Partial white mold resistance in a Brazilian-adapted common bean panel

Partial white mold resistance in a Brazilian-adapted common bean panel | 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 Partial white mold resistance in a Brazilian-adapted common bean panel Givanildo Rodrigues Silva, Thiago Alexandre Santana Gilio, Maria Celeste Gonçalves-Vidigal, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4921482/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Oct, 2024 Read the published version in Genetic Resources and Crop Evolution → Version 1 posted 10 You are reading this latest preprint version Abstract The pathogen Sclerotinia sclerotiorum (Lib.) de Bary is a fungus that causes white mold (WM) in many crops, and it is one of the greatest phytosanitary problems that compromises the productivity and quality of common bean ( Phaseolus vulgaris L.). This study aimed to characterize a panel composed of common bean lines (BLs) from Brazilian farmers with WM resistance using two methods/tests under controlled conditions. The “straw test” (ST - Terán et al., 2006 ) and “seedling straw test” (SST - Arkwazee & Myers, 2017 ) were used to screen the panel. The disease score (DS) and relative disease progress (RDP) were calculated from consecutive evaluations to obtain the area under the disease progress curve (AUDPC). In addition, the phenotypic means were used to identify genomic regions associated with the WM reaction using the genome-wide association study (GWAS) approach. In total, fifteen accessions (eleven Mesoamerican and four Andean) were selected showing high to moderate resistance, and three regions were identified on chromosomes Pv01, Pv02 and Pv03, coinciding with previously reported quantitative trait loci (QTLs), additionally, twelve genes were indicated for validation. We identified putative regions and genes contributing to physiological resistance to WM in a well-adapted common bean panel. The regions indicated in this panel that are adapted to the Brazilian climate may be important in common bean breeding programs. Genome-wide association study Phaseolus vulgaris L. Sclerotinia sclerotiorum (Lib) de Bary Physiological resistance Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The common bean ( Phaseolus vulgaris L.) is a crucial factor in food security, particularly in countries in Africa and South America (Blair et al., 2010; Broughton et al., 2003). Native to the American continent, this species exhibits genetic and phenotypic diversity represented by two well-studied gene pools: Andean and Mesoamerican. Extensive research has been conducted on its morphological, genetic, and physiological characteristics, highlighting their (dis)similarities (Gepts and Bliss 1986 ; Singh et al. 1991 ; Durán et al. 2005 ; Bitocchi et al. 2012 ; Cortes 2013 ; Bitocchi et al. 2013 ). Naturally, a wide variety of this species is maintained in different regions where it has been introduced, as it is subjected to different environmental conditions, production systems, and dietary preferences. These locally maintained varieties represent valuable sources of resistance to various pests and diseases that affect this crop. Disease susceptibility is a major challenge in common bean cultivation, and white mold [ Sclerotinia sclerotiorum (Lib.) de Bary] can significantly reduce grain quality and yield up to 100% (Schwartz and Singh 2013 ). The complexity of resistance to this pathogen has been reported, with several genes and quantitative trait loci (QTLs) involved (Kolkman and Kelly 2002 ; Ender and Kelly 2005 ; Antonio et al. 2008 ; Soule et al. 2011 ; Miklas et al. 2013 ). Some researchers have reported ‘partial’ or ‘physiological resistance’ (detected by the greenhouse straw test). Commonly, known susceptibility factors, such as canopy architecture, growth habit, and plant stature, are eliminated (Miklas et al. 2001 ; Miklas 2007 ; Pérez-Vega et al. 2012 ). These evaluations are based on direct inoculation of the respective fungus into plant tissue, excluding any impeding effect caused by natural infection escape traits. Thus, physiological resistance may play a critical role when these traits are overcome by pathogen pressure in the field. Consequently, pyramiding different genes involved in the resistance reaction can be a promising approach (Singh et al. 2014 ; Vasconcellos et al. 2017 ). To identify sources of physiological resistance to white mold, the straw test (ST) method (Petzoldt and Dickson 1996 ) has been reported to be highly efficient. However, this method becomes time-consuming when large screenings are conducted. Due to this limitation, (Arkwazee and Myers 2017 ) presented an adaptation to that method, known as the seedling straw test (SST), which allows faster evaluation of large volumes of genotypes. Genome-wide association studies (GWASs) have proven useful in investigating complex traits in animals and plants (Scherer and Christensen 2016 ). It has been used to identify quantitative trait nucleotides (QTNs) associated with resistance traits in common beans (Campa et al., 2020 ; Escobar et al., 2022 ; Fritsche-Neto et al., 2019 ; Oladzadab et al., 2019; Perseguini et al., 2016 ; Raggi et al., 2019 ), providing valuable regions for validation. Various models have been developed and used to support different studies for identifying regions associated with traits. Among these models, the fixed and random model unified probability circulating (FarmCPU) (Liu et al., 2016 ) and the multilocus mixed linear model (MLMM) (Segura et al. 2012 ) implemented in the GAPIT 3 (Wang and Zhang 2021 ) package can be used. Therefore, several factors, such as population size, linkage disequilibrium, population structure, and even the reproductive system, should be considered. These intrinsic factors in the evaluated population should be considered; in this way, we can carry out more parsimonious interpretations of the results, even if the significance threshold is not an easy choice, and to overcome this, comparisons among different models could be useful for identifying false positives. Several quantitative trait loci (QTL) for resistance and avoidance have been identified using bi-parental populations, with most loci exhibiting small to moderate effects and being located on all chromosomes except Pv10 (Schwartz and Singh 2013 ; Vasconcellos et al. 2017 ; Escobar et al. 2022 ). Recently, three GWAS targeting WM were conducted, revealing new chromosomal regions associated with resistance, including chromosome Pv10 (Campa et al. 2020 ; Escobar et al. 2022 ; Arkwazee et al. 2022 ). Additionally, Campa et al. ( 2024 ) identified three distinct genomic regions via GWAS, all located on chromosome Pv08. Regarding common bean diversity in Brazil, it has been documented that the country possesses a rich diversity of common bean varieties (Burle et al. 2010 ; Valentini et al. 2018 ). These varieties represent valuable genetic diversity resources for enhancing resistance to diseases, such as anthracnose, angular leaf spot (Perseguini et al. 2016 ; Fritsche-Neto et al. 2019 ), and WM (Carvalho et al. 2013 ; Souza et al. 2014 ; Lehner et al. 2016 ). In this way, the bean line (BL) panel, composed of landraces and varieties from different regions of Brazil and maintained at the Nupagri Research Center, serves as an important genetic resource for common bean breeding in that country. This study aimed to characterize the reactions of BL genotypes to WM via two different methods. Additionally, a GWAS was performed with the objective of identifying genomic regions for validation and consequently supporting marker-assisted selection programs aimed at enhancing resistance to this disease. Materials and Methods Vegetal accessions and SNPs obtained Ninety-three common bean accessions from the BL panel were assessed (Table S1 ). This panel was well studied by Elias et al. ( 2021 ) and comprises varieties preserved by small-scale farmers from Mato Grosso, Paraná, Sergipe, and Paraíba in Brazil (Fig. 1 ). DNA extraction, library construction, and SNP genotyping were carried out by Elias et al. ( 2021 ) using genotyping-by-sequencing (GBS) methodology, which is based on the methylation-insensitive restriction enzyme CviAII . DNA quality and quantity were determined by a NanoDrop Lite (Thermo Fisher Scientific, Waltham, USA) and electrophoresis (1% agarose gel). Additionally, a QUBIT dsDNA HS assay kit was used to quantify genomic DNA and library adapters. The library was sequenced using the Illumina HiSeq4000 platform to generate 50 bp single-end reads via the QB3 Vincent J. Coates Genomics Sequencing Laboratory at the University of California, Berkeley, USA. The sequence read alignment and SNP calling steps were conducted as previously described (Ariani et al. 2016 ). For read alignment, the reference genome sequence of the P. vulgaris G19833 accession was used (Schmutz et al. 2014 ). More information about genotyping can be found in Elias et al. ( 2021 ). A call rate threshold of 0.95 and a minimum allele frequency (MAF) greater than 0.05 were applied. A set of 28,237 SNPs, which were widely distributed across the genome, was utilized to conduct associative mapping. Screening the resistance of the BL panel to WM Two methods for white mold resistance screening were used: the seedling straw test (SST) (adapted from Petzoldt & Dickson, 1996 by Arkwazee & Myers, 2017 ) and the straw test (ST) (adapted from Petzoldt & Dickson, 1996 by Terán et al., 2006 ). To assess the reaction, the UFVS-493 white mold strain was used in both screenings. For both inoculum methods, the UFVS-493 strain was prepared using Petri plates according to the method described by Arkwazee & Myers ( 2017 ), and the inoculation was carried out at the Laboratory of Genetic Resources and Biotechnology, State University of Mato Grosso. The plants were germinated in a greenhouse in 400 cm³ plastic cups filled with autoclaved PlantMax® substrate, with two seeds per cup. For both the ST and SST screenings, three cups with two plants were used. The plants were maintained in the greenhouse until they reached the appropriate stage for each method: for the SST treatment, the plants had the first pair of fully expanded leaves, while for the ST treatment, the plants had at least two fully expanded trifoliate leaves. The plants were placed in humidity chambers made of transparent polypropylene plastic bags, maintaining a humidity level of 98% inside the bags under a temperature of 22°C and a 12-hour photoperiod. After 48 hours in the humid chamber, the plants were removed and kept in the laboratory at room temperature (25°C) for the same photoperiod. Disease severity (DS) was assessed individually in each plant using the scales proposed by Arkwazee & Myers, ( 2017 ) for SST and Terán et al. ( 2006 ) for ST at 48-hour intervals (Fig. 2 A and B). This way, the evaluations were at 3, 5 and 7 days after inoculation (DAI) for the SST method and at 3, 5, 7, and 9 DAI for the ST method. According to Campa et al. (2021), values equal to or less than 4.5 were considered resistant reactions (R), values between 4.5 and 7 were considered intermediate reactions (I), and values equal to or greater than 7 were considered susceptible (S). Basically, the disease score (DS) is a trait based on the progression of symptoms; then, the progression length of the disease was measured individually on each plant and used to obtain the relative disease progress (RDP): length of disease×100/plant height. The DS and RDP were considered to determine the AUDPC_DS and AUDPC_RDP according to the following formulas (Shaner and Finney 1977 ): AUDPC = \(\:\sum\:_{i=1}^{n}\left[\left(\frac{{Y}_{i}+{Y}_{i+1}}{2}\right)\left({T}_{i+1}+{T}_{i}\right)\right]\) where \(\:{Y}_{i}\) = the DS or RDP score at the i th observation; \(\:{T}_{i}\) = days after inoculation in the i th observation; and n is the total number of observations. The values found for the AUDPC were compared to resistant, intermediate and susceptible strains, respectively. Statistical analysis The DS and RDP, AUDPC_DS and AUDPC_RDP of each method were compared using Pearson’s correlation. The plots were created using the metan (Olivoto and Lúcio 2020 ) package. A linear mixed model was used to estimate the variance components and obtain adjusted means using restricted maximum likelihood (REML), implemented in the lm4 (Bates et al. 2015 ) package: $$\:{Y}_{ijk}=\mu\:+\:{G}_{i}+\:{B}_{j}+{GK}_{ij}+\:{\epsilon\:}_{ijk}$$ where \(\:{Y}_{ijk}\) = the phenotypic observation; \(\:\mu\:\) = the overall mean; \(\:{G}_{i}\) = the genotype random effect; \(\:{B}_{j}\) = the block, fixed effect; \(\:{GK}_{ij}\) = the repetition in the k th block, random effect; and \(\:\:{\varepsilon\:}_{ijk}\) is the residual error, a random effect. The heritability ( \(\:{H}^{2}\) ) was obtained following \(\:=\:{\sigma\:}_{g}^{2}{/(\sigma\:}_{g}^{2}+{\sigma\:}_{e}^{2}/r)\times\:100\) . where \(\:{\sigma\:}_{g}^{2}\) is the genotypic variance and \(\:{\sigma\:}_{e}^{2}\) is the residual variance. To assess the disparities between complete models incorporating the studied effects and those without them, a likelihood ratio test (LRT) was employed. The LRT test involved comparing the chi-squared value against the critical value at a 5% probability level based on the degrees of freedom. The best linear unbiased estimator (BLUE) values for each accession trait were used as the input phenotypic data for conducting the GWAS. Genome-wide association To conduct the GWAS, the mixed linear model approach implemented in the GAPIT (Wang and Zhang 2021 ) package in R software (R Development Core Team 2010 ) was used. To overcome population structure bias, the first two principal component analysis (PCA) (Q) and the VanRaden kinship matrix were employed. The GWAS analysis was performed by two algorithms, FarmCPU (Liu et al., 2016 ) and MLMM (Segura et al. 2012 ), both of which were implemented in the GAPIT 3.0 package. The Bonferroni correction was applied at 5% probability. Putative genes were identified by scanning windows of 0.1 Mb centered on the significant SNP (Garris et al. 2005 ; Patishtan et al. 2018 ; Raggi et al. 2019 ). These regions were aligned against the reference genome of P. vulgaris v1.0 (genotype G19833; Schmutz et al., 2014 ), which is accessible through the National Center for Biotechnology Information (NCBI; https://www.ncbi.nlm.nih.gov/ ). The functional annotation of the candidate genes was performed using the same reference genome. Results Correlations among the different methods in the BL panel Phenotypic evaluation of the BL panel using the ST and SST methods indicated wide variation in response to the UFVSs-493 white mold strain. Significant genotypic effects ( p < 0.01) were detected for all combinations of methods (SST or ST), traits (DS, RDP and AUPDC) and days after inoculation (DAI), indicating genetic variability in this panel (Table 1 ). Progress in identifying fewer diseases is the objective, and it highlights plant genotype resistance. Notably, the BLUP values reinforced the genotypic effect found (Figure S1 and Figure S2 ). Table 1 LRT and Wald tests were used for fixed and random effects traits, respectively. The inoculation methods used were SST (Arkwazee & Myers, 2017 ) and ST (Terán et al., 2006 ), and the disease score (DS) and relative disease progress (RDP) on the 3rd, 5th, 7th, and 9th days after inoculation (DAI) and the area under the disease progress (AUDPC) were considered. Genotype Rep:Block Block Method Variable LRT P value LRT P value Wald value P value SST DS_3DAI 30.7 < 0.0001 0.0 1.000 17.2 < 0.0001 DS_5DAI 36.1 < 0.0001 0.0 1.000 13.9 < 0.0001 DS_7DAI 18.4 < 0.0001 6.0 0.014 1.8 0.2164 RDP_3DAI 21.7 < 0.0001 0.0 1.000 12.9 < 0.0001 RDP_5DAI 38.7 < 0.0001 0.0 1.000 38.5 < 0.0001 RDP_7DAI 29.8 < 0.0001 0.0 0.857 5.1 0.04224 AUDPC_DS 36.7 < 0.0001 0.1 0.808 10.2 0.0301 AUDPC_RDP 40.6 < 0.0001 0.0 1.000 26.1 < 0.0001 ST DS_3DAI 18.9 < 0.0001 0.1 0.743 13.8 < 0.0001 DS_5DAI 45.0 < 0.0001 2.1 0.147 34.1 < 0.0001 DS_7DAI 25.0 < 0.0001 0.1 0.787 24.6 < 0.0001 DS_9DAI 30.7 < 0.0001 0.0 1.000 30.8 < 0.0001 RDP_3DAI 9.7 0.0018 0.0 1.000 31.3 < 0.0001 RDP_5DAI 15.0 0.0001 0.9 0.336 34.9 < 0.0001 RDP_7DAI 36.4 < 0.0001 0.4 0.552 11.1 0.0001 RDP_9DAI 59.8 < 0.0001 0.0 1.000 21.1 < 0.0001 AUDPC_DS 39.8 < 0.0001 0.9 0.344 32.2 < 0.0001 AUDPC_RDP 36.7 < 0.0001 0.2 0.674 25.7 < 0.0001 The evaluated methods did not have a strong positive correlation, as reported in previous studies. It is important to note that typically, only the final evaluation is considered to discriminate the response of common bean genotypes. However, even the nonadjusted disease scores did not show a correlation or relationship (Figure S3, A and B). The correlation values and the network plot are shown in Fig. 3 . The genotype score classifications between the methods used are notable. A greater correlation between different methods was shown for SST_DS_7DAI – ST_DS_5DAI (0.39). However, the smallest correlation is 0.20 for SST_DS_3DAI – ST_AUPDC_DS. On the other hand, the correlation observed within methods is considerably moderate to high, ranging from 0.23–0.97 for ST and 0.52–0.98 for SST. In this case, the significant correlation observed within methods suggests that indirect selection can be efficiently applied, and lower means can correspond to other small values of correlated traits. Figure 3 . Heatmap of Pearson correlation coefficients among genotypic values (a) and networking plot (b) of traits related to white mold reactions according to the straw test (ST , Terán et al., 2006 ) and seedling straw test (SST , Arkwazee & Myers, 2017 ) in the BL panel. The methods are indicated by the ST and SST prefixes, respectively. Disease score by DS, the relative disease progress by RDP on the 3rd, 5th, 7th, and 9th days after inoculation (DAI). The area under the disease progression curve (AUDPC) is included. The susceptibility through days after inoculation An excessive number of genotypes were identified as resistant on the 3rd day after inoculation (3 DAI) for both methods (74 for SST and 53 for ST) considering a score of 4.5 as considered for Campa et al. ( 2020 ). Consequently, 3DAI was disregarded for the identification of WM resistance. Additionally, the 3rd and 5th days after inoculation (3 DAI and 5 DAI, respectively) were discarded to indicate an intermediate response. The descriptive statistics are shown in Table 2 , and the changes in distribution on different days after inoculation are shown in Figure S4. The adjusted means were used to identify the resistance response, revealing that white mold progression progressed over time on the stems. The variance estimates ranged from 16% (ST_RDP_3DAI) to 60% (ST_RDP_9DAI), and the heritability (H 2 ) ranged from 32–64%. Table 2 Descriptive statistics using the adjusted values, genetic variance, and broad-sense heritability (H 2 ) were estimated for 10 traits in the straw test (ST – Terán et al., 2006 ) and 8 traits in the seedling straw test (SST – Arkwazee & Myers, 2017 ) in the BL panel. SST ST Variable Range Mean \(\:{\sigma\:}_{g}^{2}\) (%) H 2 (%) Range mean \(\:{\sigma\:}_{g}^{2}\) (%) H 2 (%) DS_3DAI 2.4–5.1 3.9 39 54 2.9–6 4.7 23 41 DS_5DAI 3.5–9.3 7 40 55 3.7–8.7 6.4 44 57 DS_7DAI 5.2–9.2 8.6 22 40 4.5–9.7 7.7 30 47 DS_9DAI - - - - 4.5–10 8.4 35 51 RDP_3DAI 14.4–47 29.6 28 46 5.8–34.5 16.9 16 32 RDP_5DAI 26.1–108.7 63 43 56 10.3–56.3 31.2 21 39 RDP_7DAI 45.1–106.9 91.2 32 49 14.9–94.5 48.5 41 55 RDP_9DAI - - - - 27.5–113.9 67.3 60 64 AUDPC_DS 16.5–32.6 26.5 42 56 24.2–51.1 41.3 42 55 AUDPC_RDP 133.8–357.7 246.9 43 56 84.2–421.3 244.1 41 55 Responses to the Seedling Straw Test (SST) In the SST method, SST_DS_5DAI indicates three resistant genotypes (BL15, BL18 