to identify markers associated with genotype-by-environment interactions but found only a small number of significantly associated markers, possibly due to the highly polygenic nature of genotype-by-environment interactions in this population.

Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations

Integration of multi-omics data improved prediction accuracies of oat agronomic and seed nutritional traits in multi-environment trials and distantly related populations in addition to the single-environment prediction. Multi-omics prediction has been shown to be superior to genomic prediction with genome-wide DNA-based genetic markers (G) for predicting phenotypes. However, most of the existing studies were based on historical datasets from one environment; therefore, they were unable to evaluate the efficiency of multi-omics prediction in multi-environment trials and distantly related populations. To fill those gaps, we designed a systematic experiment to collect omics data and evaluate 17 traits in two oat breeding populations planted in single and multiple environments. In the single-environment trial, transcriptomic BLUP (T), metabolomic BLUP (M), G + T, G + M, and G + T + M models showed greater prediction accuracy than GBLUP for 5, 10, 11, 17, and 17 traits, respectively, and metabolites generally performed better than transcripts when combined with SNPs. In the multi-environment trial, multi-trait models with omics data outperformed both counterpart multi-trait GBLUP models and single-environment omics models, and the highest prediction accuracy was achieved when modeling genetic covariance as an unstructured covariance model. We also demonstrated that omics data can be used to prioritize loci from one population with omics data to improve genomic prediction in a distantly related population using a two-kernel linear model that accommodated both likely casual loci with large-effect and loci that explain little or no phenotypic variance. We propose that the two-kernel linear model is superior to most genomic prediction models that assume each variant is equally likely to affect the trait and can be used to improve prediction accuracy for any trait with prior knowledge of genetic architecture.

Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations

Integration of multi-omics data improved prediction accuracies of oat agronomic and seed nutritional traits in multi-environment trials and distantly related populations in addition to the single-environment prediction. Multi-omics prediction has been shown to be superior to genomic prediction with genome-wide DNA-based genetic markers (G) for predicting phenotypes. However, most of the existing studies were based on historical datasets from one environment; therefore, they were unable to evaluate the efficiency of multi-omics prediction in multi-environment trials and distantly related populations. To fill those gaps, we designed a systematic experiment to collect omics data and evaluate 17 traits in two oat breeding populations planted in single and multiple environments. In the single-environment trial, transcriptomic BLUP (T), metabolomic BLUP (M), G + T, G + M, and G + T + M models showed greater prediction accuracy than GBLUP for 5, 10, 11, 17, and 17 traits, respectively, and metabolites generally performed better than transcripts when combined with SNPs. In the multi-environment trial, multi-trait models with omics data outperformed both counterpart multi-trait GBLUP models and single-environment omics models, and the highest prediction accuracy was achieved when modeling genetic covariance as an unstructured covariance model. We also demonstrated that omics data can be used to prioritize loci from one population with omics data to improve genomic prediction in a distantly related population using a two-kernel linear model that accommodated both likely casual loci with large-effect and loci that explain little or no phenotypic variance. We propose that the two-kernel linear model is superior to most genomic prediction models that assume each variant is equally likely to affect the trait and can be used to improve prediction accuracy for any trait with prior knowledge of genetic architecture.

Multi-trait genomic-enabled prediction enhances accuracy in multi-year wheat breeding trials

Implementing genomic-based prediction models in genomic selection requires an understanding of the measures for evaluating prediction accuracy from different models and methods using multi-trait data. In this study, we compared prediction accuracy using six large multi-trait wheat data sets (quality and grain yield). The data were used to predict 1 year (testing) from the previous year (training) to assess prediction accuracy using four different prediction models. The results indicated that the conventional Pearson’s correlation between observed and predicted values underestimated the true correlation value, whereas the corrected Pearson’s correlation calculated by fitting a bivariate model was higher than the division of the Pearson’s correlation by the squared root of the heritability across traits, by 2.53–11.46%. Across the datasets, the corrected Pearson’s correlation was higher than the uncorrected by 5.80–14.01%. Overall, we found that for grain yield the prediction performance was highest using a multi-trait compared to a single-trait model. The higher the absolute genetic correlation between traits the greater the benefits of multi-trait models for increasing the genomic-enabled prediction accuracy of traits.

