Genome-wide Association Study Identifies Candidate Loci with Major Contributions to the Genetic Control of Pod Morphological Traits in Snap Bean

Authors:
Ana Saballos Global Change and Photosynthesis Research Unit, US Department of Agriculture–Agricultural Research Service, Urbana, IL 61801, USA

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Martin M. Williams II Global Change and Photosynthesis Research Unit, US Department of Agriculture–Agricultural Research Service, Urbana, IL 61801, USA

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Abstract

Snap beans are cultivars of common bean (Phaseolus vulgaris) that are cultivated for their fleshy immature pods that exhibit a wide diversity of pod shapes and sizes. The genetic basis of the snap bean pod shape is complex and involves the interaction of multiple genes. This study used a snap bean diversity panel composed of heirloom and improved cultivars used in North America and genome-wide association studies (GWAS) to investigate the genetic basis of pod morphological characteristics, including length, width, height, width/height ratio, and coefficients of variation (CVs). The GWAS detected multiple genomic regions associated with each pod trait, with a total of 20 quantitative trait loci (QTLs) for pod length, 9 for pod width, 14 for pod height, and 10 for pod width/height ratio. Regarding the CV of each pod trait, genome-wide association analyses detected six QTL for length CVs, five for width CVs, 15 for height CVs, and six for width/height ratio CVs. Thirteen regions in seven chromosomes were associated with two or more pod traits. Eighteen QTLs for pod traits in this study colocated with previously reported QTLs for pod and seed traits. The QTL intervals encompass gene models with homologues in other species that are involved in the control of developmental processes. These results capture the complex nature of the genetic control of snap bean pod traits and confirm the significance of genomic regions harboring overlapping QTLs identified in this and other studies. The phenotypic expression of pod traits in snap bean appears to be under the control of a few genomic regions with a strong effect with additional contributions of multiple small-effect regions. Validation of the function of the candidate genes identified in associated regions will contribute to our understanding of legume pod development.

Snap beans are cultivars of common bean (Phaseolus vulgaris) cultivated for their fleshy immature pods and eaten as a vegetable. They have been independently selected from ancestral common bean gene pools of Middle-American (formerly referred as Mesoamerican) and Andean origins (Wallace et al. 2018). Commercial snap bean cultivars have high admixture between the genetic pools, reflecting adaptive historical introgression events (Bellucci et al. 2023) and intentional crosses to create improved cultivars (Wallace et al. 2018). Snap beans are marketed under different classes that include large-sieve green beans, wax beans, flat-podded Romano beans, and small-sieve whole bean (Myers and Baggett 1999). Morphological factors of the pod, including, shape, thickness, length, and color, determine the downstream fresh market or processing of the product. The ideal pod depends on the target market segment. In general, dark green, cylindrical, straight pods that mature at a uniform sieve size are preferred for the canning and packaging sector, whereas a variety of colors and shapes are found in the fresh market sector (Myers and Baggett 1999).

Many studies have sought to determine the genetics of pod morphological characteristics. Control of the pod cross-section, pod membrane, shattering, stringless pod, fiber content, and straight pods of common bean have been investigated (Gioia et al. 2013; Hagerty 2013; Koinange et al. 1996; Lamprecht 1947; Li et al. 2023; Liu et al. 2022; Parker et al. 2022). The pod of wild-type P. vulgaris is composed of a fused carpel, with a dorsal and ventral suture connecting the pod walls (Santos et al. 2023 and references within). Lignification occurs in vascular bundles along the sutures and in the wall fiber layer, creating what is commonly referred to as the suture string and wall parchment layer (Parker et al. 2021; Santos et al. 2023). The selection of tender, succulent, stringless snap bean from thin-walled, fibrous stringy dry bean partially consisted of the stepwise selection of types with no or weak wall fiber layer and stringless types (Al-Bader 2014, and references within). Classical genetic studies have reported the presence of dwarf seed (ds), a recessive gene that produces short pods (Bassett 1982), and polymeric genes Elliptische (Ea Eb), which conditioned elliptical cross-section pods compared to ea eb, which resulted in round cross-sections (Lamprech 1947, as cited in Hagerty et al. 2016).

Quantitative genetic studies suggest that morphological characteristics are quantitatively inherited traits with a few major genes and many genes with smaller effects. García-Fernández (2021) used genome-wide association studies (GWAS) to identify genetic variants associated with pod morphological and color characters of snap bean belonging to a Spanish diversity panel. That study identified several key regions potentially involved in the regulation of the traits as well as the genetic basis of the variation in the traits within the Spanish population. Murube et al. (2020) identified multiple quantitative trait loci (QTLs) for pod traits in two nested populations of dry bean, including four regions that were present in both populations. Hagerty et al. (2016) used a population derived from a cross between a dry bean and snap bean to map processing traits, including pod wall fiber, pod height, pod width, and pod wall thickness. A region in Chr04 was found to be associated with multiple processing traits. A large-scale GWAS of 683 lines of common bean genotyped with nearly five million single-nucleotide polymorphisms (SNPs) investigated the genetic basis of multiple yield component traits, including seed and pod measurements. This study produced marker trait associations for pod length, width, and height, with the associations for width proven to be the most stable across environments (Wu et al. 2020). A GWAS study by Liu et al. (2022) identified that PvGUX1_1 was associated with stringless pod. A different gene in close proximity, PvIND, was identified in an RNA expression study by Parker et al. (2022), and it was associated with the same trait.

Multiple pathways determine the final size and morphology of plant organs. Genes involved in cell proliferation rate, timing of proliferation arrest, and rate and duration of cell expansion (Hepworth and Lenhard 2014) are potential candidates for controlling pod size. The composition of the different pod tissues may influence shape. Genes involved in secondary wall formation and lignification could influence the size of the cells composing the pod walls, as demonstrated in the Arabidopsis thaliana Fruitful overexpressing lines, in which the endocarp b layer, composed of small, lignified cells in the wild-type, become larger and mesocarp-like (Roeder and Yanofsky 2006).

