Association mapping of bean reaction to common bacterial blight by SNP markers

ABSTRACT

Common bacterial blight (CBB), caused by Xanthomonas axonopodis pv. phaseoli, is the main bacterial disease affecting the production of beans (Phaseolus vulgaris L.), with significant impacts on Brazilian agriculture. The search for resistant genotypes is a fundamental strategy, given the prevalence and persistence of the pathogen in tropical conditions. Therefore, this study aimed to identify candidate genes for resistance to CBB through association mapping studies, using 126 Middle-American bean genotypes from the germplasm bank of the Instituto de Desenvolvimento Rural do Paraná. A randomized block design with four replications was used. The inoculation method was by cutting the leaves with scissors, using the isolate Xap-139y at a concentration of 10⁷ CFU·mL^-1, and the variables final score and area under the disease progress curve (AUDPC) were evaluated. The expected performance of the means and the genetic parameters were estimated using the Selegen software. The GEN203 genotype was considered resistant for AUDPC and final score. Using multi-locus models (FASTmrMLM, ISIS-EM-BLASSO, mrMLM, pLARmEB), we identified 11 quantitative trait nucleotides associated with CBB resistance and 48 candidate genes in the resistance response. These genes act in several metabolic pathways, suggesting a complex polygenic inheritance. This work contributes significantly to understanding the genetic basis of resistance to CBB and the development of bean cultivars with better adaptability and productivity in regions affected by the disease.

Key words
Xanthomonas axonopodis pv. phaseoli ; multi-locus; genome wide association studies; resistance

Introduction

Common bean (Phaseolus vulgaris L.) is the most widely consumed legume in the world, with extensive cultivation covering approximately 36 million hectares (FAO 2022). It is grown both by large producers with technology and financial resources, as well as by family farmers, who, despite the low technology employed, are responsible for 42% of the beans produced in Brazil (CONAFER 2022). Consumption is strongly favored by Middle-American varieties, mostly attributed to the carioca bean, which is characterized by its cream-colored seed coat with brown stripes, accounting for 70% of national production. Black beans are also very popular, accounting for 15% of production (Pereira et al. 2019).

Beans are a highly productive crop. However, the impact of diseases can lead to a significant loss of productivity or even make certain areas unviable for cultivation, causing a drastic drop in economic production (Canale et al. 2021).

Among the main crop diseases in terms of economic importance, there is common bacterial blight (CBB), caused by the bacterium Xanthomonas axonopodis pv. phaseoli, stand out for reduction in quality: physiological, health, nutritional, and commercial quality (Ferreira 2021). The symptoms of the disease are dark necrotic spots with a yellow halo on leaves, steams and pods. It is spread by irrigation water, rain, wind, and contaminated farm equipment (Ferreira 2021).

The use of pest and disease management is necessary to reduce disease pressure in the field such as CBB. These can be defined as a set of tools (biological, genetic, cultural, physical, behavioral and chemical) which, if managed strategically, can keep pest and pathogen populations under control. Therefore, genetic management is a sustainable and profitable method in the long term, being compatible with other alternative control measures, as well as reducing production costs and environmental impact (Masironi et al. 2023).

The use of tools such as genome wide association studies (GWAS) becomes a powerful strategy for identifying genomic regions associated with phenotypic traits under genetic control. These tools are applied to populations with diverse genetic material, including genotypes from germplasm collections, identifying which single nucleotide polymorphisms (SNPs) markers have a significant association with complex phenotypic traits, making it possible to identify multiple polymorphisms that occur naturally within a species (Schmutz et al. 2014, Delfini et al. 2021, Simons et al. 2021, Barbosa et al. 2022, Zia et al. 2022).

After to Schmutz et al.’s (2014) publication, researchers have been able to identify association between traits and markers. This includes studies on resistance to the Xanthomonas axonopodis pv. phaseoli pathosystem (Simons et al. 2021, Barbosa et al. 2022), to optimize the selection of resistant genotypes.

Therefore, due to the representativeness of the cultivation and consumption of the bean crop for Brazilians and the expressiveness of the CBB disease compromising production, the purpose of this study was to uncover genotype-to-phenotype relationships for CBB resistance in bean genotypes, to identify candidate genes for resistance, through associative mapping studies, using multi-locus models. This study aimed to contribute to understanding the genetic basis of resistance to CBB and to help develop bean varieties with better adaptability and productivity in regions affected by the disease.

