New genomic regions in dry bean (Phaseolus vulgaris L.) associated with stem diameter, plant height and other plant architecture traits

New genomic regions in dry bean (Phaseolus vulgaris L.) associated with stem diameter, plant height and other plant architecture traits | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article New genomic regions in dry bean (Phaseolus vulgaris L.) associated with stem diameter, plant height and other plant architecture traits Oscar Rodríguez, Jayanta Roy, Didier Murillo, Kristin Simons, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8634079/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract Growth habit is one of the most important domestication traits in dry bean ( Phaseolus vulgaris L.). In the U.S. for example, Type II indeterminate upright plant varieties have allowed farmers to switch from historic two-pass harvest to one-pass direct harvest. Previous work suggested a stem diameter of 5.6 mm as threshold to select Type II architecture genotypes suitable for direct combining. This study aimed to validate the correlation between stem diameter and other agronomic traits using lines comprising various market classes from a public breeding program. It also assesses if stem diameter could be used to select genotypes that combine high seed yield and upright architecture. GWAS was also used to identify genomic regions related to plant height and stem diameter. Mean stem diameter among breeding lines was 7.7 mm, higher than the proposed threshold. Stem diameter showed no significant GxE interactions and the highest broad-sense heritabilities were for regular-darkening pinto (pinto) and slow darkening (SD) pinto. Plant height was the most relevant trait for seed yield variation in black, great northern, and navy beans. In contrast, both plant height and stem diameter are required to explain part of seed yield variability and selecting upright plants for pinto, red/pink, and SD-pinto. GWAS revealed significant regions located on chromosomes Pv03, Pv07, and Pv11 depending on trait and race used. A 40.7–40.8 Mb interval on Pv07 was associated with both plant height and stem diameter, suggesting further studies on indeterminate upright dry bean plant architecture should focus on this region. Growth habit stem diameter plant height dry bean GWAS Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Growth habit is one of the primary traits that has dramatically changed in dry bean ( Phaseolus vulgaris L.) from the wild relative to its domesticated form (Smartt 1976 ). Wild beans had a climbing growth habit as an adaptation to an environment in which trees and shrubs were competitors for light but also helped as a support. Wild bean plants were branched and had many nodes, long and weak internodes, and twining ability (Gepts and Debouck 1991 ). The domestication process generated new environmental conditions for dry bean. Hence, new growth habits appeared due to reduced competition for light; therefore, less excessive growth was required. These new conditions led to the selection of cultivars that matured earlier, dried down synchronously, and had a compact growth habit with fewer branching and nodes. In Mesoamerica, for example, dry beans were traditionally grown alongside maize ( Zea mays L.) and squash ( Cucurbita spp.) in a multi-crop system called “Milpa”, which provided support for climbing beans (Zizumbo-Villarreal and Colunga-García, 2010). Subsequent breeding mostly focused on the selection of upright plants that do not need any external support from trees or trellises. These selected genotype plants had stout stems, fewer nodes, shorter internodes, and reduced twining (Gepts and Debouck 1991 ), which later helped in the introduction of monocropping. In general, a loss of genetic diversity is observed when comparing wild to cultivated dry bean. However, the opposite is observed for growth habit (Kelly 2001 ). While most wild beans tend to climb (Gepts 1998 ), cultivated beans display a wider range of growth habits (Kelly 2001 ). This growth habit has been related to three factors: the presence of axial or terminal inflorescence, stem length, and twining ability (Norton 1915 ). This higher diversity led to different classifications. The simplest one is the differentiation between determinate (apical growth ends up in an inflorescence) and indeterminate plants (apical growth continues vegetatively, no terminal inflorescence, and all inflorescences are axillary) (Ojehomon and Morgan 1969 ). A more detailed classification is based on indeterminate or determinate growth habit, stem stiffness, and climbing ability. Based on this, Singh ( 1982 ) classified beans into four growth habits namely, Type I, II, III, and IV. Thus, growth habit could be considered an important distinguishing criterion for differentiating dry bean germplasm (Beebe et al. 2000 ). This trait and other morphological, agronomic, and molecular traits were used to classify dry bean in two major gene pools, Middle-American and Andean. Each gene pool has different races, within Middle American, races Durango/Jalisco, Mesoamerica, and Guatemala are found. While races Chile, Nueva Granada, and Peru belong to the Andean gene pool (Singh 1989 ; Singh et al. 1991 ; Beebe et al. 2000 ). Breeding for upright plant architecture has significantly expanded dry bean production in the midwestern states (Singh et al. 2007 ). Today, Type II indeterminate growth is highly preferred across U.S. and for most dry bean market classes because the pods do not touch the ground which improved white mold avoidance, and reduced losses during harvest (Eckert et al. 2011 ). The only exceptions would be determinate growth habit Andean cultivars such as kidney, cranberry, and yellow market classes plants which usually maintain an upright bushy growth across most environments. Different traits related to upright plant selection have been described (Denis and Adams 1978 ; Adams 1982 ). However, stem diameter, plant height, branch angle, and pod distribution have been suggested as the most important and reliable traits to select plants with upright Type II architecture due to their high correlation with this trait (Acquaah et al. 1991 , 1992 ; Moura et al. 2013 ). In addition, different grading scales for plant architecture have been generated, some of them combine traits such as plant height, growth habit and architecture, producing a range of qualitative values to select (Collicchio et al. 1997 ; Kelly and Adams 1987 ; Melo 2009 ). However, these scales are subjective and require training to reduce errors and ensure accurate scoring. Therefore, a key consideration for the breeder is to determine if a trait used for selection can be easily measured (Acquaah et al. 1992 ), requiring less effort, time, and resources to obtain reliable data. Silva et al. ( 2013 ) determined stem diameter and mean plant height are highly correlated to plant architectural grade. Additionally, the authors found that stem diameter was controlled by additive effects, which was confirmed by Oliveira et al. ( 2015 ). These findings suggest that stem diameter is more precise and accurate than simply applying a plant architecture scale. In addition to stem diameter, plant height is another highly relevant trait for upright plant selection (Schwartz et al. 1987 ). Mulube ( 2017 ) found that plant height, stem diameter, number of pods per plant, and seed yield are the most important traits related to mechanical harvest. In the US pinto bean cultivars, plant height was increased from a mean of 34 cm in 1965 to a mean of 57 cm by 2008, likely due to the introgression of Mesoamerica race germplasm with upright Type II architecture (Vandemark et al. 2014 ). This strategy has been used to achieve upright architecture in market classes where this characteristic is uncommon (Kelly 2001 ). Introgression of Mesoamerica race Type II growth habit into Type III race Durango market classes (e.g. pinto and great northern) resulted in Durango race Type II cultivars (Kelly and Adams 1987 ). The importance of plant height and stem diameter for selecting genotypes with upright plant architecture was further confirmed by Soltani et al. ( 2016 ) with the Durango Diversity Panel (DDP). Stem diameter was highly correlated with plant height ( r = 0.80), and both traits showed moderate correlations with seed yield ( r = 0.42) and ( r = 0.40), respectively. Therefore, stem diameter and plant height could be essential traits in selecting genotypes with upright architecture. Indeed, a stem diameter value of 5.6 mm has been suggested as a threshold to choose plants with reduced lodging and improved Type II architecture suitable for mechanical direct harvest (Soltani et al. 2016 ). Besides its phenotypic importance, understanding the genetic control of growth habit is essential to detect possible regions or genes related to the trait of interest as genomic tools increase accuracy of selection. Kornegay et al. ( 1992 ) suggested a simple genetic control for growth habit, with Type I or determinate recessively inherited, and indeterminant (semi-climbing and climbing types) as dominant. Jung et al. ( 1996 ) suggested the Fin locus was the determinacy gene and linked with the photoperiod sensitivity gene ( ppd ) which possibly exerted pleiotropic effects on twining. Single gene inheritance for growth habit and pleiotropic relationships with flowering, maturity, and branch angle were confirmed by Tar’an et al. ( 2002 ) and Repinski et al. ( 2012 ). Using Mesoamerican and Andean germplasm as parents, Kolkman and Kelly ( 2003 ) confirmed the Fin gene weas located on chromosome Pv01. Fin is considered a homolog of the TFL1 (Terminal flower 1) in Arabidopsis (Repinski et al. 2012 ). Another QTL for growth habit in chromosome Pv07 was found for small-seeded (navy) genotypes, also associated with flowering, branching pattern, lodging, and disease severity index (Kwak et al. 2008 ). These findings suggest different determinacy origins, the Andean gene pool as the primary source to determinate growth habit (related to Fin gene) and the Middle-American pool as the source of indeterminate growth habit. Using the Middle-American Diversity Panel (MDP), which includes genotypes with different growth habits, Moghaddam et al. ( 2016 ) found a strong signal in chromosome Pv01 associated with Type I genotypes. However, when only Type II and Type III genotypes were used in the GWAS analysis, the most significant peak was observed on chromosome Pv07. Meta-analyses by MacQueen et al. ( 2020 ) and Izquierdo et al. ( 2023 ) identified strong signals on Pv01 and Pv07, with additional associations on Pv03, Pv06, Pv08, and Pv11 linked to phenology and architectural traits (growth habit, plant height) using both Andean and Middle American germplasm. The molecular function of some genes related to growth habit include regulation by plant hormones auxin, cytokinins and giberelins (Yoshida et al. 2018 ; McKim 2020 ), transcription factors and proteins related to these mechanisms such as WD-40, F-box (Ho et al. 2008 ); and growth-regulating factors (GRFs) with roles in stem and leaf development (Omidbakhshfard et al. 2015 ; Piya et al. 2020 ). Floral activators and floral inhibitors (Jin et al. 2021 ; Krylova et al. 2021 ) and Ring/U-box protein related to Ubiquitin ligase genes in response to abiotic and biotic stresses (Zhang et al. 2020 ; Kim et al. 2021 ) were others identified. Despite these advances and several efforts to understand the relationship among plant height, stem diameter, and upright plant selection, there is still a gap to a fuller understanding of how stem diameter and plant height jointly influence upright architecture in commercial breeding programs and within/among different market classes. Moreover, integrating phenotypic and genomic data could enhance upright plant selection strategies in the commercial breeding program Therefore, this study aimed to validate the correlation between stem diameter and other agronomic traits such as plant height and seed yield among others using breeding lines from a public breeding program. Additionally, it sought to investigate stem diameter as a criterion to select genotypes that combine high seed yield and upright architecture. Furthermore, the study aimed to identify genomic regions related to the traits of stem diameter and plant height using a genome wide association study (GWAS) approach. Materials and methods Plant material Two hundred and thirty-three (233) advanced dry bean breeding lines from the Nort Dakota State University (NDSU) dry bean breeding program and 28 cultivars commonly grown in North Dakota were evaluated across 3 growing seasons from 2020 to 2022. The lines are from the six major market classes in the U.S. including black, navy, great northern, red, pink, regular-darkening pinto (pinto), and slow-darkening (SD) pinto (Table 1 ). Table 1 Number of breeding lines and cultivars evaluated in 2020, 2021, and 2022. Race Market Class Total number of lines evaluated (with checks) Total Number of Environments (Year x Location) Mesoamerica Black 45 8 Navy 44 9 Durango / Jalisco Great Northern 55 9 Red and Pink 32 9 Pinto 32 9 SD Pinto 53 9 Total 261 53 Field experiments Breeding lines and check cultivars were evaluated in separate advanced yield trials (AYT) within each market class, except for red and pinks which were grown within the same trial because they are considered genetically close. Trails were established at four representative locations in North Dakota − Hatton, Johnstown, Carrington, and Prosper, during the growing seasons (May-September) of the years 2020, 2021, and 2022. These locations are representative of the major dry bean growing areas within the state. For each year, the number of genotypes evaluated varied, because different AYT lines from the breeding program were included annually. The number of environments (location by market class) varied between 8 and 9. The trials followed the standard management practices used by the dry bean farmers in the Red River Valley of North Dakota (Kandel and Endres 2019 ), with the exception of controlling for natural disease pressure. Each field trial was conducted in a randomized complete block design (RCBD) with three replications. The experimental plots consisted of 2 rows; each row had a length of 3.65 m, spaced 0.76 m apart, for a total plot area of 5.54 m 2 . Morpho-agronomic traits data collection The following traits were measured: Plant height: The distance in cm from the soil surface and the top of the canopy at bloom, 50% of plants at pod setting, without stretching out the plant. Stem diameter: Measured in mm by randomly selecting two plants per plot with a caliper brand tool shop model SKU 243–2377, right above the soil surface at maturity (80% of the pods and leaf area dry). Seed size: Expressed as 100-seed weight: the weight of 100 seeds in g. Seed yield: The seed yield at harvest in kg ha –1 . Maturity: Days to maturity when 80% of the pods and leaf area dry, in days after planting (DAP) (Soltani et al. 2016 ). Phenotypic Data analysis Data analysis was performed with SAS 9.4 (SAS Institute, Cary, NC, USA) to display numerical data distribution with histograms and detect outliers within boxplots, which were removed from the analysis. This dataset is unbalanced since all the breeding lines were not evaluated in the same years. Homogeneity of variances was performed using the “10x rule”. In this test, trials were considered homogeneous if the largest error variance was not 10 times larger than the smallest one (Patterson and Silvey 1980 ). After confirming homogeneity of variances, for each market class, the MIXED procedure was conducted in SAS 9.4 to perform a combined analysis of variance (ANOVA). Given the unbalanced nature of the dataset, the Best Linear Unbiased Predictor (BLUP) was obtained for all the recorded traits. The sources of variation used were genotype, environment, genotype x environment (GxE), and blocks nested within each environment. Environment was considered as the combination between location and year. The genotype effect was considered fixed, while the remaining terms were considered random. The probability of observing differences between group means was tested at significance levels of 0.05 and 0.01. Heritability estimates Variance components were obtained using covtest in PROC MIXED with SAS 9.4, considering genotype, environment and GxE as random effects. Using covariance estimates, the broad-sense heritability based on the entry mean was calculated (Fehr 1991 ). $$\:{H}^{2}=\frac{{\sigma\:}_{G}^{2}}{{\sigma\:}_{G}^{2}+{\sigma\:}_{\frac{GE}{e}}^{2}+{\sigma\:}_{\frac{E}{re}}^{2}}$$ Where, \(\:{\sigma\:}_{G}^{2}\) = Genotypic variance \(\:{\sigma\:}_{GE}^{2}\) = Genotype x environment variance \(\:{\sigma\:}_{E}^{2}\) = Environmental variance e = number of environments r = number of replicates Correlation analysis with agronomic traits Pearson’s correlation coefficients were calculated to determine the strength and direction of the linear relationship between pairs of quantitative traits. Correlation coefficients and matrices were calculated and plotted using the functions cor.matix () and corrplot () from the corrplot package in R (Wei and Simko 2017 ). Regressions Simple and multiple linear regression analyses were performed to confirm a cause-and-effect relationship among the different independent variables and seed yield. Plant height, stem diameter, maturity, and seed weight were considered as independent variables, while seed yield was the dependent variable. The R function lm () was used. Each independent trait was considered for simple regression, while combinations of the most relevant independent traits were considered for the multiple linear regressions. Principal component analysis (PCA) Principal component analysis was performed using the function PCA in R package FactoMineR (Husson et al. 2023 ). This analysis was performed for the whole data set, using all market classes and within each market class. Genetic material, DNA extraction, and GBS libraries generation A total of 544 advanced breeding lines evaluated between the years 2020, 2021, and 2022, were used to develop a representative panel comprising multiple dry bean market classes. For DNA collection, two dry bean seeds for each line were planted and the first trifoliate leaf from a single plant was taken and placed in a single well on a plate of 96 wells. Liquid nitrogen was used to preserve the collected samples. DNA was isolated using the Mag-Bind® Plant DNA Plus kit (Omega Bio-Tek, Norcross, Georgia, USA) and quantified using Nanodrop. GBS libraries were developed using the optimized two-enzyme protocol [MseI and Taqα1] protocol described by Schröder et al. ( 2016 ). Six GBS libraries were sequenced in paired-end runs (2 x 150 bp) using Illumina HiSeq 2500 sequencing system at the HudsonAlpha institute for Biotechnology (Hunstville, AL, USA). Sequence data processing and SNP Calling Low quality reads (quality threshold < 20) and reads with less than 80 bp length were discarded using SICKLE software (Joshi and Fass 2011 ). The filtered reads were mapped to the P. vulgaris reference genome UI111 v 1.1(available at: https://phytozome-next.jgi.doe.gov/info/PvulgarisUI111_v1_1 ) using Burrows-Wheeler Alignment Tool (BWA-mem) (Li, 2013 ). Subsequently, SAMtools (Li et al. 2009 ) were used to sort and index the mapping results. Read group information was added using Picard tools ( http://broadinstitute.github.io/picard ). SNP calling was performed using the MultisampleVariantsDetector module embedded in the NGSEPcore_4.2.0 software with the -maxAlnsPerStartPos 100 parameter (Tello et al. 2023 ). Multiallelic SNPs were discarded and SNPs with a minimum read depth ≥ 3, were selected. Further filtering was done to remove genotypes that had more than 75% missing sites, resulting in a total of 508 genotypes. SNPs with less than 25% missing sites and 10% heterozygotes were imputed using Beagle v5.4 (Browning et al. 2018 ). Finally, a subset of 214 genotypes with a total of 80,578 high quality SNPs after applying 5% minor allele frequency (MAF), were used for