For producers of horticultural crops, minimal variation in key traits is important because products that fail to meet certain standards may be rejected by the consumers or processors (USDA 1959, 1990). Uniformity at maturity of beans for processing maximizes the proportion of the harvested product that meets the correct standards of the target market segment and allows accurate grading of the lot through the sieve size (Al-Bader 2014). Cultivars with higher variations in pod shape and size at harvest time may result in higher pod rejection rates and lower quality of the lots. Kerr (1971) described the appearance of rogue “flat pod” beans in round-podded cultivars of snap beans. The appearance of the flat pod phenotype often occurs together with reversions to the stringy pods in stringless cultivars (Al-Bader 2014). Progeny of rogue plants tend to present the rogue phenotype, indicating that rogue traits have a genetic component, even when segregation ratios did not fit previous inheritance models (Al-Bader 2014; Kerr 1971). The flat pod rogues have higher pod wall fiber contents than the true-to-type round pods (Al-Bader 2014; Hagerty 2013). No consistent model of inheritance of this rogue trait has emerged from the studies conducted. This non-Mendelian variation of the pod traits can be considered stochastic noise. Stochastic noise, the residual variation of the phenotypic values that cannot be explained by the measured genetic and environmental factors, is often considered identical for each genotype. However, it has long been recognized that the stochastic noise itself is heritable (Falconer 1965; Lynch and Walsh 1998), as has been demonstrated by plant and animal studies (Jimenez-Gomez et al. 2011; Shi 2022). The use of the variance of a trait within a genotype as a phenotype of interest has the potential to identify additional factors modulating the phenotypic expression of the trait, complementing traditional studies of quantitative traits. Moreover, the biological mechanisms that control trait variability are not well-understood. The expression of a trait is the result of a network of biological processes. The specific topology of a network can increase or decrease the robustness (stability) of the output (Kitano 2004). Genetic variation for loci within these networks could lead to allele-specific changes in the robustness of the phenotype. Epigenetic control of gene expression, recovery of function caused by transposon excision, and regulation of gene expression caused by gene fragments captured by transposons may result in variations that do not conform with classical genetic segregation (Dooner et al. 2019; Li et al. 2013). Transcription factors, which are regulatory elements that affect gene expression and splicing factors, have been enriched in variance QTL studies (Jimenez-Gomez et al. 2011; Shi 2022). Despite the importance of the uniform appearance of horticultural crops, there has been little research regarding the genetic basis of phenotypic noise. In this respect, knowledge of the genetic markers associated with stability of the traits could be another tool for the development of commercially attractive cultivars.

Overall, the genetic basis of snap bean pod shape is complex and involves the interaction of multiple genes. The identification of QTLs in different genetic backgrounds and environments is advantageous because it provides the basis for selecting the most stable QTL for use in plant breeding. Moreover, the identification of QTLs helps identify genetic variants that may be present only in certain populations. Further research is needed to improve the understanding of the mechanisms underlying pod traits and aid the development of new cultivars with improved traits for agricultural and horticultural purposes.

We used a snap bean diversity panel composed of heirloom and improved cultivars used in North America and GWAS to investigate the genetic basis of pod morphological characteristics and within-cultivar variability of pod traits. The objectives of this study were to characterize phenotypic and genotypic variability in cultivars with diverse pod morphologies and identify quantitative trait nucleotides (QTNs) associated with pod traits. The results provide molecular markers that can be used in breeding programs and aid in the identification of candidate genes conditioning snap bean pod traits.

Materials and Methods

Germplasm

The snap bean association panel (SnAP) comprising 378 cultivars was used (Hart et al. 2015). Because of seed limitations, cultivars SnAP135, SnAP162, SnAP259, and SnAP356 were not included in the present study. The panel represents a sample of the diversity of snap bean grown in the United States over the last century, including bush and pole growth habits, fresh and processing markets, numerous pod sieve classes, and variations of several other traits. The SnAP included 149 cultivars previously characterized as part of the Common Bean Coordinated Agricultural Project (BeanCAP) diversity panel (Wallace et al. 2018). The original SNAP population was genotyped using genotyping-by-sequencing and aligned to the reference Andean G19833 P. vulgaris version 2.1 genome sequence (Schmutz et al. 2014). A total of 20,619 SNPs with a minimum allele frequency of 5% were included in the analysis (Saballos et al. 2022).

Phylogenetic analysis

A clustering neighbor-joining (NJ) tree was created from the kinship matrix calculated by GAPIT version 3 (Wang and Zhang 2021) with default parameters. To evaluate the correspondence of the NJ tree clusters of the cultivars in the panel with the Middle-American and Andean germplasm pools, we compared the reported Middle-American or Andean classification of the cultivars of the SNAP in common with the BeanCap panel, as described by Wallace et al. (2018). The K = 2% of the subpopulation belonging to each cultivar was obtained as described by Soler-Garzón et al. (2023). Briefly, the SnAP data set was pruned based on linkage disequilibrium with an r2 threshold of 0.5. The pruned set of SNPs with <20% missing values and minor allele frequency >0.01 were retained. The population structure was estimated using a Bayesian Markov chain Monte Carlo model implemented using STRUCTURE 2.3 software (Pritchard et al. 2000).

Field experiment

A field experiment was conducted at the University of Illinois Vegetable Crop Farm near Urbana, IL, USA, during 2022 and 2023. The soil was a Flanagan silt loam (fine, smectitic, mesic Aquic Argiudolls) with an average of 3.5% organic matter and a pH of 5.9. Two passes of a field cultivator equipped with rolling baskets were used to prepare the seedbed. A different field was used each year.

The experimental design was a randomized complete block with two blocks (replications). Cultivars were planted in single rows (76-cm spacing). Each plot was 2.4 m in length and planted with 30 seeds to a depth of 2.5 cm. Water was applied with an overhead sprinkler irrigation system as needed to facilitate uniform emergence.

Data collection

The flowering date was determined as the date when 50% of the plants in a plot had at least one flower open. Harvest was staggered based on the recorded flowering date to maximize uniformity of the developing stage of the pods. For each plot, the pods were harvested 17 d after the recorded flowering date, when most pods had reached the maximum length, and before seed bulging was observed through the pod wall. Ten pods were collected in each plot. Pod length was measured with a metric ruler affixed to a clipboard as the distance from the style attachment point to the calyx attachment point (Fig. 1A). For curved pods, the distance followed the curve. Pod width and height were measured in the cross-section at the middle of pod length. Pod width was the distance from the ventral to dorsal sutures (Fig. 1C). Pod height (Fig. 1G) was the distance perpendicular to the axis of bilateral symmetry created by the ventral and dorsal sutures. The pod width/height ratio (hereafter called the “pod ratio”) was calculated by dividing the pod width by its height (Fig. 1I). Measurements were obtained with a digital caliper (model CD-6”CS; Mitutoyo Corp, Aurora, IL, USA). The coefficient of variation (CV) was calculated within individual years and over both years, and then expressed as a percentage. For individual years, the SD of the total observations per cultivar over both blocks (n = 20) was divided by the year mean of the cultivar. For the overall CV, the total number of observations per cultivar (n = 40) was divided by the overall mean of the cultivar.

Fig. 1.
Fig. 1.

Graphic description of the measured pod traits, their 2-year average phenotypic distribution, and estimated genetic component for 374 cultivars of the snap bean (Phaseolus vulgaris) association panel grown at Urbana, IL, USA, in 2022 and 2023. Top left panel: Pod length. Top right panel: Pod width. Bottom left panel: Pod height. Bottom right panel: Pod ratio. (A, C, G, and I) Graphical descriptions of the measured trait. (B, D, H, and J) Pie charts depicting the percentage of phenotypic variance explained by the mixed linear model including all the single nucleotide polymorphism markers. (E, F, K, and L) Histograms of the distribution of the trait values.