Material and methods

Location, germplasm, and experimental design

The experiment to evaluate the resistance of genotypes to Xanthomonas axonopodis pv. phaseoli in bean leaves was carried out in a greenhouse belonging to the Instituto de Desenvolvimento Rural do Paraná (IDR-Paraná), Londrina, Paraná, Brazil. The average temperatures inside the greenhouse for the experiments were 35°C day and 26°C night, and 31°C day and 20°C night for the first and second experiments, respectively, with a 12-hour photoperiod for both experiments. A total of 126 bean genotypes from the IDR-Paraná Middle-American Bean Germplasm Bank was evaluated. The bank comprises genotypes from different Brazilian research institutions, containing strains and local races from breeding programs.

The experiment to evaluate the reaction to CBB in leaves was conducted using a randomized block design with four replications. Seeds were sown in trays containing substrate “Carolina soil padrão” (Carolina soil^TM). After seven days, the seedlings were transplanted into 5-L pots. The experiment was conducted in October 2021 and March 2022.

Inoculum preparation

The isolate Xap 139-y of Xanthomonas axonopodis pv. phaseoli, belonging to the collection of the Laboratório de Melhoramento de Plantas of the Universidade Estadual do Norte Fluminense Darcy Ribeiro, was used. The isolate was grown in solid dextrose yeast glucose sucrose culture medium and incubated in biochemical oxygen demand at 26°C for 36 hours (Rodrigues Neto et al. 1986).

After the colonies had grown, they were suspended in autoclaved water, and the concentration was adjusted to 10⁷ CFU·mL^-1 (Trindade et al. 2015).

Plant inoculation

Inoculation was carried out during the V3-V4 vegetative stage. One trefoil from each plant was subjected to the inoculation method by cutting with scissors, using a concentration of 10⁷ CFU·mL^-1, two parallel cuts of 1 cm each were made along the median margin of the leaflet, introducing approximately 0.1 mL of the suspension into each leaflet, and a second trefoil was used as a control by inoculating with deionized water (Andrus 1948).

Evaluation of the reaction to common bacterial blight

Evaluations were carried out 24 hours after the pathogen was inoculated. Severity was determined daily for 16 days in the first experiment and 20 days in the second experiment. Plants were evaluated visually using a severity scale ranging from 1 to 5:

1. highly resistant (no apparent lesion);

2. resistant (1 to 5% necrosis);

3. moderately resistant (6 to 25% necrosis);

4. susceptible (26 to 50% necrosis);

5. highly susceptible (> 50% necrosis)¹.

The scores obtained from the daily assessments of disease severity were used to calculate the area under the disease progress curve (AUDPC), according to the following equation by Shaner and Finney (1977) (Eq. 1):

$AACPD=\underset{1}{\overset{n-1}{\Sigma }} \left(\frac{x_{\mathrm{i}}+x_{\mathrm{i}+1}}{2}\right)\left({\mathrm{t}}_{\mathrm{i}+1}-{\mathrm{t}}_{\mathrm{i}}\right)$ (1)

Where: n = number of observations; x_i = disease severity in the “i”-th observation; t_i = time in days in the “i”-th observation.

Statistical analysis

The variance components and genetic parameters were estimated by the restricted maximum likelihood (REML) method, and the prediction of genetic values performed by best linear unbiased prediction (BLUP) using the 25 complete blocks model in various locations, available in the SELEGEN-REML/BLUP software (Resende 2016) (Eq. 2).

$\mathrm{y} = \mathrm{Xr} + \mathrm{Zg} + \mathrm{Wi} + \mathrm{e}$ (2)

Where: y = the vector of data; r = the vector of repetition effects (assumed to be fixed) added to the general average; g = the vector of genotypic effects (assumed to be random); i = the vector of genotype × environment interaction effects (random); e = the vector of errors or residues (random).

Genotypic analysis

Genotyping with SNPs obtained by sequencing amounted to 25,011 SNPs DNA was extracted from lyophilized leaves collected from a single plant of each accession. The extracted DNA was purified using the Genomic DNA Clean and Concentrator Kit (Zymo Research, CA, United States of America). Genomic DNA was quantified using the Quant-iT PicoGreen dsDNA Assay Kit (Thermo Fisher Scientific), and 100 ng of DNA from each genotype was used to prepare the libraries.