the GWAS analysis. However, after applying same quality filtering with 5% MAF, a total of 72,494 and 41,799 SNPs were used for GWAS analysis for the 132 Durango and 82 MA race genotypes, respectively. Single trait and multi-trait GWAS Genome-wide association analyses for stem diameter and plant height were conducted using GEMMA v0.98.1 (Genome-wide Efficient Mixed Model Analysis: Zhou and Stephens 2012 ). The analysis was performed for combined datasets (all 214 genotypes) and separately for Durango race and Mesoamerican race genotypes. Two models were implemented: i) the mixed linear model (GEMMA-MLM) including population structure (PCA) and relatedness (kinship matrix), and ii) efficient mixed-model analysis (EMMA) (efficient mixed-model analysis) in which only kinship matrix was included. The first two PCA calculated by prcomp () function in R was used as a covariate. The kinship matrix was computed using the centered relatedness procedure in GEMMA. The lower mean square deviation (MSD) (Mamidi et al. 2011 ) and q-q plots were considered to select the best model for further analysis. The significant threshold of P -value with multiple testing correction was calculated following the method proposed by Li and Ji, ( 2005 ), to declare association between SNPs and trait of interest. This method uses the eigenvalues of the correlation matrix between the SNPs to calculate the effective number of independent loci ( M eff ). The significance threshold was calculated as \(\:{\alpha\:}_{p}=1\:-{(1-\:{\alpha\:}_{e})}^{1/{M}_{eff}}\) , where the experiment-wise error rate was α e = 0.05. For significant SNPs, the proportion of phenotypic variance explained (PVE; R 2 ) was calculated using the genABEL package available in R (Aulchenko et al. 2007 ; R Core Team 2015 ). Manhattan plots were generated using the R function mhplot() (Zhao 2007 ). Additionally, a multi-trait GWAS was performed in GEMMA v0.98.1 using both plant height and stem diameter as response variables. The analysis was performed across the full panel as well as within each race. The GEMMA-MLM model was implemented for both traits, incorporating the first two principal components (PCA) and kinship matrix to account for population structure and relatedness. Search for putative candidate genes Common bean UI111 v 1.1 genome annotation was scanned for potential candidate genes present within the ± 50 Kb genomic regions from the significant peak SNPs. This range was selected based on the average LD blocks, which is ~ 100kb. Genes within a region were investigated through available literature search and their putative biological functions. The potential candidate genes were selected based on their potential relationship to stem diameter and plant height metabolic pathways. Haplotype block analysis Haplotype blocks were constructed surrounding the significant SNPs for the respective chromosome according to the solid spine method of linkage disequilibrium (LD) and the extended spine if D’ > 0.8 implemented in the Haploview v 4.1 software (Barrett et al. 2005 ). This method considers that the first and last markers in a haplotype block are in strong LD with all intermediate markers, thereby providing more robust block boundaries. Results Evaluation of traits across locations Genotype, environment, and GxE effects were highly significant ( P ≤ 0.01), either on the combined or for the market class analyses, for nearly all traits in the study. GxE was significant ( P ≤ 0.05) for plant height, in great northern, and red and pinks. Stem diameter did not exhibit a significant GxE for the pinto and SD-pinto market classes while this interaction was significant ( P ≤ 0.05) for the other market classes (Table 2 ). Table 2 Statistic parameters for all traits within each market class across eight to nine environments Combined Black † Great northern Navy Pinto Red and Pinks SD-pinto F Values Stem Diameter G 2.7 *** 1.9 *** 1.4 * 2.0 *** 5.2 *** 3.4 *** 3.5 *** E 51.2 *** 18.0 *** 12.7 *** 41.4 *** 13.1 *** 10.4 *** 19.2 *** G x E 1.3 *** 1.2 * 1.3 * 1.3 * 0.8 n.s. 1.4 * 1.1 n.s. Plant Height G 3.4 *** 1.6 * 4.9 *** 2.7 *** 6.4 *** 5.3 *** 2.7 *** E 69.0 *** 29.8 *** 36.3 *** 14.7 *** 9.0 *** 29.2 *** 37.7 *** G x E 2.05 *** 1.6 *** 1.3 * 1.5 *** 1.8 *** 1.3 * 1.8 *** Maturity G 2.4 *** 3.0 *** 3.7 *** 2.9 *** 3.4 *** 2.2 *** 5.1 *** E 55.2 *** 36.0 *** 43.7 *** 11.7 *** 24.5 *** 62.6 *** 26.2 *** G x E 3.3 *** 2.4 *** 2.2 *** 2.7 *** 2.3 *** 2.3 *** 1.5 ** Seed Weight G 133.5 *** 18.5 *** 12.8 *** 12 *** 10.7 *** 11.2 *** 7.9 *** E 118.5 *** 117.1 *** 48.2 *** 18.9 *** 52.6 *** 31.6 *** 37.9 *** G x E 2.9 *** 2.0 *** 2.2 *** 2.1 *** 2.5 *** 2.2 *** 2 *** Seed Yield G 2.7 *** 2.5 *** 5.5 *** 2.9 *** 1.9 ** 5.4 *** 2.8 *** E 49.2 *** 57.0 *** 30.0 *** 12.2 *** 12.8 *** 17.7 *** 39.5 *** G x E 2.7 *** 1.7 *** 2.4 *** 1.7 *** 2.6 *** 1.8 *** 1.5 *** G: Genotype, E: Environment and G X E: Genotype x Environment interaction. *** P value < 0.0001; ** P value < 0.01; * P value < 0.05; n.s. non significant. † For black, eight environments were evaluated, for the remaining market classes nine environments were taken. Mean values by market class Means values for plant height were between 49.3 cm for great northern and 53.6 cm for pinto genotypes. Stem diameter mean values across all market classes ranged between 7.6 and 8.0 mm (Table 3 ). The most common value for stem diameter among market classes was 7.6 mm, observed in black, navy, great northern, and red/pink. The highest mean stem diameter for pinto with 8.0 mm was significantly higher than the remaining market classes. According to the mean separation values for plant height, pinto and red/pink had the highest plant height with 53.6 and 52.3 cm, respectively. In addition, these two market classes showed the highest seed yield with 1827 kg ha -1 for red/pink and 1754 kg ha -1 for pinto. In most cases, pintos showed either the highest or some of the highest values for stem diameter, plant height, and seed yield. Table 3 Means for agronomic traits across environments (2020–2022) Stem diameter Plant height Maturity 100-Seed weight Seed yield Market Class Mean Mm cm d g kg ha-1 Combined 7.7 50.9 102 28.4 1662 Pinto 8.0 a 53.6 a 101 bc 35.0 b 1754 b SD Pinto 7.7 b 50.2 cd 102 ab 35.7 a 1664 c Red and Pinks 7.6 b 52.3 b 103 a 31.3 d 1827 a Black 7.6 b 50.4 c 103 a 19.0 e 1694 bc Navy 7.6 b 51.0 c 101 c 17.9 f 1582 d Great Northern 7.6 b 49.3 d 101 c 34.0 c 1539 d LSD 0.12 0.91 1.03 0.37 67.44 Mean with different letters indicate significantly different Market Classes (LSD test P < 0.05) Heritabilities The heritability estimates for stem diameter ranged between 0.43 for great northern and 0.87 for pinto (Table 4 ). The two market classes with the highest heritability were pinto and SD-Pinto (0.84), which also showed non-significant GxE interactions for the trait. Heritability for plant height, ranged between 0.46 for black and 0.89 for pinto and red/pink. Except for black, all market classes showed heritability higher than 0.70 for this trait. Table 4 Broad-sense heritability values for each market class Heritability (H 2 ) Combined Black Great Northern Navy Pinto Red and Pinks SD Pinto Stem diameter 0.75 0.58 0.43 0.63 0.87 0.82 0.84 Plant height 0.81 0.46 0.88 0.74 0.89 0.89 0.78 Maturity 0.73 0.74 0.84 0.76 0.81 0.73 0.91 Seed weight 0.99 0.96 0.96 0.95 0.94 0.95 0.93 Seed yield 0.75 0.69 0.89 0.76 0.56 0.89 0.79 H 2 : Broad sense heritability using entry-mean. Correlations among traits Among all market classes, all correlations were significant except plant height and seed weight. The two most important correlations were plant height and seed yield ( r = 0.5); plant height and stem diameter (0.4). Conversely, stem diameter and seed yield had a low correlation value (0.2). In both analyses, combined and within market classes, the most important correlation was found between plant height and seed yield. Small differences were observed among market classes for this correlation, with navy (0.6), showing the highest value. In contrast, pinto, red/pink, and SD-pinto shared the lowest correlation value of 0.4 (Fig. 1 ). Likewise, plant height and stem diameter were positively correlated. Great northern, pinto, and SD-pinto scored the lowest value of 0.3, while the highest were found for navy (0.5), red/pink, and black (0.4). In contrast to the combined analysis, higher correlation values between stem diameter and seed yield were found within market classes. Maturity and seed yield were negatively correlated for red/pink and SD-pinto (-0.3), while the remaining market classes showed weak negative or non-significant correlations (Fig. 1 ). X for non-significant correlation. PH: Plant height, SD: Stem diameter, MAT: Maturity, SW: Seed weight and Y: Plant yield. Regressions Simple linear regressions for the combined data set, plant height, stem diameter, maturity, and seed weight explained 24.1%, 8.9%, 2.7%, and 0.1% of seed yield variation, respectively (Table 5 ). In addition, multiple linear regressions that were performed using the three most important traits showed that, plant height + stem diameter (25.6%) explained most of seed yield variation; adding maturity to the model explained less variation than considering only plant height. In conclusion, plant height and stem diameter explained most of the seed yield variation. When regressions were performed within each market class, two main patterns were observed. The first pattern was detected for black, great northern, and navy, where plant height explained more than 24.4% of seed yield variation. In contrast, stem diameter only explained between 4.7 to 8.5% of the variation. For black and navy when plant height and stem diameter were used in multiple regressions, plant height was the only significant trait. Conversely, for great northern, both were significant and explained 24.0% and 7.3% of the variation, respectively. However, when a model including plant height + stem diameter was used, variation reached 26.3%. In this group, plant height was the most important trait explaining 24.4% of yield variation. The second pattern was observed for pinto, red/pink, and SD-pinto, in which plant height explained less variation than the previous group, with values of 15.1% for pinto, 16.5% for red/pink, and 20.9% for SD-pinto (Table 5 ). In contrast, stem diameter explained between 10.6 and 11.7%, suggesting a higher relevance of this trait for this group. In all market classes within this pattern, the model plant height + stem diameter showed an increase on the explained variation in comparison to the single use of plant height data. For this group, plant height was also the most important trait, however, it explained a smaller variation in seed yield. In contrast, stem diameter was relevant, explaining around 11% of the variation (Table 5 ). Table 5 Linear and multiple regressions between the traits of study and seed yield Trait Combined Black Navy Great Pinto Red SD-pinto Simple linear regression % of variation explained Plant height (PH) 24.1 29.1 36.2 24.4 15.1 16.5 20.9 Stem diameter (SD) 8.9 4.7 8.5 7.3 10.6 11.7 11.0 Maturity (MAT) 2.7 4.4 0.1 * 0.0 * 1.8 8.3 11.9 100-Seed weight (SW) 0.1 0.7 4.6 0.5 − 0.2 * 0.3 * − 0.1 * Multiple linear regression PH + SD 25.6 29.0 * 36.2 ** 26.3 19.0 20.0 24.5 PH + MAT 23.2 29.9 35.1 24.0 * 16.3 18.3 23.2 SD + MAT 10.5 7.9 * 8.6 * 8.1 * 12.8 18.0 19.7 PH + SD + MAT 24.7 29.8 * 35.0 ** 26.4 * 20.7 22.5 26.8 Simple linear regression : * n.s. non significant. Multiple regression : Black : *SD non-significant. Great northern : *MAT non-significant. Navy : **SD non-significant and *MAT non-significant. Principal component analysis When the PCA for the combined phenotypic analysis was performed, PC1 and PC2 explained 41.9% and 20.4% of the variation, respectively, reaching a total of 62.3% of the variation (Fig. 2 ). Stem diameter, seed yield, and plant height were clustered, but plant height was the closer vector to seed yield. Maturity and seed weight appear to have an opposite relationship. These last two variables showed no relationship with stem diameter, seed yield, and plant height. When this analysis was performed within each market class, there were some similarities to correlations and regressions results. However, some changes regarding the distribution of the variables were found. Similar to the combined analysis, seed weight and maturity were not clustered with plant height, stem diameter, or seed yield in most market classes. Nevertheless, some differences were observed for each market class and race. Within the Mesoamerican race, plant height, stem diameter, and seed yield were clustered together. However, a closer relationship between plant height and stem diameter was present in black compared to navy. For black beans, these two traits had almost the same vector length and direction (Fig. 2 ). For great northern and red/pink beans, a close relationship was observed between plant height and stem diameter (Fig. 3 ). Nevertheless, for great northern these traits were also highly associated with seed yield. Differences for plant height and stem diameter were observed in pinto and SD-pinto (Fig. 3 ). In these cases, and especially for SD-pinto beans, the vectors of these two variables were not in the same direction. Maturity for pinto was not related to any of the two principal components (Fig. 3 ). GWAS The Bonferroni-Holm correction for multiple testing proved too conservative, as it assumes all tests are independent. However, many SNPs might be linked due to their physical proximity or other associated factors, meaning they are not truly independent. Consequently, applying this stringent threshold resulted in no significant associations being detected for any traits. Therefore, P -value threshold to declare significant marker trait associations was determined using the Li and Ji ( 2005 ) method. Based on this method, the P -value thresholds corresponding to a 5% experiment-wise error rate were: 1.61 × 10⁻⁴ (LOD = 3.8) for the combined analyses (single trait and multi-trait), 4.27 × 10⁻⁴ (LOD = 3.4) for the Mesoamerican race, and 2.67 × 10⁻⁴ (LOD = 3.6) for the Durango race. Plant height For plant height, significant peaks were found on chromosomes Pv03 and Pv07 (Fig. 4 ). The major GWAS signal was located on Pv07, with the lead SNP S07_40778504 located within a 40.7–40.8 Mb genomic interval. In this region, five SNPs surpassed the 1.58 x 10 − 5 (LOD = 4.8) threshold. Within this interval, gene model PvUI111.07G214700, annotated as a growth-regulating factor was identified as a strong candidate gene (Table 6 ). Additional genes in these regions included those encoding serine/threonine-protein kinase, ring/zinc finger protein, E3 ubiquitin-protein ligase MGRN1 (MGRN1), SF98-Membrane associated ring fingers, and leucine rich repeats. Table 6 Candidate genes on chromosomes Pv03 and Pv07 for plant height Chr Region SNP Peak Putative Genes Gene Function Pv03 36.7 – 36.9 Mb S03_36917109 3.22 x 10 − 5 PvUI111.03G154900.1 PvUI111.03G157000.1 and PvUI111.03G157100.1 Serine/Threonine-Protein Kinase F-box associated domain (FBA_3) Pv07 40.7 – 40.8 Mb S07_40778587 1.05 x 10 − 5 PvUI111.07G214700.1 (1 copy .2) PvUI111.07G213900.1 (1 copy.2) and PvUI111.07G213300.1 PvUI111.07G213500.1 PvUI111.07G213800.1 PvUI111.07G214300.1 Growth-regulating factor related-3-related Serine/Threonine-protein kinase ring/Zinc Finger Protein Jackdaw E3 ubiquitin-protein ligase MGRN1 (MGRN1) SF98-Membrane associated ring finger. Leucine Rich Repeat (LRR_1), (LRRNT_2), and (LRR_8) The second most important peak SNP, S03_36917109, was identified on Pv03 with a P -value of 3.22 x 10 − 5 (LOD = 4.5). A cluster of 16 SNPs within the 36.7–36.9 Mb region exceeded the P -value threshold of 5.68 x 10 − 5 (LOD = 4.2). Notable gene models such as PvUI111.03G154900 related to a serine/threonine protein kinase; PvUI111.03G157000 and PvUI111.03G157100, both associated with an F-box associated domain (FBA-3), were the most relevant and considered as candidate genes governing plant height. Collectively, the significant SNPs explained 39% of the phenotypic variation for plant height, with the Pv07 significant region alone accounting for 16.6% of the variation. When GWAS analyses were conducted separately by race, four significant SNPs were detected in the Durango race. One SNP (S06_3623485) on Pv06 and three SNPs on Pv03, located between 36.8–37.2 Mb interval, corresponding to the same genomic region detected in the combined plant height GWAS analysis. For race Mesoamerica, 27 significant SNPs were found, distributed on chromosomes Pv01 (1), Pv05 (7), and Pv07 (17) (Fig. 5 ). The strongest signals with majority of the significant SNPs (7) were identified at ~ 40.7 Mb on Pv07, overlapping with the combined analysis. Additionally, five SNPs were detected on Pv07 around 2.2 Mb, highlighting the relevance of these genomic regions for plant height regulation. GWAS results pointed to chromosomes Pv03 (36.8–37.2 Mb) and Pv07 (40.7–40.8 Mb) as the most important genomic regions related to plant height for dry bean. These genomic regions were simultaneously detected in both races as well as in combined GWAS analyses. Stem diameter. GWAS identified two highly significant genomic regions located on Pv11 and Pv07 for stem diameter when all the genotypes were combined (Fig. 6 ). The most significant SNP was observed on Pv11, where the peak SNP (S11_8564171) was located around 8.5 Mb ( P = 1.42 x 10 − 5 ; LOD = 4.8). This region harbored two candidate genes with a possible relationship with stem diameter, PvUI111.11G089600.1, a cyclin-dependent kinase regulatory subunit (CKS1) and PvUI111.11G089800.1, an apoptosis inhibitor//Ring//U-Box domain-containing protein. On Pv07, two major genomic regions were detected, spanning 33.5–34.7 Mb (23 SNPs) and 40.7 Mb (5 SNPs). Haplotype block analysis revealed four distinct haplotype blocks: The first block (Block 1) extends about 379 Kb from 33.4 to 33.7 Mb consisting of five significant SNPs. The other three blocks were Block 2: 33.7 Mb (1 SNP; ~139 Kb); Block 3: 34.5–34.6 Mb (17 SNPs); Block 4: 35.7 Mb (1 SNP). An additional block of five significant SNPs clustered around 40.7 Mb spanned a 112 Kb haplotype block. Collectively, these significant genomic regions explained 56.2% of the phenotypic variation in stem diameter. Among these, the Pv07 region spanning 34.5 ̶ 34.6 Mb (Block 3) was the most significant containing several functionally relevant gene models: PvUI111.07G159600 (gibberellin receptor GID1), PvUI111.07G159700 (WD repeat containing protein), and PvUI111.07G159800 (leucine rich repeat protein) (Table 7 ). Another significant region at 33.5 Mb, with five significant SNPs, contained two notable gene models: PvUI111.07G153300 (defense-related protein containing SCP domain) and PvUI111.07G153400 (PPR repeat family protein). Notably, the 40.7 Mb region on Pv07 was significantly associated with stem diameter, overlapped with the major regions identified for plant height, suggesting a shared genetic control for upright plant architecture. Table 7 Candidate genes for stem diameter found on chromosomes Pv11 and Pv07. Chr Region SNP Peak Genes Genes Function Pv07 33.5 Mb S07_33538378 7.85 x 10 − 5 PvUI111.07G153300.1 PvUI111.07G153400.1 Defense-related protein containing SCP domain PPR repeat (PPR) // PPR repeat family (PPR_2) Pv07 34.5–34.6 Mb S07_34570692 3.11 x 10 − 5 PvUI111.07G159600.1 PvUI111.07G159700.1 (1 copy) PvUI111.07G159800.1 Gibberellin receptor GID1 (GID1) WD repeat-containing protein 23 (WDR23) Leucine Rich Repeat (LRR_1) (LRRNT_2) Pv07 40.7 Mb S07_40778429 1.47 x 10 − 5 Same region as plant height Pv11 8.5–8.9 Mb S11_8564171 1.42 x 10 − 5 PvUI111.11G089600.1 PvUI111.11G089800.1 (1 Copy) Cyclin-dependent kinase regulatory subunit CKS1 (CKS1) Inhibitor of apoptosis //Ring//U-Box domain-containing protein. When stem diameter GWAS was performed separately by race, the MLM was identified as the best-fitting model for both Durango and Mesoamerican races. In the Durango race, significant regions for stem diameter were detected on Pv07 and Pv11, with 67 and 3 significant SNPs, respectively. The significant SNPs found on