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Article

Marker-Assisted Breeding for Pyramiding Multiple Resistance to Soybean Fungal Diseases

by
Carla María Lourdes Rocha
1,
María Gabriela García
1,
Esteban Mariano Pardo
1,
José Ramón Sánchez
1,
Atilio Pedro Castagnaro
1,* and
María Amalia Chiesa
2,*
1
Instituto de Tecnología Agroindustrial del Noroeste Argentino (ITANOA), Estación Experimental Agroindustrial Obispo Colombres (EEAOC)-Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), CCT NOA Sur. Av. William Cross 3150, C.P. T4101XAC Las Talitas, Tucumán, Argentina
2
Laboratorio de Ecofisiología Vegetal (LEFIVE), Instituto de Investigaciones en Ciencias Agrarias de Rosario (IICAR), Universidad Nacional de Rosario (UNR)-Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Parque Villarino S/N, C. P. S2125ZAA Zavalla, Santa Fe, Argentina
*
Authors to whom correspondence should be addressed.
Agronomy 2026, 16(7), 754; https://doi.org/10.3390/agronomy16070754
Submission received: 6 February 2026 / Revised: 30 March 2026 / Accepted: 31 March 2026 / Published: 2 April 2026
(This article belongs to the Special Issue Functional Genomics and Molecular Breeding of Soybeans—2nd Edition)
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Abstract

Fungal diseases such as soybean stem canker (SSC), frogeye leaf spot (FLS), and sudden death syndrome (SDS) cause substantial yield losses in soybean worldwide. This study aimed to pyramid major resistance genes and QTLs against these diseases through marker-assisted backcrossing (MABC). Diagnostic SSR markers, linked to Rdm4 (SSC), Rcs3 (FLS), and SDS resistance QTLs, were validated and successfully employed for foreground and background selection in crosses between the elite cultivar A8100RR and the resistant donor ‘Forrest’. Molecular analyses confirmed the effective introgression and fixation of multiple resistance loci in BC2F5 lines. Under artificial inoculation, lines R30-11 and R25-13 displayed high resistance levels to Diaporthe aspalathi, Cercospora sojina, Fusarium virguliforme, and F. tucumaniae. Genotype R30-11 exhibited the most consistent resistance across pathogens, while R25-13 combined multi-disease resistance with glyphosate tolerance and stable agronomic performance under field conditions comparable to commercial cultivars. These results represent, to our knowledge, the first report of successful pyramiding genes and QTLs against three distinct fungal diseases (SSC, FLS, and SDS) in soybean through MABC. The developed lines constitute valuable germplasm for breeding programs designed to achieve broad-spectrum, durable and sustainable disease management.