MegaLMM: Mega-scale linear mixed models for genomic predictions with thousands of traits

Large-scale phenotype data can enhance the power of genomic prediction in plant and animal breeding, as well as human genetics. However, the statistical foundation of multi-trait genomic prediction is based on the multivariate linear mixed effect model, a tool notorious for its fragility when applied to more than a handful of traits. We present MegaLMM, a statistical framework and associated software package for mixed model analyses of a virtually unlimited number of traits. Using three examples with real plant data, we show that MegaLMM can leverage thousands of traits at once to significantly improve genetic value prediction accuracy.

Social network analysis of the genealogy of strawberry: retracing the wild roots of heirloom and modern cultivars

The widely recounted story of the origin of cultivated strawberry (Fragaria × ananassa) oversimplifies the complex interspecific hybrid ancestry of the highly admixed populations from which heirloom and modern cultivars have emerged. To develop deeper insights into the three-century-long domestication history of strawberry, we reconstructed the genealogy as deeply as possible—pedigree records were assembled for 8,851 individuals, including 2,656 cultivars developed since 1775. The parents of individuals with unverified or missing pedigree records were accurately identified by applying an exclusion analysis to array-genotyped single-nucleotide polymorphisms. We identified 187 wild octoploid and 1,171 F. × ananassa founders in the genealogy, from the earliest hybrids to modern cultivars. The pedigree networks for cultivated strawberry are exceedingly complex labyrinths of ancestral interconnections formed by diverse hybrid ancestry, directional selection, migration, admixture, bottlenecks, overlapping generations, and recurrent hybridization with common ancestors that have unequally contributed allelic diversity to heirloom and modern cultivars. Fifteen to 333 ancestors were predicted to have transmitted 90% of the alleles found in country-, region-, and continent-specific populations. Using parent–offspring edges in the global pedigree network, we found that selection cycle lengths over the past 200 years of breeding have been extraordinarily long (16.0-16.9 years/generation), but decreased to a present-day range of 6.0-10.0 years/generation. Our analyses uncovered conspicuous differences in the ancestry and structure of North American and European populations, and shed light on forces that have shaped phenotypic diversity in F. × ananassa.

Implications of genetic heterogeneity for plant translocation during ecological restoration

Ecological restoration often requires translocating plant material from distant sites. Importing suitable plant material is important for successful establishment and persistence. Yet, published guidelines for seed transfer are available for very few species. Accurately predicting how transferred plants will perform requires multiyear and multi-environment field trials and comprehensive follow-up work, and is therefore infeasible given the number of species used in restoration programs. Alternative methods to predict the outcomes of seed transfer are valuable for species without published guidelines. In this study, we analyzed the genetic structure of an important shrub used in ecological restoration in the Southern Rocky Mountains called alder-leaf mountain mahogany (Cercocarpus montanus). We sequenced DNA from 1,440 plants in 48 populations across a broad geographic range. We found that genetic heterogeneity among populations reflected the complex climate and topography across which the species is distributed. We identified temperature and precipitation variables that were useful predictors of genetic differentiation and can be used to generate seed transfer recommendations. These results will be valuable for defining management and restoration practices for mountain mahogany.

VARIABLE PRIORITIZATION IN NONLINEAR BLACK BOX METHODS: A GENETIC ASSOCIATION CASE STUDY1

The central aim in this paper is to address variable selection questions in nonlinear and nonparametric regression. Motivated by statistical genetics, where nonlinear interactions are of particular interest, we introduce a novel and interpretable way to summarize the relative importance of predictor variables. Methodologically, we develop the "RelATive cEntrality" (RATE) measure to prioritize candidate genetic variants that are not just marginally important, but whose associations also stem from significant covarying relationships with other variants in the data. We illustrate RATE through Bayesian Gaussian process regression, but the methodological innovations apply to other "black box" methods. It is known that nonlinear models often exhibit greater predictive accuracy than linear models, particularly for phenotypes generated by complex genetic architectures. With detailed simulations and two real data association mapping studies, we show that applying RATE enables an explanation for this improved performance.