The genomic DNA was digested with the restriction enzyme CviAII (recognition site C’ATG). Genomic libraries were sequenced on the Illumina HiSeq 4000 sequencer (Illumina, San Diego, CA, United States of America) using the 100-bp single-end protocol at the DNA Technologies and Expression Analysis Core Laboratory, located at the Genome Center, University of California, Davis, CA, United States of America (Delfini et al. 2021). Genotypic analyses were carried out using TASSEL software version 5.2.53 (Bradbury et al. 2007).

Population structure

Population structure was inferred using the Bayesian clustering algorithm in the STRUCTURE 2.3.4 software framework (Pritchard et al. 2000). The model comprised 10,000 burn-ins, 10,000 Monte Carlo Markov Chain simulations, and 10 replicates for hypothetical numbers of subpopulations (K) between 1 and 10. The statistical parameter ΔK was used to determine the number of groups (Evanno et al. 2005). Only genotypes with a membership coefficient ≥ 0.6 were assigned to a genetic group, while those with coefficients < 0.6 were placed in a group designated as a mixture.

Multi-locus genome wide association analysis

To identify the SNPs associated with resistance to CBB in bean leaves, the R platform was used, with the mrMLM.GUI package (Zhang et al. 2018). The analysis was enriched by applying six advanced multi-locus GWAS approaches: FASTmrEMMA (Wen et al. 2018), pKWmEB (Ren et al. 2018), FASTmrMLM (Tamba and Zhang 2018), ISIS-EM-BLASSO (Tamba et al. 2017), mrMLM (Wang et al. 2016), and pLARmEB (Zhang et al. 2017). For all these methodologies, a critical LOD value of 3 was established for the identification of significant quantitative trait nucleotides (QTNs). The phenotypic data used in the GWAS analyses comprised the averages of the AUDPC and final score (FS) variables. The significance of QTNs was determined by their detection in at least two different methods, a rigorous strategy aimed at ensuring reliability in the selection of favorable alleles and the identification of candidate genes. In addition, the GAPIT3 package (Wang and Zhang 2021) was used to draw up SNP density distribution graphs, making it easier to interpret the genomic data.

Identification of favorable alleles

Favorable alleles were identified for each of the QTNs that showed repeatability, i.e., the alleles associated with reduced disease severity. The number of favorable alleles in each accession was then identified, and it was visualized in a boxplot whether the accumulation of these favorable alleles resulted in phenotypes with more desirable characteristics.

Search for candidate genes

A study was performed to identify genes for genes in the genome around each QTN identified. The search radius was 294 kbp, determined according to the decay of linkage disequilibrium corrected for population structure and kinship (r²vs) (Delfini et al. 2021). The genes were grouped by defense mechanisms generated by physiological processes that induce the formation of resistance signaling proteins.

Results and discussion

Genetic parameters

Most of the phenotypic variance observed in the variables was attributed to the environment, which means that external conditions had a greater impact on the expression of these characteristics than genetic differences between individuals (Table 1). It was found that genotypic and environmental variance accounted for 46 and 40.5% of the phenotypic variance in the final score, respectively. For the AUDPC variable, genotypic and environmental variance accounted for 44 and 34% of the phenotypic variance, respectively. This shows that the characteristics related to the severity of the disease in question are highly influenced by environmental conditions. This influence must be considered when studying and managing the disease, as well as when selecting more resistant genotypes.

Table 1
Estimates of the genetic parameters via the restricted maximum likelihood procedure, for the resistance variables final score (FS), and area under the disease progress curve in bean leaves in two seasons, inoculated with Xanthomonas axonopodis pv. phaseoli, at a concentration of 10⁷ CFU·mL^-1, using the scissor inoculation method. Universidade Estadual do Norte Fluminense Darcy Ribeiro, Campos dos Goytacazes, RJ, Brazil, 2024.

Genetic parameters	Variable
	FS	AUDPC
Vg	0.318860	16.961017
Vint	0.090499	8.164510
Ve	0.278983	13.054553
Vf	0.688342	38.180080
h2g	0.463230	0.444237
c2int	0.131474	0.213842
h2mc	0.799184	0.748002
Acclon	0.893971	0.864871
rgloc	0.778926	0.675051
Overall mean	4.121618	40.163174

• Vg: genotypic variance; Vint: variance of the genotype × environment interaction; Ve: residual variance; Vf: individual phenotypic variance; h2g: heritability of individual plots in the broad sense, i.e. of the total genotypic effects; c2int: coefficient of determination of the effects of the genotype × environment interaction; h2mc: heritability of the genotype mean, assuming complete survival; Acclon: accuracy of genotype selection, assuming complete survival; rgloc: genotypic correlation between performance in the various environments.

