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[This corrects the article DOI: 10.1371/journal.pgen.1008367.].

Plant developmental dynamics can be heritable, genetically correlated with fitness and yield, and undergo selection. Therefore, characterizing the mechanistic connections between the genetic architecture governing plant development and the resulting ontogenetic dynamics of plants in field settings is critically important for agricultural production and evolutionary ecology. We use hierarchical Bayesian Function-Valued Trait (FVT) models to estimate Brassica rapa growth curves throughout ontogeny, across two treatments, and in two growing seasons. We find genetic variation for plasticity of growth rates and final sizes, but not the inflection point (transition from accelerating to decelerating growth) of growth curves. There are trade-offs between growth rate and duration, indicating that selection for maximum yields at early harvest dates may come at the expense of late harvest yields and vice versa. We generate eigengene modules and determine which are co-expressed with FVT traits using a Weighted Gene Co-expression Analysis. Independently, we seed a Mutual Rank co-expression network model with FVT traits to identify specific genes and gene networks related to FVT. GO-analyses of eigengene modules indicate roles for actin/cytoskeletal genes, herbivore resistance/wounding responses, and cell division, while MR networks demonstrate a close association between metabolic regulation and plant growth. We determine that combining FVT Quantitative Trait Loci (QTL) and MR genes/WGCNA eigengene expression profiles better characterizes phenotypic variation than any single data type (i.e. QTL, gene, or eigengene alone). Our network analysis allows us to employ a targeted eQTL analysis, which we use to identify regulatory hotspots for FVT. We examine cis vs. trans eQTL that mechanistically link FVT QTL with structural trait variation. Colocalization of FVT, gene, and eigengene eQTL provide strong evidence for candidate genes influencing plant height. The study is the first to explore eQTL for FVT, and specifically do so in agroecologically relevant field settings.

Brassica rapa is a model species for agronomic, ecological, evolutionary, and translational studies. Here, we describe high-density SNP discovery and genetic map construction for a B. rapa recombinant inbred line (RIL) population derived from field collected RNA sequencing (RNA-Seq) data. This high-density genotype data enables the detection and correction of putative genome misassemblies and accurate assignment of scaffold sequences to their likely genomic locations. These assembly improvements represent 7.1-8.0% of the annotated B. rapa genome. We demonstrate how using this new resource leads to a significant improvement for QTL analysis over the current low-density genetic map. Improvements are achieved by the increased mapping resolution and by having known genomic coordinates to anchor the markers for candidate gene discovery. These new molecular resources and improvements in the genome annotation will benefit the Brassicaceae genomics community and may help guide other communities in fine-tuning genome annotations.

The rhizosphere microbiome influences many aspects of plant fitness, including production of secondary compounds and defence against insect herbivores. Plants also modulate the composition of the microbial community in the rhizosphere via secretion of root exudates. We tested both the effect of the rhizosphere microbiome on plant traits, and host plant effects on rhizosphere microbes using recombinant inbred lines (RILs) of Brassica rapa that differ in production of glucosinolates (GLS), secondary metabolites that contribute to defence against insect herbivores. First, we investigated the effect of genetic variation in GLS production on the composition of the rhizosphere microbiome. Using a Bayesian Dirichlet‐multinomial regression model (DMBVS), we identified both negative and positive associations between bacteria from six genera and the concentration of five GLS compounds produced in plant roots. Additionally, we tested the effects of microbial inoculation (an intact vs. disrupted soil microbiome) on GLS production and insect damage in these RILs. We found a significant microbial treatment × genotype interaction, in which total GLS was higher in the intact relative to the disrupted microbiome treatment in some RILs. However, despite differences in GLS production between microbial treatments, we observed no difference in insect damage between treatments. Together, these results provide evidence for a full feedback cycle of plant–microbe interactions mediated by GLS; that is, GLS compounds produced by the host plant "feed‐down" to influence rhizosphere microbial community and rhizosphere microbes "feed‐up" to influence GLS production. [ABSTRACT FROM AUTHOR]

The importance of genes of major effect for evolutionary trajectories within and among natural populations has long been the subject of intense debate. For example, if allelic variation at a major-effect locus fundamentally alters the structure of quantitative trait variation, then fixation of a single locus tan have rapid and profound effects on the rate or direction of subsequent evolutionary change. Using an Arabidopsis thaliana RIL mapping population, we compare G-matrix structure between lines possessing different alleles at ERECTA, a locus known to affect ecologically relevant variation in plant architecture. We find that the allele present at ERECTA significantly alters G-matrix structure--in particular the genetic correlations between branch number and flowering time traits--and may also modulate the strength of natural selection on these traits. Despite these differences, however, when we extend our analysis to determine how evolution might differ depending on the ERECTA allele, we lind that predicted responses to selection are similar. To compare responses to selection between allele classes, we developed a resampling strategy that incorporates uncertainty in estimates of selection that can also be used for statistical comparisons of G matrices. DOI: 10.1534/genetics.108.097030