Graphical Abstract

1. Introduction

Soybean (Glycine max (L.) Merrill) is one of the most nutritionally and economically important legume crops worldwide, providing a major source of protein and oil for both human and animal nutrition [1]. Global soybean production reached approximately 397 million metric tons across 140 million hectares in 2023/24, reflecting its central role in global food and feed systems. However, sustained soybean productivity is increasingly challenged by biotic and abiotic stresses that significantly reduce yield stability and seed quality [2]. Over recent decades, yield losses attributable to biotic stress have increased markedly, with fungal pathogens representing the most damaging group affecting soybean production at a global scale [3,4]. In the context of climate change and rising food demand, the development of high-yielding cultivars with durable, broad-spectrum disease resistance has become a critical objective of modern soybean breeding programs [5].
Among the most economically important fungal diseases of soybean worldwide are soybean stem canker (SSC), frogeye leaf spot (FLS), and sudden death syndrome (SDS), which affect aerial tissues, roots, and vascular systems, respectively [6,7,8]. Although integrated disease management strategies based on fungicides, crop rotation, and seed sanitation are widely used, host genetic resistance remains the most effective, economical, and environmentally sustainable approach for long-term disease control. SSC is a destructive disease that manifests typical symptoms of necrotic cankers in the main stem and in the branch insertions and interveinal necrosis in leaves, from mid-season to crop maturity, often leading to plant death and severe yield losses [3]. It is caused primarily by two causal agents: Diaporthe aspalathi (E. Jansen, Castl. & Crous), Da (syn. Diaporthe phaseolorum var. meridionalis) and D. caulivora, Dc (Athow & Caldwell) J.M. Santos, Vrandecic & A.J.L. Phillips (syn. D. phaseolorum var. caulivora, Dpc) [9,10,11,12]. Five non-allelic resistance (R) genes (Rdm1–Rdm5) control SSC caused by D. aspalathi [13,14,15,16], although none individually provide complete resistance across all pathogen races [11]. Particularly, Rdm4 and Rdm5 were mapped on soybean chromosome 8, enabling the identification of tightly linked molecular markers suitable for marker-assisted selection (MAS) [17]. Previous studies have demonstrated that Rdm4 is a particularly stable and effective source of resistance across diverse genetic backgrounds [14].
FLS, caused by Cercospora sojina Hara, reduces yields up to 60% under warm, humid conditions. Symptoms of FLS develop primarily on foliage even though seeds, pods, and stems can also become infected [18,19]. The pathogen exhibits high genetic and physiological diversity, with multiple races reported worldwide [20]. Resistance to FLS is conferred by several dominant genes, including Rcs1, Rcs2, Rcs3, and RcsPeking [21,22,23,24]; later, an additional resistance source, RcsMte.Rdo, was identified in cv. ‘Monte Redondo’ (Bulos, personal communication). Notably, Rcs3 has shown broad effectiveness against all currently characterized isolates [25]. The genetic mapping of Rcs3 has led to the identification of diagnostic SSR markers such as Satt244, which have been widely used to accelerate the development of FLS-resistant cultivars through MAS [19].
SDS, caused by soil-borne species of Fusarium, including F. virguliforme and F. tucumaniae, represents another major constraint to soybean production across continents [26,27]. Disease development leads to root necrosis and toxin-mediated foliar symptoms, resulting in yield losses ranging from 20 to over 80% under favorable conditions [28,29,30]. Resistance is conferred by several quantitative trait loci (QTLs) and strongly influenced by environmental factors [31]. More than 200 quantitative trait loci (QTLs) associated with SDS resistance have been reported [32,33,34,35]. Interestingly, key QTLs mapped in cv. ‘Forrest’ explain almost 91% of resistance variance [36].
A main goal of current soybean breeding programs is the development of elite cultivars combining high yield potential with durable, broad-spectrum resistance to multiple pathogens. Molecular markers facilitate efficient germplasm screening and enable the identification and combination of specific resistance loci, helping to overcome the narrow genetic base characteristic of modern soybean breeding [37]. In this context, the gene pyramiding strategy, defined as the combination of multiple resistance genes and/or QTLs into a single genetic background through targeted crosses, represents a powerful yet underutilized strategy for enhancing disease durability and stability across environments.
Therefore, the aims of this work were (i) to validate molecular markers (MM) linked to Rdm4-5 and Rcs3, RcsMte.Rdo, and RcsPeking genes; (ii) to identify prevalent alleles conferring resistance to SSC and FLS in elite soybean germplasm using SSR-based genotyping; and (iii) to develop improved soybean lines carrying pyramided resistance to SSC, FLS, and SDS through marker-assisted backcrossing (MABC), providing a breeding strategy with broad applicability for sustainable soybean disease management.

2. Materials and Methods

2.1. Plant Material

A total of 56 soybean genotypes belonging to the Soybean Breeding Program (SBP) of the Estación Experimental Agroindustrial Obispo Colombres (EEAOC) germplasm collection, including commercial varieties and experimental lines, were characterized. Reference genotypes for each disease carrying different R genes were also pheno- and genotypically evaluated. Cultivar ‘Dowling’ was used as a reference for Rdm4 presence, cv. ‘Hutcheson’ for Rdm4 and Rdm5, cv. ‘Davis’ for Rcs3, cv. ‘Monte Redondo’ for RcsMte.Rdo, cv. ‘Peking’ for RcsPeking, and cv. ‘Forrest’ for SDS QTLS (Table S1). The behavior of genotypes against natural inocula of D. aspalathi, C. sojina, and Fusarium spp. under field conditions was recorded in two seasons in macro plots situated at different locations across the northwest region of Argentina (NWA).

2.2. Genotyping

2.2.1. DNA Purification

Seedlings of the cultivars were grown under greenhouse conditions in EEAOC, Las Talitas, Tucumán, Argentina (26°50′ S, 65°12′ W). Young leaf tissue was collected at vegetative stage V2 and flash-frozen in liquid nitrogen. Total DNA was extracted using the CTAB method [38] with minor modifications. DNA concentration was measured with a spectrophotometer, and DNA quality was assessed by running the samples through agarose gel (0.7% w/v) electrophoresis, stained with GelRed® (1/100 dilution) (Biotium, Inc., Hayward, CA, USA). The DNA concentration of each sample was estimated, and dilutions of 100 ng/μL were prepared.