High broad-sense heritability and selection accuracy indicate the potential for selecting superior genotypes. According to Resende (2002), selective accuracy is a crucial indicator when selecting genotypes for genetic improvement. It assesses the reliability with which genotypes are ranked according to their true genetic values, providing information on the accuracy of inferences about genotypic means, the correct ordering of genotypes for selection, and the effectiveness of the inference about the genotypic value. The broad-sense heritability estimated in this study indicates that the CBB resistance variables obtained values of medium magnitude (between 0.15 and 0.50) according to Resende’s classification (2002). Even though a broad-sense heritability coefficient of medium magnitude was observed, there are still good possibilities for selection, given the high average heritability estimates and selective accuracy (Table 1). This means that the variability observed in the characters is partly explained by genetic differences between individuals, which is an important factor for successful genetic improvement. The results obtained in this study suggest that selecting individuals with desirable characteristics for these traits can be effective in obtaining superior lines for resistance to CBB.

The variance of the genotype × environment interaction (Vint) combined with the coefficient of determination of the effects of the genotype × environment interaction (c2int) 13% for FS and 21% for AUDPC tells us how much of the phenotypic variance was explained by the G×A interaction, indicating that the complex type of interaction was not predominant in this experiment (Table 1). The genotype × environment interaction data shows that there was good agreement between the two experiment seasons. The results of Vint, c2int and rgloc indicate that the genotype × environment interaction was not a determining factor in the performance of the genotypes.

Genetic parameters such as heritability, selection accuracy, phenotypic variance, and genotypic variance are highly specific to each variable, population, and environment. This means that they can be easily altered by various factors, such as sample size, number of repetitions, population density, and the estimation methods used. As a result, comparing genetic parameters from different studies can become inconsistent most of the time, as the conditions under which the studies were carried out can be very different (Vencovsky and Barriga 1992).

The averages evaluated for FS and AUDPC were classified according to the REML/BLUP adjusted averages, and the degree of resistance based on AUDPC was stratified into four groups showing a range of 23.72 to 46.30. It was identified that 64.56% of the genotypes were highly susceptible, 33.07% of the genotypes were susceptible, 1.57% of the genotypes were considered moderately resistant, and only 0.78% of the genotypes were classified as resistant for the FS variable, i.e., only the GEN203 genotype. For the degree of resistance based on AUDPC, it was observed that 33.07% of the genotypes were highly susceptible, 48.03% susceptible, 14.96% moderately resistant, 3.14% of the genotypes were considered resistant, and only one genotype was considered highly resistant, GEN203 corresponding to 0.78% (data not shown).

Genotyping, population structure, and linkage disequilibrium decay

In total 25,011 SNPs were used for GWAS analysis across 126 genotypes phenotyped for CBB (Fig. 1). The population structure analysis indicates the formation of two large groups based on the SNPs, one made up predominantly of genotypes from the carioca bean commercial group and the other made up of genotypes from the black bean group (Fig. 2).

Figure 1
Density distribution of single nucleotide polymorphisms (SNPs) identified by genotyping by sequencing along the common bean genome at a window size of 1 Mb.

Figure 2
Population structure analysis using 126 genotypes of Phaseolus vulgaris L. with K = 2: (a) made up preferentially of genotypes from the carioca bean commercial group, identified in red; (b) made up preferentially of genotypes from the black bean commercial group, identified in green.

GWAS result multi-locus method

It has been found that the inheritance of CBB resistance in bean is thought to be oligogenic or polygenic, involving multiple genes with variation in expression levels and genetic interactions (Viteri et al. 2014). Therefore, using multi-locus approaches, it was possible to identify QTNs. Manhattan plot analysis and QQplot graphs (Fig. 3) revealed a clear association between the target phenotypic traits and the QTNs identified, showing consistency in the results for all the variables studied and in both experiments conducted. However, only QTNs detected by more than one method were considered reliable for inclusion in this study, resulting in the selection of the FASTmrMLM, ISIS-EM-BLASSO, mrMLM and pLARmEB methods as the most relevant (Table 2). In total, 11 QTNs were identified. The detailed analysis enabled the chromosomal location of these QTNs and their specific position in the genome, facilitating the subsequent identification of the genes involved and their possible functions.

