Impacts of sample handling and storage conditions on archiving physiologically active soil microbial communities.

Soil microbial communities are fundamental to ecosystem processes and plant growth, yet community composition is seasonally and successionally dynamic, which interferes with long-term iterative experimentation of plant-microbe interactions. We explore how soil sample handling (e.g. filtering) and sample storage conditions impact the ability to revive the original, physiologically active, soil microbial community. We obtained soil from agricultural fields in Montana and Oklahoma, USA and samples were sieved to 2 mm or filtered to 45 µm. Sieved and filtered soil samples were archived at -20°C or -80°C for 50 days and revived for 2 or 7 days. We extracted DNA and the more transient RNA pools from control and treatment samples and characterized microbial communities using 16S amplicon sequencing. Filtration and storage treatments significantly altered soil microbial communities, impacting both species richness and community composition. Storing sieved soil at -20°C did not alter species richness and resulted in the least disruption to the microbial community composition in comparison to nonarchived controls as characterized by RNA pools from soils of both sites. Filtration significantly altered composition but not species richness. Archiving sieved soil at -20°C could allow for long-term and repeated experimentation on preserved physiologically active microbial communities.

The rhizosphere microbiome and host plant glucosinolates exhibit feedback cycles in Brassica rapa.

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.

Spatiotemporal Heterogeneity and Intragenus Variability in Rhizobacterial Associations with Brassica rapa Growth.

Microbial communities in the rhizosphere are distinct from those in soils and are influenced by stochastic and deterministic processes during plant development. These communities contain bacteria capable of promoting growth in host plants through various strategies. While some interactions are characterized in mechanistic detail using model systems, others can be inferred from culture-independent methods, such as 16S amplicon sequencing, using machine learning methods that account for this compositional data type. To characterize assembly processes and identify community members associated with plant growth amid the spatiotemporal variability of the rhizosphere, we grew Brassica rapa in a greenhouse time series with amended and reduced microbial treatments. Inoculation with a native soil community increased plant leaf area throughout the time series by up to 28%. Despite identifying spatially and temporally variable amplicon sequence variants (ASVs) in both treatments, inoculated communities were more highly connected and assembled more deterministically overall. Using a generalized linear modeling approach controlling for spatial variability, we identified 43 unique ASVs that were positively or negatively associated with leaf area, biomass, or growth rates across treatments and time stages. ASVs of the genus Flavobacterium dominated rhizosphere communities and showed some of the strongest positive and negative correlations with plant growth. Members of this genus, and growth-associated ASVs more broadly, exhibited variable connectivity in networks independent of growth association (positive or negative). These findings suggest host-rhizobacterial interactions vary temporally at narrow taxonomic scales and present a framework for identifying rhizobacteria that may work independently or in concert to improve agricultural yields. IMPORTANCE The rhizosphere, the zone of soil surrounding plant roots, is a hot spot for microbial activity, hosting bacteria capable of promoting plant growth in ways like increasing nutrient availability or fighting plant pathogens. This microbial system is highly diverse and most bacteria are unculturable, so to identify specific bacteria associated with plant growth, we used culture-independent community DNA sequencing combined with machine learning techniques. We identified 43 specific bacterial sequences associated with the growth of the plant Brassica rapa in different soil microbial treatments and at different stages of plant development. Most associations between bacterial abundances and plant growth were positive, although similar bacterial groups sometimes had different effects on growth. Why this happens will require more research, but overall, this study provides a way to identify native bacteria from plant roots that might be isolated and applied to boost agricultural yields.

Time outweighs the effect of host developmental stage on microbial community composition.