and BL10) (Fig. 3 ). At the SST_DS_7DAI, three genotypes exhibited an intermediate response to WM (BL15, BL18 and BL98). Figure 3 . Number of genotypes identified in each response class to white mold inoculation according to the seedling straw test ( Arkwazee & Myers, 2017 ), disease score adjusted mean (DS) and relative disease progress (RDP). To select intermediate and resistance reactions, we considered only the 5th and 7th days after inoculation (DAI). Responses to the Straw Test (ST) In the ST_DS_5DAI, four genotypes were resistant (BL14, BL15, BL86 and BL220), whereas the ST_DS_7DAI indicated only one genotype (BL14); the ST_RDP_5DAI and ST_RDP_7DAI both indicated one genotype (BL227), as did the ST_DS_9DAI (BL14). Intermediate responses were identified, and the ST_DS_7DAI and ST_RDP_7DAI indicated 20 and 15 genotypes, respectively. ST_DS_9DAI indicates eight, ST_RDP_9DAI indicates four (BL67, BL74, BL96 and BL227). Regarding the responses to WM inoculation, we detected a sudden reduction in resistance in response to both methods. As one way to identify superior responses to WM resistance, the BLUP value in each method was considered (for any method, the 3DAI was dropped); in order, the genotypes that most frequently appeared among the fifteen lowest BLUP means were BL10, BL18, BL84, BL15, BL227, BL74, BL86, BL96, BL14, BL220, BL67, BL71, BL95, BL106 and BL111, which were the first fifteen ranked accessions. In this way, we identified eleven Mesoamerican and four Andean genotypes that responded strongly to WM inoculation (Table 3 ). Therefore, it represents a valuable source of resistance with significant potential for utilization in common bean breeding programs. Table 3 Descriptive statistics of selected genotypes on the BLUP ranking for Area Under Disease Progress Curve (AUDPC) in straw test (ST – Terán et al., 2006 ) and seedling straw test (SST – Arkwazee & Myers, 2017 ) considering the Disease Score (DS) and Relative Disease Progress (RDP) in the BL panel. Access Pool SST_AUDPC_RDP SST_AUDPC_DS ST_AUDPC_RDP ST_AUDPC_DS BL10 M 1 159.3 18.0 156.6 34.0 BL14 M 253.7 27.7 173.5 24.2 BL15 A 2 133.8 16.5 206.5 31.4 BL18 A 136.6 16.8 147.2 29.8 BL67 M 230.1 25.8 141.3 31.8 BL71 M 220.1 27.1 139.6 34.1 BL74 A 300.4 28.8 131.2 33.7 BL84 M 217.3 23.8 151.6 34.8 BL86 M 223.6 25.9 161.6 30.2 BL95 M 189.8 22.7 200.2 34.6 BL96 M 222.7 25.2 135.3 34.2 BL106 M 295.9 29.2 157.4 34.9 BL111 M 217.7 24.5 203.1 39.5 BL220 A 299.8 30.1 149.2 31.4 BL227 M 209.3 22.9 84.2 33.1 Mean of selected 220.7 24.3 155.9 32.8 Overall average 246.9 26.5 244.1 41.3 Overall maximum 357.7 32.6 421.3 51.1 1,2 Mesoamerican and Andean genetic pool, respectively. GWAS and candidate gene annotation The BL panel analyzed in this study comprised 28,237 SNPs distributed across the 11 common bean chromosomes. As reported by Elias et al. ( 2021 ), two clusters were identified in the Andean and Mesoamerican genetic pools, which is consistent with previous studies that highlighted two major gene pools in common bean (Debouck et al. 1993 ; Freyre et al. 1996 ; Kwak and Gepts 2009 ; Bitocchi et al. 2012 ; Kwak et al. 2012 ; Desiderio et al. 2013 ; Schmutz et al. 2014 ). The correlation values among traits can have implications for genomic association studies, as highly correlated traits may share common genomic regions. The low correlation values observed between these methods may explain why only the SST method was observed in the GWAS results (Table 4 ), which can be a combination of better discrimination between genotypes and consistency in the scores. Table 4 QTNs detected through GWAS analysis, traits (combination among methods, measurement types and days after inoculation), significant SNPs, positions, MAFs, and p values. Model Trait $ SNP Chr Position (Mb) MAF p value FarmCPU SST_DS_7DAI S01_31745487 1 31745487 0.26 1.09×10 − 10 MLMM SST_DS_7DAI S01_31745487 1 31745487 0.26 1.74×10 − 11 MLMM SST_DS_7DAI S01_36888189 1 36888189 0.30 4.84×10 − 07 FarmCPU SST_AUDPC_RDP S02_2509080 2 2509080 0.27 9.43×10 − 07 FarmCPU SST_AUDPC_RDP S02_2580500 2 2580500 0.27 9.43×10 − 07 FarmCPU SST_AUDPC_RDP S02_2582044 2 2582044 0.27 9.43×10 − 07 MLMM SST_AUDPC_RDP S02_2580500 2 2580500 0.27 1.28×10 − 06 FarmCPU SST_DS_7DAI S02_2509080 2 2509080 0.27 1.20×10 − 06 FarmCPU SST_DS_7DAI S02_2580500 2 2580500 0.27 1.20×10 − 06 FarmCPU SST_DS_7DAI S02_2582044 2 2582044 0.27 1.20×10 − 06 FarmCPU SST_RDP_5DAI S02_2509080 2 2509080 0.27 5.12×10 − 07 FarmCPU SST_RDP_5DAI S02_2580500 2 2580500 0.27 5.12×10 − 07 FarmCPU SST_RDP_5DAI S02_2582044 2 2582044 0.27 5.12×10 − 07 MLMM SST_RDP_5DAI S02_2509080 2 2509080 0.27 1.12×10 − 10 FarmCPU SST_DS_7DAI S03_36436583 3 36436583 0.26 1.45×10 − 06 MLMM SST_DS_7DAI S03_30648244 3 30648244 0.26 1.92×10 − 05 MLMM SST_DS_7DAI S03_31040939 3 31040939 0.25 8.46×10 − 05 MLMM SST_DS_7DAI S03_36436583 3 36436583 0.26 6.46×10 − 09 MLMM SST_DS_7DAI S04_36532214 4 36532214 0.25 8.46×10 − 05 MLMM SST_RDP_5DAI S05_1926309 5 1926309 0.29 8.79×10 − 09 MLMM SST_AUDPC_RDP S11_44416290 11 44416290 0.29 1.96×10 − 05 MLMM SST_RDP_5DAI S11_44111449 11 44111449 0.29 1.67×10 − 07 1, 2 , reference allele; and alternative allele; $ Method and trait combination. The p values for significant SNPs according to the MLMM model ranged from 1.92×10 − 05 (SST_DS_7DAI) to 1.74×10 − 11 (SST_DS_7DAI). The q-q plots are shown in Figure S5. The GWAS models revealed 12 significantly associated SNPs located on chromosomes Pv01, Pv02, Pv03, Pv04, Pv05 and Pv11. For gene annotation, we considered only those indicated by both models or more than one trait; SST_RDP_5DAI, SST_DS_7DAI and SST_AUDPC_RDP were the traits that showed significant SNPs in common, as shown in the Manhattan plots (Fig. 5). Additionally, twelve stress response-related genes were annotated as potential candidates (Table S2 ). The most significant marker was identified by MLMM on Pv01 (1.74×10 − 11 ) at the SST_DS_7DAI. The SNPs S02_2580500, S02_2582044 and S02_2509080 were associated with SST_RDP_5DAI, SST_DS_7DAI and SST_AUDPC_RDP, which are located on chromosome Pv02; S01_31745487 and S01_36888189 were associated with SST_DS_7DAI on Pv01. However, S03_30648244, S03_31040939 and S03_36436583, located on Pv03, were also associated with the SST_DS7DAI. On Pv04 and Pv05, S04_36532214 and S05_1926309, respectively, are indicated. The SNPs S11_44416290 and S11_44111449 located on Pv11 are indicated by SST_AUDPC_RDP and SST_RDP_5DAI, respectively. Except for the significant SNP on Pv01, all the identified regions corresponded to previously mapped quantitative trait loci (QTLs) associated with white mold resistance. On Pv01, the SNP S01_31745487 is located in the PHAVU_001G113400g intragenic region and is related to cell cycle control proteins and, previously, to responses to stresses, such as temperature and pathogen infections (Bao and Hua 2015 ; Qi and Zhang 2020 ). On Pv02, the block indicated by all the identified SNPs appears to correspond to haplotypes of the QTLs WM2.2 AN (field condition), WM2.2 BV (ST) (Miklas, 2007 ), WM2.2 R31 (field condition) (Soule et al., 2011 ; Vasconcellos et al., 2017 ), WM2.2 R31 (ST), and WM2.2 Z0726 − 9 (ST), the latter being reported in a population derived from the cross PS02-029C-20 × AN-37, known as the Z0726-9 population. Together, these QTLs spanned an extension of 22.87 Mb, ranging from 3.54 to 26.41 Mb on Pv02. Among the significant SNPs on Pv02, some were in intergenic regions. For instance, S02_2509080 ( PHAVU_002G024100g ) is associated with the PPR (pentatricopeptide repeat) family of proteins, which are known to be involved in various biological processes related to stress responses (Cushing et al. 2005 ; Saha et al. 2007 ; Yuan and Liu 2012 ; Barkan and Small 2014 ). Similarly, S02_2582044, located in an intragenic region of PHAVU_002G024600g , is associated with histone-lysine N-methyltransferase (HKMT)ases, which play important roles in histone modifications. This protein family has been implicated in gametophytic development, flowering and morphology, and responses to various stresses (Kim et al., 2015 ; Yan et al., 2019 ; Zhou et al., 2020 ). On chromosome Pv03, the SNP S03_36436583 putatively corresponds to the same region of QTLs WM3.1 AN (Miklas 2007 ), WM3.1 AP (Hoyos-Villegas et al. 2015 ), and WM3.1 XC (Pérez-Vega et al., 2012 ). However, Vasconcellos et al. ( 2017 ) determined through a meta-QTL study that WM3.1 XC and WM3.1 occupy the same physical position, with an extension spanning from 34.33 to 48.32 Mb. Discussion In this study, we evaluated the reaction of common bean accessions to the S. sclerotiorum strain UFVSs-493 using two inoculation methods. Different levels of resistance were identified in a diverse and well tropical-adapted panel. According to previous studies, the majority of WM resistance sources originate from the Andean region or secondary gene pools, such as P. coccineus (Vasconcellos et al., 2017 ). In our study, we identified promising Andean and Mesoamerican accessions with significant levels of resistance to WM that were well adapted to Brazilian climatic conditions. Understanding the complexity of resistance to WM can contribute to more sustainable common bean production; to verify the effect of this method, we evaluated the BL panel using two different methods in a controlled environment. In accordance with previous studies (Miklas et al. 2001 ; Miklas et al. 2004 ; Vasconcellos et al. 2017 ; Campa et al. 2020 ), our findings suggest that resistance to white mold has low to moderate heritability. However, the significant correlations observed between the methods (Fig. 2 ) indicate that the identification of resistant genotypes may be influenced by the inoculation method employed and the intrinsic characteristics of the evaluated panel. For this reason, it may be important to use different methods to confirm the results obtained. In the BL panel, based on the BLUP values, we selected eleven Mesoamerican and four Andean genotypes that responded strongly to WM inoculation. These accessions are potentially valuable sources of resistance to WM, and if validated, those regions could be useful in a breeding program for resistance improvement. Resistance to white mold has been recognized as a complex trait influenced by many morphological characteristics (Ando, 2007 ; Vansconcellos et al., 2017), and previous studies have mapped QTLs (or QTNs) associated with this trait based on field evaluations or controlled inoculations (Arkwazee et al., 2022 ; Campa et al., 2020 ). We identified twelve SNPs on chromosomes Pv01, Pv02, Pv03, Pv04, Pv05 and Pv11, but we considered only three regions significantly identified on chromosomes Pv01, Pv02 and Pv03 (Fig. 3 ); this way, twelve genes related to biotic stresses were annotated (Table S2 ). Among the annotated genes, PHAVU_002G024100g (pentatricopeptide repeat - PPR), indicated by S02_2509080 on Pv02, is known to be involved in posttranscriptional modifications and is expressed in different stages during salt and drought stress (Chen et al. 2018 ). It also regulates the homeostasis of reactive oxygen species in mitochondria during abiotic and biotic stress responses (Laluk et al. 2011 ; Xing et al. 2018 ). Protein kinases, which act in signaling pathways involving cell-surface receptors and other pathways, have been extensively studied in the context of plant immune functions. These proteins were first reported in Arabidopsis thaliana L. (He et al. 1998 ) and have since been associated with resistance in various plant‒pathogen interactions, such as resistance to Pseudomonas syringae pv. tomato (Rosli et al. 2013 ), corn resistance to leaf rust and smut disease (Hurni et al. 2015 ; Zhang et al. 2017 ; Yang et al. 2021 ), and wheat resistance to Zymoseptoria tritici (Saintenac et al. 2018 ). In our study, the gene PHAVU_002G023400g (wall-associated receptor kinase) was also found to be related to protein kinases. PHAVU_002G024400g (pathogenesis-related 1 protein - PR) encodes a class of proteins that accumulate in response to abiotic and biotic stresses, protecting tissues from damage. Its homologous PnPR-like gene has been shown to confer greater resistance to Fusarium solani and other pathogens in transgenic Nicotiana tabacum plants ; Boccardo et al., 2019 ). The hypersensitive response (HR) is a rapid programmed cell death that occurs at the site of pathogen entry. It is associated with restricted growth of the pathogen in infected areas and resistance to the disease (Morel and Dangl 1997 ; Dangl and Jones 2001 ; Park 2005 ; Balint-Kurti 2019 ). Additionally, the genes PHAVU_002G022800g and PHAVU_002G022900g are related to the cytochrome P450 superfamily, which encodes enzymes involved in various plant functions, including responses to biotic stresses (Pandian et al. 2020 ; Yang et al. 2021 ). This gene family is also associated with hypersensitivity responses (Kim et al., 2006) and the rapid induction of cell death in response to Pseudomonas syringae infections in Arabidopsis (Godiard et al. 1998 ). Conclusions In this study, we evaluated the response to a Brazilian strain of WM and identified fifteen accessions that exhibited a high level of physiological resistance. Among these accessions, eleven belong to the Mesoamerican group and four to the Mesoamerican group, all of which have been traditionally cultivated and are well adapted to tropical climatic conditions. Additionally, employing a GWAS approach, we identified regions on chromosomes Pv02 and Pv03 that are associated with previously reported QTLs for resistance to WM. These findings suggest the presence of potential haplotypes and reveal 12 putative genes associated with physiological resistance, which can serve as valuable targets for future validation studies. Supplemental Material Figure S1 . BLUP values of the seedling straw test (SST). Figure S2 . BLUP values of the Straw test (ST). Figure S3. Heatmap of Pearson correlation coefficients using the no adjusted disease score (A) and network plot (B) of traits related to white mold reactions according to the straw test (ST, Terán et al., 2006 ) and seedling straw test (SST, Arkwazee & Myers, 2017 ) in the BL panel. Figure S4. Histogram of the adjusted values of the evaluated traits in the BL panel. Figure S5. Q‒Q plots of p values obtained for traits considered for gene annotation. Table S1 . Genotypes screened for white mold resistance. Accession, gene pool, common name, type, state and city of origin, and altitude of the city. Table S2 . Gene annotation, SNP nearest of gene, strand and gene position considering two different reference genomes. Declarations Competing Interests The authors declare no conflicts of interest. Funding This work was supported by National Council for Scientific and Technological Development (CNPq) grant number Proc. 437310/2018-3 and the Brazilian Federal Agency for Support and Evaluation of Graduate Education (CAPES) through scholarship grants. Author Contribution All authors contributed to the study conception and design. GRS wrote the first draft of the manuscript, and all the authors commented on previous versions. All the authors have read and approved the final manuscript. Acknowledgement We deeply thank Eduardo Seiti Gomide Mizubuti, Titular Professor at Viçosa Federal University (UFV) who generously provided us with the isolate UFVS-493. References Ando K (2007) Manipulation of plant architecture to enhance crop disease control. CAB Rev Perspect Agric Vet Sci Nutr Nat Resour 2(026). https://doi.org/10.1079/PAVSNNR20072026 Antonio RP, Santos JB, Souza TP, Carneiro FF (2008) Genetic control of the resistance of common beans to white mold using the reaction to oxalic acid. Genet Mol Res 7(3):733–740. https://doi.org/10.4238/vol7-3gmr466 Ariani A, Berny Mier y Teran JC, Gepts P (2016) Genome-wide identification of SNPs and copy number variation in common bean (Phaseolus vulgaris L.) using genotyping-by-sequencing (GBS). Mol Breed 36(7):87. https://doi.org/10.1007/s11032-016-0512-9 Arkwazee H, Myers JR (2017) Seedling straw test: a rapid and resource-efficiente method for evaluationg white mold resistance. Annu Rep Bean Improv Coop 60:3 Arkwazee HA, Wallace LT, Hart JP, Griffiths PD, Myers JR (2022) Genome-Wide Association Study (GWAS) of White Mold Resistance in Snap Bean. Genes 13(12):2297. https://doi.org/10.3390/genes13122297 Balint-Kurti P (2019) The plant hypersensitive response: concepts, control and consequences. Mol Plant Pathol:mpp.12821. https://doi.org/10.1111/mpp.12821 Bao Z, Hua J (2015) Linking the Cell Cycle with Innate Immunity in Arabidopsis. Mol Plant 8(7):980–982. https://doi.org/10.1016/j.molp.2015.03.013 Barkan A, Small I (2014) Pentatricopeptide Repeat Proteins in Plants. Annu Rev Plant Biol 65(1):415–442. https://doi.org/10.1146/annurev-arplant-050213-040159 Bates D, Mächler M, Bolker B, Walker S (2015) Fitting Linear Mixed-Effects Models Using lme4. J Stat Softw 67(1). https://doi.org/10.18637/jss.v067.i01 Bitocchi E, Bellucci E, Giardini A, Rau D, Rodriguez M, Biagetti E, Santilocchi R, Spagnoletti Zeuli P, Gioia T, Logozzo G, Attene G, Nanni L, Papa R (2013) Molecular analysis of the parallel domestication of the common bean ( P haseolus vulgaris ) in Mesoamerica and the Andes. New Phytol 197(1):300–313. https://doi.org/10.1111/j.1469-8137.2012.04377.x Bitocchi E, Nanni L, Bellucci E, Rossi M, Giardini A, Zeuli PS, Logozzo G, Stougaard J, McClean P, Attene G, Papa R (2012) Mesoamerican origin of the common bean ( Phaseolus vulgaris L.) is revealed by sequence data. Proc Natl Acad Sci 109(14). https://doi.org/10.1073/pnas.1108973109 Boccardo NA, Segretin ME, Hernandez I, Mirkin FG, Chacón O, Lopez Y, Borrás-Hidalgo O, Bravo-Almonacid FF (2019) Expression of pathogenesis-related proteins in transplastomic tobacco plants confers resistance to filamentous pathogens under field trials. Sci Rep 9(1):2791. https://doi.org/10.1038/s41598-019-39568-6 Burle ML, Fonseca JR, Kami JA, Gepts P (2010) Microsatellite diversity and genetic structure among common bean (Phaseolus vulgaris L.) landraces in Brazil, a secondary center of diversity. Theor Appl Genet 121(5):801–813. https://doi.org/10.1007/s00122-010-1350-5 Campa A, García-Fernández C, Ferreira JJ (2020) Genome-Wide Association Study (GWAS) for Resistance to Sclerotinia sclerotiorum in Common Bean. Genes 11(12):1496. https://doi.org/10.3390/genes11121496 Campa A, Geffroy V, Bitocchi E, Noly A, Papa R, Ferreira JJ (2024) Screening for resistance to four fungal diseases and associated genomic regions in a snap bean diversity panel. Front Plant Sci 15. https://doi.org/10.3389/fpls.2024.1386877 Carvalho RSB, Lima IA, Alves FC, Santos JB dos (2013) Selection of carioca common bean progenies resistant to white mold. Crop Breed Appl Biotechnol 13(3):172–177. https://doi.org/10.1590/S1984-70332013000300004 Chen G, Zou Y, Hu J, Ding Y (2018) Genome-wide analysis of the rice PPR gene family and their expression profiles under different stress treatments. BMC Genomics 19(1):720. https://doi.org/10.1186/s12864-018-5088-9 Cortes A (2013) On the Origin of the Common Bean (Phaseolus vulgaris L.). Am J Plant Sci 4:1998–2000. https://doi.org/10.4236/ajps.2013.410248 . Cushing DA, Forsthoefel NR, Gestaut DR, Vernon DM (2005) Arabidopsis emb175 and other ppr knockout mutants reveal essential roles for pentatricopeptide repeat (PPR) proteins in plant embryogenesis. Planta 221(3):424–436. https://doi.org/10.1007/s00425-004-1452-x Dangl JL, Jones JDG (2001) Plant pathogens and integrated defence responses to infection. Nature 411(6839):826–833. https://doi.org/10.1038/35081161 Debouck DG, Toro O, Paredes OM, Johnson WC, Gepts P (1993) Genetic diversity and ecological distribution ofPhaseolus vulgaris (Fabaceae) in northwestern South