Pv11 overlapped with those identified in the combined analysis. On Pv07, 67 significant SNPs were distributed in the 31.4–31.8 Mb (5 SNPs), 32.4–32.9 (36 SNPs), 33 -33.1 (16 SNPs), and 34.5–34.6 Mb (10 SNPs) genomic regions. Most of these intervals coincided with those found in the combined analysis. In the Mesoamerica race, 11 significant SNPs were found exclusively on chromosome Pv07, with seven SNPs clustered around 2.2 Mb. This region was also observed in the Mesoamerican plant height analysis. Overall, GWAS results indicate that multiple genomic regions on Pv07 contribute to stem diameter variation. The 2.2 Mb and 40.7 Mb genomic regions are primarily related to the Mesoamerican race, while the 33.5–33.6 Mb and 34.5–34.6 Mb regions are associated with the Durango race. Multi-trait GWAS Single-trait GWAS analyses identified overlapping genomic regions associated with both plant height and stem diameter, suggesting potential pleiotropic effects. Therefore, a multi-trait GWAS was conducted to detect common genomic loci influencing both traits simultaneously. In combined panel, the multi-trait GWAS detected significant signals on chromosomes Pv03, Pv05, Pv07, and Pv11 (Fig. 7 ). Notably, the significant signals on Pv07 and P11 overlapped with those detected in the single-trait GWAS for both plant height and stem diameter. The genomic region at 40.7–40.8 Mb on Pv07, consistently detected across multiple analyses, indicates shared genetic control underlying the two traits. Similar patterns were also observed in the race-specific multi-trait GWAS analyses. In the Durango race, multi-trait GWAS detected major peaks Pv03, Pv05, Pv07, and Pv11 (Fig. 8 ). Consistent with the single-trait GWAS, significant genome regions were located on Pv07 spanning between 31.6–33.1 Mb (54 SNPs) and on Pv11 at 8.5 Mb, both associated with stem diameter. For the Mesoamerican race, significant associations were detected on Pv01, Pv05, Pv06, Pv07, and Pv08 (Fig. 9 ). The peak genomic region spanning 40.7–40.8 Mb on Pv07 was simultaneously detected in both single-trait and multi-trait GWAS analysis, confirming the importance of this locus in governing plant height and stem diameter in Mesoamerican genotypes. Discussion The present study highlights the importance of key agronomic traits for seed yield and upright plant selection in dry bean. Using a combined analysis and six different market classes, from two races, allowed a better understanding of the different traits used for plant selection. Among all traits studied, both plant height and stem diameter were the most important for seed yield and upright plant selection. Nevertheless, their importance changed based on the race and the market class evaluated. Therefore, the discussion will focus on these traits. GWAS analyses further elucidated the genetic architecture underlying these traits, offering valuable opportunities to develop markers for breeding programs aimed at optimizing beneficial ideotype development. Stem diameter Across all market classes, stem diameter averaged 7.7 mm, with pinto showing the thickest stem (8.0 mm), significantly greater than other market classes. These values are higher than the ones found by Oliveira et al. ( 2015 ) (5.1 mm) and Moura et al. ( 2013 ) (5.4 mm) who mainly used Type III cultivars. Our results were similar to the ones found by Brothers and Kelly, ( 1993 ) and Mulube, ( 2017 ) who observed diameters ranging from 5.7 and 8.3 mm in crosses involving either small or medium seed sizes or Andean genotypes. Most importantly, the stem diameter in this study surpass the 5.6 mm threshold proposed by Soltani et al. ( 2016 ) for selecting upright cultivars. Values from our study were even greater than the thickest stems found by these authors, 5.7 mm in Type II plants, and 5.0 mm in Type III plants. Soltani et al. ( 2016 ) used a diverse panel of genotypes (Durango Diversity Panel – DDP), which included old and new germplasm and several cultivars with growth type III. In contrast, our study used elite/advanced lines from a public breeding program in which selection for upright plant architecture is a routine selection component of the breeding pipeline. Thus, it is not surprising that selection for upright architecture within the breeding program may have indirectly increased stem diameter. Stem diameter showed no significant GxE interaction for pinto and SD-pinto, indicating high phenotypic stability across environments (Fehr 1991 ). Similar stability was reported by Mulube, ( 2017 ) and Oliveira et al. ( 2015 ) for released cultivars. In contrast, Moura et al. ( 2013 ) and Soltani et al. ( 2016 ), observed significant GxE interaction when analyzing panels with contrasting growth habits. Nevertheless, Soltani et al. ( 2016 ), showed non-significant GxE when the analysis was performed within Type II and III growth habits separately. These findings suggests that GxE effects diminishes within populations with similar architecture, highlighting the need for pattern-specific evaluations within populations. Heritability estimates also showed differences among market classes. Pinto (0.87) and SD-pinto (0.84) showed the highest heritability. This reinforces the stability and reliability of the trait for selecting upright plants especially in these two market classes. These values are comparable to those reported by Silva et al. ( 2013 ) (0.81) and Oliveira et al. ( 2015 ) (0.75–0.77); but substantially higher those obtained by Soltani et al. ( 2016 ), who found a broad-sense heritability of 0.33 and found higher heritability values for Type II compared to Type III plants. This could be explained by the inherent nature of stiff-stem and upright plants in Type II compared to Type III plants, which have prostate habit and weaker stems (Singh et al. 1991 ). However, in this study, Durango market classes showed higher heritability values than Mesoamerican. It could show that breeding efforts to select upright plants likely are changing traits in different market classes (Kelly, 2001 ), one of them could be stem diameter. Plant height Plant height had a highly significant GxE interaction across all market classes, indicating lower stability compared to stem diameter. Mulube, ( 2017 ), Moura et al. ( 2013 ) and Soltani et al. ( 2016 ) also reinforced that plant height is more environment-sensitive. Heritability values differed among market classes, ranging from 0.46 in black to 0.89 for red/pinks. However, most heritability values were above 0.75. These results are higher than the 0.66 reported by Soltani et al. ( 2016 ), and the 0.6 found by Oliveira et al. ( 2015 ). Mean plant height exceeded 49 cm across all market classes, with pinto and red/pink reaching 53.6 and 52.3 cm, respectively. These values were higher than the mean of completely upright, Type II plants (42.3 cm), or Type III (33.3), reported by Soltani et al. ( 2016 ). Silva et al. ( 2013 ) also observed taller and thicker Type II (6.3 mm and 50 cm) than Type III plants (5.3 mm and 38 cm), suggesting that differences of 12 cm in plant height and 1 mm in stem diameter could imply a change in growth habit. This contrasts with our results given that some Durango/Jalisco market classes are showing taller and thicker plants. This shows again a likely influence of the breeding efforts to bring these characteristics from Mesoamerican to Durango races (Kelly, 2001 ). In spite of the significant GxE interaction for plant height, its heritability was high, which confirms its importance for plant architecture. Thus, plant height and stem diameter could be considered as appropriate selection traits for dry bean, although their use in combination would depend on the market class undergoing selection. Relationships between stem diameter, plant height and other traits Plant height showed a stronger correlation with seed yield than stem diameter in this study. In contrast, Soltani et al. ( 2016 ), found that plant height (0.42) and stem diameter (0.4) were equally correlated with seed yield. Within market classes, plant height and yield were also significantly correlated, however, its magnitude changed based on the market class. For black, navy, and great northern, correlation values were higher than 0.5. Plant height and stem diameter correlation ranged between 0.3 and 0.5 across all market classes and was stronger for Mesoamerican market classes black (0.4) and navy (0.5). Higher correlation values of 0.8 for this combination of traits was found by Silva et al. ( 2013 ) and Soltani et al. ( 2016 ). Then, this study confirms a positive correlation between plant height, stem diameter, and seed yield; however, their magnitude will vary based on the market class. The correlations between stem diameter and plant height, plant height and seed yield tended to be higher in Mesoamerican market classes compared to Durango/Jalisco. This could be related to inherent traits nature for each race, but also to breeding efforts to bring upright characteristic from Mesoamerican into Durango (Kelly, 2001 ). Regression analyses further confirmed correlations results regarding the observed two patterns. In black, navy, and great northern market classes, plant height explained more than 24.4% of seed yield variation, while stem diameter showed non-significant or minor effects. Interestingly, great northern beans were more closely related to Mesoamerican than Durango/Jalisco, likely due to the use of navy beans as parents to generate early upright great northern cultivars (McClean et al. 2018 ). In addition, comparisons between pinto and great northern DNA sequence data have shown differences between these two market classes regarding selection hotspots for plant architecture (unpublished data). In contrast, pinto, red/pink, and SD-pinto showed similar contributions of plant height and stem diameter to seed yield, reflecting dual role of these traits in upright architecture. Tall plants prevent pods contact with soil, while thicker stem provide structural support (Acquaah et al. 1992 ). Historical trends support these findings, Vandemark et al. ( 2014 ) documented significant increase in plant height (34 cm to 57 cm) in pinto cultivars released after 1980, coinciding with the shift from prostrate Type III to upright Type II architecture. However, no-significant differences were found for navy, likely, due to their common upright architecture. Our study further indicates that stem diameter was increased mainly in pinto cultivars, likely due to the indirect selection for upright architecture. According to Soltani et al. ( 2016 ) an increase in stem diameter and plant height could result from converting Type III genotypes (thinner stems) to Type II (stiff stem). Changes in plant height in the newly released pinto cultivars have allowed a more efficient direct harvest for these cultivars (Eckert et al. 2011 ). While Soltani et al. ( 2016 ) suggested a 5.6 mm threshold for stem diameter, our results indicate that this benchmark may need revision for commercial breeding programs and for specific market classes, given historic differences in growth habits. Stem diameter was a stable and heritable trait, which explained a high amount of variation in the seed yield of Durango/Jalisco market classes such as pinto and SD-pinto. It is suggested to continue selecting for this trait and plant height in these market classes. In contrast, plant height is a reliable trait for upright selection in the Mesoamerican market classes. Genetic architecture of plant height GWAS results for plant height revealed significant regions on chromosomes Pv07 and Pv03. On Pv07, the region close to 40.7 Mb, has several plant growth-related genes, including PvUI111.07G214700, which encodes a growth-regulating factor (GRF). GRFs are plant-specific transcription factor that controls cell expansion (size) and proliferation (number), influencing stem and leaf development (Omidbakhshfard et al. 2015 ). In rice ( Oryza sativa L.), they play important roles in stem growth and development (Wang et al. 2018 ) and coordinate levels of defense and plant development hormones in opposite directions (Omidbakhshfard et al. 2015 ). Under normal conditions, GRF1/GRF3 invest energy in plant growth. Under stress conditions, they inhibit growth directing resources to stress tolerance processes (Piya et al. 2020 ). The second major region found on Pv03 (36.7–36.9 Mb) includes gene models related to serine/threonine-protein kinases and F-box associated domains. According to Kuzbakova et al. ( 2022 ), these kinases have been associated with plant height. For instance, in soybean ( Glycine max L.), a serine/threonine-protein phosphatase is encoded by the gene GmPP1-like. Mutants of this gene show shorter internode length and height. Gene model PvUI111.03G157000.1 was also found in this region, encoding an F-box domain, with protein destruction functions (Ho et al. 2008 ). F-box domains, create dimers to regulate auxins, giberellins (GA), jasmonate (JA), and ethylene expressions (Prigge et al. 2016 ). Combined and race-specific single trait and multi-trait GWAS analysis confirmed the relevance of Pv03 (36 Mb) and Pv07 (40.7 Mb), with Pv03 (36 Mb) and Pv06 (3.6 Mb) significant for Durango, and Pv07 (40.7 Mb) was highly relevant for Mesoamerican races. These findings support previous report of MacQueen et al. ( 2020 ) that several chromosomes could be related to plant height and highlight Pv03 and Pv07 as key targets for marker-assisted selection. Genetic architecture of stem diameter For stem diameter, the most important peak was located on chromosome Pv11 (8.5 and 8.9 Mb), which includes PvUI111.11G089600 encoding a cyclin-dependent kinase regulatory subunit (CKS1). CSK1 proteins, regulate the cell cycle affecting growth and development (Tamirisa et al. 2017 ). A second most important peak on Pv07 (34.5 and 34.6 Mb) harbors gene model PvUI111.07G159600 encodes a gibberellin receptor GID1, which interacts with DELLA proteins, to modulate GA signaling repressors (Yoshida et al. 2018 ). A highly relevant finding is the region around 40.7 Mb on Pv07, detected for both plant height and stem diameter traits. This region contains genes related to growth regulating factors, serine/threonine protein kinase, E3 ubiquitin-protein ligase, membrane associated ring finger and leucine rich repeats. According to (Wang et al. 2018 ), the growth regulating factors are not only related to plant height but to stem thickness. In rice, similar pleiotropic loci named Ideal Plant architecture (IPA1) have been reported, encoding OsSPL14 (SQUAMOSA PROMOTER BINDING PROTEIN-LIKE 14), which controls plant height, stem diameter, tiller numbers and number of productive tillers (Jiao et al. 2010 ). Thus, Pv07 (40.7 Mb) becomes interesting and important target for further indeterminate growth plant architecture studies. Chromosome Pv07 could be related to indeterminate growth in dry bean Our current findings reinforce the critical role of chromosome Pv07 in plant architecture and growth habit. Genes such as PvTFL1y or Fin locus on Pv01 and PvTFL1z (likely a duplication of the first one) on Pv07 (Krylova et al. 2021 ; Kwak et al. 2008 ) have been related to plant height. Genes on Pv01, are associated with determinant Andean genotypes (Cichy et al. 2015 ; Resende et al. 2018 ), whereas Pv07 could be related to indeterminacy in the Middle American gene pool, particularly in navy (Kolkman and Kelly, 2003 ). This suggests two distinct domestication processes, one on Pv01 for Andean (determinant) and another on Pv07 for Mesoamerican (indeterminant). Our GWAS results could confirm an indeterminacy region on Pv07. This region was suggested by Moghaddam et al. ( 2016 ) who used Type I, II, and III plants and confirmed a determinacy region on Pv01. However, it was only found when Andean determinate genotypes were present. When Andean cultivars were removed, peaks on Pv04, Pv06, Pv07, and Pv11 appeared. Being the peaks on Pv07, (46.11 Mb) and (35.42 Mb) the most relevant. Similarly, our results showed significant regions on Pv03, Pv07, and Pv11 using indeterminate Durango and Mesoamerica germplasm. Notably, the genomic region 40.7 Mb on Pv07, was significant for plant height and stem diameter. This region was also validated and identified by multi-trait GWAS analysis. However, genome comparisons showed that this region is different to the one found by Moghaddam et al. ( 2016 ) on Pv07 (46 Mb), which was mapped based on G19833v2.1 reference genome (Schmutz et al. 2014). Recent findings by Beagley et al. ( 2025 ), further support this by identifying Pv07 regions associated with stem elongation of the first five nodes independent of flowering time, which consistent with the results. Candidate genes identified in our study, such as PvUI111.07G141800 and PvUI111.07G153300 located at 31.4 Mb and 33.5 Mb, respectively, share 100% similarity with G19833v2.1 genome homologs Phvul.007G149432 and Phvul.007G161200, respectively reported by Beagley et al. ( 2025 ) and are implicated in stem elongation. These findings highlight Pv07 as a hotspot for growth habit regulation, with potential pleiotropic effects on plant height, stem diameter and node elongation. Domestication process likely led to different growth habits. For instance, due to maize’s late arrival to the Andes some mutations were generated to give Andean plants own support. In contrast, the use of maize in Middle America, allowed the bean plants to climb (Koinange et al. 1996 ). However, it is possible that some Middle American climbing Type IV plants adapted to less support conditions, generating Type III plants. This could be partially shown by Tobar-Piñon, (2020) who found higher admixture between Guatemalan climbing beans (Type IV) and Durango/Jalisco race (Type III and IV) than with Mesoamerican race (Type II). Likely a plant biomass reduction was required to adapt to temperature and altitude variations, changing from Type IV to Type II progressively. Evidence suggests that Pv07 could be highly relevant for indeterminate dry bean germplasm growth habit. Similar to dry bean, soybean has growth habits such as determinate, indeterminate, and semideterminate, which are mastered by two genes ( Dt1 and Dt2 ), Dt1 controls determinacy, and Dt2 , semideterminacy (Ping et al. 2014 ; Clark and Ma 2023 ). Dt2 is related to branch number (Virdi et al. 2023 ), generates differences in height and diverse degrees of stem termination, with pleiotropic effects on node number and internode length (Kou et al. 2021 ). It is possible that within the indeterminate dry bean germplasm, there are genes controlling specific height or vine length, having therefore a similar function to Dt2 . This region could be located on Pv07 and could be important to improve traits such as first pod height, plant stature, and mechanical harvesting, which are key for dry bean commercialization. Our GWAS analyses revealed multiple Pv07 regions contributing to stem diameter variation (2.2 Mb, 33.5–34.6 Mb, and 40.7 Mb), with race-specific associations: 2.2 Mb and 40.7 Mb for Mesoamerica and 33.5–33.6 Mb and 34.5–34.6 Mb for Durango. In general, regions on Pv07 not only can help to understand differences among indeterminant growth habits grown in the US (Type II and III); but could also help to trace their domestication traits. Only a few studies available related to stem diameter genetics have been reported. Therefore, the effect of intervals located mainly on Pv07 is an important result of this study. This is one of the few studies that show genetic architecture and field importance of stem diameter and plant height. Collectively, Pv07 significant regions explained 56.2% genetic variation for stem diameter and 16.6% for plant height, underscoring its central role in indeterminate architecture. These regions could help explain differences in Type II and III growth habits in beans with indeterminant growth habits. These findings provide a foundation for developing functional markers for fine-mapping and cloning causative genes, enabling targeted breeding for upright plants with thicker stems-traits essential for mechanical harvesting and improved yield stability. Conclusions Overall, stem diameter was larger for current advanced lines than values suggested by Soltani et al. ( 2016 ) of 5.6 mm., indicating that selection for upright architecture within the breeding program may have indirectly increased stem diameter. Stem diameter showed the highest heritability values in pinto and SD-pinto. Additionally, non-significant GxE interaction was found in these market classes. This suggests that it is a stable and heritable trait for selection of upright plant architecture, especially in these two market classes. Plant height could be a better indicator for seed yield than stem diameter in black, great northern, and navy. While both plant height and stem diameter are required to continue selecting upright and high-yielding plants for pinto, red/pink and SD-pinto. Specially, considering that these three last market classes tend to have a Type III prostrate genetic background. For both plant height and stem diameter there is a shared genetic effect in an interval centered on region centered at 40.7 Mb at Pv07. This common region is related to growth regulating factors, serine/threonine protein kinase, E3 ubiquitin-protein ligase, membrane associated ring finger, and leucine rich repeat. Most of these gene models have relation to plant growth and disease avoidance, which makes this region an interesting area to continue with further investigation and exploit by developing functional molecular markers to assist bean breeding for selecting bean genotypes with improved plant architecture. Declarations Acknowledgments This study was supported by USDA-ARS Pulse Crop Health Initiative, Northarvest Bean Growers Association, and USDA-NIFA through Hatch project ND1508 and W4150 multi-state project. Funding This study was supported by USDA-ARS Pulse Crop Health Initiative, Northarvest Bean Growers Association, and USDA-NIFA through Hatch project ND1508 and W4150 multi-state project. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Author Contribution Statement Oscar Rodriguez: Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing - original draft Jayanta Roy: Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing - review & editing Philip E. McClean: Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - review & editing Didier Murillo: Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing - review & editing Kristin Simons: Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing - review & editing Nusrat Khan: Data curation, Formal analysis, Investigation, Methodology, Software, Supervision, Validation, Visualization, Writing - review & editing Jose Figueroa: Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing - review & editing Juan Osorno: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - review & editing Data availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. 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07:11:59","extension":"xml","order_by":21,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":210234,"visible":true,"origin":"","legend":"","description":"","filename":"91334f3eff47489eac390c35dc670f851structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8634079/v1/920e7d15e02ed9f97e877fb7.xml"},{"id":100943225,"identity":"cabc95c7-40c6-4812-86d4-ebc10e59787c","added_by":"auto","created_at":"2026-01-23 05:26:17","extension":"html","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":225543,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8634079/v1/c626fb28e776a0f9c010656e.html"},{"id":100943171,"identity":"10242f07-0a9f-4e7b-8ae5-80330f83c887","added_by":"auto","created_at":"2026-01-23 05:26:16","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":152351,"visible":true,"origin":"","legend":"\u003cp\u003ePearson’s correlations among traits within each market class\u003c/p\u003e\n\u003cp\u003eX for non-significant correlation. PH: Plant height, SD: Stem diameter, MAT: Maturity, SW: Seed weight and Y: Plant yield.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8634079/v1/838353370b930163aa26826c.jpeg"},{"id":100943173,"identity":"52ab22c9-1eed-471f-9bcf-f435a8e32a3e","added_by":"auto","created_at":"2026-01-23 05:26:16","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":51487,"visible":true,"origin":"","legend":"\u003cp\u003ePCA Analysis for all traits in Mesoamerica race (black and navy). PH: Plant height, SD: Stem diameter, MAT: Maturity, SW: Seed weight and Y: Seed yield.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8634079/v1/bc3f59262086002892e07769.jpeg"},{"id":100943224,"identity":"150e20d2-6480-45ae-aa5b-b924ae4e6f61","added_by":"auto","created_at":"2026-01-23 05:26:17","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":53007,"visible":true,"origin":"","legend":"\u003cp\u003ePCA Analysis for all traits in Durango/Jalisco race (Pinto and SD-pinto). PH: Plant height, SD: Stem diameter, MAT: Maturity, SW: Seed weight and Y: Seed yield.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8634079/v1/3c6dda0cbafd070d42d8bc66.jpeg"},{"id":100943200,"identity":"f1880866-7123-43df-b655-0cd8f687bf3f","added_by":"auto","created_at":"2026-01-23 05:26:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":116517,"visible":true,"origin":"","legend":"\u003cp\u003eManhattan plot and q-q plot for plant height using MLM in GEMMA\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8634079/v1/f1dacb65110b7c2f5cf600d8.png"},{"id":100952218,"identity":"5d07186c-7b8d-488e-a839-f1cd519c2d40","added_by":"auto","created_at":"2026-01-23 07:12:14","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":103086,"visible":true,"origin":"","legend":"\u003cp\u003eManhattan and q-q plot for plant height within Mesoamerican race using MLM in GEMMA\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8634079/v1/03642c6bc935b089286be792.png"},{"id":100943186,"identity":"1b056500-d9fa-409e-919e-a7ec650ba654","added_by":"auto","created_at":"2026-01-23 05:26:16","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":110882,"visible":true,"origin":"","legend":"\u003cp\u003eManhattan and q-q plot for stem diameter using MLM in GEMMA\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8634079/v1/4d664215bb20dcfbe7b471f7.png"},{"id":100951268,"identity":"a9c76db8-fc39-4697-9904-1cfaacb01948","added_by":"auto","created_at":"2026-01-23 07:10:22","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":159540,"visible":true,"origin":"","legend":"\u003cp\u003eManhattan and q-q plot for a multi-trait analysis using MLM in GEMMA\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8634079/v1/45b8c35869bc843418b4943d.png"},{"id":100952276,"identity":"eb67da8a-a2bb-45d6-9d56-93e3eb75376a","added_by":"auto","created_at":"2026-01-23 07:12:27","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":206009,"visible":true,"origin":"","legend":"\u003cp\u003eManhattan and q-q plot for a multi-trait analysis within Durango race using MLM in GEMMA\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8634079/v1/cc433f9af3d10ea0c31a8393.png"},{"id":100943172,"identity":"1b190c9b-b331-4454-9789-382194bd3af5","added_by":"auto","created_at":"2026-01-23 05:26:16","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":235203,"visible":true,"origin":"","legend":"\u003cp\u003eManhattan and q-q plot for a multi-trait within Mesoamerica race using MLM and GEMMA\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-8634079/v1/ca9c9bce1d1a80efad139c29.png"},{"id":100953163,"identity":"df83498e-0c0f-47c7-9920-01e28d4b799c","added_by":"auto","created_at":"2026-01-23 07:20:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2545234,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8634079/v1/70446a9b-1300-47a3-955d-8979a4459351.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"New genomic regions in dry bean (Phaseolus vulgaris L.) associated with stem diameter, plant height and other plant architecture traits","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGrowth habit is one of the primary traits that has dramatically changed in dry bean (\u003cem\u003ePhaseolus vulgaris\u003c/em\u003e L.) from the wild relative to its domesticated form (Smartt \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e1976\u003c/span\u003e). Wild beans had a climbing growth habit as an adaptation to an environment in which trees and shrubs were competitors for light but also helped as a support. Wild bean plants were branched and had many nodes, long and weak internodes, and twining ability (Gepts and Debouck \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). The domestication process generated new environmental conditions for dry bean. Hence, new growth habits appeared due to reduced competition for light; therefore, less excessive growth was required. These new conditions led to the selection of cultivars that matured earlier, dried down synchronously, and had a compact growth habit with fewer branching and nodes. In Mesoamerica, for example, dry beans were traditionally grown alongside maize (\u003cem\u003eZea mays\u003c/em\u003e L.) and squash (\u003cem\u003eCucurbita\u003c/em\u003e spp.) in a multi-crop system called \u0026ldquo;Milpa\u0026rdquo;, which provided support for climbing beans (Zizumbo-Villarreal and Colunga-Garc\u0026iacute;a, 2010). Subsequent breeding mostly focused on the selection of upright plants that do not need any external support from trees or trellises. These selected genotype plants had stout stems, fewer nodes, shorter internodes, and reduced twining (Gepts and Debouck \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1991\u003c/span\u003e), which later helped in the introduction of monocropping. In general, a loss of genetic diversity is observed when comparing wild to cultivated dry bean. However, the opposite is observed for growth habit (Kelly \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). While most wild beans tend to climb (Gepts \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), cultivated beans display a wider range of growth habits (Kelly \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). This growth habit has been related to three factors: the presence of axial or terminal inflorescence, stem length, and twining ability (Norton \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1915\u003c/span\u003e). This higher diversity led to different classifications. The simplest one is the differentiation between determinate (apical growth ends up in an inflorescence) and indeterminate plants (apical growth continues vegetatively, no terminal inflorescence, and all inflorescences are axillary) (Ojehomon and Morgan \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1969\u003c/span\u003e). A more detailed classification is based on indeterminate or determinate growth habit, stem stiffness, and climbing ability. Based on this, Singh (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1982\u003c/span\u003e) classified beans into four growth habits namely, Type I, II, III, and IV. Thus, growth habit could be considered an important distinguishing criterion for differentiating dry bean germplasm (Beebe et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). This trait and other morphological, agronomic, and molecular traits were used to classify dry bean in two major gene pools, Middle-American and Andean. Each gene pool has different races, within Middle American, races Durango/Jalisco, Mesoamerica, and Guatemala are found. While races Chile, Nueva Granada, and Peru belong to the Andean gene pool (Singh \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Singh et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Beebe et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBreeding for upright plant architecture has significantly expanded dry bean production in the midwestern states (Singh et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Today, Type II indeterminate growth is highly preferred across U.S. and for most dry bean market classes because the pods do not touch the ground which improved white mold avoidance, and reduced losses during harvest (Eckert et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The only exceptions would be determinate growth habit Andean cultivars such as kidney, cranberry, and yellow market classes plants which usually maintain an upright bushy growth across most environments. Different traits related to upright plant selection have been described (Denis and Adams \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1978\u003c/span\u003e; Adams \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1982\u003c/span\u003e). However, stem diameter, plant height, branch angle, and pod distribution have been suggested as the most important and reliable traits to select plants with upright Type II architecture due to their high correlation with this trait (Acquaah et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1991\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Moura et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In addition, different grading scales for plant architecture have been generated, some of them combine traits such as plant height, growth habit and architecture, producing a range of qualitative values to select (Collicchio et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Kelly and Adams \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1987\u003c/span\u003e; Melo \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). However, these scales are subjective and require training to reduce errors and ensure accurate scoring. Therefore, a key consideration for the breeder is to determine if a trait used for selection can be easily measured (Acquaah et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1992\u003c/span\u003e), requiring less effort, time, and resources to obtain reliable data. Silva et al. (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) determined stem diameter and mean plant height are highly correlated to plant architectural grade. Additionally, the authors found that stem diameter was controlled by additive effects, which was confirmed by Oliveira et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). These findings suggest that stem diameter is more precise and accurate than simply applying a plant architecture scale. In addition to stem diameter, plant height is another highly relevant trait for upright plant selection (Schwartz et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). Mulube (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) found that plant height, stem diameter, number of pods per plant, and seed yield are the most important traits related to mechanical harvest. In the US pinto bean cultivars, plant height was increased from a mean of 34 cm in 1965 to a mean of 57 cm by 2008, likely due to the introgression of Mesoamerica race germplasm with upright Type II architecture (Vandemark et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This strategy has been used to achieve upright architecture in market classes where this characteristic is uncommon (Kelly \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Introgression of Mesoamerica race Type II growth habit into Type III race Durango market classes (e.g. pinto and great northern) resulted in Durango race Type II cultivars (Kelly and Adams \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1987\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe importance of plant height and stem diameter for selecting genotypes with upright plant architecture was further confirmed by Soltani et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) with the Durango Diversity Panel (DDP). Stem diameter was highly correlated with plant height (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.80), and both traits showed moderate correlations with seed yield (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.42) and (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.40), respectively. Therefore, stem diameter and plant height could be essential traits in selecting genotypes with upright architecture. Indeed, a stem diameter value of 5.6 mm has been suggested as a threshold to choose plants with reduced lodging and improved Type II architecture suitable for mechanical direct harvest (Soltani et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBesides its phenotypic importance, understanding the genetic control of growth habit is essential to detect possible regions or genes related to the trait of interest as genomic tools increase accuracy of selection. Kornegay et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1992\u003c/span\u003e) suggested a simple genetic control for growth habit, with Type I or determinate recessively inherited, and indeterminant (semi-climbing and climbing types) as dominant. Jung et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) suggested the \u003cem\u003eFin\u003c/em\u003e locus was the determinacy gene and linked with the photoperiod sensitivity gene (\u003cem\u003eppd\u003c/em\u003e) which possibly exerted pleiotropic effects on twining. Single gene inheritance for growth habit and pleiotropic relationships with flowering, maturity, and branch angle were confirmed by Tar\u0026rsquo;an et al. (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) and Repinski et al. (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Using Mesoamerican and Andean germplasm as parents, Kolkman and Kelly (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) confirmed the \u003cem\u003eFin\u003c/em\u003e gene weas located on chromosome Pv01. \u003cem\u003eFin\u003c/em\u003e is considered a homolog of the \u003cem\u003eTFL1\u003c/em\u003e (Terminal flower 1) in Arabidopsis (Repinski et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Another QTL for growth habit in chromosome Pv07 was found for small-seeded (navy) genotypes, also associated with flowering, branching pattern, lodging, and disease severity index (Kwak et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). These findings suggest different determinacy origins, the Andean gene pool as the primary source to determinate growth habit (related to \u003cem\u003eFin\u003c/em\u003e gene) and the Middle-American pool as the source of indeterminate growth habit.\u003c/p\u003e \u003cp\u003eUsing the Middle-American Diversity Panel (MDP), which includes genotypes with different growth habits, Moghaddam et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) found a strong signal in chromosome Pv01 associated with Type I genotypes. However, when only Type II and Type III genotypes were used in the GWAS analysis, the most significant peak was observed on chromosome Pv07. Meta-analyses by MacQueen et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Izquierdo et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) identified strong signals on Pv01 and Pv07, with additional associations on Pv03, Pv06, Pv08, and Pv11 linked to phenology and architectural traits (growth habit, plant height) using both Andean and Middle American germplasm.\u003c/p\u003e \u003cp\u003eThe molecular function of some genes related to growth habit include regulation by plant hormones auxin, cytokinins and giberelins (Yoshida et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; McKim \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), transcription factors and proteins related to these mechanisms such as WD-40, F-box (Ho et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2008\u003c/span\u003e); and growth-regulating factors (GRFs) with roles in stem and leaf development (Omidbakhshfard et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Piya et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Floral activators and floral inhibitors (Jin et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Krylova et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Ring/U-box protein related to Ubiquitin ligase genes in response to abiotic and biotic stresses (Zhang et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kim et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) were others identified.