2.2.2. SSR Amplifications

DNA samples from each cultivar were characterized by amplification of SSR Sat_162 and Satt233 linked to Rdm4 and Rdm5 genes, respectively [17]; Satt244 was linked to Rcs3, RcsPeking [23] and RcsMte.Rdo (Bulos, personal communication), and Satt163, Satt214, Satt270, Satt309, Satt354, Satt371, Satt570 were linked to different QTLs for SDS resistance [36] (Table S1). SSRs were amplified according to Chiesa et al. [17] by using a MyCycler Bio-Rad thermal cycler (Hercules, CA, USA). Amplification products were separated by electrophoresis on denaturing polyacrylamide gels in a 4300 DNA Analyzer (Li-Cor Inc., Lincoln, NE, USA). Images were analyzed by using the SagaMX Automated AFLP Analysis Software version 3.3 (Li-Cor Inc., Lincoln, NE, USA). The size of the amplicons was determined by comparison with the LI-COR IRDye 50–700 bp Size Standard (LICOR Biosciences, Lincoln, NE, USA; product number 4200-60) weight marker.

2.3. Disease Evaluation Under Semi-Controlled Conditions

2.3.1. Soybean Stem Canker

SSC was evaluated under semi-controlled conditions in a greenhouse at the Laboratorio de Ecofisiología Vegetal (LEFIVE) of Instituto de Investigaciones en Ciencias Agrarias de Rosario, Universidad Nacional de Rosario (IICAR-UNR), Zavalla, Santa Fe, Argentina. The phenotyping of the selected genotypes was performed under natural sunlight during the spring of 2019. Inoculation assays were carried out with isolate CCC123-09 of D. aspalathi (Table S2) obtained from Centro de Referencia de Micología (CEREMIC-UNR). For each genotype, 10 inoculated and 10 non-inoculated seedlings were analyzed. All inoculations were performed on seedlings in the fully expanded trifoliate leaf stage V2, evaluated from 7 to 42 days post-inoculation (dpi). The inoculation methodology and phenotypic response of the genotypes were characterized according to Chiesa et al. [14]. The cv. ‘RA702’ (rdm/rdm) and the experimental line J77-339 (rdm/rdm) were used as susceptible (S) controls [39]; meanwhile, Tracy-M (Rdm1/Rdm1; Rdm2/Rdm2), Crockett (Rdm3/Rdm3), Dowling (Rdm4/Rdm4), and ‘Hutcheson’ (Rdm4/Rdm4; Rdm5/Rdm5) were used as reference resistant genotypes carrying the known Rdm genes.

2.3.2. Frogeye Leaf Spot

FLS evaluation under semi-controlled conditions was conducted in an inoculation chamber at the laboratory of Biotechnology of EEAOC, Las Talitas, Tucumán, Argentina. Artificial inoculations were carried out with isolate CCC232-10 of C. sojina obtained from CEREMIC (Table S2). For each genotype, the experimental unit consisted of five pots with three seedlings each. All inoculations were performed on seedlings in the fully expanded trifoliate leaf stage V3. Two trifoliate leaves per plant were inoculated with 0.3 mL of the conidial suspension adjusted to 6 × 104 conidia mL−1 on the upper leaf surface [19]. Control plants were sprayed with sterile distilled water. Plants were maintained at a humidity of 80% and 26 °C for 72 h in the inoculation chamber. Then, the environmental conditions were returned to 26 °C and 40% humidity. Plants were visually assessed for disease symptoms at 14 and 21 dpi. The severity of the disease (% of the leaf area affected with symptoms) was evaluated using a disease evaluation scale based on a protocol proposed by Martins et al. (2004) [40] for end-of-cycle diseases, adapted for FLS. The cv. ‘Anta 8.2’ (rcs/rcs) and HO6620 were used as susceptibility controls; meanwhile, ‘Davis’ (Rcs3/Rcs3), ‘Peking’ (RcsPeking/RcsPeking), and ‘Monte Redondo’ (RcsMte.Rdo/RcsMte.Rdo) were used as resistant control genotypes carrying the known Rcs genes.