America. Econ Bot 47(4):408–423. https://doi.org/10.1007/BF02907356 Desiderio F, Bitocchi E, Bellucci E, Rau D, Rodriguez M, Attene G, Papa R, Nanni L (2013) Chloroplast Microsatellite Diversity in Phaseolus vulgaris. Front Plant Sci 3. https://doi.org/10.3389/fpls.2012.00312 Durán LA, Blair MW, Giraldo MC, Macchiavelli R, Prophete E, Nin JC, Beaver JS (2005) Morphological and Molecular Characterization of Common Bean Landraces and Cultivars from the Caribbean. Crop Sci 45(4):1320–1328. https://doi.org/10.2135/cropsci2004.0501 Elias JCF, Gonçalves-Vidigal MC, Ariani A, Valentini G, Martiniano-Souza M da C, Vaz Bisneta M, Gepts P (2021) Genome-Environment Association Analysis for Bio-Climatic Variables in Common Bean (Phaseolus vulgaris L.) from Brazil. Plants 10(8):1572. https://doi.org/10.3390/plants10081572 Ender M, Kelly JD (2005) Identification of QTL Associated with White Mold Resistance in Common Bean. Crop Sci 45(6):2482–2490. https://doi.org/10.2135/cropsci2005.0064 Escobar E, Oladzad A, Simons K, Miklas P, Lee RK, Schroder S, Bandillo N, Wunsch M, McClean PE, Osorno JM (2022) New genomic regions associated with white mold resistance in dry bean using a MAGIC population. Plant Genome 15(1). https://doi.org/10.1002/tpg2.20190 Freyre R, Ríos R, Guzmán L, Debouck DG, Gepts P (1996) Ecogeographic distribution ofPhaseolus spp. (Fabaceae) in Bolivia. Econ Bot 50(2):195–215. https://doi.org/10.1007/BF02861451 Fritsche-Neto R, Souza TLPO de, Pereira HS, Faria LC de, Melo LC, Novaes E, Brum IJB, Jannink J-L (2019) Association mapping in common bean revealed regions associated with Anthracnose and Angular Leaf Spot resistance. Sci Agric 76(4):321–327. https://doi.org/10.1590/1678-992x-2017-0306 Garris AJ, Tai TH, Coburn J, Kresovich S, McCouch S (2005) Genetic Structure and Diversity in Oryza sativa L. Genetics 169(3):1631–1638. https://doi.org/10.1534/genetics.104.035642 Gepts P, Bliss FA (1986) Phaseolin variability among wild and cultivated common beans (Phaseolus vulgaris) from Colombia. Econ Bot 40:469–478. https://doi.org/10.1007/BF02859660 Godiard L, Sauviac L, Dalbin N, Liaubet L, Callard D, Czernic P, Marco Y (1998) CYP76C2 , an Arabidopsis thaliana cytochrome P450 gene expressed during hypersensitive and developmental cell death. FEBS Lett 438(3):245–249. https://doi.org/10.1016/S0014-5793(98)01309-X He Z, He D, Kohorn BD (1998) Requirement for the induced expression of a cell wall associated receptor kinase for survival during the pathogen response. Plant J 14(1):55–63. https://doi.org/10.1046/j.1365-313X.1998.00092.x Hoyos-Villegas V, Mkwaila W, Cregan PB, Kelly JD (2015) Quantitative Trait Loci Analysis of White Mold Avoidance in Pinto Bean. Crop Sci 55(5):2116–2129. https://doi.org/10.2135/cropsci2015.02.0106 Hurni S, Scheuermann D, Krattinger SG, Kessel B, Wicker T, Herren G, Fitze MN, Breen J, Presterl T, Ouzunova M, Keller B (2015) The maize disease resistance gene Htn1 against northern corn leaf blight encodes a wall-associated receptor-like kinase. Proc Natl Acad Sci 112(28):8780–8785. https://doi.org/10.1073/pnas.1502522112 Kim J-M, Sasaki T, Ueda M, Sako K, Seki M (2015) Chromatin changes in response to drought, salinity, heat, and cold stresses in plants. Front Plant Sci 6. https://doi.org/10.3389/fpls.2015.00114 Kolkman JM, Kelly JD (2002) Agronomic Traits Affecting Resistance to White Mold in Common Bean. Crop Sci 42(3):693–699. https://doi.org/10.2135/cropsci2002.6930 Kwak M, Gepts P (2009) Structure of genetic diversity in the two major gene pools of common bean (Phaseolus vulgaris L., Fabaceae). Theor Appl Genet 118(5):979–992. https://doi.org/10.1007/s00122-008-0955-4 Kwak M, Toro O, Debouck DG, Gepts P (2012) Multiple origins of the determinate growth habit in domesticated common bean (Phaseolus vulgaris). Ann Bot 110(8):1573–1580. https://doi.org/10.1093/aob/mcs207 Laluk K, AbuQamar S, Mengiste T (2011) The Arabidopsis Mitochondria-Localized Pentatricopeptide Repeat Protein PGN Functions in Defense against Necrotrophic Fungi and Abiotic Stress Tolerance. Plant Physiol 156(4):2053–2068. https://doi.org/10.1104/pp.111.177501 Lehner M da S, Paula Júnior TJ de, Vieira RF, Lima RC, Soares B de A, Silva RA (2016) Reaction of sources of resistance to white mold to microsatellite haplotypes of Sclerotinia sclerotiorum. Sci Agric 73(2):184–188. https://doi.org/10.1590/0103-9016-2015-0085 Li S, Wang Z, Tang B, Zheng L, Chen H, Cui X, Ge F, Liu D (2021) A Pathogenesis-Related Protein-Like Gene Is Involved in the Panax notoginseng Defense Response to the Root Rot Pathogen. Front Plant Sci 11:2217. https://doi.org/10.3389/fpls.2020.610176 Liu X, Huang M, Fan B, Buckler ES, Zhang Z (2016) Iterative Usage of Fixed and Random Effect Models for Powerful and Efficient Genome-Wide Association Studies. PLOS Genet 12(2):e1005767. https://doi.org/10.1371/journal.pgen.1005767 Miklas PN (2007) Marker-Assisted Backcrossing QTL for Partial Resistance to Sclerotinia White Mold in Dry Bean. Crop Sci 47(3):935–942. https://doi.org/10.2135/cropsci2006.08.0525 Miklas PN, Hauf DC, Henson RA, Grafton KF (2004) Inheritance of ICA Bunsi-Derived Resistance to White Mold in a Navy × Pinto Bean Cross. Crop Sci 44(5):1584–1588. https://doi.org/10.2135/cropsci2004.1584 Miklas PN, Johnson WC, Delorme R, Gepts P (2001) QTL Conditioning Physiological Resistance and Avoidance to White Mold in Dry Bean. Crop Sci 41(2):309–315. https://doi.org/10.2135/cropsci2001.412309x Miklas PN, Porter LD, Kelly JD, Myers JR (2013) Characterization of white mold disease avoidance in common bean. Eur J Plant Pathol 135(3):525–543. https://doi.org/10.1007/s10658-012-0153-8 Morel J-B, Dangl JL (1997) The hypersensitive response and the induction of cell death in plants. Cell Death Differ 4(8):671–683. https://doi.org/10.1038/sj.cdd.4400309 Oladzadabbasabadi A, Mamidi S, Miklas PN, Lee R, McClean P (2019) Linked candidate genes of different functions for white mold resistance in common bean (Phaseolus vulgaris. L) are identified by QTL-based pooled sequencing. In Review Olivoto T, Lúcio AD (2020) metan: An R package for multi-environment trial analysis. Methods Ecol Evol 11(6):783–789. https://doi.org/10.1111/2041-210X.13384 Pandian BA, Sathishraj R, Djanaguiraman M, Prasad PVV, Jugulam M (2020) Role of Cytochrome P450 Enzymes in Plant Stress Response. Antioxidants 9(5):454. https://doi.org/10.3390/antiox9050454 Park J-M (2005) The Hypersensitive Response. A Cell Death during Disease Resistance. Plant Pathol J 21(2):99–101. https://doi.org/10.5423/PPJ.2005.21.2.099 Patishtan J, Hartley TN, Fonseca de Carvalho R, Maathuis FJM (2018) Genome-wide association studies to identify rice salt-tolerance markers: Salinity GWAS. Plant Cell Environ 41(5):970–982. https://doi.org/10.1111/pce.12975 Pérez-Vega E, Pascual A, Campa A, Giraldez R, Miklas PN, Ferreira JJ (2012) Mapping quantitative trait loci conferring partial physiological resistance to white mold in the common bean RIL population Xana × Cornell 49242. Mol Breed 29(1):31–41. https://doi.org/10.1007/s11032-010-9522-1 Perseguini JMKC, Oblessuc PR, Rosa JRBF, Gomes KA, Chiorato AF, Carbonell SAM, Garcia AAF, Vianello RP, Benchimol-Reis LL (2016) Genome-Wide Association Studies of Anthracnose and Angular Leaf Spot Resistance in Common Bean (Phaseolus vulgaris L.). PLOS ONE 11(3):e0150506. https://doi.org/10.1371/journal.pone.0150506 Petzoldt PN, Dickson MH (1996) Straw test for resistance to white mold in beans. Annu Report Bean Improv Coop 39:142–143 Qi F, Zhang F (2020) Cell Cycle Regulation in the Plant Response to Stress. Front Plant Sci 10:1765. https://doi.org/10.3389/fpls.2019.01765 R Development Core Team (2010) a language and environment for statistical computing: reference index. R Foundation for Statistical Computing, Vienna Raggi L, Caproni L, Carboni A, Negri V (2019) Genome-Wide Association Study Reveals Candidate Genes for Flowering Time Variation in Common Bean (Phaseolus vulgaris L.). Front Plant Sci 10:962. https://doi.org/10.3389/fpls.2019.00962 Rosli HG, Zheng Y, Pombo MA, Zhong S, Bombarely A, Fei Z, Collmer A, Martin GB (2013) Transcriptomics-based screen for genes induced by flagellin and repressed by pathogen effectors identifies a cell wall-associated kinase involved in plant immunity. Genome Biol 14(12):R139. https://doi.org/10.1186/gb-2013-14-12-r139 Saha D, Prasad AM, Srinivasan R (2007) Pentatricopeptide repeat proteins and their emerging roles in plants. Plant Physiol Biochem 45(8):521–534. https://doi.org/10.1016/j.plaphy.2007.03.026 Saintenac C, Lee W-S, Cambon F, Rudd JJ, King RC, Marande W, Powers SJ, Bergès H, Phillips AL, Uauy C, Hammond-Kosack KE, Langin T, Kanyuka K (2018) Wheat receptor-kinase-like protein Stb6 controls gene-for-gene resistance to fungal pathogen Zymoseptoria tritici. Nat Genet 50(3):368–374. https://doi.org/10.1038/s41588-018-0051-x Scherer A, Christensen GB (2016) Concepts and Relevance of Genome-Wide Association Studies. Sci Prog 99(1):59–67. https://doi.org/10.3184/003685016X14558068452913 Schmutz J, McClean PE, Mamidi S, Wu GA, Cannon SB, Grimwood J, Jenkins J, Shu S, Song Q, Chavarro C, Torres-Torres M, Geffroy V, Moghaddam SM, Gao D, Abernathy B, Barry K, Blair M, Brick MA, Chovatia M, Gepts P, Goodstein DM, Gonzales M, Hellsten U, Hyten DL, Jia G, Kelly JD, Kudrna D, Lee R, Richard MMS, Miklas PN, Osorno JM, Rodrigues J, Thareau V, Urrea CA, Wang M, Yu Y, Zhang M, Wing RA, Cregan PB, Rokhsar DS, Jackson SA (2014) A reference genome for common bean and genome-wide analysis of dual domestications. Nat Genet 46(7):707–713. https://doi.org/10.1038/ng.3008 Schwartz HF, Singh SP (2013) Breeding Common Bean for Resistance to White Mold: A Review. Crop Sci 53(5):1832–1844. https://doi.org/10.2135/cropsci2013.02.0081 Segura V, Vilhjálmsson BJ, Platt A, Korte A, Seren Ü, Long Q, Nordborg M (2012) An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations. Nat Genet 44(7):825–830. https://doi.org/10.1038/ng.2314 Shaner G, Finney RE (1977) The Effect of Nitrogen Fertilization on the Expression of Slow-Mildewing Resistance in Knox Wheat. Phytopathology 77(8):1051. https://doi.org/10.1094/Phyto-67-1051 Singh SP, Gutiérrez JA, Molina A, Urrea C, Gepts P (1991) Genetic Diversity in Cultivated Common Bean: II. Marker-Based Analysis of Morphological and Agronomic Traits. Crop Sci 31(1):23–29. https://doi.org/10.2135/cropsci1991.0011183X003100010005x Singh SP, Schwartz HF, Terán H, Viteri D, Otto K (2014) Pyramiding white mould resistance between and within common bean gene pools. Can J Plant Sci 94(5):947–954. https://doi.org/10.4141/cjps2013-321 Soule M, Porter L, Medina J, Santana GP, Blair MW, Miklas PN (2011) Comparative QTL Map for White Mold Resistance in Common Bean, and Characterization of Partial Resistance in Dry Bean Lines VA19 and I9365-3. Crop Sci 51(1):123–139. https://doi.org/10.2135/cropsci2010.06.0356 Souza DA de, Pereira FAC, Dias JA, Leite ME, Santos JB dos (2014) Reaction of common bean progenies to white mold derived from recurrent selection. Ciênc Rural 44(4):583–587. https://doi.org/10.1590/S0103-84782014000400001 Terán H, Lema M, Schwartz HF, Duncan R, Gilbertson R, Singh SP (2006) Modified Petzoldt and Dickson scale for white mold rating of common bean.:2 Valentini G, Gonçalves-Vidigal MC, Elias JCF, Moiana LD, Mindo NNA (2018) Population Structure and Genetic Diversity of Common Bean Accessions from Brazil. Plant Mol Biol Report 36(5–6):897–906. https://doi.org/10.1007/s11105-018-1129-4 Vasconcellos RCC, Oraguzie OB, Soler A, Arkwazee H, Myers JR, Ferreira JJ, Song Q, McClean P, Miklas PN (2017) Meta-QTL for resistance to white mold in common bean. PLOS ONE 12(2):e0171685. https://doi.org/10.1371/journal.pone.0171685 Wang J, Zhang Z (2021) GAPIT Version 3: Boosting Power and Accuracy for Genomic Association and Prediction. Genomics Proteomics Bioinformatics 19(4):629–640. https://doi.org/10.1016/j.gpb.2021.08.005 Xing H, Fu X, Yang C, Tang X, Guo L, Li C, Xu C, Luo K (2018) Genome-wide investigation of pentatricopeptide repeat gene family in poplar and their expression analysis in response to biotic and abiotic stresses. Sci Rep 8(1):2817. https://doi.org/10.1038/s41598-018-21269-1 Yan H, Liu Y, Zhang K, Song J, Xu W, Su Z (2019) Chromatin State-Based Analysis of Epigenetic H3K4me3 Marks of Arabidopsis in Response to Dark Stress. Front Genet 10:306. https://doi.org/10.3389/fgene.2019.00306 Yang P, Scheuermann D, Kessel B, Koller T, Greenwood JR, Hurni S, Herren G, Zhou S, Marande W, Wicker T, Krattinger SG, Ouzunova M, Keller B (2021) Alleles of a wall-associated kinase gene account for three of the major northern corn leaf blight resistance loci in maize. Plant J 106(2):526–535. https://doi.org/10.1111/tpj.15183 Yuan H, Liu D (2012) Functional disruption of the pentatricopeptide protein SLG1 affects mitochondrial RNA editing, plant development, and responses to abiotic stresses in Arabidopsis: PPR protein SLG1 and mitochondrial RNA editing. Plant J 70(3):432–444. https://doi.org/10.1111/j.1365-313X.2011.04883.x Zhang N, Zhang B, Zuo W, Xing Y, Konlasuk S, Tan G, Zhang Q, Ye J, Xu M (2017) Cytological and Molecular Characterization of ZmWAK -Mediated Head-Smut Resistance in Maize. Mol Plant-Microbe Interactions® 30(6):455–465. https://doi.org/10.1094/MPMI-11-16-0238-R Zhou H, Liu Y, Liang Y, Zhou D, Li S, Lin S, Dong H, Huang L (2020) The function of histone lysine methylation related SET domain group proteins in plants. Protein Sci 29(5):1120–1137. https://doi.org/10.1002/pro.3849 Statements & declarations Additional Declarations No competing interests reported. Supplementary Files SuplementalFiguresPartialwhitemoldresistanceinaBrazilianadaptedcommonbeanpanel.docx SupplementalTablesPartialwhitemoldresistanceinaBrazilianadaptedcommonbeanpanel.xlsx Cite Share Download PDF Status: Published Journal Publication published 10 Oct, 2024 Read the published version in Genetic Resources and Crop Evolution → Version 1 posted Editorial decision: Revision requested 30 Aug, 2024 Reviews received at journal 21 Aug, 2024 Reviews received at journal 21 Aug, 2024 Reviewers agreed at journal 20 Aug, 2024 Reviewers agreed at journal 20 Aug, 2024 Reviewers agreed at journal 19 Aug, 2024 Reviewers invited by journal 16 Aug, 2024 Editor assigned by journal 16 Aug, 2024 Submission checks completed at journal 16 Aug, 2024 First submitted to journal 15 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4921482","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":347239683,"identity":"2559ff37-8ad4-4303-8f37-0cc4bee24b62","order_by":0,"name":"Givanildo Rodrigues Silva","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYLACHgYJEMX4AEQakKKF2YAULWDAJkGUFnkH3ocP3u6xsNtwvP1ZNc+fOwzm0gfwazE8wG5sOOeZRPKGM2fMbvO2PWOw7EsgoKWBjU2a54BEssGNHLbbvA2HGQzOEHAYQsv958+Kef4QoUWeAaLFzuAGgxkzDxsRWgyY2ZgN5xyQSJA8k2MsObftGY9lDyFb2tsYH7w5UGfPd/z4ww9v/tyRM+chZMthCJ3YAKEPENIAtAWq1B7KP0BQxygYBaNgFIw8AAC92T93On7z9QAAAABJRU5ErkJggg==","orcid":"","institution":"University of Mato Grosso State “Carlos Alberto Ryes Maldonado”","correspondingAuthor":true,"prefix":"","firstName":"Givanildo","middleName":"Rodrigues","lastName":"Silva","suffix":""},{"id":347239684,"identity":"d99fc4b2-9517-4cda-8052-99ec9c49bbb0","order_by":1,"name":"Thiago Alexandre Santana Gilio","email":"","orcid":"","institution":"University of Mato Grosso State “Carlos Alberto Ryes Maldonado”","correspondingAuthor":false,"prefix":"","firstName":"Thiago","middleName":"Alexandre Santana","lastName":"Gilio","suffix":""},{"id":347239685,"identity":"4415ba30-3ce7-4dad-8b43-ec32872b0d66","order_by":2,"name":"Maria Celeste Gonçalves-Vidigal","email":"","orcid":"","institution":"State University Maringá","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"Celeste","lastName":"Gonçalves-Vidigal","suffix":""},{"id":347239686,"identity":"2330ab39-a13d-4b14-8692-47ead29fb391","order_by":3,"name":"Kelly Lana Araújo","email":"","orcid":"","institution":"University of Mato Grosso State “Carlos Alberto Ryes Maldonado”","correspondingAuthor":false,"prefix":"","firstName":"Kelly","middleName":"Lana","lastName":"Araújo","suffix":""},{"id":347239687,"identity":"870e74b1-f711-4eec-b70d-6138b7e22778","order_by":4,"name":"Marco Antonio Aparecido Barelli","email":"","orcid":"","institution":"University of Mato Grosso State “Carlos Alberto Ryes Maldonado”","correspondingAuthor":false,"prefix":"","firstName":"Marco","middleName":"Antonio Aparecido","lastName":"Barelli","suffix":""},{"id":347239688,"identity":"c874f681-577d-42af-9809-0b69264a667a","order_by":5,"name":"Lorenna Lopes Souza","email":"","orcid":"","institution":"State University Maringá","correspondingAuthor":false,"prefix":"","firstName":"Lorenna","middleName":"Lopes","lastName":"Souza","suffix":""},{"id":347239690,"identity":"7cb0e846-1fbe-443d-9a32-a3b08d474abb","order_by":6,"name":"Leonarda Grillo Neves","email":"","orcid":"","institution":"University of Mato Grosso State “Carlos Alberto Ryes Maldonado”","correspondingAuthor":false,"prefix":"","firstName":"Leonarda","middleName":"Grillo","lastName":"Neves","suffix":""},{"id":347239692,"identity":"344e9c2e-d5e6-4582-b5da-8b04aa6007b7","order_by":7,"name":"Marcial Pastor-Corrales","email":"","orcid":"","institution":"Beltsville Agricultural Research Center","correspondingAuthor":false,"prefix":"","firstName":"Marcial","middleName":"","lastName":"Pastor-Corrales","suffix":""}],"badges":[],"createdAt":"2024-08-15 22:36:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4921482/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4921482/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10722-024-02209-7","type":"published","date":"2024-10-10T15:57:43+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":64477977,"identity":"0132e022-ba64-49be-8a3d-978dfdeff63a","added_by":"auto","created_at":"2024-09-13 15:50:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":36896,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOrigin of the BL panel accessions. State origins of the 93 Brazilian varieties evaluated for resistance to white mold.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4921482/v1/b47809dc38dd87678a620fe1.png"},{"id":64477975,"identity":"b97addfd-d785-4a00-975f-7543b0bf0065","added_by":"auto","created_at":"2024-09-13 15:50:43","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":556760,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTwo different methods for evaluating white mold reactions in common beans. A) Straw test proposed by Arkwazee \u0026amp; Myers (2017) and; B) the adaptation proposed by Terán et al. (2006). The colors are the reactions suggested by the authors, where green, yellow and red indicate resistant, intermediate and susceptiblereactions, respectively. The white bar indicates 2.54 cm.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4921482/v1/d912e93d709740ca443fe999.jpeg"},{"id":64478497,"identity":"da628aa1-38cf-491c-932d-f09fca675519","added_by":"auto","created_at":"2024-09-13 15:58:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":505009,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHeatmap of Pearson correlation coefficients among genotypic values (a) and networking plot (b) of traits related to white mold reactions according to the straw test (ST, Terán et al., 2006) and seedling straw test (SST, Arkwazee \u0026amp; Myers, 2017) in the BL panel. The methods are indicated by the ST and SST prefixes, respectively. Disease score by DS, the relative disease progress by RDP on the 3rd, 5th, 7th, and 9th days after inoculation (DAI). The area under the disease progression curve (AUDPC) is included.