\u003c/p\u003e \u003cp\u003eDespite these advances and several efforts to understand the relationship among plant height, stem diameter, and upright plant selection, there is still a gap to a fuller understanding of how stem diameter and plant height jointly influence upright architecture in commercial breeding programs and within/among different market classes. Moreover, integrating phenotypic and genomic data could enhance upright plant selection strategies in the commercial breeding program Therefore, this study aimed to validate the correlation between stem diameter and other agronomic traits such as plant height and seed yield among others using breeding lines from a public breeding program. Additionally, it sought to investigate stem diameter as a criterion to select genotypes that combine high seed yield and upright architecture. Furthermore, the study aimed to identify genomic regions related to the traits of stem diameter and plant height using a genome wide association study (GWAS) approach.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlant material\u003c/h2\u003e \u003cp\u003eTwo hundred and thirty-three (233) advanced dry bean breeding lines from the Nort Dakota State University (NDSU) dry bean breeding program and 28 cultivars commonly grown in North Dakota were evaluated across 3 growing seasons from 2020 to 2022. The lines are from the six major market classes in the U.S. including black, navy, great northern, red, pink, regular-darkening pinto (pinto), and slow-darkening (SD) pinto (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNumber of breeding lines and cultivars evaluated in 2020, 2021, and 2022.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarket Class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal number of lines evaluated (with checks)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal Number of Environments (Year x Location)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMesoamerica\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNavy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eDurango / Jalisco\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGreat Northern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRed and Pink\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePinto\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSD Pinto\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eField experiments\u003c/h3\u003e\n\u003cp\u003eBreeding lines and check cultivars were evaluated in separate advanced yield trials (AYT) within each market class, except for red and pinks which were grown within the same trial because they are considered genetically close. Trails were established at four representative locations in North Dakota\u0026thinsp;\u0026minus;\u0026thinsp;Hatton, Johnstown, Carrington, and Prosper, during the growing seasons (May-September) of the years 2020, 2021, and 2022. These locations are representative of the major dry bean growing areas within the state. For each year, the number of genotypes evaluated varied, because different AYT lines from the breeding program were included annually. The number of environments (location by market class) varied between 8 and 9. The trials followed the standard management practices used by the dry bean farmers in the Red River Valley of North Dakota (Kandel and Endres \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), with the exception of controlling for natural disease pressure. Each field trial was conducted in a randomized complete block design (RCBD) with three replications. The experimental plots consisted of 2 rows; each row had a length of 3.65 m, spaced 0.76 m apart, for a total plot area of 5.54 m\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eMorpho-agronomic traits data collection\u003c/h3\u003e\n\u003cp\u003eThe following traits were measured:\u003c/p\u003e \u003cp\u003ePlant height: The distance in cm from the soil surface and the top of the canopy at bloom, 50% of plants at pod setting, without stretching out the plant.\u003c/p\u003e \u003cp\u003eStem diameter: Measured in mm by randomly selecting two plants per plot with a caliper brand tool shop model SKU 243\u0026ndash;2377, right above the soil surface at maturity (80% of the pods and leaf area dry).\u003c/p\u003e \u003cp\u003eSeed size: Expressed as 100-seed weight: the weight of 100 seeds in g.\u003c/p\u003e \u003cp\u003eSeed yield: The seed yield at harvest in kg ha\u003csup\u003e\u0026ndash;1\u003c/sup\u003e. Maturity: Days to maturity when 80% of the pods and leaf area dry, in days after planting (DAP) (Soltani et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003ePhenotypic Data analysis\u003c/h3\u003e\n\u003cp\u003eData analysis was performed with SAS 9.4 (SAS Institute, Cary, NC, USA) to display numerical data distribution with histograms and detect outliers within boxplots, which were removed from the analysis. This dataset is unbalanced since all the breeding lines were not evaluated in the same years. Homogeneity of variances was performed using the \u0026ldquo;10x rule\u0026rdquo;. In this test, trials were considered homogeneous if the largest error variance was not 10 times larger than the smallest one (Patterson and Silvey \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1980\u003c/span\u003e). After confirming homogeneity of variances, for each market class, the MIXED procedure was conducted in SAS 9.4 to perform a combined analysis of variance (ANOVA). Given the unbalanced nature of the dataset, the Best Linear Unbiased Predictor (BLUP) was obtained for all the recorded traits. The sources of variation used were genotype, environment, genotype x environment (GxE), and blocks nested within each environment. Environment was considered as the combination between location and year. The genotype effect was considered fixed, while the remaining terms were considered random. The probability of observing differences between group means was tested at significance levels of 0.05 and 0.01.\u003c/p\u003e\n\u003ch3\u003eHeritability estimates\u003c/h3\u003e\n\u003cp\u003eVariance components were obtained using covtest in PROC MIXED with SAS 9.4, considering genotype, environment and GxE as random effects. Using covariance estimates, the broad-sense heritability based on the entry mean was calculated (Fehr \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1991\u003c/span\u003e).\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{H}^{2}=\\frac{{\\sigma\\:}_{G}^{2}}{{\\sigma\\:}_{G}^{2}+{\\sigma\\:}_{\\frac{GE}{e}}^{2}+{\\sigma\\:}_{\\frac{E}{re}}^{2}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere,\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{G}^{2}\\)\u003c/span\u003e \u003c/span\u003e = Genotypic variance\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{GE}^{2}\\)\u003c/span\u003e \u003c/span\u003e = Genotype x environment variance\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{E}^{2}\\)\u003c/span\u003e \u003c/span\u003e = Environmental variance\u003c/p\u003e \u003cp\u003ee\u0026thinsp;=\u0026thinsp;number of environments\u003c/p\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;number of replicates\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation analysis with agronomic traits\u003c/h2\u003e \u003cp\u003ePearson\u0026rsquo;s correlation coefficients were calculated to determine the strength and direction of the linear relationship between pairs of quantitative traits. Correlation coefficients and matrices were calculated and plotted using the functions \u003cem\u003ecor.matix ()\u003c/em\u003e and \u003cem\u003ecorrplot ()\u003c/em\u003e from the corrplot package in R (Wei and Simko \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRegressions\u003c/h3\u003e\n\u003cp\u003eSimple and multiple linear regression analyses were performed to confirm a cause-and-effect relationship among the different independent variables and seed yield. Plant height, stem diameter, maturity, and seed weight were considered as independent variables, while seed yield was the dependent variable. The R function \u003cem\u003elm ()\u003c/em\u003e was used. Each independent trait was considered for simple regression, while combinations of the most relevant independent traits were considered for the multiple linear regressions.\u003c/p\u003e\n\u003ch3\u003ePrincipal component analysis (PCA)\u003c/h3\u003e\n\u003cp\u003ePrincipal component analysis was performed using the function PCA in R package FactoMineR (Husson et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This analysis was performed for the whole data set, using all market classes and within each market class.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGenetic material, DNA extraction, and GBS libraries generation\u003c/h2\u003e \u003cp\u003eA total of 544 advanced breeding lines evaluated between the years 2020, 2021, and 2022, were used to develop a representative panel comprising multiple dry bean market classes. For DNA collection, two dry bean seeds for each line were planted and the first trifoliate leaf from a single plant was taken and placed in a single well on a plate of 96 wells. Liquid nitrogen was used to preserve the collected samples. DNA was isolated using the Mag-Bind\u0026reg; Plant DNA Plus kit (Omega Bio-Tek, Norcross, Georgia, USA) and quantified using Nanodrop. GBS libraries were developed using the optimized two-enzyme protocol [MseI and Taqα1] protocol described by Schr\u0026ouml;der et al. (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Six GBS libraries were sequenced in paired-end runs (2 x 150 bp) using Illumina HiSeq 2500 sequencing system at the HudsonAlpha institute for Biotechnology (Hunstville, AL, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSequence data processing and SNP Calling\u003c/h2\u003e \u003cp\u003eLow quality reads (quality threshold\u0026thinsp;\u0026lt;\u0026thinsp;20) and reads with less than 80 bp length were discarded using SICKLE software (Joshi and Fass \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The filtered reads were mapped to the \u003cem\u003eP. vulgaris\u003c/em\u003e reference genome UI111 \u003cem\u003ev\u003c/em\u003e1.1(available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://phytozome-next.jgi.doe.gov/info/PvulgarisUI111_v1_1\u003c/span\u003e\u003cspan address=\"https://phytozome-next.jgi.doe.gov/info/PvulgarisUI111_v1_1\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) using Burrows-Wheeler Alignment Tool (BWA-mem) (Li, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Subsequently, SAMtools (Li et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) were used to sort and index the mapping results. Read group information was added using Picard tools (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://broadinstitute.github.io/picard\u003c/span\u003e\u003cspan address=\"http://broadinstitute.github.io/picard\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). SNP calling was performed using the \u003cem\u003eMultisampleVariantsDetector\u003c/em\u003e module embedded in the NGSEPcore_4.2.0 software with the \u003cem\u003e-maxAlnsPerStartPos\u003c/em\u003e 100 parameter (Tello et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Multiallelic SNPs were discarded and SNPs with a minimum read depth\u0026thinsp;\u0026ge;\u0026thinsp;3, were selected. Further filtering was done to remove genotypes that had more than 75% missing sites, resulting in a total of 508 genotypes. SNPs with less than 25% missing sites and 10% heterozygotes were imputed using Beagle \u003cem\u003ev5.4\u003c/em\u003e (Browning et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Finally, a subset of 214 genotypes with a total of 80,578 high quality SNPs after applying 5% minor allele frequency (MAF), were used for the GWAS analysis. However, after applying same quality filtering with 5% MAF, a total of 72,494 and 41,799 SNPs were used for GWAS analysis for the 132 Durango and 82 MA race genotypes, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSingle trait and multi-trait GWAS\u003c/h2\u003e \u003cp\u003eGenome-wide association analyses for stem diameter and plant height were conducted using GEMMA v0.98.1 (Genome-wide Efficient Mixed Model Analysis: Zhou and Stephens \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The analysis was performed for combined datasets (all 214 genotypes) and separately for Durango race and Mesoamerican race genotypes. Two models were implemented: i) the mixed linear model (GEMMA-MLM) including population structure (PCA) and relatedness (kinship matrix), and ii) efficient mixed-model analysis (EMMA) (efficient mixed-model analysis) in which only kinship matrix was included. The first two PCA calculated by \u003cem\u003eprcomp ()\u003c/em\u003e function in R was used as a covariate. The kinship matrix was computed using the centered relatedness procedure in GEMMA. The lower mean square deviation (MSD) (Mamidi et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and q-q plots were considered to select the best model for further analysis. The significant threshold of \u003cem\u003eP\u003c/em\u003e-value with multiple testing correction was calculated following the method proposed by Li and Ji, (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), to declare association between SNPs and trait of interest. This method uses the eigenvalues of the correlation matrix between the SNPs to calculate the effective number of independent loci (\u003cem\u003eM\u003c/em\u003e\u003csub\u003e\u003cem\u003eeff\u003c/em\u003e\u003c/sub\u003e). The significance threshold was calculated as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\alpha\\:}_{p}=1\\:-{(1-\\:{\\alpha\\:}_{e})}^{1/{M}_{eff}}\\)\u003c/span\u003e\u003c/span\u003e, where the experiment-wise error rate was \u003cem\u003eα\u003c/em\u003e\u003csub\u003e\u003cem\u003ee\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.05. For significant SNPs, the proportion of phenotypic variance explained (PVE; \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e) was calculated using the genABEL package available in R (Aulchenko et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; R Core Team \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Manhattan plots were generated using the R function \u003cem\u003emhplot()\u003c/em\u003e (Zhao \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditionally, a multi-trait GWAS was performed in GEMMA v0.98.1 using both plant height and stem diameter as response variables. The analysis was performed across the full panel as well as within each race. The GEMMA-MLM model was implemented for both traits, incorporating the first two principal components (PCA) and kinship matrix to account for population structure and relatedness.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSearch for putative candidate genes\u003c/h2\u003e \u003cp\u003eCommon bean UI111 \u003cem\u003ev\u003c/em\u003e1.1 genome annotation was scanned for potential candidate genes present within the \u0026plusmn;\u0026thinsp;50 Kb genomic regions from the significant peak SNPs. This range was selected based on the average LD blocks, which is ~\u0026thinsp;100kb. Genes within a region were investigated through available literature search and their putative biological functions. The potential candidate genes were selected based on their potential relationship to stem diameter and plant height metabolic pathways.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eHaplotype block analysis\u003c/h2\u003e \u003cp\u003eHaplotype blocks were constructed surrounding the significant SNPs for the respective chromosome according to the solid spine method of linkage disequilibrium (LD) and the extended spine if D\u0026rsquo; \u0026gt; 0.8 implemented in the Haploview \u003cem\u003ev\u003c/em\u003e4.1 software (Barrett et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). This method considers that the first and last markers in a haplotype block are in strong LD with all intermediate markers, thereby providing more robust block boundaries.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of traits across locations\u003c/h2\u003e \u003cp\u003eGenotype, environment, and GxE effects were highly significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.01), either on the combined or for the market class analyses, for nearly all traits in the study. GxE was significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05) for plant height, in great northern, and red and pinks. Stem diameter did not exhibit a significant GxE for the pinto and SD-pinto market classes while this interaction was significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05) for the other market classes (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistic parameters for all traits within each market class across eight to nine environments\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCombined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBlack \u0026dagger;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGreat northern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNavy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePinto\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRed and Pinks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSD-pinto\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c9\" namest=\"c3\"\u003e \u003cp\u003eF Values\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eStem\u003c/p\u003e \u003cp\u003eDiameter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.7 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.9 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.4 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.0 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.2 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.4 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.5 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.2 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.0 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.7 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41.4 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.1 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.4 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e19.2 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG x E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.3 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.3 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.3 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.8 n.s.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.4 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.1 n.s.