Figure 3
Manhattan plot graphs with the multi-locus methods FASTmrMLM, ISIS-EM-BLASSO, mrMLM and pLARmEB. Axis X represents the chromosomes, and Axis Y represents -log10(p-value). Quantile-quantile plots with the multi-locus methods FASTmrMLM, ISIS-EM-BLASSO, mrMLM and pLARmEB.

Table 2
Quantitative trait nucleotides (QTNs) associated with resistance to common bacterial blight in bean leaves, which were detected at least twice using different methods and/or in different environments.

Trait	SNP	Chr	Position (pb)	QTN effect	LOD score	PVE (%)	MAF	Gen	Env	Methods
AUDPC	S02_339452	2	339,452	0.86~1.02	3.72~3.79	2.78~3.93	0.264	A	1	2,4
	S03_49566239	3	49,566,239	1.37~1.46	4.0~5.08	5.43~6.14	0.184	A	1	1,2
	S05_11203900	5	11,203,900	-2.63~-1.48	3.21~4.55	3.38~10.13	0.0894	A	1	3,4
	S05_180803	5	180,803	-1.27~-1.25	3.62~5.13	7.49~7.77	0.48	G	1	1,2,4
	S09_7645146	9	7,645,146	1.73~1.74	3.27~4.48	5.34~5.43	0.104	T	1	1,2,4
	S01_43414676	1	43,414,676	1.94~2.28	4.19~5.53	8.07~11.10	0.252	G	2	2,3
	S01_50735044	1	50,735,044	2.43~3.79	3.21~4.46	4.86~11.83	0.0813	T	2	1,2,4
	S07_29142414	7	29,142,414	1.43~1.93	3.90~4.00	5.65~10.25	0.4228	A	2	2,3
FS	S08_1771253	8	1,771,253	0.46~0.62	3.07~4.26	3.09~9.75	0.0244	C	1	1,2,3
	S09_14077754	9	14,077,754	0.22~0.29	4.29~5.21	7.10~11.38	0.2846	C	2	3,4
	S11_50935764	11	50,935,764	-0.35~-0.24	4.4~5.66	7.34~14.46	0.2439	G	2	3,4

• AUDPC: area under the disease progress curve; FS: final score; LOD: the significant threshold for transformed P-value; PVE: phenotypic variation explained; MAF: minor allele frequency; Env: environment: 1) summer, 2) winter; methods: 1) FASTmrMLM, 2) ISIS-EM-BLASSO, 3) mrMLM, 4) pLARmEB.

Favorable alleles

Favorable alleles were identified for each of the QTNs that showed repeatability for lower disease incidence. The box-plot results showed a gradual reduction for all variables in both experiments (Fig. 4).

Figure 4
Accumulation of favorable alleles in relation to the averages for each of the two variables in two seasons, for leaf resistance to common bacterial blight present in the common bean panel.

Identification of candidate genes

Forty-eight candidate genes were identified that are directly related to resistance or to the physiological processes of the plant that lead to conferring stimuli for resistance to bean leaf blight (Table 3).

Table 3
Functional annotation of the 48 favorable genes close to quantitative trait nucleotides according to linkage disequilibrium decay, associated with resistance to bacterial leaf blight in beans.

Candidate Genes	Trat	Position	Chr	Functional annotation
Phvul.001G175900	AUDPC	43414676	1	Defense response
Phvul.001G176800	AUDPC	43414676	1	Activity/serine/threonine protein kinase
Phvul.001G178400	AUDPC	43414676	1	Protein kinase activity/protein tyrosine kinase
Phvul.001G255800	AUDPC	50735044	1	Protein tyrosine/serine/threonine phosphatase
Phvul.002G002000	AUDPC	339452	2	Jasmonate domain-containing protein
Phvul.002G002200	AUDPC	339452	2	Serine/threonine kinase
Phvul.002G002600	AUDPC	339452	2	Protein kinase
Phvul.003G255801	AUDPC	49566239	3	Jasmonate o-methyltransferase
Phvul.003G255900	AUDPC	49566239	3	Jasmonate o-methyltransferase
Phvul.003G256100	AUDPC	49566239	3	Jasmonate o-methyltransferase
Phvul.003G256300	AUDPC	49566239	3	Jasmonate o-methyltransferase
Phvul.007G172900	AUDPC	29142414	7	Tpl-binding domain in jasmonate signalling
Phvul.007G174200	AUDPC	29142414	7	Serine/threonine-protein kinase
Phvul.007G174700	AUDPC	29142414	7	Protein serine/threonine phosphatase activity
Phvul.008G018200	FS	1771253	8	Protein serine/threonine phosphatase activity
Phvul.008G019200	FS	1771253	8	Protein tyrosine kinase activity
Phvul.008G021001	FS	1771253	8	Protein tyrosine kinase activity
Phvul.008G021800	FS	1771253	8	Stress-induced protein kin1-related
Phvul.008G022300	FS	1771253	8	Stress-induced protein kin1-related
Phvul.008G022400	FS	1771253	8	Stress-induced protein kin1-related
Phvul.009G085400	FS	14077754	9	Protein phosphatase/protein serine/threonine
Phvul.009G085500	FS	14077754	9	Protein kinase superfamily protein/protein Kinase-like
Phvul.009G086400	FS	14077754	9	Protein phosphatase/protein serine/threonine
Phvul.009G087500	FS	14077754	9	Interacting serine/threonine-protein kinase
Phvul.011G192900	FS	50935764	11	Disease resistance protein signature
Phvul.011G193100	FS	50935764	11	Disease resistance protein signature
Phvul.011G193800	FS	50935764	11	Disease resistance protein signature
Phvul.011G193966	FS	50935764	11	Disease resistance protein signature
Phvul.011G194800	FS	50935764	11	Disease resistance protein signature
Phvul.011G195000	FS	50935764	11	Disease resistance protein signature
Phvul.011G195200	FS	50935764	11	Disease resistance protein signature
Phvul.011G195300	FS	50935764	11	Disease resistance protein signature
Phvul.011G195400	FS	50935764	11	Disease resistance protein signature
Phvul.011G195500	FS	50935764	11	Disease resistance protein signature
Phvul.011G195600	FS	50935764	11	Disease resistance protein signature
Phvul.011G195751	FS	50935764	11	Disease resistance protein signature
Phvul.011G193200	FS	50935764	11	Protein tyrosine kinase activity
Phvul.011G193400	FS	50935764	11	Protein serine/threonine kinase activity
Phvul.011G193900	FS	50935764	11	Protein kinase activity
Phvul.011G194100	FS	50935764	11	Threonine-specific protein kinase
Phvul.011G194200	FS	50935764	11	Protein serine/threonine kinase activity
Phvul.011G194300	FS	50935764	11	Threonine-specific protein kinase
Phvul.011G194401	FS	50935764	11	Protein serine/threonine kinase activity
Phvul.011G194600	FS	50935764	11	Protein serine/threonine kinase activity
Phvul.011G194700	FS	50935764	11	Protein serine/threonine kinase activity
Phvul.011G196200	FS	50935764	11	Protein serine/threonine kinase activity
Phvul.011G197500	FS	50935764	11	Protein tyrosine kinase activity threonine protein kinase
Phvul.011G197700	FS	50935764	11	Serine/threonine protein kinase

• AUDPC: area under the disease progress curve; FS: final score.

Previous studies have emphasized the importance of jasmonate acid in pathogen resistance in various plant species, highlighting the function of these phytohormones as mediators of complex defensive responses (Ferraz et al. 2015). These investigations highlight the ability of plants to use hormonal signals to orchestrate a coordinated response to pathogenic stress.

The initiation of the defense system in plants is triggered by the recognition of pathogens by specific receptors, which activate a complex intracellular signaling network, regulating defense responses (Spoel and Dong 2012). This network involves several gene families that encode crucial proteins in the immune response, including serine/threonine kinases (RSTKs), which are essential in innate immunity triggered by microorganism-associated molecular patterns (MAMPs) (Ranf 2018). In this work, it was possible to identify 19 genes that code for RSTK proteins, highlighting their central role in defense against pathogens through the recognition of MAMPs and the subsequent activation of immune responses. In addition, 10 genes associated with RLK-type receptor protein kinases were identified, emphasizing the importance of these proteins in the perception of extracellular signals and the initiation of signaling pathways that mediate defensive responses. Melotto et al. (2017) highlighted the role of receptor-like kinases (RLKs) and receptor-like proteins in mediating resistance to bacterial pathogens in common bean. These proteins are involved in the recognition of microbial elicitors, activating defense responses such as the production of antimicrobial compounds and reinforcement of the plant cell wall.