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4921482/v1/6e11699a26b8c31d5eecfa6e.png"},{"id":64477976,"identity":"1605b668-9577-41b2-a89c-305a6a17c722","added_by":"auto","created_at":"2024-09-13 15:50:44","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":41229,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNumber of genotypes identified in each response class to white mold inoculation according to the seedling straw test (Arkwazee \u0026amp; Myers, 2017), disease score adjusted mean (DS) and relative disease progress (RDP). To select intermediate and resistance reactions, we considered only the 5\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003eth\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e and 7\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003eth\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e days after inoculation (DAI).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4921482/v1/a53a7f3806be15d731199c60.png"},{"id":64477981,"identity":"e56b5641-b9bb-4060-b295-f90dadb0c74b","added_by":"auto","created_at":"2024-09-13 15:50:44","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":47128,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNumber of genotypes identified in each response class to white mold inoculation according to the straw test (Terán et al., 2006), disease score adjusted mean (DS) and relative disease progress (RDP). To select intermediate and resistance reactions, we considered only the 7\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003eth\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e and 9\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003eth\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e days after inoculation (DAI) and the area under disease progress (AUDPC).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4921482/v1/8b6483cd24e290018d1f998c.png"},{"id":64479085,"identity":"95a13dcb-9c44-479e-848b-798b978b22cf","added_by":"auto","created_at":"2024-09-13 16:06:44","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1584315,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSNP density and Manhattan plots of the markers considered for gene annotation. The arrows indicate the regions indicated at least twice.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4921482/v1/5c8b21533442173e447174fd.png"},{"id":66597398,"identity":"8b2c693d-70e5-4e77-95a5-c89bf86554d5","added_by":"auto","created_at":"2024-10-14 16:10:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4066578,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4921482/v1/0d7f5b01-15cb-44dd-81bf-105d54757c55.pdf"},{"id":64478498,"identity":"2c7ebcc1-e336-4e03-9bee-336ee8e4d05b","added_by":"auto","created_at":"2024-09-13 15:58:44","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2362098,"visible":true,"origin":"","legend":"","description":"","filename":"SuplementalFiguresPartialwhitemoldresistanceinaBrazilianadaptedcommonbeanpanel.docx","url":"https://assets-eu.researchsquare.com/files/rs-4921482/v1/f74bca4c35876e460c91dcac.docx"},{"id":64477978,"identity":"593a841b-7381-49f0-8d97-5e013e2a2864","added_by":"auto","created_at":"2024-09-13 15:50:44","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":18003,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTablesPartialwhitemoldresistanceinaBrazilianadaptedcommonbeanpanel.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4921482/v1/627234552d954e0040a5405c.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Partial white mold resistance in a Brazilian-adapted common bean panel","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe common bean (\u003cem\u003ePhaseolus vulgaris\u003c/em\u003e L.) is a crucial factor in food security, particularly in countries in Africa and South America (Blair et al., 2010; Broughton et al., 2003). Native to the American continent, this species exhibits genetic and phenotypic diversity represented by two well-studied gene pools: Andean and Mesoamerican. Extensive research has been conducted on its morphological, genetic, and physiological characteristics, highlighting their (dis)similarities (Gepts and Bliss \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Singh et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Dur\u0026aacute;n et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Bitocchi et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Cortes \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Bitocchi et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Naturally, a wide variety of this species is maintained in different regions where it has been introduced, as it is subjected to different environmental conditions, production systems, and dietary preferences. These locally maintained varieties represent valuable sources of resistance to various pests and diseases that affect this crop.\u003c/p\u003e \u003cp\u003eDisease susceptibility is a major challenge in common bean cultivation, and white mold [\u003cem\u003eSclerotinia sclerotiorum\u003c/em\u003e (Lib.) de Bary] can significantly reduce grain quality and yield up to 100% (Schwartz and Singh \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The complexity of resistance to this pathogen has been reported, with several genes and quantitative trait loci (QTLs) involved (Kolkman and Kelly \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Ender and Kelly \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Antonio et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Soule et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Miklas et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Some researchers have reported \u0026lsquo;partial\u0026rsquo; or \u0026lsquo;physiological resistance\u0026rsquo; (detected by the greenhouse straw test). Commonly, known susceptibility factors, such as canopy architecture, growth habit, and plant stature, are eliminated (Miklas et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Miklas \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; P\u0026eacute;rez-Vega et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). These evaluations are based on direct inoculation of the respective fungus into plant tissue, excluding any impeding effect caused by natural infection escape traits. Thus, physiological resistance may play a critical role when these traits are overcome by pathogen pressure in the field. Consequently, pyramiding different genes involved in the resistance reaction can be a promising approach (Singh et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Vasconcellos et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo identify sources of physiological resistance to white mold, the straw test (ST) method (Petzoldt and Dickson \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) has been reported to be highly efficient. However, this method becomes time-consuming when large screenings are conducted. Due to this limitation, (Arkwazee and Myers \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) presented an adaptation to that method, known as the seedling straw test (SST), which allows faster evaluation of large volumes of genotypes.\u003c/p\u003e \u003cp\u003eGenome-wide association studies (GWASs) have proven useful in investigating complex traits in animals and plants (Scherer and Christensen \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). It has been used to identify quantitative trait nucleotides (QTNs) associated with resistance traits in common beans (Campa et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Escobar et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Fritsche-Neto et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Oladzadab et al., 2019; Perseguini et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Raggi et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), providing valuable regions for validation. Various models have been developed and used to support different studies for identifying regions associated with traits. Among these models, the fixed and random model unified probability circulating (FarmCPU) (Liu et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and the multilocus mixed linear model (MLMM) (Segura et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) implemented in the GAPIT 3 (Wang and Zhang \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) package can be used. Therefore, several factors, such as population size, linkage disequilibrium, population structure, and even the reproductive system, should be considered. These intrinsic factors in the evaluated population should be considered; in this way, we can carry out more parsimonious interpretations of the results, even if the significance threshold is not an easy choice, and to overcome this, comparisons among different models could be useful for identifying false positives.\u003c/p\u003e \u003cp\u003eSeveral quantitative trait loci (QTL) for resistance and avoidance have been identified using bi-parental populations, with most loci exhibiting small to moderate effects and being located on all chromosomes except Pv10 (Schwartz and Singh \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Vasconcellos et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Escobar et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Recently, three GWAS targeting WM were conducted, revealing new chromosomal regions associated with resistance, including chromosome Pv10 (Campa et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Escobar et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Arkwazee et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additionally, Campa et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) identified three distinct genomic regions via GWAS, all located on chromosome Pv08. Regarding common bean diversity in Brazil, it has been documented that the country possesses a rich diversity of common bean varieties (Burle et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Valentini et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These varieties represent valuable genetic diversity resources for enhancing resistance to diseases, such as anthracnose, angular leaf spot (Perseguini et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Fritsche-Neto et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and WM (Carvalho et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Souza et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Lehner et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In this way, the bean line (BL) panel, composed of landraces and varieties from different regions of Brazil and maintained at the Nupagri Research Center, serves as an important genetic resource for common bean breeding in that country. This study aimed to characterize the reactions of BL genotypes to WM via two different methods. Additionally, a GWAS was performed with the objective of identifying genomic regions for validation and consequently supporting marker-assisted selection programs aimed at enhancing resistance to this disease.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eVegetal accessions and SNPs obtained\u003c/h2\u003e \u003cp\u003eNinety-three common bean accessions from the BL panel were assessed (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). This panel was well studied by Elias et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and comprises varieties preserved by small-scale farmers from Mato Grosso, Paran\u0026aacute;, Sergipe, and Para\u0026iacute;ba in Brazil (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDNA extraction, library construction, and SNP genotyping were carried out by Elias et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) using genotyping-by-sequencing (GBS) methodology, which is based on the methylation-insensitive restriction enzyme \u003cem\u003eCviAII\u003c/em\u003e. DNA quality and quantity were determined by a NanoDrop Lite (Thermo Fisher Scientific, Waltham, USA) and electrophoresis (1% agarose gel). Additionally, a QUBIT dsDNA HS assay kit was used to quantify genomic DNA and library adapters. The library was sequenced using the Illumina HiSeq4000 platform to generate 50 bp single-end reads via the QB3 Vincent J. Coates Genomics Sequencing Laboratory at the University of California, Berkeley, USA. The sequence read alignment and SNP calling steps were conducted as previously described (Ariani et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). For read alignment, the reference genome sequence of the \u003cem\u003eP. vulgaris\u003c/em\u003e G19833 accession was used (Schmutz et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). More information about genotyping can be found in Elias et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA call rate threshold of 0.95 and a minimum allele frequency (MAF) greater than 0.05 were applied. A set of 28,237 SNPs, which were widely distributed across the genome, was utilized to conduct associative mapping.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eScreening the resistance of the BL panel to WM\u003c/h2\u003e \u003cp\u003eTwo methods for white mold resistance screening were used: the seedling straw test (SST) (adapted from Petzoldt \u0026amp; Dickson, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1996\u003c/span\u003e by Arkwazee \u0026amp; Myers, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and the straw test (ST) (adapted from Petzoldt \u0026amp; Dickson, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1996\u003c/span\u003e by Ter\u0026aacute;n et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). To assess the reaction, the UFVS-493 white mold strain was used in both screenings.\u003c/p\u003e \u003cp\u003eFor both inoculum methods, the UFVS-493 strain was prepared using Petri plates according to the method described by Arkwazee \u0026amp; Myers (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and the inoculation was carried out at the Laboratory of Genetic Resources and Biotechnology, State University of Mato Grosso.\u003c/p\u003e \u003cp\u003eThe plants were germinated in a greenhouse in 400 cm\u0026sup3; plastic cups filled with autoclaved PlantMax\u0026reg; substrate, with two seeds per cup. For both the ST and SST screenings, three cups with two plants were used. The plants were maintained in the greenhouse until they reached the appropriate stage for each method: for the SST treatment, the plants had the first pair of fully expanded leaves, while for the ST treatment, the plants had at least two fully expanded trifoliate leaves. The plants were placed in humidity chambers made of transparent polypropylene plastic bags, maintaining a humidity level of 98% inside the bags under a temperature of 22\u0026deg;C and a 12-hour photoperiod. After 48 hours in the humid chamber, the plants were removed and kept in the laboratory at room temperature (25\u0026deg;C) for the same photoperiod.\u003c/p\u003e \u003cp\u003eDisease severity (DS) was assessed individually in each plant using the scales proposed by Arkwazee \u0026amp; Myers, (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) for SST and Ter\u0026aacute;n et al. (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) for ST at 48-hour intervals (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and B). This way, the evaluations were at 3, 5 and 7 days after inoculation (DAI) for the SST method and at 3, 5, 7, and 9 DAI for the ST method. According to Campa et al. (2021), values equal to or less than 4.5 were considered resistant reactions (R), values between 4.5 and 7 were considered intermediate reactions (I), and values equal to or greater than 7 were considered susceptible (S).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBasically, the disease score (DS) is a trait based on the progression of symptoms; then, the progression length of the disease was measured individually on each plant and used to obtain the relative disease progress (RDP): length of disease\u0026times;100/plant height.\u003c/p\u003e \u003cp\u003eThe DS and RDP were considered to determine the AUDPC_DS and AUDPC_RDP according to the following formulas (Shaner and Finney \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e1977\u003c/span\u003e):\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAUDPC =\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sum\\:_{i=1}^{n}\\left[\\left(\\frac{{Y}_{i}+{Y}_{i+1}}{2}\\right)\\left({T}_{i+1}+{T}_{i}\\right)\\right]\\)\u003c/span\u003e\u003c/span\u003e\u003c/h2\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Y}_{i}\\)\u003c/span\u003e\u003c/span\u003e = the DS or RDP score at the \u003cem\u003ei\u003c/em\u003eth observation; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{T}_{i}\\)\u003c/span\u003e\u003c/span\u003e = days after inoculation in the \u003cem\u003ei\u003c/em\u003eth observation; and \u003cem\u003en\u003c/em\u003e is the total number of observations.\u003c/p\u003e \u003cp\u003eThe values found for the AUDPC were compared to resistant, intermediate and susceptible strains, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe DS and RDP, AUDPC_DS and AUDPC_RDP of each method were compared using Pearson\u0026rsquo;s correlation. The plots were created using the \u003cem\u003emetan\u003c/em\u003e (Olivoto and L\u0026uacute;cio \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) package.\u003c/p\u003e \u003cp\u003eA linear mixed model was used to estimate the variance components and obtain adjusted means using restricted maximum likelihood (REML), implemented in the \u003cem\u003elm4\u003c/em\u003e (Bates et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) package:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{Y}_{ijk}=\\mu\\:+\\:{G}_{i}+\\:{B}_{j}+{GK}_{ij}+\\:{\\epsilon\\:}_{ijk}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Y}_{ijk}\\)\u003c/span\u003e\u003c/span\u003e = the phenotypic observation; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\mu\\:\\)\u003c/span\u003e\u003c/span\u003e = the overall mean; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{G}_{i}\\)\u003c/span\u003e\u003c/span\u003e = the genotype random effect; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{B}_{j}\\)\u003c/span\u003e\u003c/span\u003e = the block, fixed effect; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{GK}_{ij}\\)\u003c/span\u003e\u003c/span\u003e = the repetition in the \u003cem\u003ek\u003c/em\u003eth block, random effect; and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:{\\varepsilon\\:}_{ijk}\\)\u003c/span\u003e\u003c/span\u003e is the residual error, a random effect. The heritability (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{H}^{2}\\)\u003c/span\u003e\u003c/span\u003e) was obtained following \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:=\\:{\\sigma\\:}_{g}^{2}{/(\\sigma\\:}_{g}^{2}+{\\sigma\\:}_{e}^{2}/r)\\times\\:100\\)\u003c/span\u003e\u003c/span\u003e. where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{g}^{2}\\)\u003c/span\u003e\u003c/span\u003e is the genotypic variance and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{e}^{2}\\)\u003c/span\u003e\u003c/span\u003e is the residual variance.\u003c/p\u003e \u003cp\u003eTo assess the disparities between complete models incorporating the studied effects and those without them, a likelihood ratio test (LRT) was employed. The LRT test involved comparing the chi-squared value against the critical value at a 5% probability level based on the degrees of freedom. The best linear unbiased estimator (BLUE) values for each accession trait were used as the input phenotypic data for conducting the GWAS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eGenome-wide association\u003c/h2\u003e \u003cp\u003eTo conduct the GWAS, the mixed linear model approach implemented in the \u003cem\u003eGAPIT\u003c/em\u003e (Wang and Zhang \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) package in R software (R Development Core Team \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) was used. To overcome population structure bias, the first two principal component analysis (PCA) (Q) and the VanRaden kinship matrix were employed.