Thousands of microbial taxa in the soil form symbioses with host plants, and due to their contribution to plant performance, these microbes are often considered an extension of the host genome. Given microbial effects on host performance, it is important to understand factors that govern microbial community assembly. Host developmental stage could affect rhizosphere microbial diversity while, alternatively, microbial assemblages could change simply as a consequence of time and the opportunity for microbial succession. Previous studies suggest that rhizosphere microbial assemblages shift across plant developmental stages, but time since germination is confounded with developmental stage. We asked how elapsed time and potential microbial succession relative to host development affected microbial diversity in the rhizosphere using monogenic flowering-time mutants of Arabidopsis thaliana. Under our experimental design, different developmental stages were present among host genotypes after the same amount of time following germination, e.g. at 76 days following germination some host genotypes were flowering while others were fruiting or senescing. We found that elapsed time was a strong predictor of microbial diversity whereas there were few differences among developmental stages. Our results support the idea that time and, likely, microbial succession more strongly affect microbial community assembly than host developmental stage.

Soil Microsite Outweighs Cultivar Genotype Contribution to Brassica Rhizobacterial Community Structure.

Microorganisms residing on root surfaces play a central role in plant development and performance and may promote growth in agricultural settings. Studies have started to uncover the environmental parameters and host interactions governing their assembly. However, soil microbial communities are extremely diverse and heterogeneous, showing strong variations over short spatial scales. Here, we quantify the relative effect of meter-scale variation in soil bacterial community composition among adjacent field microsites, to better understand how microbial communities vary by host plant genotype as well as soil microsite heterogeneity. We used bacterial 16S rDNA amplicon sequencing to compare rhizosphere communities from four Brassica rapa cultivars grown in three contiguous field plots (blocks) and evaluated the relative contribution of resident soil communities and host genotypes in determining rhizosphere community structure. We characterize concomitant meter-scale variation in bacterial community structure among soils and rhizospheres and show that this block-scale variability surpasses the influence of host genotype in shaping rhizosphere communities. We identified biomarker amplicon sequence variants (ASVs) associated with bulk soil and rhizosphere habitats, each block, and three of four cultivars. Numbers and percent abundances of block-specific biomarkers in rhizosphere communities far surpassed those from bulk soils. These results highlight the importance of fine-scale variation in the pool of colonizing microorganisms during rhizosphere assembly and demonstrate that microsite variation may constitute a confounding effect while testing biotic and abiotic factors governing rhizosphere community structure.

Correction: Integrating transcriptomic network reconstruction and eQTL analyses reveals mechanistic connections between genomic architecture and Brassica rapa development.

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

A Nested Association Mapping Panel in Arabidopsis thaliana for Mapping and Characterizing Genetic Architecture.

Linkage and association mapping populations are crucial public resources that facilitate the characterization of trait genetic architecture in natural and agricultural systems. We define a large nested association mapping panel (NAM) from 14 publicly available recombinant inbred line populations (RILs) of Arabidopsis thaliana, which share a common recurrent parent (Col-0). Using a genotype-by-sequencing approach (GBS), we identified single nucleotide polymorphisms (SNPs; range 563-1525 per population) and subsequently built updated linkage maps in each of the 14 RIL sets. Simulations in individual RIL populations indicate that our GBS markers have improved power to detect small effect QTL and enhanced resolution of QTL support intervals in comparison to original linkage maps. Using these robust linkage maps, we imputed a common set of publicly available parental SNPs into each RIL linkage map, generating overlapping markers across all populations. Though ultimately depending on allele frequencies at causal loci, simulations of the NAM panel suggest that surveying between 4 to 7 of the 14 RIL populations provides high resolution of the genetic architecture of complex traits, relative to a single mapping population.

Integrating transcriptomic network reconstruction and eQTL analyses reveals mechanistic connections between genomic architecture and Brassica rapa development.

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.

QTL Underlying Circadian Clock Parameters Under Seasonally Variable Field Settings in Arabidopsis thaliana.

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.

The effect of rhizosphere microbes outweighs host plant genetics in reducing insect herbivory.