In Jurado et al.’s (2022) work with sequencing analysis, the Phvul.011G193400 gene located on chromosome 11 was differentially expressed against Colletotrichum lindemuthianum in bean. The Phvul.011G193400 gene is a typical R gene with a serine/threonine protein kinase that may be associated with the initiation of plant defense response signals and pathogen recognition (Monson et al. 2022).

The Phvul.011G193100 gene was differentially expressed in bean 72 hours after inoculation with C. lindemuthianum and was identified as a protein containing leucine-rich regions that play a crucial role in activating defense responses when detecting virulent pathogen proteins during infection (Alvarez-Diaz et al. 2022).

The genes Phvul.011G194600, Phvul.011G194401, Phvul.011G194700, and Phvul.011G196200 were identified in Quezada et al.’s (2019) work with bean plants, demonstrating that they are Cysteine (C)-rich receptor-like kinases (CRKs) are a class of RLK that play vital roles in disease resistance and cell death in plants.

The Phvul.002G002000 gene has been identified as a jasmonate-responsive transcriptional regulator (Aparicio-Fabre et al. 2013). Jasmonate signaling involving transcriptional genes plays an essential role in growth and development, stress responses, including defense against insects and microbial pathogens, as well as responses to abiotic stresses such as drought, ultraviolet radiation and ozone (Browse 2009).

The most commonly used markers for assisted selection and the development of CBB-resistant cultivars are BC420 and SAP 6. Based on the results of this work, the QTNs identified showed promising potential for use in resistance disease breeding programs, focusing on pyramiding of favorable alleles, which could be assessed by Marker-assisted selection.

The results presented here highlight the significance of quantitative resistance genes in managing resistance to CBB in bean plants. The discovery of these genes makes it possible to develop breeding strategies aimed at combining these resistance genes to provide effective and long-lasting protection against pathogens. Quantitative resistance, by imposing less selective pressure on pathogens, offers a sustainable approach to disease management in agricultural crops, promoting the durability of resistance and mitigating the risk of resistance outgrowth.

Conclusion

With the results obtained in this work, it was possible to observe a high level of genetic variability among the bean genotypes studied for resistance to the disease common bacterial leaf blight. Using the leaf score scale, it was possible to determine the identification of a genotype that is highly resistant to CBB, which will serve as a source of resistance to be introduced into bean breeding programs, with the aim of improving the bean crop in terms of resistance to CBB.

Through the study of genome-wide association analysis, it was possible to search for genomic regions of interest for resistance to CBB. Many polymorphisms were identified, which resulted in 11 QTNs associated with resistance to CBB, of which 48 candidate genes were corroborated. The genes identified in this work play different roles in the plant’s metabolism, and it is therefore recommended that they be used as targets for future studies to improve resistance to CBB in beans. The associated SNPs have the potential to be used in assisted selection.

¹

Pastor-Corrales, M. A., Beebe, S. E. and Correa, F. J. (1981). Comparing two inoculation techniques for evaluating resistance in beans to Xanthomonas campestris pv. phaseoli. In: International Conference on Plant Pathogenic Bacteria. Centro Internacional de Agricultura Tropical, 493-503.

ACKNOWLEDGMENTS

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, that financed part of this study.

How to cite: Fukuji, K. K. N., Fukuji, A. Y. S., Gonçalves, L. S. A., Delfini, J., Cirino, V. M. and Rodrigues, R. (2025). Association mapping of bean reaction to common bacterial blight by SNP markers. Bragantia, 84, e20240120. https://doi.org/10.1590/1678-4499.20240120

FUNDING

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Finance Code 001

DATA AVAILABILITY STATEMENT

The data will be available to the corresponding author on request.

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*

Corresponding author: [e-mail]

Section Editor:

Carlos Alberto Scapim https://orcid.org/0000-0002-7047-9606

CONFLICT OF INTEREST

Nothing to declare.

Publication Dates

• Publication in this collection
  20 Dec 2024
• Date of issue
  2025

History

• Received
  09 June 2024
• Accepted
  10 Sept 2024