\u003c/p\u003e \u003cp\u003eThe GWAS analysis was performed by two algorithms, FarmCPU (Liu et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and MLMM (Segura et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), both of which were implemented in the GAPIT 3.0 package. The Bonferroni correction was applied at 5% probability.\u003c/p\u003e \u003cp\u003ePutative genes were identified by scanning windows of 0.1 Mb centered on the significant SNP (Garris et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Patishtan et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Raggi et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These regions were aligned against the reference genome of \u003cem\u003eP. vulgaris\u003c/em\u003e v1.0 (genotype G19833; Schmutz et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), which is accessible through the National Center for Biotechnology Information (NCBI; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The functional annotation of the candidate genes was performed using the same reference genome.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCorrelations among the different methods in the BL panel\u003c/h2\u003e \u003cp\u003ePhenotypic evaluation of the BL panel using the ST and SST methods indicated wide variation in response to the UFVSs-493 white mold strain.\u003c/p\u003e \u003cp\u003eSignificant genotypic effects (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) were detected for all combinations of methods (SST or ST), traits (DS, RDP and AUPDC) and days after inoculation (DAI), indicating genetic variability in this panel (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Progress in identifying fewer diseases is the objective, and it highlights plant genotype resistance. Notably, the BLUP values reinforced the genotypic effect found (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e and Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\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\u003eLRT and Wald tests were used for fixed and random effects traits, respectively. The inoculation methods used were SST (Arkwazee \u0026amp; Myers, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and ST (Ter\u0026aacute;n et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), and the disease score (DS) and relative disease progress (RDP) on the 3rd, 5th, 7th, and 9th days after inoculation (DAI) and the area under the disease progress (AUDPC) were considered.\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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eGenotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eRep:Block\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eBlock\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMethod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWald value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDS_3DAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\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\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\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\u003eDS_5DAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\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\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\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\u003eDS_7DAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.014\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\u003e0.2164\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\u003eRDP_3DAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\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\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\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\u003eRDP_5DAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\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\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e38.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\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\u003eRDP_7DAI\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\u003e\u0026lt;\u0026thinsp;0.0001\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\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.04224\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\u003eAUDPC_DS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0301\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\u003eAUDPC_RDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\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\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e26.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDS_3DAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\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\u003eDS_5DAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\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\u003eDS_7DAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\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\u003eDS_9DAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\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\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\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\u003eRDP_3DAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0018\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\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\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\u003eRDP_5DAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.336\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\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\u003eRDP_7DAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0001\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\u003eRDP_9DAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\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\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\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\u003eAUDPC_DS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e32.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\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\u003eAUDPC_RDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\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 evaluated methods did not have a strong positive correlation, as reported in previous studies. It is important to note that typically, only the final evaluation is considered to discriminate the response of common bean genotypes. However, even the nonadjusted disease scores did not show a correlation or relationship (Figure S3, A and B). The correlation values and the network plot are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The genotype score classifications between the methods used are notable. A greater correlation between different methods was shown for SST_DS_7DAI \u0026ndash; ST_DS_5DAI (0.39). However, the smallest correlation is 0.20 for SST_DS_3DAI \u0026ndash; ST_AUPDC_DS.\u003c/p\u003e \u003cp\u003eOn the other hand, the correlation observed within methods is considerably moderate to high, ranging from 0.23\u0026ndash;0.97 for ST and 0.52\u0026ndash;0.98 for SST. In this case, the significant correlation observed within methods suggests that indirect selection can be efficiently applied, and lower means can correspond to other small values of correlated traits.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e. \u003cb\u003eHeatmap of Pearson correlation coefficients among genotypic values (a) and networking plot (b) of traits related to white mold reactions according to the straw test (ST\u003c/b\u003e, Ter\u0026aacute;n et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) \u003cb\u003eand seedling straw test (SST\u003c/b\u003e, Arkwazee \u0026amp; Myers, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e\u003cb\u003e) in the BL panel. The methods are indicated by the ST and SST prefixes, respectively. Disease score by DS, the relative disease progress by RDP on the 3rd, 5th, 7th, and 9th days after inoculation (DAI). The area under the disease progression curve (AUDPC) is included.\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eThe susceptibility through days after inoculation\u003c/h2\u003e \u003cp\u003eAn excessive number of genotypes were identified as resistant on the 3rd day after inoculation (3 DAI) for both methods (74 for SST and 53 for ST) considering a score of 4.5 as considered for Campa et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Consequently, 3DAI was disregarded for the identification of WM resistance. Additionally, the 3rd and 5th days after inoculation (3 DAI and 5 DAI, respectively) were discarded to indicate an intermediate response.\u003c/p\u003e \u003cp\u003eThe descriptive statistics are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, and the changes in distribution on different days after inoculation are shown in Figure S4.\u003c/p\u003e \u003cp\u003eThe adjusted means were used to identify the resistance response, revealing that white mold progression progressed over time on the stems. The variance estimates ranged from 16% (ST_RDP_3DAI) to 60% (ST_RDP_9DAI), and the heritability (H\u003csup\u003e2\u003c/sup\u003e) ranged from 32\u0026ndash;64%.\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\u003eDescriptive statistics using the adjusted values, genetic variance, and broad-sense heritability (H\u003csup\u003e2\u003c/sup\u003e) were estimated for 10 traits in the straw test (ST \u0026ndash; Ter\u0026aacute;n et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) and 8 traits in the seedling straw test (SST \u0026ndash; Arkwazee \u0026amp; Myers, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) in the BL panel.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eSST\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003eST\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{g}^{2}\\)\u003c/span\u003e\u003c/span\u003e (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eH\u003csup\u003e2\u003c/sup\u003e(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003emean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{g}^{2}\\)\u003c/span\u003e\u003c/span\u003e(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eH\u003csup\u003e2\u003c/sup\u003e(%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDS_3DAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.4\u0026ndash;5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.9\u0026ndash;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDS_5DAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.5\u0026ndash;9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.7\u0026ndash;8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDS_7DAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.2\u0026ndash;9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.5\u0026ndash;9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDS_9DAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.5\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDP_3DAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.4\u0026ndash;47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.8\u0026ndash;34.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDP_5DAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.1\u0026ndash;108.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.3\u0026ndash;56.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e31.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDP_7DAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.1\u0026ndash;106.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e91.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.9\u0026ndash;94.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e48.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDP_9DAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27.5\u0026ndash;113.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e67.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAUDPC_DS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.5\u0026ndash;32.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24.2\u0026ndash;51.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e41.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAUDPC_RDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e133.8\u0026ndash;357.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e246.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e84.2\u0026ndash;421.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e244.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e55\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 \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eResponses to the Seedling Straw Test (SST)\u003c/h2\u003e \u003cp\u003eIn the SST method, SST_DS_5DAI indicates three resistant genotypes (BL15, BL18 and BL10) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e). At the SST_DS_7DAI, three genotypes exhibited an intermediate response to WM (BL15, BL18 and BL98).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e. \u003cb\u003eNumber of genotypes identified in each response class to white mold inoculation according to the seedling straw test (\u003c/b\u003eArkwazee \u0026amp; Myers, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e\u003cb\u003e), disease score adjusted mean (DS) and relative disease progress (RDP). To select intermediate and resistance reactions, we considered only the 5th and 7th days after inoculation (DAI).\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eResponses to the Straw Test (ST)\u003c/h2\u003e \u003cp\u003eIn the ST_DS_5DAI, four genotypes were resistant (BL14, BL15, BL86 and BL220), whereas the ST_DS_7DAI indicated only one genotype (BL14); the ST_RDP_5DAI and ST_RDP_7DAI both indicated one genotype (BL227), as did the ST_DS_9DAI (BL14).\u003c/p\u003e \u003cp\u003eIntermediate responses were identified, and the ST_DS_7DAI and ST_RDP_7DAI indicated 20 and 15 genotypes, respectively. ST_DS_9DAI indicates eight, ST_RDP_9DAI indicates four (BL67, BL74, BL96 and BL227).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRegarding the responses to WM inoculation, we detected a sudden reduction in resistance in response to both methods. As one way to identify superior responses to WM resistance, the BLUP value in each method was considered (for any method, the 3DAI was dropped); in order, the genotypes that most frequently appeared among the fifteen lowest BLUP means were BL10, BL18, BL84, BL15, BL227, BL74, BL86, BL96, BL14, BL220, BL67, BL71, BL95, BL106 and BL111, which were the first fifteen ranked accessions. In this way, we identified eleven Mesoamerican and four Andean genotypes that responded strongly to WM inoculation (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Therefore, it represents a valuable source of resistance with significant potential for utilization in common bean breeding programs.\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\u003eDescriptive statistics of selected genotypes on the BLUP ranking for Area Under Disease Progress Curve (AUDPC) in straw test (ST \u0026ndash; Ter\u0026aacute;n et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) and seedling straw test (SST \u0026ndash; Arkwazee \u0026amp; Myers, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) considering the Disease Score (DS) and Relative Disease Progress (RDP) in the BL panel.\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccess\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePool\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSST_AUDPC_RDP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSST_AUDPC_DS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eST_AUDPC_RDP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eST_AUDPC_DS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBL10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e159.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e156.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e34.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBL14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e253.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e173.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e24.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBL15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e133.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e206.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e31.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBL18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e136.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e147.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBL67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e230.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e141.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e31.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBL71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e220.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e139.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e34.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBL74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e300.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e131.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBL84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e217.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e151.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e34.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBL86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e223.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e161.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e30.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBL95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e189.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e200.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e34.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBL96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e222.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e135.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e34.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBL106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e295.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e157.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e34.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBL111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e217.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e203.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e39.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBL220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e299.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e149.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e31.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBL227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e209.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e84.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean of selected\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e220.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e155.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e32.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e246.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e244.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e41.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall maximum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e357.