Exotic plant species can threaten closely related native congeners through asymmetric hybridization and subsequent backcrossing, the process known as genetic assimilation. I explore the initial stages of this process in Taraxacum ceratophorum (Asteraceae), the native alpine dandelion, and the invasive apomict T. officinale. In central Colorado, seven T. ceratophorum populations all occur in sympatry with T officinale. In one large population on Pennsylvania Mountain, surveys further revealed that flowering phenologies and visiting insect taxa overlap almost completely for both Taraxacum species. Together these results indicated that heterospecific pollen transfer is likely. Crossing experiments showed that T. ceratophorum is an obligate outcrosser, and interspecific hand pollinations resulted in 37.3% seed set. However, molecular analysis of the F1 offspring indicated that only 33.2% of germinating seeds were hybrids; the remainder were selfed offspring produced from a breakdown in self-incompatibility (the mentor effect). Although the mentor effect helps reduce the production of hybrids, the asymmetrical direction of hybridization creates the potential for genetic assimilation of T. ceratophorum by T. officinale. Key words: Asteraceae; exotic species; genetic assimilation; hybridization; Taraxacum.

The circadian clock facilitates coordination of the internal rhythms of an organism to daily environmental conditions, such as the light-dark cycle of one day. Circadian period length (the duration of one endogenous cycle) and phase (the timing of peak activity) exhibit quantitative variation in natural populations. Here, we measured circadian period and phase in June, July and September in three Arabidopsis thaliana recombinant inbred line populations. Circadian period and phase were estimated from bioluminescence of a genetic construct between a native circadian clock gene (COLD CIRCADIAN RHYTHM RNA BINDING 2) and the reporter gene (LUCIFERASE) after lines were entrained under field settings. Using a Bayesian mapping approach, we estimated the median number and effect size of genomic regions (Quantitative Trait Loci, QTL) underlying circadian parameters and the degree to which these regions overlap across months of the growing season. We also tested for QTL associations between the circadian clock and plant morphology. The genetic architecture of circadian phase was largely independent across months, as evidenced by the fact that QTL determining phase values in one month of the growing season were different from those determining phase in a second month. QTL for circadian parameters were shared with both cauline and rosette branching in at least one mapping population. The results provide insights into the QTL architecture of the clock under field settings, and suggest that the circadian clock is highly responsive to changing environments and that selection can act on clock phase in a nuanced manner. [ABSTRACT FROM AUTHOR]

Key message: We develop Bayesian function-valued trait models that mathematically isolate genetic mechanisms underlying leaf growth trajectories by factoring out genotype-specific differences in photosynthesis. Remote sensing data can be used instead of leaf-level physiological measurements. Abstract: Characterizing the genetic basis of traits that vary during ontogeny and affect plant performance is a major goal in evolutionary biology and agronomy. Describing genetic programs that specifically regulate morphological traits can be complicated by genotypic differences in physiological traits. We describe the growth trajectories of leaves using novel Bayesian function-valued trait (FVT) modeling approaches in Brassica rapa recombinant inbred lines raised in heterogeneous field settings. While frequentist approaches estimate parameter values by treating each experimental replicate discretely, Bayesian models can utilize information in the global dataset, potentially leading to more robust trait estimation. We illustrate this principle by estimating growth asymptotes in the face of missing data and comparing heritabilities of growth trajectory parameters estimated by Bayesian and frequentist approaches. Using pseudo-Bayes factors, we compare the performance of an initial Bayesian logistic growth model and a model that incorporates carbon assimilation ( A ) as a cofactor, thus statistically accounting for genotypic differences in carbon resources. We further evaluate two remotely sensed spectroradiometric indices, photochemical reflectance (pri2) and MERIS Terrestrial Chlorophyll Index (mtci) as covariates in lieu of A , because these two indices were genetically correlated with A across years and treatments yet allow much higher throughput compared to direct leaf-level gas-exchange measurements. For leaf lengths in uncrowded settings, including A improves model fit over the initial model. The mtci and pri2 indices also outperform direct A measurements. Of particular importance for evolutionary biologists and plant breeders, hierarchical Bayesian models estimating FVT parameters improve heritabilities compared to frequentist approaches. [ABSTRACT FROM AUTHOR]