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePlant\u003c/p\u003e \u003cp\u003eHeight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.4 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.6 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.9 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.7 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.4 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.3 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.7 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.0 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.8 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.3 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.7 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.0 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e29.2 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e37.7 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG x E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.05 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.6 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.3 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.5 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.8 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.3 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.8 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eMaturity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.4 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.0 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.7 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.9 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.4 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.2 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.1 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.2 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.0 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43.7 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.7 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24.5 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e62.6 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e26.2 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG x E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.3 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.4 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.2 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.7 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.3 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.3 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.5 **\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSeed\u003c/p\u003e \u003cp\u003eWeight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e133.5 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.5 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.8 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.7 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11.2 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.9 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118.5 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e117.1 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48.2 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.9 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e52.6 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e31.6 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e37.9 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG x E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.9 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.0 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.2 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.1 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.5 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.2 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eSeed Yield\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.7 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.5 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.5 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.9 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.9 **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.4 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.8 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.2 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.0 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.0 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.2 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.8 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e17.7 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e39.5 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG x E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.7 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.7 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.4 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.7 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.6 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.8 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.5 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eG: Genotype, E: Environment and G X E: Genotype x Environment interaction. *** \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; **\u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.01; * \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05; n.s. non significant. \u0026dagger; For black, eight environments were evaluated, for the remaining market classes nine environments were taken.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eMean values by market class\u003c/h2\u003e \u003cp\u003eMeans values for plant height were between 49.3 cm for great northern and 53.6 cm for pinto genotypes. Stem diameter mean values across all market classes ranged between 7.6 and 8.0 mm (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The most common value for stem diameter among market classes was 7.6 mm, observed in black, navy, great northern, and red/pink. The highest mean stem diameter for pinto with 8.0 mm was significantly higher than the remaining market classes.\u003c/p\u003e \u003cp\u003e According to the mean separation values for plant height, pinto and red/pink had the highest plant height with 53.6 and 52.3 cm, respectively. In addition, these two market classes showed the highest seed yield with 1827 kg ha\u003csup\u003e-1\u003c/sup\u003e for red/pink and 1754 kg ha\u003csup\u003e-1\u003c/sup\u003efor pinto. In most cases, pintos showed either the highest or some of the highest values for stem diameter, plant height, and seed yield.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMeans for agronomic traits across environments (2020\u0026ndash;2022)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStem diameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlant height\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMaturity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100-Seed weight\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSeed yield\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarket Class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ekg ha-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCombined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1662\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePinto\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.0 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.6 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101 bc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.0 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1754 b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD Pinto\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.7 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.2 cd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102 ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35.7 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1664 c\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRed and Pinks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.6 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.3 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e103 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.3 d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1827 a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.6 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.4 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e103 a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.0 e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1694 bc\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNavy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.6 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.0 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.9 f\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1582 d\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreat Northern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.6 b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.3 d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.0 c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1539 d\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e67.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMean with different letters indicate significantly different Market Classes (LSD test P\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eHeritabilities\u003c/h2\u003e \u003cp\u003eThe heritability estimates for stem diameter ranged between 0.43 for great northern and 0.87 for pinto (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The two market classes with the highest heritability were pinto and SD-Pinto (0.84), which also showed non-significant GxE interactions for the trait. Heritability for plant height, ranged between 0.46 for black and 0.89 for pinto and red/pink. Except for black, all market classes showed heritability higher than 0.70 for this trait.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBroad-sense heritability values for each market class\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003eHeritability (H\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCombined\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGreat Northern\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNavy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePinto\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRed and Pinks\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSD Pinto\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStem diameter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlant height\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaturity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeed weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeed yield\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eH\u003csup\u003e2\u003c/sup\u003e: Broad sense heritability using entry-mean.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eCorrelations among traits\u003c/h2\u003e \u003cp\u003eAmong all market classes, all correlations were significant except plant height and seed weight. The two most important correlations were plant height and seed yield (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.5); plant height and stem diameter (0.4). Conversely, stem diameter and seed yield had a low correlation value (0.2). In both analyses, combined and within market classes, the most important correlation was found between plant height and seed yield. Small differences were observed among market classes for this correlation, with navy (0.6), showing the highest value. In contrast, pinto, red/pink, and SD-pinto shared the lowest correlation value of 0.4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Likewise, plant height and stem diameter were positively correlated. Great northern, pinto, and SD-pinto scored the lowest value of 0.3, while the highest were found for navy (0.5), red/pink, and black (0.4). In contrast to the combined analysis, higher correlation values between stem diameter and seed yield were found within market classes. Maturity and seed yield were negatively correlated for red/pink and SD-pinto (-0.3), while the remaining market classes showed weak negative or non-significant correlations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eX for non-significant correlation. PH: Plant height, SD: Stem diameter, MAT: Maturity, SW: Seed weight and Y: Plant yield.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eRegressions\u003c/h2\u003e \u003cp\u003eSimple linear regressions for the combined data set, plant height, stem diameter, maturity, and seed weight explained 24.1%, 8.9%, 2.7%, and 0.1% of seed yield variation, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In addition, multiple linear regressions that were performed using the three most important traits showed that, plant height\u0026thinsp;+\u0026thinsp;stem diameter (25.6%) explained most of seed yield variation; adding maturity to the model explained less variation than considering only plant height. In conclusion, plant height and stem diameter explained most of the seed yield variation.\u003c/p\u003e \u003cp\u003eWhen regressions were performed within each market class, two main patterns were observed. The first pattern was detected for black, great northern, and navy, where plant height explained more than 24.4% of seed yield variation. In contrast, stem diameter only explained between 4.7 to 8.5% of the variation. For black and navy when plant height and stem diameter were used in multiple regressions, plant height was the only significant trait. Conversely, for great northern, both were significant and explained 24.0% and 7.3% of the variation, respectively. However, when a model including plant height\u0026thinsp;+\u0026thinsp;stem diameter was used, variation reached 26.3%. In this group, plant height was the most important trait explaining 24.4% of yield variation.\u003c/p\u003e \u003cp\u003eThe second pattern was observed for pinto, red/pink, and SD-pinto, in which plant height explained less variation than the previous group, with values of 15.1% for pinto, 16.5% for red/pink, and 20.9% for SD-pinto (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In contrast, stem diameter explained between 10.6 and 11.7%, suggesting a higher relevance of this trait for this group. In all market classes within this pattern, the model plant height\u0026thinsp;+\u0026thinsp;stem diameter showed an increase on the explained variation in comparison to the single use of plant height data. For this group, plant height was also the most important trait, however, it explained a smaller variation in seed yield. In contrast, stem diameter was relevant, explaining around 11% of the variation (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLinear and multiple regressions between the traits of study and seed yield\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrait\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCombined\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNavy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGreat\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePinto\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRed\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSD-pinto\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eSimple linear regression\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e% of variation explained\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlant height (PH)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStem diameter (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaturity (MAT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100-Seed weight (SW)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0.2 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.3 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0.1 *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eMultiple linear regression\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePH\u0026thinsp;+\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.0 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.2 **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePH\u0026thinsp;+\u0026thinsp;MAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.0 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSD\u0026thinsp;+\u0026thinsp;MAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.9 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.6 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.1 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePH\u0026thinsp;+\u0026thinsp;SD\u0026thinsp;+\u0026thinsp;MAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.8 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.0 **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.4 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e26.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSimple linear regression\u003c/b\u003e: * n.s. non significant. \u003cb\u003eMultiple regression\u003c/b\u003e: \u003cb\u003eBlack\u003c/b\u003e: *SD non-significant. \u003cb\u003eGreat northern\u003c/b\u003e: *MAT non-significant. \u003cb\u003eNavy\u003c/b\u003e: **SD non-significant and *MAT non-significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003ePrincipal component analysis\u003c/h2\u003e \u003cp\u003eWhen the PCA for the combined phenotypic analysis was performed, PC1 and PC2 explained 41.9% and 20.4% of the variation, respectively, reaching a total of 62.3% of the variation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Stem diameter, seed yield, and plant height were clustered, but plant height was the closer vector to seed yield. Maturity and seed weight appear to have an opposite relationship. These last two variables showed no relationship with stem diameter, seed yield, and plant height. When this analysis was performed within each market class, there were some similarities to correlations and regressions results. However, some changes regarding the distribution of the variables were found. Similar to the combined analysis, seed weight and maturity were not clustered with plant height, stem diameter, or seed yield in most market classes. Nevertheless, some differences were observed for each market class and race. Within the Mesoamerican race, plant height, stem diameter, and seed yield were clustered together. However, a closer relationship between plant height and stem diameter was present in black compared to navy. For black beans, these two traits had almost the same vector length and direction (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor great northern and red/pink beans, a close relationship was observed between plant height and stem diameter (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Nevertheless, for great northern these traits were also highly associated with seed yield. Differences for plant height and stem diameter were observed in pinto and SD-pinto (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In these cases, and especially for SD-pinto beans, the vectors of these two variables were not in the same direction. Maturity for pinto was not related to any of the two principal components (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eGWAS\u003c/h2\u003e \u003cp\u003eThe Bonferroni-Holm correction for multiple testing proved too conservative, as it assumes all tests are independent. However, many SNPs might be linked due to their physical proximity or other associated factors, meaning they are not truly independent. Consequently, applying this stringent threshold resulted in no significant associations being detected for any traits. Therefore, \u003cem\u003eP\u003c/em\u003e-value threshold to declare significant marker trait associations was determined using the Li and Ji (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) method. Based on this method, the \u003cem\u003eP\u003c/em\u003e-value thresholds corresponding to a 5% experiment-wise error rate were: 1.61 \u0026times; 10⁻⁴ (LOD\u0026thinsp;=\u0026thinsp;3.8) for the combined analyses (single trait and multi-trait), 4.27 \u0026times; 10⁻⁴ (LOD\u0026thinsp;=\u0026thinsp;3.4) for the Mesoamerican race, and 2.67 \u0026times; 10⁻⁴ (LOD\u0026thinsp;=\u0026thinsp;3.6) for the Durango race.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003ePlant height\u003c/h2\u003e \u003cp\u003eFor plant height, significant peaks were found on chromosomes Pv03 and Pv07 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The major GWAS signal was located on Pv07, with the lead SNP S07_40778504 located within a 40.7\u0026ndash;40.8 Mb genomic interval. In this region, five SNPs surpassed the 1.58 x 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e (LOD\u0026thinsp;=\u0026thinsp;4.8) threshold.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWithin this interval, gene model PvUI111.07G214700, annotated as a growth-regulating factor was identified as a strong candidate gene (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Additional genes in these regions included those encoding serine/threonine-protein kinase, ring/zinc finger protein, E3 ubiquitin-protein ligase MGRN1 (MGRN1), SF98-Membrane associated ring fingers, and leucine rich repeats.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCandidate genes on chromosomes Pv03 and Pv07 for plant height\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePeak\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePutative Genes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGene Function\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePv03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.7 \u0026ndash;\u003c/p\u003e \u003cp\u003e36.9 Mb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS03_36917109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.22 x 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePvUI111.03G154900.1\u003c/p\u003e \u003cp\u003ePvUI111.03G157000.1 and\u003c/p\u003e \u003cp\u003ePvUI111.03G157100.