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e421.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e51.1\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 \u003csup\u003e1,2\u003c/sup\u003e Mesoamerican and Andean genetic pool, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eGWAS and candidate gene annotation\u003c/h2\u003e \u003cp\u003eThe BL panel analyzed in this study comprised 28,237 SNPs distributed across the 11 common bean chromosomes. As reported by Elias et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), two clusters were identified in the Andean and Mesoamerican genetic pools, which is consistent with previous studies that highlighted two major gene pools in common bean (Debouck et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Freyre et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Kwak and Gepts \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Bitocchi et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Kwak et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Desiderio et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Schmutz et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe correlation values among traits can have implications for genomic association studies, as highly correlated traits may share common genomic regions. The low correlation values observed between these methods may explain why only the SST method was observed in the GWAS results (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), which can be a combination of better discrimination between genotypes and consistency in the scores.\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\u003eQTNs detected through GWAS analysis, traits (combination among methods, measurement types and days after inoculation), significant SNPs, positions, MAFs, and \u003cem\u003ep\u003c/em\u003e values.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"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=\"\u0026times;\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrait\u003csup\u003e$\u003c/sup\u003e\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\u003eChr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePosition (Mb)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMAF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarmCPU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSST_DS_7DAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS01_31745487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31745487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c7\"\u003e \u003cp\u003e1.09\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMLMM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSST_DS_7DAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS01_31745487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31745487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c7\"\u003e \u003cp\u003e1.74\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMLMM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSST_DS_7DAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS01_36888189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36888189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c7\"\u003e \u003cp\u003e4.84\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;07\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarmCPU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSST_AUDPC_RDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS02_2509080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2509080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c7\"\u003e \u003cp\u003e9.43\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;07\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarmCPU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSST_AUDPC_RDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS02_2580500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2580500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c7\"\u003e \u003cp\u003e9.43\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;07\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarmCPU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSST_AUDPC_RDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS02_2582044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2582044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c7\"\u003e \u003cp\u003e9.43\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;07\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMLMM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSST_AUDPC_RDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS02_2580500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2580500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c7\"\u003e \u003cp\u003e1.28\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;06\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarmCPU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSST_DS_7DAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS02_2509080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2509080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c7\"\u003e \u003cp\u003e1.20\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;06\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarmCPU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSST_DS_7DAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS02_2580500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2580500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c7\"\u003e \u003cp\u003e1.20\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;06\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarmCPU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSST_DS_7DAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS02_2582044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2582044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c7\"\u003e \u003cp\u003e1.20\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;06\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarmCPU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSST_RDP_5DAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS02_2509080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2509080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c7\"\u003e \u003cp\u003e5.12\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;07\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarmCPU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSST_RDP_5DAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS02_2580500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2580500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c7\"\u003e \u003cp\u003e5.12\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;07\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarmCPU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSST_RDP_5DAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS02_2582044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2582044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c7\"\u003e \u003cp\u003e5.12\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;07\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMLMM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSST_RDP_5DAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS02_2509080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2509080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c7\"\u003e \u003cp\u003e1.12\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;10\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarmCPU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSST_DS_7DAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS03_36436583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36436583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c7\"\u003e \u003cp\u003e1.45\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;06\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMLMM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSST_DS_7DAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS03_30648244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30648244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c7\"\u003e \u003cp\u003e1.92\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;05\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMLMM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSST_DS_7DAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS03_31040939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31040939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c7\"\u003e \u003cp\u003e8.46\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;05\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMLMM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSST_DS_7DAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS03_36436583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36436583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c7\"\u003e \u003cp\u003e6.46\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;09\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMLMM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSST_DS_7DAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS04_36532214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36532214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c7\"\u003e \u003cp\u003e8.46\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;05\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMLMM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSST_RDP_5DAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS05_1926309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1926309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c7\"\u003e \u003cp\u003e8.79\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;09\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMLMM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSST_AUDPC_RDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS11_44416290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e44416290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c7\"\u003e \u003cp\u003e1.96\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;05\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMLMM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSST_RDP_5DAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS11_44111449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e44111449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c7\"\u003e \u003cp\u003e1.67\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;07\u003c/sup\u003e\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 \u003csup\u003e \u003cb\u003e1, 2\u003c/b\u003e \u003c/sup\u003e, \u003cb\u003ereference allele; and alternative allele;\u003c/b\u003e \u003csup\u003e\u003cb\u003e$\u003c/b\u003e\u003c/sup\u003e \u003cb\u003eMethod and trait combination.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe \u003cem\u003ep\u003c/em\u003e values for significant SNPs according to the MLMM model ranged from 1.92\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;05\u003c/sup\u003e (SST_DS_7DAI) to 1.74\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e (SST_DS_7DAI). The q-q plots are shown in Figure S5. The GWAS models revealed 12 significantly associated SNPs located on chromosomes Pv01, Pv02, Pv03, Pv04, Pv05 and Pv11. For gene annotation, we considered only those indicated by both models or more than one trait; SST_RDP_5DAI, SST_DS_7DAI and SST_AUDPC_RDP were the traits that showed significant SNPs in common, as shown in the Manhattan plots (Fig.\u0026nbsp;5). Additionally, twelve stress response-related genes were annotated as potential candidates (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe most significant marker was identified by MLMM on Pv01 (1.74\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e) at the SST_DS_7DAI. The SNPs S02_2580500, S02_2582044 and S02_2509080 were associated with SST_RDP_5DAI, SST_DS_7DAI and SST_AUDPC_RDP, which are located on chromosome Pv02; S01_31745487 and S01_36888189 were associated with SST_DS_7DAI on Pv01. However, S03_30648244, S03_31040939 and S03_36436583, located on Pv03, were also associated with the SST_DS7DAI. On Pv04 and Pv05, S04_36532214 and S05_1926309, respectively, are indicated. The SNPs S11_44416290 and S11_44111449 located on Pv11 are indicated by SST_AUDPC_RDP and SST_RDP_5DAI, respectively.\u003c/p\u003e \u003cp\u003eExcept for the significant SNP on Pv01, all the identified regions corresponded to previously mapped quantitative trait loci (QTLs) associated with white mold resistance. On Pv01, the SNP S01_31745487 is located in the \u003cem\u003ePHAVU_001G113400g\u003c/em\u003e intragenic region and is related to cell cycle control proteins and, previously, to responses to stresses, such as temperature and pathogen infections (Bao and Hua \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Qi and Zhang \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOn Pv02, the block indicated by all the identified SNPs appears to correspond to haplotypes of the QTLs WM2.2\u003csup\u003eAN\u003c/sup\u003e (field condition), WM2.2\u003csup\u003eBV\u003c/sup\u003e (ST) (Miklas, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), WM2.2\u003csup\u003eR31\u003c/sup\u003e (field condition) (Soule et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Vasconcellos et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), WM2.2\u003csup\u003eR31\u003c/sup\u003e (ST), and WM2.2\u003csup\u003eZ0726\u0026thinsp;\u0026minus;\u0026thinsp;9\u003c/sup\u003e (ST), the latter being reported in a population derived from the cross PS02-029C-20 \u0026times; AN-37, known as the Z0726-9 population. Together, these QTLs spanned an extension of 22.87 Mb, ranging from 3.54 to 26.41 Mb on Pv02.\u003c/p\u003e \u003cp\u003eAmong the significant SNPs on Pv02, some were in intergenic regions. For instance, S02_2509080 (\u003cem\u003ePHAVU_002G024100g\u003c/em\u003e) is associated with the PPR (pentatricopeptide repeat) family of proteins, which are known to be involved in various biological processes related to stress responses (Cushing et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Saha et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Yuan and Liu \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Barkan and Small \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Similarly, S02_2582044, located in an intragenic region of \u003cem\u003ePHAVU_002G024600g\u003c/em\u003e, is associated with histone-lysine N-methyltransferase (HKMT)ases, which play important roles in histone modifications. This protein family has been implicated in gametophytic development, flowering and morphology, and responses to various stresses (Kim et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Yan et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhou et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOn chromosome Pv03, the SNP S03_36436583 putatively corresponds to the same region of QTLs WM3.1\u003csup\u003eAN\u003c/sup\u003e (Miklas \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), WM3.1\u003csup\u003eAP\u003c/sup\u003e (Hoyos-Villegas et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and WM3.1\u003csup\u003eXC\u003c/sup\u003e (P\u0026eacute;rez-Vega et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). However, Vasconcellos et al. (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) determined through a meta-QTL study that WM3.1\u003csup\u003eXC\u003c/sup\u003e and WM3.1 occupy the same physical position, with an extension spanning from 34.33 to 48.32 Mb.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we evaluated the reaction of common bean accessions to the \u003cem\u003eS. sclerotiorum\u003c/em\u003e strain UFVSs-493 using two inoculation methods. Different levels of resistance were identified in a diverse and well tropical-adapted panel.\u003c/p\u003e \u003cp\u003eAccording to previous studies, the majority of WM resistance sources originate from the Andean region or secondary gene pools, such as \u003cem\u003eP. coccineus\u003c/em\u003e (Vasconcellos et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In our study, we identified promising Andean and Mesoamerican accessions with significant levels of resistance to WM that were well adapted to Brazilian climatic conditions.\u003c/p\u003e \u003cp\u003eUnderstanding the complexity of resistance to WM can contribute to more sustainable common bean production; to verify the effect of this method, we evaluated the BL panel using two different methods in a controlled environment. In accordance with previous studies (Miklas et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Miklas et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Vasconcellos et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Campa et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), our findings suggest that resistance to white mold has low to moderate heritability. However, the significant correlations observed between the methods (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) indicate that the identification of resistant genotypes may be influenced by the inoculation method employed and the intrinsic characteristics of the evaluated panel. For this reason, it may be important to use different methods to confirm the results obtained.\u003c/p\u003e \u003cp\u003eIn the BL panel, based on the BLUP values, we selected eleven Mesoamerican and four Andean genotypes that responded strongly to WM inoculation. These accessions are potentially valuable sources of resistance to WM, and if validated, those regions could be useful in a breeding program for resistance improvement.\u003c/p\u003e \u003cp\u003eResistance to white mold has been recognized as a complex trait influenced by many morphological characteristics (Ando, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Vansconcellos et al., 2017), and previous studies have mapped QTLs (or QTNs) associated with this trait based on field evaluations or controlled inoculations (Arkwazee et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Campa et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). We identified twelve SNPs on chromosomes Pv01, Pv02, Pv03, Pv04, Pv05 and Pv11, but we considered only three regions significantly identified on chromosomes Pv01, Pv02 and Pv03 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e); this way, twelve genes related to biotic stresses were annotated (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmong the annotated genes, \u003cem\u003ePHAVU_002G024100g\u003c/em\u003e (pentatricopeptide repeat - PPR), indicated by S02_2509080 on Pv02, is known to be involved in posttranscriptional modifications and is expressed in different stages during salt and drought stress (Chen et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). It also regulates the homeostasis of reactive oxygen species in mitochondria during abiotic and biotic stress responses (Laluk et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Xing et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eProtein kinases, which act in signaling pathways involving cell-surface receptors and other pathways, have been extensively studied in the context of plant immune functions. These proteins were first reported in \u003cem\u003eArabidopsis thaliana\u003c/em\u003e L. (He et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) and have since been associated with resistance in various plant‒pathogen interactions, such as resistance to \u003cem\u003ePseudomonas syringae\u003c/em\u003e pv. \u003cem\u003etomato\u003c/em\u003e (Rosli et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), corn resistance to leaf rust and smut disease (Hurni et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and wheat resistance to \u003cem\u003eZymoseptoria tritici\u003c/em\u003e (Saintenac et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In our study, the gene \u003cem\u003ePHAVU_002G023400g\u003c/em\u003e (wall-associated receptor kinase) was also found to be related to protein kinases.\u003c/p\u003e \u003cp\u003e \u003cem\u003ePHAVU_002G024400g\u003c/em\u003e (pathogenesis-related 1 protein - PR) encodes a class of proteins that accumulate in response to abiotic and biotic stresses, protecting tissues from damage. Its homologous \u003cem\u003ePnPR-like\u003c/em\u003e gene has been shown to confer greater resistance to \u003cem\u003eFusarium solani\u003c/em\u003e and other pathogens in transgenic \u003cem\u003eNicotiana tabacum\u003c/em\u003e plants ; Boccardo et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe hypersensitive response (HR) is a rapid programmed cell death that occurs at the site of pathogen entry. It is associated with restricted growth of the pathogen in infected areas and resistance to the disease (Morel and Dangl \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Dangl and Jones \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Park \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Balint-Kurti \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Additionally, the genes \u003cem\u003ePHAVU_002G022800g\u003c/em\u003e and \u003cem\u003ePHAVU_002G022900g\u003c/em\u003e are related to the cytochrome P450 superfamily, which encodes enzymes involved in various plant functions, including responses to biotic stresses (Pandian et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This gene family is also associated with hypersensitivity responses (Kim et al., 2006) and the rapid induction of cell death in response to \u003cem\u003ePseudomonas syringae\u003c/em\u003e infections in Arabidopsis (Godiard et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1998\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study, we evaluated the response to a Brazilian strain of WM and identified fifteen accessions that exhibited a high level of physiological resistance. Among these accessions, eleven belong to the Mesoamerican group and four to the Mesoamerican group, all of which have been traditionally cultivated and are well adapted to tropical climatic conditions. Additionally, employing a GWAS approach, we identified regions on chromosomes Pv02 and Pv03 that are associated with previously reported QTLs for resistance to WM. These findings suggest the presence of potential haplotypes and reveal 12 putative genes associated with physiological resistance, which can serve as valuable targets for future validation studies.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eSupplemental Material\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. BLUP values of the seedling straw test (SST).\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e. BLUP values of the Straw test (ST).\u003c/p\u003e \u003cp\u003eFigure S3. Heatmap of Pearson correlation coefficients using the no adjusted disease score (A) and network plot (B) of traits related to white mold reactions according to the straw test (ST, Ter\u0026aacute;n et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) and seedling straw test (SST, Arkwazee \u0026amp; Myers, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) in the BL panel.\u003c/p\u003e \u003cp\u003eFigure S4. Histogram of the adjusted values of the evaluated traits in the BL panel.\u003c/p\u003e \u003cp\u003eFigure S5. Q‒Q plots of \u003cem\u003ep\u003c/em\u003e values obtained for traits considered for gene annotation.\u003c/p\u003e \u003cp\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Genotypes screened for white mold resistance. Accession, gene pool, common name, type, state and city of origin, and altitude of the city.\u003c/p\u003e \u003cp\u003eTable \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e. Gene annotation, SNP nearest of gene, strand and gene position considering two different reference genomes.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by National Council for Scientific and Technological Development (CNPq) grant number Proc. 437310/2018-3 and the Brazilian Federal Agency for Support and Evaluation of Graduate Education (CAPES) through scholarship grants.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study conception and design. GRS wrote the first draft of the manuscript, and all the authors commented on previous versions. All the authors have read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe deeply thank Eduardo Seiti Gomide Mizubuti, Titular Professor at Vi\u0026ccedil;osa Federal University (UFV) who generously provided us with the isolate UFVS-493.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAndo K (2007) Manipulation of plant architecture to enhance crop disease control. CAB Rev Perspect Agric Vet Sci Nutr Nat Resour 2(026). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1079/PAVSNNR20072026\u003c/span\u003e\u003cspan address=\"10.1079/PAVSNNR20072026\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAntonio RP, Santos JB, Souza TP, Carneiro FF (2008) Genetic control of the resistance of common beans to white mold using the reaction to oxalic acid. Genet Mol Res 7(3):733\u0026ndash;740. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4238/vol7-3gmr466\u003c/span\u003e\u003cspan address=\"10.4238/vol7-3gmr466\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAriani A, Berny Mier y Teran JC, Gepts P (2016) Genome-wide identification of SNPs and copy number variation in common bean (Phaseolus vulgaris L.) using genotyping-by-sequencing (GBS). Mol Breed 36(7):87. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11032-016-0512-9\u003c/span\u003e\u003cspan address=\"10.1007/s11032-016-0512-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArkwazee H, Myers JR (2017) Seedling straw test: a rapid and resource-efficiente method for evaluationg white mold resistance. Annu Rep Bean Improv Coop 60:3\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArkwazee HA, Wallace LT, Hart JP, Griffiths PD, Myers JR (2022) Genome-Wide Association Study (GWAS) of White Mold Resistance in Snap Bean. Genes 13(12):2297. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/genes13122297\u003c/span\u003e\u003cspan address=\"10.3390/genes13122297\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBalint-Kurti P (2019) The plant hypersensitive response: concepts, control and consequences. Mol Plant Pathol:mpp.12821. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/mpp.12821\u003c/span\u003e\u003cspan address=\"10.1111/mpp.12821\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBao Z, Hua J (2015) Linking the Cell Cycle with Innate Immunity in Arabidopsis. Mol Plant 8(7):980\u0026ndash;982. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.molp.2015.03.013\u003c/span\u003e\u003cspan address=\"10.1016/j.molp.2015.03.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarkan A, Small I (2014) Pentatricopeptide Repeat Proteins in Plants. Annu Rev Plant Biol 65(1):415\u0026ndash;442. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1146/annurev-arplant-050213-040159\u003c/span\u003e\u003cspan address=\"10.1146/annurev-arplant-050213-040159\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBates D, M\u0026auml;chler M, Bolker B, Walker S (2015) Fitting Linear Mixed-Effects Models Using lme4. J Stat Softw 67(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18637/jss.v067.i01\u003c/span\u003e\u003cspan address=\"10.18637/jss.v067.i01\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBitocchi E, Bellucci E, Giardini A, Rau D, Rodriguez M, Biagetti E, Santilocchi R, Spagnoletti Zeuli P, Gioia T, Logozzo G, Attene G, Nanni L, Papa R (2013) Molecular analysis of the parallel domestication of the common bean ( \u003cem\u003eP haseolus vulgaris\u003c/em\u003e) in Mesoamerica and the Andes. New Phytol 197(1):300\u0026ndash;313. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1469-8137.2012.04377.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1469-8137.2012.04377.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBitocchi E, Nanni L, Bellucci E, Rossi M, Giardini A, Zeuli PS, Logozzo G, Stougaard J, McClean P, Attene G, Papa R (2012) Mesoamerican origin of the common bean (\u003cem\u003ePhaseolus vulgaris\u003c/em\u003e L.) is revealed by sequence data. Proc Natl Acad Sci 109(14). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.1108973109\u003c/span\u003e\u003cspan address=\"10.1073/pnas.1108973109\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoccardo NA, Segretin ME, Hernandez I, Mirkin FG, Chac\u0026oacute;n O, Lopez Y, Borr\u0026aacute;s-Hidalgo O, Bravo-Almonacid FF (2019) Expression of pathogenesis-related proteins in transplastomic tobacco plants confers resistance to filamentous pathogens under field trials. Sci Rep 9(1):2791. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-019-39568-6\u003c/span\u003e\u003cspan address=\"10.1038/s41598-019-39568-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurle ML, Fonseca JR, Kami JA, Gepts P (2010) Microsatellite diversity and genetic structure among common bean (Phaseolus vulgaris L.) landraces in Brazil, a secondary center of diversity. Theor Appl Genet 121(5):801\u0026ndash;813. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00122-010-1350-5\u003c/span\u003e\u003cspan address=\"10.1007/s00122-010-1350-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCampa A, Garc\u0026iacute;a-Fern\u0026aacute;ndez C, Ferreira JJ (2020) Genome-Wide Association Study (GWAS) for Resistance to Sclerotinia sclerotiorum in Common Bean. Genes 11(12):1496. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/genes11121496\u003c/span\u003e\u003cspan address=\"10.3390/genes11121496\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCampa A, Geffroy V, Bitocchi E, Noly A, Papa R, Ferreira JJ (2024) Screening for resistance to four fungal diseases and associated genomic regions in a snap bean diversity panel. Front Plant Sci 15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2024.1386877\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2024.1386877\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarvalho RSB, Lima IA, Alves FC, Santos JB dos (2013) Selection of carioca common bean progenies resistant to white mold. Crop Breed Appl Biotechnol 13(3):172\u0026ndash;177. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1590/S1984-70332013000300004\u003c/span\u003e\u003cspan address=\"10.1590/S1984-70332013000300004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen G, Zou Y, Hu J, Ding Y (2018) Genome-wide analysis of the rice PPR gene family and their expression profiles under different stress treatments. BMC Genomics 19(1):720. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12864-018-5088-9\u003c/span\u003e\u003cspan address=\"10.1186/s12864-018-5088-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCortes A (2013) On the Origin of the Common Bean (Phaseolus vulgaris L.). Am J Plant Sci 4:1998\u0026ndash;2000. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4236/ajps.2013.410248\u003c/span\u003e\u003cspan address=\"10.4236/ajps.2013.410248\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCushing DA, Forsthoefel NR, Gestaut DR, Vernon DM (2005) Arabidopsis emb175 and other ppr knockout mutants reveal essential roles for pentatricopeptide repeat (PPR) proteins in plant embryogenesis. Planta 221(3):424\u0026ndash;436. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00425-004-1452-x\u003c/span\u003e\u003cspan address=\"10.1007/s00425-004-1452-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDangl JL, Jones JDG (2001) Plant pathogens and integrated defence responses to infection. Nature 411(6839):826\u0026ndash;833. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/35081161\u003c/span\u003e\u003cspan address=\"10.1038/35081161\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDebouck DG, Toro O, Paredes OM, Johnson WC, Gepts P (1993) Genetic diversity and ecological distribution ofPhaseolus vulgaris (Fabaceae) in northwestern South America. Econ Bot 47(4):408\u0026ndash;423. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/BF02907356\u003c/span\u003e\u003cspan address=\"10.1007/BF02907356\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDesiderio F, Bitocchi E, Bellucci E, Rau D, Rodriguez M, Attene G, Papa R, Nanni L (2013) Chloroplast Microsatellite Diversity in Phaseolus vulgaris. Front Plant Sci 3. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2012.00312\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2012.00312\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDur\u0026aacute;n LA, Blair MW, Giraldo MC, Macchiavelli R, Prophete E, Nin JC, Beaver JS (2005) Morphological and Molecular Characterization of Common Bean Landraces and Cultivars from the Caribbean. Crop Sci 45(4):1320\u0026ndash;1328. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2135/cropsci2004.0501\u003c/span\u003e\u003cspan address=\"10.2135/cropsci2004.0501\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElias JCF, Gon\u0026ccedil;alves-Vidigal MC, Ariani A, Valentini G, Martiniano-Souza M da C, Vaz Bisneta M, Gepts P (2021) Genome-Environment Association Analysis for Bio-Climatic Variables in Common Bean (Phaseolus vulgaris L.) from Brazil. Plants 10(8):1572. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/plants10081572\u003c/span\u003e\u003cspan address=\"10.3390/plants10081572\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEnder M, Kelly JD (2005) Identification of QTL Associated with White Mold Resistance in Common Bean. Crop Sci 45(6):2482\u0026ndash;2490. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2135/cropsci2005.0064\u003c/span\u003e\u003cspan address=\"10.2135/cropsci2005.0064\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEscobar E, Oladzad A, Simons K, Miklas P, Lee RK, Schroder S, Bandillo N, Wunsch M, McClean PE, Osorno JM (2022) New genomic regions associated with white mold resistance in dry bean using a MAGIC population. Plant Genome 15(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/tpg2.20190\u003c/span\u003e\u003cspan address=\"10.1002/tpg2.20190\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFreyre R, R\u0026iacute;os R, Guzm\u0026aacute;n L, Debouck DG, Gepts P (1996) Ecogeographic distribution ofPhaseolus spp. (Fabaceae) in Bolivia. Econ Bot 50(2):195\u0026ndash;215. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/BF02861451\u003c/span\u003e\u003cspan address=\"10.1007/BF02861451\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFritsche-Neto R, Souza TLPO de, Pereira HS, Faria LC de, Melo LC, Novaes E, Brum IJB, Jannink J-L (2019) Association mapping in common bean revealed regions associated with Anthracnose and Angular Leaf Spot resistance. Sci Agric 76(4):321\u0026ndash;327. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1590/1678-992x-2017-0306\u003c/span\u003e\u003cspan address=\"10.1590/1678-992x-2017-0306\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarris AJ, Tai TH, Coburn J, Kresovich S, McCouch S (2005) Genetic Structure and Diversity in Oryza sativa L. Genetics 169(3):1631\u0026ndash;1638. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1534/genetics.104.035642\u003c/span\u003e\u003cspan address=\"10.1534/genetics.104.035642\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGepts P, Bliss FA (1986) Phaseolin variability among wild and cultivated common beans (Phaseolus vulgaris) from Colombia. Econ Bot 40:469\u0026ndash;478. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/BF02859660\u003c/span\u003e\u003cspan address=\"10.1007/BF02859660\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGodiard L, Sauviac L, Dalbin N, Liaubet L, Callard D, Czernic P, Marco Y (1998) \u003cem\u003eCYP76C2\u003c/em\u003e, an \u003cem\u003eArabidopsis thaliana\u003c/em\u003e cytochrome P450 gene expressed during hypersensitive and developmental cell death. FEBS Lett 438(3):245\u0026ndash;249. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0014-5793(98)01309-X\u003c/span\u003e\u003cspan address=\"10.1016/S0014-5793(98)01309-X\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe Z, He D, Kohorn BD (1998) Requirement for the induced expression of a cell wall associated receptor kinase for survival during the pathogen response. Plant J 14(1):55\u0026ndash;63. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1046/j.1365-313X.1998.00092.x\u003c/span\u003e\u003cspan address=\"10.1046/j.1365-313X.1998.00092.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoyos-Villegas V, Mkwaila W, Cregan PB, Kelly JD (2015) Quantitative Trait Loci Analysis of White Mold Avoidance in Pinto Bean. Crop Sci 55(5):2116\u0026ndash;2129. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2135/cropsci2015.02.0106\u003c/span\u003e\u003cspan address=\"10.2135/cropsci2015.02.0106\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHurni S, Scheuermann D, Krattinger SG, Kessel B, Wicker T, Herren G, Fitze MN, Breen J, Presterl T, Ouzunova M, Keller B (2015) The maize disease resistance gene \u003cem\u003eHtn1\u003c/em\u003e against northern corn leaf blight encodes a wall-associated receptor-like kinase. Proc Natl Acad Sci 112(28):8780\u0026ndash;8785. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.1502522112\u003c/span\u003e\u003cspan address=\"10.1073/pnas.1502522112\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim J-M, Sasaki T, Ueda M, Sako K, Seki M (2015) Chromatin changes in response to drought, salinity, heat, and cold stresses in plants. Front Plant Sci 6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2015.00114\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2015.00114\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKolkman JM, Kelly JD (2002) Agronomic Traits Affecting Resistance to White Mold in Common Bean. Crop Sci 42(3):693\u0026ndash;699. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2135/cropsci2002.6930\u003c/span\u003e\u003cspan address=\"10.2135/cropsci2002.6930\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKwak M, Gepts P (2009) Structure of genetic diversity in the two major gene pools of common bean (Phaseolus vulgaris L., Fabaceae). Theor Appl Genet 118(5):979\u0026ndash;992. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00122-008-0955-4\u003c/span\u003e\u003cspan address=\"10.1007/s00122-008-0955-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKwak M, Toro O, Debouck DG, Gepts P (2012) Multiple origins of the determinate growth habit in domesticated common bean (Phaseolus vulgaris). Ann Bot 110(8):1573\u0026ndash;1580. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/aob/mcs207\u003c/span\u003e\u003cspan address=\"10.1093/aob/mcs207\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaluk K, AbuQamar S, Mengiste T (2011) The Arabidopsis Mitochondria-Localized Pentatricopeptide Repeat Protein PGN Functions in Defense against Necrotrophic Fungi and Abiotic Stress Tolerance. Plant Physiol 156(4):2053\u0026ndash;2068. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1104/pp.111.177501\u003c/span\u003e\u003cspan address=\"10.1104/pp.111.177501\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLehner M da S, Paula J\u0026uacute;nior TJ de, Vieira RF, Lima RC, Soares B de A, Silva RA (2016) Reaction of sources of resistance to white mold to microsatellite haplotypes of Sclerotinia sclerotiorum. Sci Agric 73(2):184\u0026ndash;188. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1590/0103-9016-2015-0085\u003c/span\u003e\u003cspan address=\"10.1590/0103-9016-2015-0085\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi S, Wang Z, Tang B, Zheng L, Chen H, Cui X, Ge F, Liu D (2021) A Pathogenesis-Related Protein-Like Gene Is Involved in the Panax notoginseng Defense Response to the Root Rot Pathogen. Front Plant Sci 11:2217. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2020.610176\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2020.610176\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu X, Huang M, Fan B, Buckler ES, Zhang Z (2016) Iterative Usage of Fixed and Random Effect Models for Powerful and Efficient Genome-Wide Association Studies. PLOS Genet 12(2):e1005767. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pgen.1005767\u003c/span\u003e\u003cspan address=\"10.1371/journal.pgen.1005767\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiklas PN (2007) Marker-Assisted Backcrossing QTL for Partial Resistance to Sclerotinia White Mold in Dry Bean. Crop Sci 47(3):935\u0026ndash;942. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2135/cropsci2006.08.0525\u003c/span\u003e\u003cspan address=\"10.2135/cropsci2006.08.0525\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiklas PN, Hauf DC, Henson RA, Grafton KF (2004) Inheritance of ICA Bunsi-Derived Resistance to White Mold in a Navy \u0026times; Pinto Bean Cross. Crop Sci 44(5):1584\u0026ndash;1588. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2135/cropsci2004.1584\u003c/span\u003e\u003cspan address=\"10.2135/cropsci2004.1584\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiklas PN, Johnson WC, Delorme R, Gepts P (2001) QTL Conditioning Physiological Resistance and Avoidance to White Mold in Dry Bean. Crop Sci 41(2):309\u0026ndash;315. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2135/cropsci2001.412309x\u003c/span\u003e\u003cspan address=\"10.2135/cropsci2001.412309x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiklas PN, Porter LD, Kelly JD, Myers JR (2013) Characterization of white mold disease avoidance in common bean. Eur J Plant Pathol 135(3):525\u0026ndash;543. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10658-012-0153-8\u003c/span\u003e\u003cspan address=\"10.1007/s10658-012-0153-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorel J-B, Dangl JL (1997) The hypersensitive response and the induction of cell death in plants. Cell Death Differ 4(8):671\u0026ndash;683. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/sj.cdd.4400309\u003c/span\u003e\u003cspan address=\"10.1038/sj.cdd.4400309\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOladzadabbasabadi A, Mamidi S, Miklas PN, Lee R, McClean P (2019) Linked candidate genes of different functions for white mold resistance in common bean (Phaseolus vulgaris. L) are identified by QTL-based pooled sequencing. In Review\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOlivoto T, L\u0026uacute;cio AD (2020) metan: An R package for multi-environment trial analysis. Methods Ecol Evol 11(6):783\u0026ndash;789. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/2041-210X.13384\u003c/span\u003e\u003cspan address=\"10.1111/2041-210X.13384\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePandian BA, Sathishraj R, Djanaguiraman M, Prasad PVV, Jugulam M (2020) Role of Cytochrome P450 Enzymes in Plant Stress Response. Antioxidants 9(5):454. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/antiox9050454\u003c/span\u003e\u003cspan address=\"10.3390/antiox9050454\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark J-M (2005) The Hypersensitive Response. A Cell Death during Disease Resistance. Plant Pathol J 21(2):99\u0026ndash;101. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5423/PPJ.2005.21.2.099\u003c/span\u003e\u003cspan address=\"10.5423/PPJ.2005.21.2.099\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatishtan J, Hartley TN, Fonseca de Carvalho R, Maathuis FJM (2018) Genome-wide association studies to identify rice salt-tolerance markers: Salinity GWAS. Plant Cell Environ 41(5):970\u0026ndash;982. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/pce.12975\u003c/span\u003e\u003cspan address=\"10.1111/pce.12975\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eP\u0026eacute;rez-Vega E, Pascual A, Campa A, Giraldez R, Miklas PN, Ferreira JJ (2012) Mapping quantitative trait loci conferring partial physiological resistance to white mold in the common bean RIL population Xana \u0026times; Cornell 49242. Mol Breed 29(1):31\u0026ndash;41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11032-010-9522-1\u003c/span\u003e\u003cspan address=\"10.1007/s11032-010-9522-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerseguini JMKC, Oblessuc PR, Rosa JRBF, Gomes KA, Chiorato AF, Carbonell SAM, Garcia AAF, Vianello RP, Benchimol-Reis LL (2016) Genome-Wide Association Studies of Anthracnose and Angular Leaf Spot Resistance in Common Bean (Phaseolus vulgaris L.). PLOS ONE 11(3):e0150506. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0150506\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0150506\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePetzoldt PN, Dickson MH (1996) Straw test for resistance to white mold in beans. Annu Report Bean Improv Coop 39:142\u0026ndash;143\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQi F, Zhang F (2020) Cell Cycle Regulation in the Plant Response to Stress. Front Plant Sci 10:1765. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2019.01765\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2019.01765\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR Development Core Team (2010) a language and environment for statistical computing: reference index. R Foundation for Statistical Computing, Vienna\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaggi L, Caproni L, Carboni A, Negri V (2019) Genome-Wide Association Study Reveals Candidate Genes for Flowering Time Variation in Common Bean (Phaseolus vulgaris L.). Front Plant Sci 10:962. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2019.00962\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2019.00962\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosli HG, Zheng Y, Pombo MA, Zhong S, Bombarely A, Fei Z, Collmer A, Martin GB (2013) Transcriptomics-based screen for genes induced by flagellin and repressed by pathogen effectors identifies a cell wall-associated kinase involved in plant immunity. Genome Biol 14(12):R139. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/gb-2013-14-12-r139\u003c/span\u003e\u003cspan address=\"10.1186/gb-2013-14-12-r139\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaha D, Prasad AM, Srinivasan R (2007) Pentatricopeptide repeat proteins and their emerging roles in plants. Plant Physiol Biochem 45(8):521\u0026ndash;534. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.plaphy.2007.03.026\u003c/span\u003e\u003cspan address=\"10.1016/j.plaphy.2007.03.026\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaintenac C, Lee W-S, Cambon F, Rudd JJ, King RC, Marande W, Powers SJ, Berg\u0026egrave;s H, Phillips AL, Uauy C, Hammond-Kosack KE, Langin T, Kanyuka K (2018) Wheat receptor-kinase-like protein Stb6 controls gene-for-gene resistance to fungal pathogen Zymoseptoria tritici. Nat Genet 50(3):368\u0026ndash;374. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41588-018-0051-x\u003c/span\u003e\u003cspan address=\"10.1038/s41588-018-0051-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScherer A, Christensen GB (2016) Concepts and Relevance of Genome-Wide Association Studies. Sci Prog 99(1):59\u0026ndash;67. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3184/003685016X14558068452913\u003c/span\u003e\u003cspan address=\"10.3184/003685016X14558068452913\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchmutz J, McClean PE, Mamidi S, Wu GA, Cannon SB, Grimwood J, Jenkins J, Shu S, Song Q, Chavarro C, Torres-Torres M, Geffroy V, Moghaddam SM, Gao D, Abernathy B, Barry K, Blair M, Brick MA, Chovatia M, Gepts P, Goodstein DM, Gonzales M, Hellsten U, Hyten DL, Jia G, Kelly JD, Kudrna D, Lee R, Richard MMS, Miklas PN, Osorno JM, Rodrigues J, Thareau V, Urrea CA, Wang M, Yu Y, Zhang M, Wing RA, Cregan PB, Rokhsar DS, Jackson SA (2014) A reference genome for common bean and genome-wide analysis of dual domestications. Nat Genet 46(7):707\u0026ndash;713. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/ng.3008\u003c/span\u003e\u003cspan address=\"10.1038/ng.3008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchwartz HF, Singh SP (2013) Breeding Common Bean for Resistance to White Mold: A Review. Crop Sci 53(5):1832\u0026ndash;1844. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2135/cropsci2013.02.0081\u003c/span\u003e\u003cspan address=\"10.2135/cropsci2013.02.0081\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSegura V, Vilhj\u0026aacute;lmsson BJ, Platt A, Korte A, Seren \u0026Uuml;, Long Q, Nordborg M (2012) An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations. Nat Genet 44(7):825\u0026ndash;830. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/ng.2314\u003c/span\u003e\u003cspan address=\"10.1038/ng.2314\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShaner G, Finney RE (1977) The Effect of Nitrogen Fertilization on the Expression of Slow-Mildewing Resistance in Knox Wheat. Phytopathology 77(8):1051. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1094/Phyto-67-1051\u003c/span\u003e\u003cspan address=\"10.1094/Phyto-67-1051\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh SP, Guti\u0026eacute;rrez JA, Molina A, Urrea C, Gepts P (1991) Genetic Diversity in Cultivated Common Bean: II. Marker-Based Analysis of Morphological and Agronomic Traits. Crop Sci 31(1):23\u0026ndash;29. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2135/cropsci1991.0011183X003100010005x\u003c/span\u003e\u003cspan address=\"10.2135/cropsci1991.0011183X003100010005x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh SP, Schwartz HF, Ter\u0026aacute;n H, Viteri D, Otto K (2014) Pyramiding white mould resistance between and within common bean gene pools. Can J Plant Sci 94(5):947\u0026ndash;954. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4141/cjps2013-321\u003c/span\u003e\u003cspan address=\"10.4141/cjps2013-321\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSoule M, Porter L, Medina J, Santana GP, Blair MW, Miklas PN (2011) Comparative QTL Map for White Mold Resistance in Common Bean, and Characterization of Partial Resistance in Dry Bean Lines VA19 and I9365-3. Crop Sci 51(1):123\u0026ndash;139. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2135/cropsci2010.06.0356\u003c/span\u003e\u003cspan address=\"10.2135/cropsci2010.06.0356\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSouza DA de, Pereira FAC, Dias JA, Leite ME, Santos JB dos (2014) Reaction of common bean progenies to white mold derived from recurrent selection. Ci\u0026ecirc;nc Rural 44(4):583\u0026ndash;587. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1590/S0103-84782014000400001\u003c/span\u003e\u003cspan address=\"10.1590/S0103-84782014000400001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTer\u0026aacute;n H, Lema M, Schwartz HF, Duncan R, Gilbertson R, Singh SP (2006) Modified Petzoldt and Dickson scale for white mold rating of common bean.:2\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eValentini G, Gon\u0026ccedil;alves-Vidigal MC, Elias JCF, Moiana LD, Mindo NNA (2018) Population Structure and Genetic Diversity of Common Bean Accessions from Brazil. Plant Mol Biol Report 36(5\u0026ndash;6):897\u0026ndash;906. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11105-018-1129-4\u003c/span\u003e\u003cspan address=\"10.1007/s11105-018-1129-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVasconcellos RCC, Oraguzie OB, Soler A, Arkwazee H, Myers JR, Ferreira JJ, Song Q, McClean P, Miklas PN (2017) Meta-QTL for resistance to white mold in common bean. PLOS ONE 12(2):e0171685. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0171685\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0171685\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang J, Zhang Z (2021) GAPIT Version 3: Boosting Power and Accuracy for Genomic Association and Prediction. Genomics Proteomics Bioinformatics 19(4):629\u0026ndash;640. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.gpb.2021.08.005\u003c/span\u003e\u003cspan address=\"10.1016/j.gpb.2021.08.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXing H, Fu X, Yang C, Tang X, Guo L, Li C, Xu C, Luo K (2018) Genome-wide investigation of pentatricopeptide repeat gene family in poplar and their expression analysis in response to biotic and abiotic stresses. Sci Rep 8(1):2817. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-018-21269-1\u003c/span\u003e\u003cspan address=\"10.1038/s41598-018-21269-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan H, Liu Y, Zhang K, Song J, Xu W, Su Z (2019) Chromatin State-Based Analysis of Epigenetic H3K4me3 Marks of Arabidopsis in Response to Dark Stress. Front Genet 10:306. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fgene.2019.00306\u003c/span\u003e\u003cspan address=\"10.3389/fgene.2019.00306\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang P, Scheuermann D, Kessel B, Koller T, Greenwood JR, Hurni S, Herren G, Zhou S, Marande W, Wicker T, Krattinger SG, Ouzunova M, Keller B (2021) Alleles of a wall-associated kinase gene account for three of the major northern corn leaf blight resistance loci in maize. Plant J 106(2):526\u0026ndash;535. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/tpj.15183\u003c/span\u003e\u003cspan address=\"10.1111/tpj.15183\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan H, Liu D (2012) Functional disruption of the pentatricopeptide protein SLG1 affects mitochondrial RNA editing, plant development, and responses to abiotic stresses in Arabidopsis: PPR protein SLG1 and mitochondrial RNA editing. Plant J 70(3):432\u0026ndash;444. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-313X.2011.04883.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-313X.2011.04883.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang N, Zhang B, Zuo W, Xing Y, Konlasuk S, Tan G, Zhang Q, Ye J, Xu M (2017) Cytological and Molecular Characterization of \u003cem\u003eZmWAK\u003c/em\u003e -Mediated Head-Smut Resistance in Maize. Mol Plant-Microbe Interactions\u0026reg; 30(6):455\u0026ndash;465. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1094/MPMI-11-16-0238-R\u003c/span\u003e\u003cspan address=\"10.1094/MPMI-11-16-0238-R\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou H, Liu Y, Liang Y, Zhou D, Li S, Lin S, Dong H, Huang L (2020) The function of histone lysine methylation related SET domain group proteins in plants. Protein Sci 29(5):1120\u0026ndash;1137. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/pro.3849\u003c/span\u003e\u003cspan address=\"10.1002/pro.3849\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStatements \u0026amp; declarations\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":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"genetic-resources-and-crop-evolution","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gres","sideBox":"Learn more about [Genetic Resources and Crop Evolution](https://www.springer.com/journal/10722)","snPcode":"10722","submissionUrl":"https://submission.nature.com/new-submission/10722/3","title":"Genetic Resources and Crop Evolution","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Genome-wide association study, Phaseolus vulgaris L., Sclerotinia sclerotiorum (Lib) de Bary, Physiological resistance","lastPublishedDoi":"10.21203/rs.3.rs-4921482/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4921482/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe pathogen \u003cem\u003eSclerotinia sclerotiorum\u003c/em\u003e (Lib.) de Bary is a fungus that causes white mold (WM) in many crops, and it is one of the greatest phytosanitary problems that compromises the productivity and quality of common bean (\u003cem\u003ePhaseolus vulgaris\u003c/em\u003e L.). This study aimed to characterize a panel composed of common bean lines (BLs) from Brazilian farmers with WM resistance using two methods/tests under controlled conditions. The \u0026ldquo;straw test\u0026rdquo; (ST - Ter\u0026aacute;n et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) and \u0026ldquo;seedling straw test\u0026rdquo; (SST - Arkwazee \u0026amp; Myers, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) were used to screen the panel. The disease score (DS) and relative disease progress (RDP) were calculated from consecutive evaluations to obtain the area under the disease progress curve (AUDPC). In addition, the phenotypic means were used to identify genomic regions associated with the WM reaction using the genome-wide association study (GWAS) approach. In total, fifteen accessions (eleven Mesoamerican and four Andean) were selected showing high to moderate resistance, and three regions were identified on chromosomes Pv01, Pv02 and Pv03, coinciding with previously reported quantitative trait loci (QTLs), additionally, twelve genes were indicated for validation. We identified putative regions and genes contributing to physiological resistance to WM in a well-adapted common bean panel. The regions indicated in this panel that are adapted to the Brazilian climate may be important in common bean breeding programs.\u003c/p\u003e","manuscriptTitle":"Partial white mold resistance in a Brazilian-adapted common bean panel","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-13 15:50:39","doi":"10.21203/rs.3.rs-4921482/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-30T09:12:33+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-21T20:46:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-21T08:36:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"16531126026425511094509783048563029901","date":"2024-08-20T19:57:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"5067298584670217746632393051406941602","date":"2024-08-20T05:05:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"142892912927970851501782709330516262293","date":"2024-08-19T13:19:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-16T18:50:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-16T15:51:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-16T15:50:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Genetic Resources and Crop Evolution","date":"2024-08-15T22:35:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"genetic-resources-and-crop-evolution","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gres","sideBox":"Learn more about [Genetic Resources and Crop Evolution](https://www.springer.com/journal/10722)","snPcode":"10722","submissionUrl":"https://submission.nature.com/new-submission/10722/3","title":"Genetic Resources and Crop Evolution","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"619d8aac-e205-4f67-b4d2-23926e112e26","owner":[],"postedDate":"September 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-10-14T16:05:50+00:00","versionOfRecord":{"articleIdentity":"rs-4921482","link":"https://doi.org/10.1007/s10722-024-02209-7","journal":{"identity":"genetic-resources-and-crop-evolution","isVorOnly":false,"title":"Genetic Resources and Crop Evolution"},"publishedOn":"2024-10-10 15:57:43","publishedOnDateReadable":"October 10th, 2024"},"versionCreatedAt":"2024-09-13 15:50:39","video":"","vorDoi":"10.1007/s10722-024-02209-7","vorDoiUrl":"https://doi.org/10.1007/s10722-024-02209-7","workflowStages":[]},"version":"v1","identity":"rs-4921482","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4921482","identity":"rs-4921482","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}