Citation: J. Amer. Soc. Hort. Sci. 149, 1; 10.21273/JASHS05318-23

Statistical analysis

Phenotypic variation and correlations between traits were quantified using R studio (Posit team 2022) statistical software (R version 4.2.1) (R Core Team 2022). Pearson’s correlation coefficients (r) between traits were calculated using the procedures cor and Rcorr of the Hmisc package (Harrell 2023). Data were visualized using the Corrplot package (Wei and Simko 2021). The pod ratio captures the shape of the cross-section as cylindrical (ratio close to 1.00) or “flat” (ratio >1.50). For instance, cultivars of the Romano class (per their plant variety protection) are characterized by pods with a flat cross-section. The correlation between height and width was calculated for all lines in the panel and individually for pods with a ratio ≥1.50 (including all Romano types) and cylindrical classes.

Broad-sense heritability was calculated as the proportion of phenotypic variance (σ2p) caused by the genotypic variance (σ2g) (Schmidt et al. 2019). Specifically:

H2 = σ2g/σ2p, where
σ2p = σ2g + σ2gy/ny + σ2gb:y/nynb + σ2ϵ/nynbnr
where σ2g⋅y is the variance caused by the genotype × year interaction, g⋅b:y is the genotype × block within-year interaction, σ2ϵ is the error term, ny is the number of years, nb is the number of blocks within the year, and nr is the number of replications within the block.

For the length, width, height, and ratio traits, the variance components were calculated from the following lineal model equation:

Yijk = μ + Gi + Yj + B(Y)k(j) + GxYij + GxB(Y)ik(j) + εijlk, where

Yijk is the trait value of the plot in the kth block in the jth year, with the ith cultivar, μ is the overall mean of the experiment, Gi is the main effect of the ith cultivar, Yj is the main effect of the jth year, (GY)ij is the interaction effect between the ith cultivar and the jlth year, and B(Y)k(l) is the effect of the kth block nested within the jth year and εijk in the error term associated with the plot in the kth block in the jth year with the ith cultivar. For the CV traits, because a single value of the CV was calculated per year, the only factors were cultivar and year. The linear model was fitted using the procedure lm of the r package stats (R Core Team 2022), and the analysis of variance (ANOVA) was performed from the linear model using the Anova procedure of the car package (Fox and Weisberg 2019).

Although the heritability reported here is based on the broad sense heritability equation, in a highly inbred population such as SnAP, the dominance effects do not contribute to the phenotype of the lines (Falconer and Mackay 1996). Therefore, the broad-sense heritability will approximate the narrow-sense heritability. Chip-based heritability, which is the portion of the phenotypic variation that can be explained by the genotyped genetic markers, was automatically calculated by the program GAPIT (Lipka et al. 2012) using the mixed linear model. Only the additive effects of the loci are included in the estimate.

Genome-wide association analyses

For each pod trait, the average value of the cultivar over years was used in the joint genome-wide association analyses. The average of the cultivar over replications within the year was used for individual year analyses. Seven GWAS models were used. Multilocus random model GWAS were conducted with the MrMLM version 4.0.2 package (Zhang et al. 2020) and the multilocus mixed model (MLMM) (Segura et al. 2012), as implemented in GAPIT version 3 (Wang and Zhang 2021). MrMLM implements six multilocus, random SNP-effect models (ISIS EM-BLASSO, mrMLM, FASTmrMLM, FASmrEMMA, pLARmEB, and pKWmEB). The multilocus, random SNP-effect mixed linear models use a two-stage model to determine significant SNPs: selection of potentially significant markers with a low criterion significance test, followed by a multiple locus method for markers that have passed the initial screening. All nonzero effect markers are further identified by the likelihood ratio test for true QTN. The significant threshold is less stringent, allowing for the identification of a higher number of significant markers while controlling for type I errors (Wang et al. 2016). In the MLMM (Segura et al. 2012), associated markers are fitted as cofactors for marker tests. The cofactors are adjusted through forward inclusion and backward elimination in the regression model. For the MLMM, the procedure reported by Benjamini and Hochberg (1995) was used to control multiple testing. A kinship matrix used for analysis was calculated in GAPIT using the VanRaden method (VanRaden 2008) and provided for the MrMLM package. Three principal components generated by GAPIT were included as covariates. Associations were considered significant if the Benjamini and Hochberg adjusted P ≤ 0.05 for the MLMM model, or if the logarithm of the odds score was 4 for the MrMLM package. SNPs significantly associated with a trait are subsequently referred to as QTNs for the trait. Only QTNs that explain >1% of phenotypic variance (%PV) for at least one of the models are presented. The associated genomic locations are named according to the following format: “TraitChr_Mbp-Mbp,” in which the first part is the trait for which the association was detected, followed by the chromosome number and linkage disequilibrium interval r2 > 0.8 of the QTN. Two QTNs with linkage disequilibrium (LD) intervals that overlap were considered to belong to the same genomic region and subsequently referred to as QTLs.

Linkage disequilibrium determination of QTNs

The LD analysis was performed using Tassel 5 software for association mapping of complex traits (Bradbury et al. 2007). The square of the correlation coefficient (r2) between alleles at two loci was calculated using a sliding window LD, which calculates LD for each SNP within a window of 200 SNPs surrounding the current SNP. The LD heatmap was used to scan for high linkage disequilibrium within chromosomes based on r2 values. High LD was characterized by red squares in the generated heatmap.

To determine the linkage interval around a QTN, the LD matrix generated by Tassel was filtered for SNPs with LD r2 > 0.80 of each QTN. The position of the furthest SNP from the QTN on both the distal and proximal sides was taken to mark the linkage interval around the QTN.

The overall LD landscape of the panel was examined using the “full matrix LD” option in Tassel, which calculates LD for every combination of SNPs in the alignment.

Concordance with previously reported QTLs

Collocated associated genomic regions from previously published studies of related traits were obtained through literature search and by using the webtool ZZBrowse GWAS/QTL on the Legume Information System website (Dash et al. 2015). Genomic intervals of reported associated regions, sequence information of associated molecular markers obtained from the Pulse Crop Database (Humann et al. 2019) and the supplemental information tables of Song et al. (2015) and gene sequence information were used when available to compare the genomic intervals and genes from the literature and the intervals identified during this study. If necessary, the CoGe web-based tool (Lyons and Freeling 2008) was used to identify the P. vulgaris genome version 2.1 location of QTL mapped to P. vulgaris genome version 1. The reported QTLs within 1.5 Mbp of the QTLs in this study were considered colocalized. The 1.5-Mbp interval was chosen empirically based on the spread of colocalized QTN/QTL for the same trait from the literature around a genomic location and the uncertainty in the exact physical location because of the different populations and genome versions used in the studies.

Candidate genes

Candidate genes for associated genomic regions identified during this study were selected based on the following criteria. The first criterion was genes tagged by the associated SNP and expressed in the flowers, pods, and developing seeds of common bean. The second criterion was genes within the QTL interval and expressed in flowers, pods, and developing seeds of common bean. The third criterion was genes with experimentally verified roles in cell proliferation, organ patterning, fruit size, seed size, and seed weight in bean or related species, within 1 Mbp of QTLs, and expressed in flowers, pods, and developing seeds of common bean. Functional annotations of genes present in the QTL intervals were obtained from the Plant Comparative Genomics portal Phytozome 13 (Goodstein et al. 2012) with the P. vulgaris version 2.1 reference genome assembly of G19833 (Schmutz et al. 2014). Gene expression data from relevant tissues was obtained from the PvGEA: Common Bean Gene Expression Atlas and Network Analysis (O’Rourke et al. 2014). The putative function, gene expression, experimental evidence, and phenotype of mutants for the A. thaliana homologs of the Phaseolus genes were obtained from The Arabidopsis Information Resource (TAIR) (Berardini et al. 2015).

Genotype of flat pod types in relation to snap bean ancestry

For this analysis, cultivars with a ratio ≥1.50 were considered flat pod cultivars, and cultivars with a ratio ≤1.20 were considered cylindrical pods cultivars. Using these criteria, all the cultivars belonging to the Romano market segment are included in the flat pot category. We determined the NJ clusters in which flat pod cultivars were located. We identified cylindrical pod cultivars in the same cluster as the flat pod cultivars in the NJ tree for comparison. The allelic status of the representative QTN in the QTL with the highest %PV explained for the ratio was determined for the flat pod cultivars and selected cylindrical pod cultivars.

Results

Phenotypic variation, ANOVA, correlations, and heritability

The 374 cultivars exhibited wide morphological variation in pod traits, and the variation had a genetic component (Table 1, Figs. 1 and 2). Broad-sense heritability estimates were high (H2 > 0.81) for length, width, height, and ratio, indicating strong genetic control. Because a single value of the CV was calculated per year, no broad-sense heritability was calculated for the CV traits. Chip-based heritability estimates closely follow the broad-sense heritability estimates, indicating that variations in the genotyped markers capture a high proportion of the genetic variations of the traits. Chip-base heritability was low to moderate for CV traits, indicating that a smaller but detectable proportion of the variation is caused by genetic components. The ANOVA (Supplemental Table 1) indicated that the cultivar effect was statistically significant for all pod morphological and CV of traits, indicating that cultivar influences not only the morphology of the pod but also the degree of variability of the trait expression within cultivar. Across market classes (Fig. 3A), moderate positive correlations were observed between pod length and both pod width and height (r = 0.28 and 0.40, respectively), but not between pod width and height. The pod ratio was positively correlated with the pod width (r = 0.85) and had a moderate negative correlation with pod height (r = 0.44), as expected, because the ratio is derived from those variables. Significant correlations between the pod height and width were detected when the panel was divided into cylindrical (Fig. 3B) and flat pod cultivars (Fig. 3C). The flat pod cultivars (ratio ≥1.50; n = 34 cultivars) included the cultivars classified as sieve class flat and Romano type on their plant variety protection certificate (Hart, personal communication). For the cylindrical cultivars (ratio ≤1.20; n = 322), the Pearson’s correlation coefficient of width and height was r = 0.82. For the flat pod cultivars, Pearson’s correlation coefficient of width and height was r = 0.55. The magnitude of the relationships between ratio and height and ratio and width were similar (r = −0.32 and 0.27, respectively) for the cylindrical pod cultivars; however, for the flat pod cultivars, only the correlation between ratio and width was significant (r = 0.80). The smaller magnitude of the correlation between the ratio and height compared with the correlation between the ratio and width indicated that the shape of the cross-section is mainly controlled by the distance between the sutures.

Table 1.

Heritability, phenotypic ranges, and means of snap bean (Phaseolus vulgaris) pod traits measured in 374 cultivars of the snap bean association panel grown at Urbana, IL, USA, in 2022 and 2023.

Table 1.
Fig. 2.
Fig. 2.

Distribution of the coefficient of variation (CV) and their estimated genetic component for the pod traits for the 374 cultivars of the snap bean (Phaseolus vulgaris) association panel grown at Urbana, IL, USA, in 2022 and 2023. The CV of the trait per cultivar was calculated based on 40 observations of the trait per cultivar. Top left panel: Length CV. Top right panel: Width CV. Bottom left panel: Height CV. Bottom left panel: Ratio CV. (A, B, E, and F) Histograms of the distribution of the trait values. (C, D, G, and H) Pie charts depicting the percentage of phenotypic variance explained by the mixed linear model including all the single nucleotide polymorphism markers.

Citation: J. Amer. Soc. Hort. Sci. 149, 1; 10.21273/JASHS05318-23

Fig. 3.
Fig. 3.

Pearson correlations among the four morphological pod traits evaluated in 374 cultivars from the snap bean (Phaseolus vulgaris) association panel grown at Urbana, IL, USA, in 2022 and 2023. Empty squares: nonsignificant correlations (α = 0.05). (A) All cultivars evaluated. (B) Subset of cylindrical pod cultivars (ratio ≤1.2, n = 322). (C) Subset of flat pod cultivars (ratio ≥1.5; n = 34).

Citation: J. Amer. Soc. Hort. Sci. 149, 1; 10.21273/JASHS05318-23

Phylogenetic division

Based on the comparison of the cultivars in common between the SnAP and BeanCap, both the Andean and Middle-American germplasm pools are represented in the panel. The K = 2 analysis (Soler-Garzón et al. 2023) divided the panel into 21 cultivars predominantly belonging to population 1 (>90%). Ten of the cultivars in this group were designated as being of Middle-American origin by Wallace et al. (2018) and referred to as putative Middle-American. One hundred forty-five cultivars predominantly belonged to population 2 (>90%). Forty-five cultivars in this group were identified as being of Andean origin by Wallace et al. (2018); therefore, population 2 is considered putative Andean. The remaining 211 cultivars had admixture >10%. The number of clusters for the NJ tree was set to eight based on the general agreement of the NJ analysis with the K = 8 structure analysis reported by Wallace et al. (2018) (Supplemental Fig. 1). The division of cluster 1 with the rest of the clusters followed the ancestral division between the Andean and Middle-American germplasm pools, with cluster 1 containing the majority of cultivars classified as being of Middle-American origin by Wallace et al. (2018), except for six cultivars, with >90% belonging to population 1 (putative Middle-American) in clusters 5 and 7. The corresponding cultivar names for the codes in the NJ tree are provided in Supplemental Table 2.

Genome-wide association analyses

Multiple QTNs were detected, explaining a high proportion of the variation of each of the morphological traits (Supplemental Table 3, Fig. 4). The stability of the QTLs across years varied depending on the trait. Five QTLs for the ratio, four for the length, three for the width, and one for the height were detected in both years and in the joint analysis (Table 2). The reported results presented are from the 2022–23 joint genome-wide association analysis.

Fig. 4.
Fig. 4.

Manhattan plots summarizing the results of the genome-wide association analyses for the 2-year average of the pod morphological traits for the 374 cultivars of the snap bean (Phaseolus vulgaris) association panel grown at Urbana, IL, USA, in 2022 and 2023. Top left panel: Pod length. Top right panel: Pod width. Bottom left panel: Pod height. Bottom right panel: Pod ratio. (A, C, E, and G) Manhattan plots of the multilocus random SNP effect mixed linear models (MrMLM) and multilocus mixed model (MLMM) analyses. For the MrMLM plot, pink dots represent single nucleotide polymorphisms detected by multiple models. For both MrMLM and MLMM plots, the X-axis shows the chromosomes and the Y-axis shows the −log10 of the P value of the association. The horizontal line represents the significance threshold. (B, D, F, and H) Quantile–quantile plots depicting the observed (Y-axis) and expected (X-axis) −log10 of the P value.

Citation: J. Amer. Soc. Hort. Sci. 149, 1; 10.21273/JASHS05318-23

Table 2.

Quantitative trait loci (QTL) for pod morphological and uniformity traits detected by the joint-year analysis and genome-wide association analyses in the snap bean (Phaseolus vulgaris) association panel grown at Urbana, IL, USA, in 2022 and 2023. The analysis was performed with the cultivar 2-year average of each morphological trait, and the coefficient of variation (CV) of all the cultivars observations. During this study, 374 cultivars of the panel were included. Only the QTLs detected by both individual year analyses are shown.

Table 2.

Pod length.

A total of 20 QTNs in 11 genomic locations were associated with pod length. The %PV explained by the QTNs was between 1.37% and 11.99%, and the allelic effect was a change in length between 0.2 and 1.3 cm. SNPs within QTLs Len02_43.8–45.3, Len02_47.4–47.7, Len03_10.2–11.3, and Len07_9.1 were detected independently in both years.

Pod width.

A total of nine QTN in six genomic regions were associated with pod width, with %PV of 1.35% to 32.62%; the allele effect was a change in pod width of 0.26 to 3.28 mm. SNPs within QTLs Wid02_29.3–31.2, Wid04_43.3–45.4, and Wid10_44.1–44.2 were detected independently in both years.

Pod height.

A total of 14 QTNs in 12 genomic locations were associated with pod width. The %PV of the QTN ranged between 1.02% and 37.88%; the allele effect was a change in pod height ranging between 0.14 and 0.78 mm. SNPs within QTL Height04_44.3–45.4 were detected independently in both years.

Pod ratio.

Ten QTNs in 10 genomic locations were associated with the phenotype, with %PV of 1.00% to 56.83%; the allele effect was a change in ratio of 0.07 to 0.36. SNPs within QTLs Ratio03_36.2–36.4, Ratio04_44.4, Ratio07_0.6, Ratio09_26.9–27.9, and Ratio10_44.1–44.2 were detected independently in both years.

CV traits.

Multiple QTNs were associated with the uniformity of pod traits measured as the CV (Supplemental Table 3, Fig. 5). The stability of the QTL across years varied depending on the trait. Two QTLs each for the ratio CV and height CV were detected in both years (Table 2). For the pod length CV, six QTNs were identified in five genomic locations, with %PV of 2.11% to 7.94%. For the pod width CV, five QTNs in five genomic regions were detected, with %PV of 2.25% to 10.69%. For the pod height CV, 15 QTNs in nine genomic regions were detected, with %PV of 1.35% to 12.81%. QTL HeightCV04_46.6–47.5 and HeightCV11_1.0–1.2 were stable, as detected during individual year analyses. For the pod ratio CV, six QTN in six genomic regions were detected, with %PV of 2.14% to 10.07%. QTL RatioCV04_43.2–45.4 and RatioCV07_29.1–30.3 were stable, as detected during individual year analyses. Chromosome 2 region 41.0 to 41.8 contains associations with multiple variation traits, specifically, CVs of length, width, and height. Several other colocalizations of CV traits were detected in chromosomes 3, 4, and 6.

Fig. 5.
Fig. 5.

Manhattan plots summarizing the results of the genome-wide association analyses for the within-cultivar coefficient of variation (CV) of the pod morphological traits for the 374 cultivars of the snap bean (Phaseolus vulgaris) association panel grown at Urbana, IL, USA, in 2022 and 2023. Top left panel: Pod length CV. Top right panel: Pod width CV. Bottom left panel: Pod height CV. Bottom right panel: Pod ratio CV. (A, C, E, and G) Manhattan plots of the multilocus random SNP effect mixed linear models (MrMLM) and multilocus mixed model (MLMM) analyses. For the MrMLM plot, pink dots represent single nucleotide polymorphisms detected by multiple models. For both MrMLM and MLMM plots, the X-axis shows the chromosomes and the Y-axis shows the −log10 of the P value of the association. The horizontal line represents the significance threshold. (B, D, F, and H) Quantile–quantile plots depicting the observed (Y-axis) and expected (X-axis) −log10 of the P value.

Citation: J. Amer. Soc. Hort. Sci. 149, 1; 10.21273/JASHS05318-23

Colocalization among traits in this study

Colocalized GWAS signals were identified between traits (Supplemental Table 4). Thirteen regions in seven chromosomes were associated with two or more pod traits. Three regions were enriched for colocalized QTN for four or more traits. The region on Chr02_41.0–41.8 harbors variations in QTNs for length CV, width CV, and height CV. The minor allele of the QTN had the effect of reducing the CV for all three traits. The region on Chr04_43.3–45.0 encompassed QTNs for pod width, height, ratio, width CV, and ratio CV. The effects of the minor allele of the QTNs were positive for width, ratio, width CV, and ratio CV, and negative for height. The region on Chr06_18.2–19.6 contained QTNs for pod width height, ratio, and height CVs. The effects of the minor allele of the QTNs were positive for all traits.

Concordance with previously identified genomic regions

Our results were compared with genomic regions associated with pod and seed morphological traits of dry bean and snap bean (García-Fernández et al. 2021; González et al. 2016; Hagerty et al. 2016; Murube et al. 2020; Wu et al. 2020; Li et al. 2023). There were 18 regions in which the QTLs identified during this study colocalized with reported QTLs (Supplemental Table 5). Regions Chr02_ 43:48, Chr04_44:45, and Chr06_18:19, which were associated with multiple pod traits during our study, were also detected by others (García-Fernández et al. 2021; Li et al. 2023; Murube et al. 2020; Wu et al. 2020).

Linkage disequilibrium determination around QTN

The LD intervals (r2 > 0.80) around the QTN varied from 0 to 19 Mbp when calculated using a 200 SNP window. The median interval size was 102,200 bp. The r2 > 0.80 genomic intervals for the QTN should be taken as guidance of the most likely regions in which causal genes in LD with the QTN are located (Supplemental Table 6), but we cannot completely exclude genes outside the interval. Examination of the LD heat map from the full matrix analysis showed abnormal patterns of LD. An example of one of such regions is shown in Supplemental Fig. 2. Loci are in LD > r2 = 0.80 with loci in the distal part of the chromosome (∼20 Mbp away), but they are in LD r2 < 0.50 with neighboring loci. Because the LD of the QTN was calculated using a window with 200 sites, it is possible that the interval could include SNP located farther away in the genome and interrupted by regions of low LD.

Candidate genes

We identified 1088 genes with expression in flower, developing pod, and developing seed in or near the regions associated with pod traits (Supplemental Table 7). Given the colocalization of QTLs for multiple pod and seed traits found in this and other studies, the genomic intervals used to identify candidate genes may encompass QTN for multiple traits. Of the proposed candidate genes, 61 were tagged by a QTN, 40 were in an exon region, 2 were in the 5′UTR, and 19 were in an intron region. We identified genes whose homologs have functional annotation of involvement in organ size determination, organ development and patterning, and cell proliferation. Of those, 11 are homologs of genes implicated in the control of seed and organ size in common bean or related legume species. Soybean (Glycine max) gene model Glyma.19G196000 is implicated in the control of seed size (Zhu et al. 2022). A similar gene model, Phvul.001G192300, is in the interval of Ratio01_44.9–46.8 QTL. Phvul.002G327700, located near Height02_48.7–48.9 QTL, is a homolog of soybean Seed Thickness (GmST05, Glyma.05G244100) gene. GmST05 influences seed size in soybean (Duan et al. 2022). Soybean PP2C (Glyma.17G221100 alias Glyma17g33690), coding for a putative phosphatase, was identified as the gene underlying a QTL for seed size (Lu et al. 2017). Two phosphatases with homology to PP2C, Phvul.007G009000 and Phvul.002G309100, are located in the QTLs Ratio07_0.6 and Len02_47.4–47.7, respectively, both of which are stable across years. Phaseolin storage proteins (encoded by Phvul.007G059700 to Phvul.007G060000) have been implicated in the seed size and weight of common bean (Blair et al. 2006; Johnson et al. 1996). A cluster of genes coding for phaseolin proteins is located near the Len07_5.4–5.6 QTL. Putative homologs of genes coding for five components of the PPD2/NINJA/KIX8/TPL and CYCD3 pathway, which regulate cell proliferation (Baekelandt et al. 2018), are located in associated regions. Mutations or downregulation of homologs of PPD1 and PPD2 genes in legume species result in increased lateral organ size and seed weight because of prolonged cell proliferation (Vigna mungo VmPPD, Medicago truncatula MtBs1–1, and soybean GmBs1 and GmBs2) (Ge et al. 2016; Naito et al. 2017). The gene model with highest similarity to the PPD genes, Phvul.007G061400, is located in close proximity to the Len07_5.4–5.6 QTL. Two possible homologs of A. thaliana AtNINJA, Phvul.004G169100 and Phvul.008G032900, are located in the intervals for HeightCV04_46.6–47.5 and RatioCV08_1.3–2.7, respectively. Soybean mKIX8–1 was identified as the causative gene for the major seed weight QTL qSw17–1 (Nguyen et al. 2021). Its homolog Phvul.009G205400 is located near Height09_30.5–30.6. Phvul.003G240500, a gene model annotated as a CYCLIN-D3–2 gene, is located near RatioCV03_46.6–46.8 QTL.

Pod patterning genes are of special interest for breeding snap bean. Two genes have been recently identified as causative genes for the stringless pod trait in snap bean. PvGUX1_1 (gene model Phvul.002G270800) codes for cell wall-modifying enzymes involved in secondary wall deposition (Liu et al. 2022). PvIND (gene model Phvul.002G271000) is a transcription factor homologous of A. thaliana IND, which controls cell wall-modifying genes, resulting in tissue specific secondary wall formation and lignification in the pod (Parker et al. 2022). Interestingly, both of those genes are included in the Len02_43.8–45.3 QTL, a stable QTL detected independently in both years.

Multiple genes coding for transcription factors similar to those involved in organ morphogenesis in other species are present in the intervals. Those include genes coding for NAM-ATAF1,2-CUC2 (NAC), Apetala 2, basic helix-loop-helix (BHLH), and multiple MCM1-AGAMOUS-DEFICIENS-SRF (MADS) box and homeobox domain-containing transcription factors. Phvul.010G118700 codes a NAC domain protein homolog to AtNST1, a regulator of secondary wall thickening in A. thaliana siliques. Phvul.002G283100 codes a BHLH137 protein, whose homolog in A. thaliana functions as a cytokinin-responsive growth regulator that regulates cell expansion and cell cycle progression (Park et al. 2021). Homeodomain-containing gene Phvul.004G143500 is the homolog of ATEDT1, which codes for a transcription factor that directly upregulates the expression of several cell wall-loosening genes (Taylor-Teeples et al. 2015).

The existence of pods at different developmental stages within plants and inflorescences contributes to intracultivar variation of pod traits, and this source of variation cannot be controlled by adjusting the harvest of the pods based on flowering date. Genes that control plant determinacy and inflorescence architecture could indirectly affect pod uniformity. Two gene models located in QTL WidCV02_12.7–23.7, Phvul.002G095800 and Phvul.002 G109400, code for Wuschel-related homeobox proteins. Wuschel protein is a main determinant of shoot and inflorescence meristem activity (Benlloch et al. 2015). An Agamous-like MADS-Box gene with similarity to Pisum sativum VEGETATIVE1/FULc (VEG1) gene is located in LenCV01_51.1–51.2. The VEG1 gene specifies the formation of the secondary inflorescence meristem (Serra-Picó et al. 2022).

Spontaneous revertant from round, fleshy pods to oval pods with increased fiber may contribute to variations in the pod traits measured in this study. Intriguingly, a region in Chr02 (40.1 to 41.8 Mbp) that harbors colocalized QTLs for CVs of length, width, and height encompasses a gene coding for a transposase protein (Phvul.002G246000). Transposon activity has been implicated in the non-Mendelian variation of traits in other species such as maize (Zea mays) (Dooner et al. 2019).

Genotype of flat pod types in relation to snap bean ancestry

For cultivars with flat pods (pod ratio > 1.50), we investigated the allele status of the most robust ratio QTL in relation to their phylogenetic clusters (Supplemental Table 8). The flat pod phenotype was not restricted to a single lineage (Supplemental Fig. 1). Cluster 2 was enriched in cultivars expressing the trait. This cluster corresponds to group 5, Andean landrace and Romano, of Wallace et al. (2018) analysis. However, flat pod lines were also found in clusters 1, 3, 4, and 8 of the NJ analysis. The comparison of the genotype of the flat pod cultivars at the pod ratio QTN revealed distinct QTN composition across the putative Andean/Middle-American division. The majority of the lines (82%) with a flat pod phenotype carried the positive-effect alleles for the Chr04_44 region. However, flat pod lines with ancestry probability >90% for belonging to the Middle-American gene pool in cluster 1 of the NJ tree and one cultivar of mixed ancestry presented the flat pod phenotype while carrying the negative effect allele in that region. The Middle-American lines carried the positive effect alleles in the QTL RatioChr07_0.6 and Ratio09_26.9–27.9, and two of them also carried the positive effect allele in the Ratio10_44.1–44.2. To allow a comparison of the allelic status of ratio-associated SNPs in flat pod and cylindrical pod cultivars of similar genetic composition, a sampling of 10 lines with a pod shape close to cylindrical (pod ratio < 1.20) within the NJ clusters in which flat pod cultivars were present was included in the analysis. None of them carried the positive effect alleles in QTLs Chr04_45, Chr07_1, and Chr11_5. Taken together, these results suggest oligogenic control of the flat pod trait, and that selection of cylindrical pods from the flat pods may have occurred independently in the Andean and Middle-American pools.

Discussion

Many characteristics determine whether a cultivar is successful for production. Pod shape and size are ultimately driven by consumer preferences, where plant breeders create new cultivars tailored to specific market segments. In this study, we used a diversity panel of commercial and heirloom snap bean cultivars to identify genomic regions associated with important pod shape traits.

The panel encompasses wide genetic and phenotypic variations in pod morphology (Table 1, Fig. 1). Consistent with previous research (García-Fernández et al. 2021; González et al. 2016), genotype explains most of the phenotypic variance, with estimates of broad-sense heritability >80% for the main traits. High estimates of heritability for traits in this study indicated that the genome-wide association analysis is suitable for identifying genomic regions associated with pod traits, and that effective selection for improvement can be made. The results of the analysis of the CVs of the traits determined that there is a genetic component for stability of the expression of pod traits, albeit highly influenced by environment. These could help snap bean breeders by providing an additional tool to improve the crop.

Pod traits studied during this study had a variety of relationships with each other. When examining the entire panel, pod width and pod height were not correlated with each other, suggesting they are independently controlled. However, when the population was subdivided into flat and cylindrical types, we observed a significant correlation between height and width within each type. For cylindrical cultivars, the height and width of the pod increase in tandem, whereas for the flat pod cultivars, the increase of pod width is only moderately coupled with height. The presence of different market types influenced by pod shape in the panel hid the correlations between height and width, which are revealed when market class is considered. Based on the strength of the correlation between the pod width and ratio, the variation in width is the main driving factor for changes in pod ratio.

Multiple genomic regions are associated with pod traits. The clustering of reported associated regions of pod and seed traits from this and previous studies (García-Fernández et al. 2021; González et al. 2016; Hagerty et al. 2016; Li et al. 2023; Murube et al. 2020; Wu et al. 2020) indicate major regions controlling the pod phenotype. The overlap of associated regions for pod traits and seed traits suggests that there are master genes with pleiotropic effects in multiple pod and seed traits, or that some regions in the genome are enriched for genes controlling pod and seed traits (García-Fernández et al. 2021; González et al. 2016; Murube et al. 2020; Wu et al. 2020). Although the genome-wide association analysis alone cannot validate the involvement of genes colocalized with associated regions, comparisons of functional annotations and expression in target organs of the genes and their homologs in other species allow us to postulate candidate genes for pod characteristics. Of particular interest are regions detected in both years that explain a relatively high proportion of the phenotypic variability of the traits in this study and are associated with multiple traits. For example, Chr02:47 Mbp is detected in both years and explains the highest proportion of the variation for pod length. It overlaps with proposed associated regions for pod and seed length and number of seeds per pod. Multiple QTNs are located in this region. One of the QTN is located in an exon of candidate gene model Phvul.002G309100, a protein phosphatase with similarity to PP2C. Other gene models with predicted function in carbohydrate transport and modification are located in the interval. Such genes play a crucial role in the development of fruit organs and plant productivity because they coordinate sugar fluxes from source leaf toward sink organs (Doidy et al. 2019). Chr04:44–45 Mbp is associated with multiple pod and seed traits and explains the highest proportion of the %PV for width, height, and ratio. The interval contains the homeobox-domain genes Phvul.004G143500 and Phvul.004G146200. AT1G73360, the A. thaliana homolog of Phvul.004G143500, is implicated in the upregulation of several cell wall-loosening genes that coordinate cell wall extensibility (Taylor-Teeples et al. 2015).

For vegetable crops, uniformity of the harvested product influences visual appeal for the consumer and facilitates mechanized sorting and packing. We investigated the extent to which variability in pod traits, measured as the CV from the cultivar mean, was under genetic control. The CV QTL identified during this study open the possibility to incorporate marker-assisted selection in breeding for more uniform product size and shape. For example, region Chr02:40.3–41.8 Mbp is associated with three CV traits. A transposase coding gene, Phvul.002G246000, tagged by a QTN, is located in the interval. Transposon insertion and excision have been identified as the basis of instable mutations in maize. The production of transposase enzymes may influence the rate of transposon excision and insertion events in the bean genome, contributing to trait variation. Ultimately, an association analysis can only identify regions of interest. We hope that the robust regions identified in this study will serve as the basis for future studies that will identify the genes responsible for the traits.

To capture variations in the shape of the pod cross-section independent of the overall increase of the pod diameter, we used the ratio between the width and height of the pod. Previous research has attributed the cylindrical cross-section phenotype from the flat pod wild-type to polymeric recessives gene (Lamprecht 1947). The segregation results of flat versus cylindrical pods from several studies do not agree with a single recessive gene (Beltrán et al. 2002). The observed deviations from the expected ratios were attributed to several causes, including incomplete dominance, distorted segregation of the alleles, and epistatic effects between two genes. During this study, we identified ratio-associated regions in Chr04, Chr07, Chr09, and Ch10 that were detected in both years of the study and with %PV >3.00%. The presence of positive effect alleles in the Ch04 region appears sufficient to confer a flatter pod phenotype because all cultivars in the panel carrying those alleles present a pod ratio ≥1.40. However, the flat pod phenotype can be expressed in the absence of positive effect alleles in the Ch04 region because a group of five cultivars from the Middle-American gene pool had a ratio of 1.52 to 2.35 while carrying the negative effect alleles in the region of Chr04. These lines carry the positive effect allele in significant regions of Chr07 and Chr09, and two of them also carry the positive effect allele of the Chr10 QTL. The most extreme phenotypes in the panel tend to have stacked positive alleles for most of the associated regions (Chr02, Chr04, Chr07, and Chr10). In the absence of a positive effect allele in the Chr04 QTL, positive effect alleles of at least the Chr07 and Chr09 QTLs seem to be associated with the flat pod phenotype, but the presence of only one of them is insufficient for expression of the trait. This suggests that at least two independent genetic systems within snap bean germplasm are individually sufficient for the expression of the flat pod trait. Moreover, genes underlying associated regions appear to act additively for the expression of the phenotype. We hypothesize that selection of cylindrical pods occurred independently in the Middle-American and Andean pools. Further studies with a larger sample of snap bean lines of Middle-American origin are needed to validate this hypothesis. For breeders of snap bean, one practical implication is that the ancestry composition of their germplasm pool will determine which markers are useful in their programs.

In summary, these results confirm the complex nature of the genetic control of snap bean pod traits and confirm the significance of genomic regions harboring overlapping QTLs identified in this and related studies. The phenotypic expression of pod traits in snap bean appears to be under oligogenic control, with a few genomic regions having a strong effect and additional contributions of multiple small-effect regions. Validation of the function of the candidate genes identified in the associated regions will contribute to our understanding of legume pod development. The robust QTLs in Chr02 and Chr04, which explain a high proportion of the phenotypic variation of the traits, can be integrated into marker-assisted breeding programs to accelerate the development of snap bean cultivars.

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  • Fig. 1.

    Graphic description of the measured pod traits, their 2-year average phenotypic distribution, and estimated genetic component for 374 cultivars of the snap bean (Phaseolus vulgaris) association panel grown at Urbana, IL, USA, in 2022 and 2023. Top left panel: Pod length. Top right panel: Pod width. Bottom left panel: Pod height. Bottom right panel: Pod ratio. (A, C, G, and I) Graphical descriptions of the measured trait. (B, D, H, and J) Pie charts depicting the percentage of phenotypic variance explained by the mixed linear model including all the single nucleotide polymorphism markers. (E, F, K, and L) Histograms of the distribution of the trait values.

  • Fig. 2.

    Distribution of the coefficient of variation (CV) and their estimated genetic component for the pod traits for the 374 cultivars of the snap bean (Phaseolus vulgaris) association panel grown at Urbana, IL, USA, in 2022 and 2023. The CV of the trait per cultivar was calculated based on 40 observations of the trait per cultivar. Top left panel: Length CV. Top right panel: Width CV. Bottom left panel: Height CV. Bottom left panel: Ratio CV. (A, B, E, and F) Histograms of the distribution of the trait values. (C, D, G, and H) Pie charts depicting the percentage of phenotypic variance explained by the mixed linear model including all the single nucleotide polymorphism markers.

  • Fig. 3.

    Pearson correlations among the four morphological pod traits evaluated in 374 cultivars from the snap bean (Phaseolus vulgaris) association panel grown at Urbana, IL, USA, in 2022 and 2023. Empty squares: nonsignificant correlations (α = 0.05). (A) All cultivars evaluated. (B) Subset of cylindrical pod cultivars (ratio ≤1.2, n = 322). (C) Subset of flat pod cultivars (ratio ≥1.5; n = 34).

  • Fig. 4.

    Manhattan plots summarizing the results of the genome-wide association analyses for the 2-year average of the pod morphological traits for the 374 cultivars of the snap bean (Phaseolus vulgaris) association panel grown at Urbana, IL, USA, in 2022 and 2023. Top left panel: Pod length. Top right panel: Pod width. Bottom left panel: Pod height. Bottom right panel: Pod ratio. (A, C, E, and G) Manhattan plots of the multilocus random SNP effect mixed linear models (MrMLM) and multilocus mixed model (MLMM) analyses. For the MrMLM plot, pink dots represent single nucleotide polymorphisms detected by multiple models. For both MrMLM and MLMM plots, the X-axis shows the chromosomes and the Y-axis shows the −log10 of the P value of the association. The horizontal line represents the significance threshold. (B, D, F, and H) Quantile–quantile plots depicting the observed (Y-axis) and expected (X-axis) −log10 of the P value.

  • Fig. 5.

    Manhattan plots summarizing the results of the genome-wide association analyses for the within-cultivar coefficient of variation (CV) of the pod morphological traits for the 374 cultivars of the snap bean (Phaseolus vulgaris) association panel grown at Urbana, IL, USA, in 2022 and 2023. Top left panel: Pod length CV. Top right panel: Pod width CV. Bottom left panel: Pod height CV. Bottom right panel: Pod ratio CV. (A, C, E, and G) Manhattan plots of the multilocus random SNP effect mixed linear models (MrMLM) and multilocus mixed model (MLMM) analyses. For the MrMLM plot, pink dots represent single nucleotide polymorphisms detected by multiple models. For both MrMLM and MLMM plots, the X-axis shows the chromosomes and the Y-axis shows the −log10 of the P value of the association. The horizontal line represents the significance threshold. (B, D, F, and H) Quantile–quantile plots depicting the observed (Y-axis) and expected (X-axis) −log10 of the P value.

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Supplementary Materials

Ana Saballos Global Change and Photosynthesis Research Unit, US Department of Agriculture–Agricultural Research Service, Urbana, IL 61801, USA

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Martin M. Williams II Global Change and Photosynthesis Research Unit, US Department of Agriculture–Agricultural Research Service, Urbana, IL 61801, USA

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Contributor Notes

This research was funded by the US Department of Agriculture-Agricultural Research Service (USDA-ARS), Project number 5012-12220-010-000D, entitled “Resilience of Integrated Weed Management Systems to Climate Variability in Midwest Crop Production Systems,” and by an appointment to the Agricultural Research Service Research Participation Program administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the US Department of Energy (DOE) and the USDA. We thank Jim Myers at Oregon State University and John Hart at USDA-ARS Tropical Agriculture Research Station for the assembly of the panel and collection of the cultivars Plant Variety Protection data, and Alvaro Soler-Garzon of Washington State University for the production of the genomic data and structure analysis used in this study. We thank Nicholas Hausman for managing the field experiment and numerous students for assisting with data collection, and Felix Navarro, Tim Trump, and Jen Trapp from Seneca Foods for providing increases of the diversity panel seed. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not necessarily reflect the view of the USDOE or USDA. The mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement. Equal opportunity provider and employer.

M.W. is the corresponding author. E-mail: martin.williams@usda.gov.

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