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSerine/Threonine-Protein Kinase\u003c/p\u003e \u003cp\u003eF-box associated domain (FBA_3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePv07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.7 \u0026ndash;\u003c/p\u003e \u003cp\u003e40.8 Mb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS07_40778587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05 x 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePvUI111.07G214700.1 (1 copy .2)\u003c/p\u003e \u003cp\u003ePvUI111.07G213900.1 (1 copy.2) and PvUI111.07G213300.1\u003c/p\u003e \u003cp\u003ePvUI111.07G213500.1\u003c/p\u003e \u003cp\u003ePvUI111.07G213800.1\u003c/p\u003e \u003cp\u003ePvUI111.07G214300.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGrowth-regulating factor related-3-related\u003c/p\u003e \u003cp\u003eSerine/Threonine-protein kinase ring/Zinc Finger Protein Jackdaw\u003c/p\u003e \u003cp\u003eE3 ubiquitin-protein ligase MGRN1 (MGRN1)\u003c/p\u003e \u003cp\u003eSF98-Membrane associated ring finger.\u003c/p\u003e \u003cp\u003eLeucine Rich Repeat (LRR_1), (LRRNT_2), and (LRR_8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe second most important peak SNP, S03_36917109, was identified on Pv03 with a \u003cem\u003eP\u003c/em\u003e-value of 3.22 x 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(LOD\u0026thinsp;=\u0026thinsp;4.5). A cluster of 16 SNPs within the 36.7\u0026ndash;36.9 Mb region exceeded the \u003cem\u003eP\u003c/em\u003e-value threshold of 5.68 x 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e (LOD\u0026thinsp;=\u0026thinsp;4.2). Notable gene models such as PvUI111.03G154900 related to a serine/threonine protein kinase; PvUI111.03G157000 and PvUI111.03G157100, both associated with an F-box associated domain (FBA-3), were the most relevant and considered as candidate genes governing plant height. Collectively, the significant SNPs explained 39% of the phenotypic variation for plant height, with the Pv07 significant region alone accounting for 16.6% of the variation.\u003c/p\u003e \u003cp\u003eWhen GWAS analyses were conducted separately by race, four significant SNPs were detected in the Durango race. One SNP (S06_3623485) on Pv06 and three SNPs on Pv03, located between 36.8\u0026ndash;37.2 Mb interval, corresponding to the same genomic region detected in the combined plant height GWAS analysis. For race Mesoamerica, 27 significant SNPs were found, distributed on chromosomes Pv01 (1), Pv05 (7), and Pv07 (17) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The strongest signals with majority of the significant SNPs (7) were identified at ~\u0026thinsp;40.7 Mb on Pv07, overlapping with the combined analysis. Additionally, five SNPs were detected on Pv07 around 2.2 Mb, highlighting the relevance of these genomic regions for plant height regulation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGWAS results pointed to chromosomes Pv03 (36.8\u0026ndash;37.2 Mb) and Pv07 (40.7\u0026ndash;40.8 Mb) as the most important genomic regions related to plant height for dry bean. These genomic regions were simultaneously detected in both races as well as in combined GWAS analyses.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStem diameter.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eGWAS identified two highly significant genomic regions located on Pv11 and Pv07 for stem diameter when all the genotypes were combined (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The most significant SNP was observed on Pv11, where the peak SNP (S11_8564171) was located around 8.5 Mb (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.42 x 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e; LOD\u0026thinsp;=\u0026thinsp;4.8). This region harbored two candidate genes with a possible relationship with stem diameter, PvUI111.11G089600.1, a cyclin-dependent kinase regulatory subunit (CKS1) and PvUI111.11G089800.1, an apoptosis inhibitor//Ring//U-Box domain-containing protein.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOn Pv07, two major genomic regions were detected, spanning 33.5\u0026ndash;34.7 Mb (23 SNPs) and 40.7 Mb (5 SNPs). Haplotype block analysis revealed four distinct haplotype blocks: The first block (Block 1) extends about 379 Kb from 33.4 to 33.7 Mb consisting of five significant SNPs. The other three blocks were Block 2: 33.7 Mb (1 SNP; ~139 Kb); Block 3: 34.5\u0026ndash;34.6 Mb (17 SNPs); Block 4: 35.7 Mb (1 SNP). An additional block of five significant SNPs clustered around 40.7 Mb spanned a 112 Kb haplotype block. Collectively, these significant genomic regions explained 56.2% of the phenotypic variation in stem diameter.\u003c/p\u003e \u003cp\u003eAmong these, the Pv07 region spanning 34.5 ̶ 34.6 Mb (Block 3) was the most significant containing several functionally relevant gene models: PvUI111.07G159600 (gibberellin receptor GID1), PvUI111.07G159700 (WD repeat containing protein), and PvUI111.07G159800 (leucine rich repeat protein) (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Another significant region at 33.5 Mb, with five significant SNPs, contained two notable gene models: PvUI111.07G153300 (defense-related protein containing SCP domain) and PvUI111.07G153400 (PPR repeat family protein). Notably, the 40.7 Mb region on Pv07 was significantly associated with stem diameter, overlapped with the major regions identified for plant height, suggesting a shared genetic control for upright plant architecture.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCandidate genes for stem diameter found on chromosomes Pv11 and Pv07.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePeak\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGenes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGenes Function\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePv07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.5 Mb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS07_33538378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.85 x 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePvUI111.07G153300.1\u003c/p\u003e \u003cp\u003ePvUI111.07G153400.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDefense-related protein containing SCP domain\u003c/p\u003e \u003cp\u003ePPR repeat (PPR) // PPR repeat family (PPR_2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePv07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34.5\u0026ndash;34.6 Mb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS07_34570692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.11 x 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePvUI111.07G159600.1\u003c/p\u003e \u003cp\u003ePvUI111.07G159700.1 (1 copy)\u003c/p\u003e \u003cp\u003ePvUI111.07G159800.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGibberellin receptor GID1 (GID1)\u003c/p\u003e \u003cp\u003eWD repeat-containing protein 23 (WDR23)\u003c/p\u003e \u003cp\u003eLeucine Rich Repeat (LRR_1) (LRRNT_2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePv07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40.7 Mb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS07_40778429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.47 x 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSame region as plant height\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePv11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.5\u0026ndash;8.9 Mb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS11_8564171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.42 x 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePvUI111.11G089600.1\u003c/p\u003e \u003cp\u003ePvUI111.11G089800.1 (1 Copy)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCyclin-dependent kinase regulatory subunit CKS1 (CKS1)\u003c/p\u003e \u003cp\u003eInhibitor of apoptosis //Ring//U-Box domain-containing protein.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhen stem diameter GWAS was performed separately by race, the MLM was identified as the best-fitting model for both Durango and Mesoamerican races. In the Durango race, significant regions for stem diameter were detected on Pv07 and Pv11, with 67 and 3 significant SNPs, respectively. The significant SNPs found on Pv11 overlapped with those identified in the combined analysis. On Pv07, 67 significant SNPs were distributed in the 31.4\u0026ndash;31.8 Mb (5 SNPs), 32.4\u0026ndash;32.9 (36 SNPs), 33 -33.1 (16 SNPs), and 34.5\u0026ndash;34.6 Mb (10 SNPs) genomic regions. Most of these intervals coincided with those found in the combined analysis. In the Mesoamerica race, 11 significant SNPs were found exclusively on chromosome Pv07, with seven SNPs clustered around 2.2 Mb. This region was also observed in the Mesoamerican plant height analysis.\u003c/p\u003e \u003cp\u003eOverall, GWAS results indicate that multiple genomic regions on Pv07 contribute to stem diameter variation. The 2.2 Mb and 40.7 Mb genomic regions are primarily related to the Mesoamerican race, while the 33.5\u0026ndash;33.6 Mb and 34.5\u0026ndash;34.6 Mb regions are associated with the Durango race.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eMulti-trait GWAS\u003c/h2\u003e \u003cp\u003eSingle-trait GWAS analyses identified overlapping genomic regions associated with both plant height and stem diameter, suggesting potential pleiotropic effects. Therefore, a multi-trait GWAS was conducted to detect common genomic loci influencing both traits simultaneously.\u003c/p\u003e \u003cp\u003eIn combined panel, the multi-trait GWAS detected significant signals on chromosomes Pv03, Pv05, Pv07, and Pv11 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Notably, the significant signals on Pv07 and P11 overlapped with those detected in the single-trait GWAS for both plant height and stem diameter. The genomic region at 40.7\u0026ndash;40.8 Mb on Pv07, consistently detected across multiple analyses, indicates shared genetic control underlying the two traits.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSimilar patterns were also observed in the race-specific multi-trait GWAS analyses. In the Durango race, multi-trait GWAS detected major peaks Pv03, Pv05, Pv07, and Pv11 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Consistent with the single-trait GWAS, significant genome regions were located on Pv07 spanning between 31.6\u0026ndash;33.1 Mb (54 SNPs) and on Pv11 at 8.5 Mb, both associated with stem diameter. For the Mesoamerican race, significant associations were detected on Pv01, Pv05, Pv06, Pv07, and Pv08 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). The peak genomic region spanning 40.7\u0026ndash;40.8 Mb on Pv07 was simultaneously detected in both single-trait and multi-trait GWAS analysis, confirming the importance of this locus in governing plant height and stem diameter in Mesoamerican genotypes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study highlights the importance of key agronomic traits for seed yield and upright plant selection in dry bean. Using a combined analysis and six different market classes, from two races, allowed a better understanding of the different traits used for plant selection. Among all traits studied, both plant height and stem diameter were the most important for seed yield and upright plant selection. Nevertheless, their importance changed based on the race and the market class evaluated. Therefore, the discussion will focus on these traits. GWAS analyses further elucidated the genetic architecture underlying these traits, offering valuable opportunities to develop markers for breeding programs aimed at optimizing beneficial ideotype development.\u003c/p\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003eStem diameter\u003c/h2\u003e \u003cp\u003eAcross all market classes, stem diameter averaged 7.7 mm, with pinto showing the thickest stem (8.0 mm), significantly greater than other market classes. These values are higher than the ones found by Oliveira et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) (5.1 mm) and Moura et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) (5.4 mm) who mainly used Type III cultivars. Our results were similar to the ones found by Brothers and Kelly, (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1993\u003c/span\u003e) and Mulube, (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) who observed diameters ranging from 5.7 and 8.3 mm in crosses involving either small or medium seed sizes or Andean genotypes. Most importantly, the stem diameter in this study surpass the 5.6 mm threshold proposed by Soltani et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) for selecting upright cultivars. Values from our study were even greater than the thickest stems found by these authors, 5.7 mm in Type II plants, and 5.0 mm in Type III plants. Soltani et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) used a diverse panel of genotypes (Durango Diversity Panel \u0026ndash; DDP), which included old and new germplasm and several cultivars with growth type III. In contrast, our study used elite/advanced lines from a public breeding program in which selection for upright plant architecture is a routine selection component of the breeding pipeline. Thus, it is not surprising that selection for upright architecture within the breeding program may have indirectly increased stem diameter.\u003c/p\u003e \u003cp\u003eStem diameter showed no significant GxE interaction for pinto and SD-pinto, indicating high phenotypic stability across environments (Fehr \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). Similar stability was reported by Mulube, (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and Oliveira et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) for released cultivars. In contrast, Moura et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and Soltani et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), observed significant GxE interaction when analyzing panels with contrasting growth habits. Nevertheless, Soltani et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), showed non-significant GxE when the analysis was performed within Type II and III growth habits separately. These findings suggests that GxE effects diminishes within populations with similar architecture, highlighting the need for pattern-specific evaluations within populations.\u003c/p\u003e \u003cp\u003eHeritability estimates also showed differences among market classes. Pinto (0.87) and SD-pinto (0.84) showed the highest heritability. This reinforces the stability and reliability of the trait for selecting upright plants especially in these two market classes. These values are comparable to those reported by Silva et al. (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) (0.81) and Oliveira et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) (0.75\u0026ndash;0.77); but substantially higher those obtained by Soltani et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), who found a broad-sense heritability of 0.33 and found higher heritability values for Type II compared to Type III plants. This could be explained by the inherent nature of stiff-stem and upright plants in Type II compared to Type III plants, which have prostate habit and weaker stems (Singh et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). However, in this study, Durango market classes showed higher heritability values than Mesoamerican. It could show that breeding efforts to select upright plants likely are changing traits in different market classes (Kelly, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), one of them could be stem diameter.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003ePlant height\u003c/h2\u003e \u003cp\u003ePlant height had a highly significant GxE interaction across all market classes, indicating lower stability compared to stem diameter. Mulube, (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), Moura et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and Soltani et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) also reinforced that plant height is more environment-sensitive. Heritability values differed among market classes, ranging from 0.46 in black to 0.89 for red/pinks. However, most heritability values were above 0.75. These results are higher than the 0.66 reported by Soltani et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and the 0.6 found by Oliveira et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Mean plant height exceeded 49 cm across all market classes, with pinto and red/pink reaching 53.6 and 52.3 cm, respectively. These values were higher than the mean of completely upright, Type II plants (42.3 cm), or Type III (33.3), reported by Soltani et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Silva et al. (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) also observed taller and thicker Type II (6.3 mm and 50 cm) than Type III plants (5.3 mm and 38 cm), suggesting that differences of 12 cm in plant height and 1 mm in stem diameter could imply a change in growth habit. This contrasts with our results given that some Durango/Jalisco market classes are showing taller and thicker plants. This shows again a likely influence of the breeding efforts to bring these characteristics from Mesoamerican to Durango races (Kelly, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). In spite of the significant GxE interaction for plant height, its heritability was high, which confirms its importance for plant architecture. Thus, plant height and stem diameter could be considered as appropriate selection traits for dry bean, although their use in combination would depend on the market class undergoing selection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eRelationships between stem diameter, plant height and other traits\u003c/h2\u003e \u003cp\u003ePlant height showed a stronger correlation with seed yield than stem diameter in this study. In contrast, Soltani et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), found that plant height (0.42) and stem diameter (0.4) were equally correlated with seed yield. Within market classes, plant height and yield were also significantly correlated, however, its magnitude changed based on the market class. For black, navy, and great northern, correlation values were higher than 0.5. Plant height and stem diameter correlation ranged between 0.3 and 0.5 across all market classes and was stronger for Mesoamerican market classes black (0.4) and navy (0.5). Higher correlation values of 0.8 for this combination of traits was found by Silva et al. (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and Soltani et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Then, this study confirms a positive correlation between plant height, stem diameter, and seed yield; however, their magnitude will vary based on the market class. The correlations between stem diameter and plant height, plant height and seed yield tended to be higher in Mesoamerican market classes compared to Durango/Jalisco. This could be related to inherent traits nature for each race, but also to breeding efforts to bring upright characteristic from Mesoamerican into Durango (Kelly, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegression analyses further confirmed correlations results regarding the observed two patterns. In black, navy, and great northern market classes, plant height explained more than 24.4% of seed yield variation, while stem diameter showed non-significant or minor effects. Interestingly, great northern beans were more closely related to Mesoamerican than Durango/Jalisco, likely due to the use of navy beans as parents to generate early upright great northern cultivars (McClean et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In addition, comparisons between pinto and great northern DNA sequence data have shown differences between these two market classes regarding selection hotspots for plant architecture (unpublished data). In contrast, pinto, red/pink, and SD-pinto showed similar contributions of plant height and stem diameter to seed yield, reflecting dual role of these traits in upright architecture. Tall plants prevent pods contact with soil, while thicker stem provide structural support (Acquaah et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1992\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHistorical trends support these findings, Vandemark et al. (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) documented significant increase in plant height (34 cm to 57 cm) in pinto cultivars released after 1980, coinciding with the shift from prostrate Type III to upright Type II architecture. However, no-significant differences were found for navy, likely, due to their common upright architecture. Our study further indicates that stem diameter was increased mainly in pinto cultivars, likely due to the indirect selection for upright architecture. According to Soltani et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) an increase in stem diameter and plant height could result from converting Type III genotypes (thinner stems) to Type II (stiff stem). Changes in plant height in the newly released pinto cultivars have allowed a more efficient direct harvest for these cultivars (Eckert et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). While Soltani et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) suggested a 5.6 mm threshold for stem diameter, our results indicate that this benchmark may need revision for commercial breeding programs and for specific market classes, given historic differences in growth habits. Stem diameter was a stable and heritable trait, which explained a high amount of variation in the seed yield of Durango/Jalisco market classes such as pinto and SD-pinto. It is suggested to continue selecting for this trait and plant height in these market classes. In contrast, plant height is a reliable trait for upright selection in the Mesoamerican market classes.