2.3.3. Sudden Death Syndrome

SDS evaluation under semi-controlled conditions was conducted in an inoculation chamber at the laboratory of Biotechnology of EEAOC. Inoculation assays were carried out independently with isolates of F. tucumaniae and F. virguliforme obtained from the Phytopathology laboratory of EEAOC (Table S2). Artificial fungal inoculation was performed using the soil infestation method [41,42]. Autoclaved sorghum seeds were infested with five agar plugs (6 mm diameter) of each fungal mycelium and incubated for 2 weeks at 25 °C in the dark. A layer of infested sorghum (3 g) was placed at the time of planting before placing seeds in pots (diameter: 8 cm, height: 11 cm) filled with 250 g of GrowMix® Multipro commercial substrate (Terrafertil S.A., Buenos Aires, Argentina). The experimental design comprised five pots for each genotype and five seedlings per pot. Controls consisted of five pots with five non-inoculated seedlings per genotype. Pots were maintained at 25 ± 3 °C, and 30 dpi plants were evaluated for foliar and root severity based on visual assessments and shoot and root fresh weight at 30 dpi as described by Scandiani et al. (2011) [42]. The mean foliar and root severity and mean of root weight were averaged for each experimental unit (5 plants per pot). The cv. A8000 was used as a susceptible control and cv. Forrest as a resistant control.
For the three evaluated diseases, the pathogenicity tests were performed at least three times.

2.4. Validation of MM for SSC and FLS

The accuracy of MMs linked to Rdm4, Rdm5, and Rcs3 genes to predict resistance to SSC and FLS, respectively, was evaluated by correlating and comparing the genotypic and phenotypic data in a set of selected genotypes tested under semi-controlled conditions with local isolates of Da and C. sojina. Then, to correlate the presence or absence of the MM Sat_162, Satt233, and Satt244 with resistant or susceptible phenotypes, respectively, the Phi coefficient was calculated by regression analysis using InfoStat software [43].

2.5. Gene Pyramiding Scheme and Marker-Assisted Backcrossing Scheme (MABC)

2.5.1. Marker-Assisted Backcrossing Scheme (MABC)

Initial Cross: Single crosses to obtain the initial F1 were made between the recurrent parent (RP) cv. A8100RR (carrying the alleles linked to the Rdm4 and Rcs3 genes) and a donor parent (DP) cv. Forrest (carrying all the alleles linked to the QTLs for resistance to SDS) (Figure 1) during the 2015/2016 crop season at Monte Redondo EEAOC substation, Cruz Alta, Tucumán, Argentina (26°49′25″ S 64°51′01″ W).
BC1: The F1 plants obtained were analyzed with polymorphic SSR, and hybrids were used to perform crosses with RP A8100RR (BC1). The crosses between the effective F1 and RP A8100 were conducted in the field in the 2016/2017 crop season at Monte Redondo EEAOC substation. Seeds BC1F1 were obtained (Figure 1). BC2: Selected plants at this stage were screened with SSR for selection of the target genes to identify plants with all gene combinations (foreground selection), while background selection was applied to accelerate the recovery of the RP genome. The selected ones were used to perform BC2 with RP in September/October 2017, under greenhouse conditions in the Plant Physiology Laboratory (UNR-IICAR), Zavalla, Santa Fe, Argentina (33°1′52.76″ S, 60°53′22.27″ W). The BC2F1 seeds obtained were planted in 2018 in field plots at Monte Redondo EEOAC substation.
Plants carrying the desired resistance loci and higher genetic similarity to the RP were selected and advanced. The BC2F2 seeds were obtained in April of 2018 (Figure 1). Homozygous BC2F2 individuals were advanced through the single-seed descent (SSD) method up to the BC2F5 generation from the 2019/20 to the 2022/23 crop seasons.
Based on molecular profiles, a set of fixed BC2F5 lines carrying all resistance loci was selected and tested to verify the putative stacked resistance loci in the new genetic background (resistance response or phenotypic reaction) against D. aspalathi, C. sojina, F. virguliforme, and F. tucumaniae, as described previously.

2.5.2. Molecular Marker Analysis

Foreground Selection: After the initial crosses in each generation (F1, BC1 to BC2), seeds obtained were planted, leaf samples collected, and the extraction of genomic DNA from each plant was performed. A parental polymorphism survey for target genes Rdm4, Rcs3, and SDS7-1, SDS7-2, SDS7-3, SDS7-5, SDS15-9 was conducted using linked SSR to ensure the presence of all target loci (Table S1). Individuals carrying the alleles linked to the resistance loci were crossed with the RP cv. A8100RR (Figure 1).
Background Selection: Simultaneously, in BC2, the background selection was conducted to identify plants with maximum recovery and proportion of the genome of the RP. A set of 38 SSRs (highly polymorphic, informative, randomly distributed in the 20 soybean molecular linkage groups (MLG) that are used routinely in the EEAOC germplasm bank for genotyping) were tested. Similarity between the RC2F1 lines and the RP was calculated, taking into account only the polymorphic SSRs. A distance matrix was calculated with the Jaccard coefficient using Info-Gen software version 2008 [43].