Rhizosphere microbes affect plant performance, including plant resistance against insect herbivores; yet, a direct comparison of the relative influence of rhizosphere microbes versus plant genetics on herbivory levels and on metabolites related to defence is lacking. In the crucifer Boechera stricta, we tested the effects of rhizosphere microbes and plant population on herbivore resistance, the primary metabolome, and select secondary metabolites. Plant populations differed significantly in the concentrations of six glucosinolates (GLS), secondary metabolites known to provide herbivore resistance in the Brassicaceae. The population with lower GLS levels experienced ~60% higher levels of aphid (Myzus persicae) attack; no association was observed between GLS and damage by a second herbivore, flea beetles (Phyllotreta cruciferae). Rhizosphere microbiome (disrupted vs. intact native microbiome) had no effect on plant GLS concentrations. However, aphid number and flea beetle damage were respectively about three- and seven-fold higher among plants grown in the disrupted versus intact native microbiome treatment. These differences may be attributable to shifts in primary metabolic pathways previously implicated in host defence against herbivores, including increases in pentose and glucoronate interconversion among plants grown with an intact microbiome. Furthermore, native microbiomes with distinct community composition (as estimated from 16s rRNA amplicon sequencing) differed two-fold in their effect on host plant susceptibility to aphids. The findings suggest that rhizosphere microbes, including distinct native microbiomes, can play a greater role than population in defence against insect herbivores, and act through metabolic mechanisms independent of population.

Circadian rhythms are associated with shoot architecture in natural settings.

Circadian rhythms are key regulators of diverse biological processes under controlled settings. Yet, the phenotypic and fitness consequences of quantitative variation in circadian rhythms remain largely unexplored in the field. As with other pathways, phenotypic characterization of circadian outputs in the field may reveal novel clock functions. Across consecutive growing seasons, we test for associations between clock variation and flowering phenology, plant size, shoot architecture, and fruit set in clock mutants and segregating progenies of Arabidopsis thaliana expressing quantitative variation in circadian rhythms. Using structural equation modeling, we find that genotypic variation in circadian rhythms within a growing season is associated directly with branching, which in turn affects fruit production. Consistent with direct associations between the clock and branching in segregating progenies, cauline branch number is lower and rosette branch number higher in a short-period mutant relative to wild-type and long-period genotypes, independent of flowering time. Differences in branching arise from variation in meristem fate as well as leaf production rate before flowering and attendant increases in meristem number. Our results suggest that clock variation directly affects shoot architecture in the field, suggesting a novel clock function and means by which the clock affects performance.

The plant circadian clock influences rhizosphere community structure and function.

Plants alter chemical and physical properties of soil, and thereby influence rhizosphere microbial community structure. The structure of microbial communities may in turn affect plant performance. Yet, outside of simple systems with pairwise interacting partners, the plant genetic pathways that influence microbial community structure remain largely unknown, as are the performance feedbacks of microbial communities selected by the host plant genotype. We investigated the role of the plant circadian clock in shaping rhizosphere community structure and function. We performed 16S ribosomal RNA gene sequencing to characterize rhizosphere bacterial communities of Arabidopsis thaliana between day and night time points, and tested for differences in community structure between wild-type (Ws) vs clock mutant (toc1-21, ztl-30) genotypes. We then characterized microbial community function, by growing wild-type plants in soils with an overstory history of Ws, toc1-21 or ztl-30 and measuring plant performance. We observed that rhizosphere community structure varied between day and night time points, and clock misfunction significantly altered rhizosphere communities. Finally, wild-type plants germinated earlier and were larger when inoculated with soils having an overstory history of wild-type in comparison with clock mutant genotypes. Our findings suggest the circadian clock of the plant host influences rhizosphere community structure and function.

Bayesian estimation and use of high-throughput remote sensing indices for quantitative genetic analyses of leaf growth.

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. 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 max) 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 max, because these two indices were genetically correlated with A max 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 max improves model fit over the initial model. The mtci and pri2 indices also outperform direct A max measurements. Of particular importance for evolutionary biologists and plant breeders, hierarchical Bayesian models estimating FVT parameters improve heritabilities compared to frequentist approaches.

Circadian rhythms vary over the growing season and correlate with fitness components.