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGenetic architecture of plant height\u003c/h3\u003e\n\u003cp\u003eGWAS results for plant height revealed significant regions on chromosomes Pv07 and Pv03. On Pv07, the region close to 40.7 Mb, has several plant growth-related genes, including PvUI111.07G214700, which encodes a growth-regulating factor (GRF). GRFs are plant-specific transcription factor that controls cell expansion (size) and proliferation (number), influencing stem and leaf development (Omidbakhshfard et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In rice (\u003cem\u003eOryza sativa\u003c/em\u003e L.), they play important roles in stem growth and development (Wang et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and coordinate levels of defense and plant development hormones in opposite directions (Omidbakhshfard et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Under normal conditions, GRF1/GRF3 invest energy in plant growth. Under stress conditions, they inhibit growth directing resources to stress tolerance processes (Piya et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe second major region found on Pv03 (36.7\u0026ndash;36.9 Mb) includes gene models related to serine/threonine-protein kinases and F-box associated domains. According to Kuzbakova et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), these kinases have been associated with plant height. For instance, in soybean (\u003cem\u003eGlycine max\u003c/em\u003e L.), a serine/threonine-protein phosphatase is encoded by the gene GmPP1-like. Mutants of this gene show shorter internode length and height. Gene model PvUI111.03G157000.1 was also found in this region, encoding an F-box domain, with protein destruction functions (Ho et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). F-box domains, create dimers to regulate auxins, giberellins (GA), jasmonate (JA), and ethylene expressions (Prigge et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCombined and race-specific single trait and multi-trait GWAS analysis confirmed the relevance of Pv03 (36 Mb) and Pv07 (40.7 Mb), with Pv03 (36 Mb) and Pv06 (3.6 Mb) significant for Durango, and Pv07 (40.7 Mb) was highly relevant for Mesoamerican races. These findings support previous report of MacQueen et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) that several chromosomes could be related to plant height and highlight Pv03 and Pv07 as key targets for marker-assisted selection.\u003c/p\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003eGenetic architecture of stem diameter\u003c/h2\u003e \u003cp\u003eFor stem diameter, the most important peak was located on chromosome Pv11 (8.5 and 8.9 Mb), which includes PvUI111.11G089600 encoding a cyclin-dependent kinase regulatory subunit (CKS1). CSK1 proteins, regulate the cell cycle affecting growth and development (Tamirisa et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). A second most important peak on Pv07 (34.5 and 34.6 Mb) harbors gene model PvUI111.07G159600 encodes a gibberellin receptor GID1, which interacts with DELLA proteins, to modulate GA signaling repressors (Yoshida et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA highly relevant finding is the region around 40.7 Mb on Pv07, detected for both plant height and stem diameter traits. This region contains genes related to growth regulating factors, serine/threonine protein kinase, E3 ubiquitin-protein ligase, membrane associated ring finger and leucine rich repeats. According to (Wang et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), the growth regulating factors are not only related to plant height but to stem thickness. In rice, similar pleiotropic loci named \u003cem\u003eIdeal Plant architecture\u003c/em\u003e (IPA1) have been reported, encoding OsSPL14 (SQUAMOSA PROMOTER BINDING PROTEIN-LIKE 14), which controls plant height, stem diameter, tiller numbers and number of productive tillers (Jiao et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Thus, Pv07 (40.7 Mb) becomes interesting and important target for further indeterminate growth plant architecture studies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003eChromosome Pv07 could be related to indeterminate growth in dry bean\u003c/h2\u003e \u003cp\u003eOur current findings reinforce the critical role of chromosome Pv07 in plant architecture and growth habit. Genes such as \u003cem\u003ePvTFL1y\u003c/em\u003e or \u003cem\u003eFin\u003c/em\u003e locus on Pv01 and \u003cem\u003ePvTFL1z\u003c/em\u003e (likely a duplication of the first one) on Pv07 (Krylova et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kwak et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) have been related to plant height. Genes on Pv01, are associated with determinant Andean genotypes (Cichy et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Resende et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), whereas Pv07 could be related to indeterminacy in the Middle American gene pool, particularly in navy (Kolkman and Kelly, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). This suggests two distinct domestication processes, one on Pv01 for Andean (determinant) and another on Pv07 for Mesoamerican (indeterminant). Our GWAS results could confirm an indeterminacy region on Pv07. This region was suggested by Moghaddam et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) who used Type I, II, and III plants and confirmed a determinacy region on Pv01. However, it was only found when Andean determinate genotypes were present. When Andean cultivars were removed, peaks on Pv04, Pv06, Pv07, and Pv11 appeared. Being the peaks on Pv07, (46.11 Mb) and (35.42 Mb) the most relevant. Similarly, our results showed significant regions on Pv03, Pv07, and Pv11 using indeterminate Durango and Mesoamerica germplasm. Notably, the genomic region 40.7 Mb on Pv07, was significant for plant height and stem diameter. This region was also validated and identified by multi-trait GWAS analysis. However, genome comparisons showed that this region is different to the one found by Moghaddam et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) on Pv07 (46 Mb), which was mapped based on G19833v2.1 reference genome (Schmutz et al. 2014). Recent findings by Beagley et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), further support this by identifying Pv07 regions associated with stem elongation of the first five nodes independent of flowering time, which consistent with the results. Candidate genes identified in our study, such as PvUI111.07G141800 and PvUI111.07G153300 located at 31.4 Mb and 33.5 Mb, respectively, share 100% similarity with G19833v2.1 genome homologs Phvul.007G149432 and Phvul.007G161200, respectively reported by Beagley et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and are implicated in stem elongation. These findings highlight Pv07 as a hotspot for growth habit regulation, with potential pleiotropic effects on plant height, stem diameter and node elongation.\u003c/p\u003e \u003cp\u003eDomestication process likely led to different growth habits. For instance, due to maize\u0026rsquo;s late arrival to the Andes some mutations were generated to give Andean plants own support. In contrast, the use of maize in Middle America, allowed the bean plants to climb (Koinange et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). However, it is possible that some Middle American climbing Type IV plants adapted to less support conditions, generating Type III plants. This could be partially shown by Tobar-Pi\u0026ntilde;on, (2020) who found higher admixture between Guatemalan climbing beans (Type IV) and Durango/Jalisco race (Type III and IV) than with Mesoamerican race (Type II). Likely a plant biomass reduction was required to adapt to temperature and altitude variations, changing from Type IV to Type II progressively. Evidence suggests that Pv07 could be highly relevant for indeterminate dry bean germplasm growth habit. Similar to dry bean, soybean has growth habits such as determinate, indeterminate, and semideterminate, which are mastered by two genes (\u003cem\u003eDt1\u003c/em\u003e and \u003cem\u003eDt2\u003c/em\u003e), \u003cem\u003eDt1\u003c/em\u003e controls determinacy, and \u003cem\u003eDt2\u003c/em\u003e, semideterminacy (Ping et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Clark and Ma \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). \u003cem\u003eDt2\u003c/em\u003e is related to branch number (Virdi et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), generates differences in height and diverse degrees of stem termination, with pleiotropic effects on node number and internode length (Kou et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It is possible that within the indeterminate dry bean germplasm, there are genes controlling specific height or vine length, having therefore a similar function to \u003cem\u003eDt2\u003c/em\u003e. This region could be located on Pv07 and could be important to improve traits such as first pod height, plant stature, and mechanical harvesting, which are key for dry bean commercialization.\u003c/p\u003e \u003cp\u003eOur GWAS analyses revealed multiple Pv07 regions contributing to stem diameter variation (2.2 Mb, 33.5\u0026ndash;34.6 Mb, and 40.7 Mb), with race-specific associations: 2.2 Mb and 40.7 Mb for Mesoamerica and 33.5\u0026ndash;33.6 Mb and 34.5\u0026ndash;34.6 Mb for Durango. In general, regions on Pv07 not only can help to understand differences among indeterminant growth habits grown in the US (Type II and III); but could also help to trace their domestication traits. Only a few studies available related to stem diameter genetics have been reported. Therefore, the effect of intervals located mainly on Pv07 is an important result of this study. This is one of the few studies that show genetic architecture and field importance of stem diameter and plant height. Collectively, Pv07 significant regions explained 56.2% genetic variation for stem diameter and 16.6% for plant height, underscoring its central role in indeterminate architecture. These regions could help explain differences in Type II and III growth habits in beans with indeterminant growth habits. These findings provide a foundation for developing functional markers for fine-mapping and cloning causative genes, enabling targeted breeding for upright plants with thicker stems-traits essential for mechanical harvesting and improved yield stability.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOverall, stem diameter was larger for current advanced lines than values suggested by Soltani et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) of 5.6 mm., indicating that selection for upright architecture within the breeding program may have indirectly increased stem diameter. Stem diameter showed the highest heritability values in pinto and SD-pinto. Additionally, non-significant GxE interaction was found in these market classes. This suggests that it is a stable and heritable trait for selection of upright plant architecture, especially in these two market classes. Plant height could be a better indicator for seed yield than stem diameter in black, great northern, and navy. While both plant height and stem diameter are required to continue selecting upright and high-yielding plants for pinto, red/pink and SD-pinto. Specially, considering that these three last market classes tend to have a Type III prostrate genetic background. For both plant height and stem diameter there is a shared genetic effect in an interval centered on region centered at 40.7 Mb at Pv07. This common region is related to growth regulating factors, serine/threonine protein kinase, E3 ubiquitin-protein ligase, membrane associated ring finger, and leucine rich repeat. Most of these gene models have relation to plant growth and disease avoidance, which makes this region an interesting area to continue with further investigation and exploit by developing functional molecular markers to assist bean breeding for selecting bean genotypes with improved plant architecture.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by USDA-ARS Pulse Crop Health Initiative, Northarvest Bean Growers Association, and USDA-NIFA through Hatch project ND1508 and W4150 multi-state project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by USDA-ARS Pulse Crop Health Initiative, Northarvest Bean Growers Association, and USDA-NIFA through Hatch project ND1508 and W4150 multi-state project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOscar Rodriguez: Data curation, Formal analysis, Investigation, Methodology, Validation, Visualization, Writing - original draft\u003c/p\u003e\n\u003cp\u003eJayanta Roy: Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing - review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003ePhilip E. McClean: Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eDidier Murillo: Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing - review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eKristin Simons: Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing - review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eNusrat Khan: Data curation, Formal analysis, Investigation, Methodology, Software, Supervision, Validation, Visualization, Writing - review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eJose Figueroa: Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing - review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003eJuan\u0026nbsp;Osorno: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAcquaah G, Adams MW, Kelly JD (1991) Identification of effective indicators of erect plant architecture in dry beans. 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Genetic Resources and Crop Evolution 57:813\u0026ndash;825. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10722-009-9521-4\u003c/span\u003e\u003cspan address=\"10.1007/s10722-009-9521-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"euphytica","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"euph","sideBox":"Learn more about [Euphytica](https://www.springer.com/journal/10681)","snPcode":"10681","submissionUrl":"https://submission.springernature.com/new-submission/10681/3","title":"Euphytica","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Growth habit, stem diameter, plant height, dry bean, GWAS","lastPublishedDoi":"10.21203/rs.3.rs-8634079/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8634079/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGrowth habit is one of the most important domestication traits in dry bean (\u003cem\u003ePhaseolus vulgaris\u003c/em\u003e L.). In the U.S. for example, Type II indeterminate upright plant varieties have allowed farmers to switch from historic two-pass harvest to one-pass direct harvest. Previous work suggested a stem diameter of 5.6 mm as threshold to select Type II architecture genotypes suitable for direct combining. This study aimed to validate the correlation between stem diameter and other agronomic traits using lines comprising various market classes from a public breeding program. It also assesses if stem diameter could be used to select genotypes that combine high seed yield and upright architecture. GWAS was also used to identify genomic regions related to plant height and stem diameter. Mean stem diameter among breeding lines was 7.7 mm, higher than the proposed threshold. Stem diameter showed no significant GxE interactions and the highest broad-sense heritabilities were for regular-darkening pinto (pinto) and slow darkening (SD) pinto. Plant height was the most relevant trait for seed yield variation in black, great northern, and navy beans. In contrast, both plant height and stem diameter are required to explain part of seed yield variability and selecting upright plants for pinto, red/pink, and SD-pinto. GWAS revealed significant regions located on chromosomes Pv03, Pv07, and Pv11 depending on trait and race used. A 40.7\u0026ndash;40.8 Mb interval on Pv07 was associated with both plant height and stem diameter, suggesting further studies on indeterminate upright dry bean plant architecture should focus on this region.\u003c/p\u003e","manuscriptTitle":"New genomic regions in dry bean (Phaseolus vulgaris L.) associated with stem diameter, plant height and other plant architecture traits","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-23 05:26:11","doi":"10.21203/rs.3.rs-8634079/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-23T21:12:25+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-23T14:47:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-11T12:55:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"98311302482966584789339616476523306942","date":"2026-01-30T00:55:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"245772613451237909573740885947685480152","date":"2026-01-21T14:50:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-21T14:36:15+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-19T13:54:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-19T13:53:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Euphytica","date":"2026-01-19T01:27:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"euphytica","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"euph","sideBox":"Learn more about [Euphytica](https://www.springer.com/journal/10681)","snPcode":"10681","submissionUrl":"https://submission.springernature.com/new-submission/10681/3","title":"Euphytica","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"c0f2cf29-b915-43f1-b5b0-c2f691d2f8d6","owner":[],"postedDate":"January 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-03-23T21:23:54+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-23 05:26:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8634079","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8634079","identity":"rs-8634079","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}