2.6. Evaluation of Agronomic Performance

Herbicide Test: The selected lines BC2F5 R30-6, 30-9, 30-11, and 25-13 were tested for herbicide resistance under greenhouse conditions. Plants were grown in pots (diameter: 20 cm, height: 50 cm) filled with 750 g of GrowMix® Multipro commercial substrate (Terrafertil S.A., Argentina). Ten pots of each line were planted with five seeds/pot. The herbicide glyphosate 1.5% (v/v) was applied at growth stage V4. Also, two cultivars, glyphosate-tolerant A8100 (RR) and glyphosate-sensitive Macxy, were included as controls.
Yield Evaluation: Line R25-13 was evaluated for its performance by comparative testing of yield in two cropping seasons (2023/2024 and 20124/2025) at three different experimental locations that represent different environments: Monte Redondo (26°49′25″ S 64°51′01″ W), Piedra Blanca (26°37′57″ S 64°42′09″ W) and Piedra Buena (26°45′21″ S 64°39′59″ W), Tucumán, Argentina. Plots were arranged in a completely randomized block design, with three repetitions and 2.6 m2 per plot. The plant density was 18 plants per meter. DM 67i70 and CZ 6505 were used as commercial standard genotypes. Insects, fungal pathogens, and weeds were chemically controlled following local agronomic practices. Data on agronomic traits such as plant height, lodging tolerance, and 1000-seed weight were recorded.

2.7. Statistical Analysis

The disease response of the pyramided lines to SSC and FLS was evaluated using a generalized linear model (GLM) with genotype as a fixed effect and a binomial distribution. Mean comparisons were performed using Fisher’s LSD test (α = 0.05).
For SDS, a linear model was applied to analyze root biomass, including genotype, inoculation treatment (inoculated/non-inoculated), and their interaction as fixed effects. A varIdent variance structure was used to allow heterogeneous variances between the two inoculation levels (k = 2). For both foliar and root disease severity index (DSI), linear models were fitted with genotype as a fixed effect, also applying a varIdent variance structure (k = 2). In all SDS analyses, mean comparisons were performed using the DGC test at α = 0.05. All analyses were conducted in Navure software v. 2023 [44].

3. Results

3.1. Molecular Screening for Rdm4 and Rdm5 Resistance Genes

Sixty soybean genotypes, including reference lines and key progenitors from the elite breeding germplasm panel, were selected based on their phenotypic reaction to D. aspalathi (disease response) under natural inoculum pressure (Table S3). Then, these cultivars were genotyped using SSR Sat_162 and Satt233 to detect the presence of Rdm4 and Rdm5 genes, respectively. The reference cultivars carrying different Rdm genes used were Hutcheson (Rdm4/Rdm4; Rdm5/Rdm5), Dowling (Rdm4/Rdm4), and Tracy-M (Rdm1/Rdm1, Rdm2/Rdm2), while J77-339 (rdm/rdm) was used as susceptible control (Table S3).
Under natural inoculum pressure, 47 genotypes were classified as resistant (R), four as moderately resistant (MR), and ten as susceptible (S) to D. aspalathi. Among the 51 genotypes characterized as R or MR, 35 (68.6%) carried the Sat_162 marker allele linked to the Rdm4 gene in the cultivar Hutcheson, seven (13.7%) carried the Satt233 allele linked to Rdm5, and five genotypes (9.8%) carried both alleles (Figure S1a). In contrast, of the 10 susceptible genotypes, nine lacked both Sat_162 and Satt233 MM. Within the R/MR group, Sat_162 was absent in 15 genotypes (29%), while Satt233 was absent in 14 genotypes (27%). Notably, one susceptible genotype carried the MM Sat_162, whereas none of the susceptible genotypes carried the Satt233 MM. However, in 24 cultivars (39%), MM Sat_162 was not detected, while the MM Satt233 allele was not found in 23 analyzed cultivars (37%) (Table S3).
The results obtained were analyzed as the frequency of Sat_162 and Satt233 in the selected cultivars. The allele associated with the Rdm4 gene showed a higher frequency of occurrence (0.6) in the set of analyzed genotypes compared to Satt233, linked to the Rdm5 gene, which was found with a frequency of 0.11. In addition, the frequency of the presence of both gene alleles was 0.08 (Figure S1b).