Translational regulation contributes to the elevated CO2 response in two Solanum species

Understanding the impact of elevated CO2 (eCO2 ) in global agriculture is important given climate change projections. Breeding climate-resilient crops depends on genetic variation within naturally varying populations. The effect of genetic variation in response to eCO2 is poorly understood, especially in crop species. We describe the different ways in which Solanum lycopersicum and its wild relative S. pennellii respond to eCO2 , from cell anatomy, to the transcriptome, and metabolome. We further validate the importance of translational regulation as a potential mechanism for plants to adaptively respond to rising levels of atmospheric CO2 .

Fast and flexible linear mixed models for genome-wide genetics

Linear mixed effect models are powerful tools used to account for population structure in genome-wide association studies (GWASs) and estimate the genetic architecture of complex traits. However, fully-specified models are computationally demanding and common simplifications often lead to reduced power or biased inference. We describe Grid-LMM (https://github.com/deruncie/GridLMM), an extendable algorithm for repeatedly fitting complex linear models that account for multiple sources of heterogeneity, such as additive and non-additive genetic variance, spatial heterogeneity, and genotype-environment interactions. Grid-LMM can compute approximate (yet highly accurate) frequentist test statistics or Bayesian posterior summaries at a genome-wide scale in a fraction of the time compared to existing general-purpose methods. We apply Grid-LMM to two types of quantitative genetic analyses. The first is focused on accounting for spatial variability and non-additive genetic variance while scanning for QTL; and the second aims to identify gene expression traits affected by non-additive genetic variation. In both cases, modeling multiple sources of heterogeneity leads to new discoveries.

Transcriptional regulation of nitrogen-associated metabolism and growth

Nitrogen is an essential macronutrient for plant growth and basic metabolic processes. The application of nitrogen-containing fertilizer increases yield, which has been a substantial factor in the green revolution¹. Ecologically, however, excessive application of fertilizer has disastrous effects such as eutrophication². A better understanding of how plants regulate nitrogen metabolism is critical to increase plant yield and reduce fertilizer overuse. Here we present a transcriptional regulatory network and twenty-one transcription factors that regulate the architecture of root and shoot systems in response to changes in nitrogen availability. Genetic perturbation of a subset of these transcription factors revealed coordinate transcriptional regulation of enzymes involved in nitrogen metabolism. Transcriptional regulators in the network are transcriptionally modified by feedback via genetic perturbation of nitrogen metabolism. The network, genes and gene-regulatory modules identified here will prove critical to increasing agricultural productivity.

Genomic Characterization of the Evolutionary Potential of the Sea Urchin Strongylocentrotus droebachiensis Facing Ocean Acidification

Ocean acidification (OA) is increasing due to anthropogenic CO₂ emissions and poses a threat to marine species and communities worldwide. To better project the effects of acidification on organisms’ health and persistence, an understanding is needed of the 1) mechanisms underlying developmental and physiological tolerance and 2) potential populations have for rapid evolutionary adaptation. This is especially challenging in nonmodel species where targeted assays of metabolism and stress physiology may not be available or economical for large-scale assessments of genetic constraints. We used mRNA sequencing and a quantitative genetics breeding design to study mechanisms underlying genetic variability and tolerance to decreased seawater pH (-0.4 pH units) in larvae of the sea urchin Strongylocentrotus droebachiensis. We used a gene ontology-based approach to integrate expression profiles into indirect measures of cellular and biochemical traits underlying variation in larval performance (i.e., growth rates). Molecular responses to OA were complex, involving changes to several functions such as growth rates, cell division, metabolism, and immune activities. Surprisingly, the magnitude of pH effects on molecular traits tended to be small relative to variation attributable to segregating functional genetic variation in this species. We discuss how the application of transcriptomics and quantitative genetics approaches across diverse species can enrich our understanding of the biological impacts of climate change.

The joint influence of photoperiod and temperature during growth cessation and development of dormancy in white spruce (Picea glauca)

Timely responses to environmental cues enable the synchronization of phenological life-history transitions essential for the health and survival of north-temperate and boreal tree species. While photoperiodic cues will remain persistent under climate change, temperature cues may vary, contributing to possible asynchrony in signals influencing developmental and physiological transitions essential to forest health. Understanding the relative contribution of photoperiod and temperature as determinants of the transition from active growth to dormancy is important for informing adaptive forest management decisions that consider future climates. Using a combination of photoperiod (long = 20 h or short = 8 h day lengths) and temperature (warm = 22 °C/16 °C and cool = 8 °C/4 °C day/night, respectively) treatments, we used microscopy, physiology and modeling to comprehensively examine hallmark traits of the growth-dormancy transition-including bud formation, growth cessation, cold hardiness and gas exchange-within two provenances of white spruce [Picea glauca (Moench) Voss] spanning a broad latitude in Alberta, Canada. Following exposure to experimental treatments, seedlings were transferred to favorable conditions, and the depth of dormancy was assessed by determining the timing and ability of spruce seedlings to resume growth. Short photoperiods promoted bud development and growth cessation, whereas longer photoperiods extended the growing season through the induction of lammas growth. In contrast, cool temperatures under both photoperiodic conditions delayed bud development. Photoperiod strongly predicted the development of cold hardiness, whereas temperature predicted photosynthetic rates associated with active growth. White spruce was capable of attaining endodormancy, but its release was environmentally determined. Dormancy depth varied substantially across experimental treatments suggesting that environmental cues experienced within one season could affect growth in the following season, which is particularly important for a determinate species such as white spruce. The joint influence of these environmental cues points toward the importance of including local constant photoperiod and shifting temperature cues into predictive models that consider how climate change may affect northern forests.

Fluctuating, warm temperatures decrease the effect of a key floral repressor on flowering time in Arabidopsis thaliana

The genetic basis of growth and development is often studied in constant laboratory environments; however, the environmental conditions that organisms experience in nature are often much more dynamic. We examined how daily temperature fluctuations, average temperature, day length and vernalization influence the flowering time of 59 genotypes of Arabidopsis thaliana with allelic perturbations known to affect flowering time. For a subset of genotypes, we also assessed treatment effects on morphology and growth. We identified 17 genotypes, many of which have high levels of the floral repressor FLOWERING LOCUS C (FLC), that bolted dramatically earlier in fluctuating - as opposed to constant - warm temperatures (mean = 22°C). This acceleration was not caused by transient VERNALIZATION INSENSITIVE 3-mediated vernalization, differential growth rates or exposure to high temperatures, and was not apparent when the average temperature was cool (mean = 12°C). Further, in constant temperatures, contrary to physiological expectations, these genotypes flowered more rapidly in cool than in warm environments. Fluctuating temperatures often reversed these responses, restoring faster bolting in warm conditions. Independently of bolting time, warm fluctuating temperature profiles also caused morphological changes associated with shade avoidance or 'high-temperature' phenotypes. Our results suggest that previous studies have overestimated the effect of the floral repressor FLC on flowering time by using constant temperature laboratory conditions.

The impact of gene expression variation on the robustness and evolvability of a developmental gene regulatory network

Regulatory interactions buffer development against genetic and environmental perturbations, but adaptation requires phenotypes to change. We investigated the relationship between robustness and evolvability within the gene regulatory network underlying development of the larval skeleton in the sea urchin Strongylocentrotus purpuratus. We find extensive variation in gene expression in this network throughout development in a natural population, some of which has a heritable genetic basis. Switch-like regulatory interactions predominate during early development, buffer expression variation, and may promote the accumulation of cryptic genetic variation affecting early stages. Regulatory interactions during later development are typically more sensitive (linear), allowing variation in expression to affect downstream target genes. Variation in skeletal morphology is associated primarily with expression variation of a few, primarily structural, genes at terminal positions within the network. These results indicate that the position and properties of gene interactions within a network can have important evolutionary consequences independent of their immediate regulatory role.

Dissecting high-dimensional phenotypes with bayesian sparse factor analysis of genetic covariance matrices

Quantitative genetic studies that model complex, multivariate phenotypes are important for both evolutionary prediction and artificial selection. For example, changes in gene expression can provide insight into developmental and physiological mechanisms that link genotype and phenotype. However, classical analytical techniques are poorly suited to quantitative genetic studies of gene expression where the number of traits assayed per individual can reach many thousand. Here, we derive a Bayesian genetic sparse factor model for estimating the genetic covariance matrix (G-matrix) of high-dimensional traits, such as gene expression, in a mixed-effects model. The key idea of our model is that we need consider only G-matrices that are biologically plausible. An organism's entire phenotype is the result of processes that are modular and have limited complexity. This implies that the G-matrix will be highly structured. In particular, we assume that a limited number of intermediate traits (or factors, e.g., variations in development or physiology) control the variation in the high-dimensional phenotype, and that each of these intermediate traits is sparse - affecting only a few observed traits. The advantages of this approach are twofold. First, sparse factors are interpretable and provide biological insight into mechanisms underlying the genetic architecture. Second, enforcing sparsity helps prevent sampling errors from swamping out the true signal in high-dimensional data. We demonstrate the advantages of our model on simulated data and in an analysis of a published Drosophila melanogaster gene expression data set.

Social environment influences the relationship between genotype and gene expression in wild baboons

Variation in the social environment can have profound effects on survival and reproduction in wild social mammals. However, we know little about the degree to which these effects are influenced by genetic differences among individuals, and conversely, the degree to which social environmental variation mediates genetic reaction norms. To better understand these relationships, we investigated the potential for dominance rank, social connectedness and group size to modify the effects of genetic variation on gene expression in the wild baboons of the Amboseli basin. We found evidence for a number of gene-environment interactions (GEIs) associated with variation in the social environment, encompassing social environments experienced in adulthood as well as persistent effects of early life social environment. Social connectedness, maternal dominance rank and group size all interacted with genotype to influence gene expression in at least one sex, and either in early life or in adulthood. These results suggest that social and behavioural variation, akin to other factors such as age and sex, can impact the genotype-phenotype relationship. We conclude that GEIs mediated by the social environment are important in the evolution and maintenance of individual differences in wild social mammals, including individual differences in responses to social stressors.

Genetics of gene expression responses to temperature stress in a sea urchin gene network

Stress responses play an important role in shaping species distributions and robustness to climate change. We investigated how stress responses alter the contribution of additive genetic variation to gene expression during development of the purple sea urchin, Strongylocentrotus purpuratus, under increased temperatures that model realistic climate change scenarios. We first measured gene expression responses in the embryos by RNA-seq to characterize molecular signatures of mild, chronic temperature stress in an unbiased manner. We found that an increase from 12 to 18 °C caused widespread alterations in gene expression including in genes involved in protein folding, RNA processing and development. To understand the quantitative genetic architecture of this response, we then focused on a well-characterized gene network involved in endomesoderm and ectoderm specification. Using a breeding design with wild-caught individuals, we measured genetic and gene-environment interaction effects on 72 genes within this network. We found genetic or maternal effects in 33 of these genes and that the genetic effects were correlated in the network. Fourteen network genes also responded to higher temperatures, but we found no significant genotype-environment interactions in any of the genes. This absence may be owing to an effective buffering of the temperature perturbations within the network. In support of this hypothesis, perturbations to regulatory genes did not affect the expression of the genes that they regulate. Together, these results provide novel insights into the relationship between environmental change and developmental evolution and suggest that climate change may not expose large amounts of cryptic genetic variation to selection in this species.