Circadian clocks have evolved independently in all three domains of life, suggesting that internal mechanisms of time-keeping are adaptive in contemporary populations. However, the performance consequences of either discrete or quantitative clock variation have rarely been tested in field settings. Clock sensitivity of diverse segregating lines to the environment remains uncharacterized as do the statistical genetic parameters that determine evolutionary potential. In field studies with Arabidopsis thaliana, we found that major perturbations to circadian cycle length (referred to as clock period) via mutation reduce both survival and fecundity. Subtler adjustments via genomic introgression of naturally occurring alleles indicated that clock periods slightly >24 hr were adaptive, consistent with prior models describing how well the timing of biological processes is adjusted within a diurnal cycle (referred to as phase). In segregating recombinant inbred lines (RILs), circadian phase varied up to 2 hr across months of the growing season, and both period and phase expressed significant genetic variances. Performance metrics including developmental rate, size and fruit set were described by principal components (PC) analyses and circadian parameters correlated with the first PC, such that period lengths slightly >24 hr were associated with improved performance in multiple RIL sets. These experiments translate functional analyses of clock behaviour performed in controlled settings to natural ones, demonstrating that quantitative variation in circadian phase is highly responsive to seasonally variable abiotic factors. The results expand upon prior studies in controlled settings, showing that discrete and quantitative variation in clock phenotypes correlates with performance in nature.

Using RNA-Seq for Genomic Scaffold Placement, Correcting Assemblies, and Genetic Map Creation in a Common Brassica rapa Mapping Population.

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.

Genetic architecture, biochemical underpinnings and ecological impact of floral UV patterning.

Floral attraction traits can significantly affect pollinator visitation patterns, but adaptive evolution of these traits may be constrained by correlations with other traits. In some cases, molecular pathways contributing to floral attraction are well characterized, offering the opportunity to explore loci potentially underlying variation among individuals. Here, we quantify the range of variation in floral UV patterning (i.e. UV 'bulls-eye nectar guides) among crop and wild accessions of Brassica rapa. We then use experimental crosses to examine the genetic architecture, candidate loci and biochemical underpinnings of this patterning as well as phenotypic manipulations to test the ecological impact. We find qualitative variation in UV patterning between wild (commonly lacking UV patterns) and crop (commonly exhibiting UV patterns) accessions. Similar to the majority of crops, recombinant inbred lines (RILs) derived from an oilseed crop × WI fast-plant® cross exhibit UV patterns, the size of which varies extensively among genotypes. In RILs, we further observe strong statistical-genetic and QTL correlations within petal morphological traits and within measurements of petal UV patterning; however, correlations between morphology and UV patterning are weak or nonsignificant, suggesting that UV patterning is regulated and may evolve independently of overall petal size. HPLC analyses reveal a high concentration of sinapoyl glucose in UV-absorbing petal regions, which, in concert with physical locations of UV-trait QTLs, suggest a regulatory and structural gene as candidates underlying observed quantitative variation. Finally, insects prefer flowers with UV bulls-eye patterns over those that lack patterns, validating the importance of UV patterning in pollen-limited populations of B. rapa.

Modeling development and quantitative trait mapping reveal independent genetic modules for leaf size and shape.

Improved predictions of fitness and yield may be obtained by characterizing the genetic controls and environmental dependencies of organismal ontogeny. Elucidating the shape of growth curves may reveal novel genetic controls that single-time-point (STP) analyses do not because, in theory, infinite numbers of growth curves can result in the same final measurement. We measured leaf lengths and widths in Brassica rapa recombinant inbred lines (RILs) throughout ontogeny. We modeled leaf growth and allometry as function valued traits (FVT), and examined genetic correlations between these traits and aspects of phenology, physiology, circadian rhythms and fitness. We used RNA-seq to construct a SNP linkage map and mapped trait quantitative trait loci (QTL). We found genetic trade-offs between leaf size and growth rate FVT and uncovered differences in genotypic and QTL correlations involving FVT vs STPs. We identified leaf shape (allometry) as a genetic module independent of length and width and identified selection on FVT parameters of development. Leaf shape is associated with venation features that affect desiccation resistance. The genetic independence of leaf shape from other leaf traits may therefore enable crop optimization in leaf shape without negative effects on traits such as size, growth rate, duration or gas exchange.

QTL architecture of reproductive fitness characters in Brassica rapa.

BACKGROUND: Reproductive output is critical to both agronomists seeking to increase seed yield and to evolutionary biologists interested in understanding natural selection. We examine the genetic architecture of diverse reproductive fitness traits in recombinant inbred lines (RILs) developed from a crop (seed oil) × wild-like (rapid cycling) genotype of Brassica rapa in field and greenhouse environments. RESULTS: Several fitness traits showed strong correlations and QTL-colocalization across environments (days to bolting, fruit length and seed color). Total fruit number was uncorrelated across environments and most QTL affecting this trait were correspondingly environment-specific. Most fitness components were positively correlated, consistent with life-history theory that genotypic variation in resource acquisition masks tradeoffs. Finally, we detected evidence of transgenerational pleiotropy, that is, maternal days to bolting was negatively correlated with days to offspring germination. A QTL for this transgenerational correlation was mapped to a genomic region harboring one copy of FLOWERING LOCUS C, a genetic locus known to affect both days to flowering as well as germination phenotypes. CONCLUSIONS: This study characterizes the genetic structure of important fitness/yield traits within and between generations in B. rapa. Several identified QTL are suitable candidates for fine-mapping for the improvement of yield in crop Brassicas. Specifically, brFLC1, warrants further investigation as a potential regulator of phenology between generations.

Quantitative trait loci × environment interactions for plant morphology vary over ontogeny in Brassica rapa.

Growth in plants occurs via the addition of repeating modules, suggesting that the genetic architecture of similar subunits may vary between earlier- and later-developing modules. These complex environment × ontogeny interactions are not well elucidated, as studies examining quantitative trait loci (QTLs) expression over ontogeny have not included multiple environments. Here, we characterized the genetic architecture of vegetative traits and onset of reproduction over ontogeny in recombinant inbred lines of Brassica rapa in the field and glasshouse. The magnitude of genetic variation in plasticity of seedling internodes was greater than in those produced later in ontogeny. We correspondingly detected that QTLs for seedling internode length were environment-specific, whereas later in ontogeny the majority of QTLs affected internode lengths in all treatments. The relationship between internode traits and onset of reproduction varied with environment and ontogenetic stage. This relationship was observed only in the glasshouse environment and was largely attributable to one environment-specific QTL. Our results provide the first evidence of a QTL × environment × ontogeny interaction, and provide QTL resolution for differences between early- and later-stage plasticity for stem elongation. These results also suggest potential constraints on morphological evolution in early vs later modules as a result of associations with reproductive timing.

Natural variation in GA1 associates with floral morphology in Arabidopsis thaliana.

• The genetic architecture of floral traits is evolutionarily important due to the fitness consequences of quantitative variation in floral morphology. Yet, little is known about the genes underlying these traits in natural populations. Using Arabidopsis thaliana, we examine molecular variation at GIBBERELLIC ACID REQUIRING 1 (GA1) and test for associations with floral morphology. • We examined full-length sequence in 32 accessions and describe two haplotypes (comprising four nonsynonymous polymorphisms) in GA1 that segregate at intermediate frequencies. In 133 A. thaliana accessions, we test for genotype-phenotype associations and corroborate these findings in segregating progenies. • The two common GA1 haplotypes were associated with the length of petals, stamens, and to a lesser extent style-stigma length. Associations were confirmed in a segregating progeny developed from 19 accessions. We find analogous results in recombinant inbred lines of the Bayreuth × Shahdara cross, which differ only at one of 4 SNPs, suggesting that this SNP may contribute to the observed association. • Assuming GA1 causally affects floral organ size, it is interesting that adjacent petal and stamen whorls are most strongly affected. This pattern suggests that GA1 could contribute to the greater strength of petal-stamen correlations relative to other floral-length correlations observed in some Brassicaceous species.