3.2. Correlation Analysis Between Phenotypic Reaction to SSC and MM Presence

From the 60 previously screened soybean cultivars, a set of 17 were artificially inoculated with the CCC123-09 isolate of D. aspalathi and tested for disease reaction under semi-controlled conditions (Table 1). After 42 dpi, 14 cultivars were characterized as R/MR. From the 14 cultivars characterized as R or MR, 12 (85.7%) amplified only the Sat_162 allele (indicating the presence of Rdm4 gene), six (42.8%) presented the Satt233 MM allele (presence of Rdm5 gene), and five (35.7%) presented both MMs, associated with the Rdm4 and Rdm5 genes, respectively (Table 1). The correlation analysis between the presence of diagnostic MM and the phenotypic response showed a statistically significant positive and directly proportional correlation [r = 0.72, p ≤ 0.05] between the presence of Sat_162 (Rdm4 gene) and the resistance response. However, the correlation between the presence of Satt233 (associated with the Rdm5 gene) and the resistance was not significant [r = 0.32; p = 0.2011].

3.3. Molecular Screening for Rcs3, RcsMte.Rdo and RcsPeking Resistance Genes

Sixty genotypes previously screened were also analyzed for the presence of MM-Satt244 linked to Rcs3, RcsMte.Rdo and RcsPeking genes for FLS resistance. Reference cultivars carrying different Rcs genes were included: ‘Davis’ (Rcs3/Rcs3), ‘Monte Redondo’ (RcsMte.Rdo/RcsMte.Rdo), and ‘Peking’ (RcsPekin/RcsPeking), and Anta 8.2 (rcs/rcs) as a susceptible genotype. Phenotypic information was obtained from field evaluation conducted under a natural inoculum pressure of C. sojina (Table S4). From this set, 25 (41.6%) were classified as R, six (10%) as MR, eight genotypes (13%) were classified as S, and the rest could not be phenotyped under field conditions (Table S4). Genotypic analysis showed that among the 31 genotypes classified as R or MR, only 12 (38.7%) presented the Satt244 allele associated with the Rcs3 gene, and four (13%) amplified the alleles associated with RcsMte.Rdo Meanwhile, the allele of Satt244 associated with RcsPeking was not detected in the analyzed genotypes. In addition, one genotype classified as S presented RcsMte.Rdo (Figure S2a).
The haplotype of the Rcs3 gene was more frequent (0.4), followed by the haplotype of the RcsMte.Rdo (0.1), while the haplotype of RcsPeking was found only in the reference cultivar (0.08) (Figure S2b). In 13 cultivars, classified as R/MR under field conditions, the Rcs3, RcsMte.Rdo and RcsPeking alleles were not detected. This could indicate that the observed phenotypic resistance may be associated with the presence of some of the remaining known genes (Rcs1 and Rcs2) or could be due to new sources of resistance to FLS.

3.4. Correlation Analysis Between Diagnostic MM and Rcs Genes

From the 60 previously screened soybean cultivars, a set of 14 genotypes was tested for disease reaction to the isolate CCC232-10 of C. sojina under semi-controlled conditions. The phenotypic responses showed that 12 were characterized as R and only one as S (Table 2). Among the 12 cultivars characterized as R, six (50%) showed only the haplotype of the Satt244 linked to the Rcs3 gene; meanwhile, RcsMte.Rdo and RcsPeking haplotypes were not found in the tested genotypes (Table 2).
The correlation between the presence of diagnostic MM Satt244 and the phenotypic response to the local isolate of C. sojina was analyzed. The analysis showed a statistically significant correlation, positive and directly proportional, r = 0.55, p ≤ 0.05, between the presence of the diagnostic marker Satt244 and the resistance to C. sojina.
Thus, both SSR Sat_162 and Satt244 were used as diagnostic MMs to MAS of the Rdm4 and Rcs genes, respectively.

3.5. Molecular Screening for SDS QTLs

A total of 60 soybean genotypes, including the resistant reference genotype cv. Forrest, were analyzed using SSR markers associated with SDS resistance QTLs: Satt214 (SDS7-1); Satt309 (SDS7-2); Satt163 (SDS8-1); Satt570 (SDS7-3); Satt371, Satt357, Satt202, Satt316, and Satt307 (SDS7-5); Satt354 (SDS7-6); and Satt270 (SDS 15-9). