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. Animal development is highly stereotypic in the face of changing environmental conditions and individual genetic differences. At the same time, developmental processes can evolve rapidly, suggesting that selectable genetic variation is hidden beneath this apparent stability. To better understand the relationship between these seemingly opposed properties of robustness and evolvability, we measured how natural variation in gene expression propagates across a network of interacting genes underlying early development in sea urchins. We found that gene interactions are not equal across development: expression variation is well buffered during early development by the use of on/off switch-like (rather than continuously tunable) regulatory mechanisms, while during later development it has a greater impact on the expression of downstream target genes and on morphology. Using a breeding design, we were able to detect a substantial genetic component to the observed variation in gene expression. Interestingly, the degree of genetic contribution was greatest during early development and specifically at points of switch-like regulation, suggesting that the properties of developmental gene regulatory networks that underlie robustness also promote the accumulation of genetic variation that could seed adaptation.

The seasonal timing of seed germination determines a plant’s realized environmental niche, and is important for adaptation to climate. The timing of seasonal germination depends on patterns of seed dormancy release or induction by cold and interacts with flowering-time variation to construct different seasonal life histories. To characterize the genetic basis and climatic associations of natural variation in seed chilling responses and associated life-history syndromes, we selected 559 fully sequenced accessions of the model annual species Arabidopsis thaliana from across a wide climate range and scored each for seed germination across a range of 13 cold stratification treatments, as well as the timing of flowering and senescence. Germination strategies varied continuously along 2 major axes: 1) Overall germination fraction and 2) induction vs. release of dormancy by cold. Natural variation in seed responses to chilling was correlated with flowering time and senescence to create a range of seasonal life-history syndromes. Genome-wide association identified several loci associated with natural variation in seed chilling responses, including a known functional polymorphism in the self-binding domain of the candidate gene DOG1. A phylogeny of DOG1 haplotypes revealed ancient divergence of these functional variants associated with periods of Pleistocene climate change, and Gradient Forest analysis showed that allele turnover of candidate SNPs was significantly associated with climate gradients. These results provide evidence that A. thaliana ’s germination niche and correlated life-history syndromes are shaped by past climate cycles, as well as local adaptation to contemporary climate.

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 , 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 can leverage thousands of traits at once to significantly improve genetic value prediction accuracy.

Chromosomal inversions play an important role in local adaptation. Inversions can capture multiple locally adaptive functional variants in a linked block by repressing recombination. However, this recombination suppression makes it difficult to identify the genetic mechanisms underlying an inversion’s role in adaptation. In this study, we used large-scale transcriptomic data to dissect the functional importance of a 13 Mb inversion locus (Inv4m) found almost exclusively in highland populations of maize (Zea mays ssp. mays). Inv4m was introgressed into highland maize from the wild relative Zea mays ssp. mexicana, also present in the highlands of Mexico, and is thought to be important for the adaptation of these populations to cultivation in highland environments. However, the specific genetic variants and traits that underlie this adaptation are not known. We created two families segregating for the standard and inverted haplotypes of Inv4m in a common genetic background and measured gene expression effects associated with the inversion across 9 tissues in two experimental conditions. With these data, we quantified both the global transcriptomic effects of the highland Inv4m haplotype, and the local cis-regulatory variation present within the locus. We found diverse physiological effects of Inv4m across the 9 tissues, including a strong effect on the expression of genes involved in photosynthesis and chloroplast physiology. Although we could not confidently identify the causal alleles within Inv4m, this research accelerates progress towards understanding this inversion and will guide future research on these important genomic features.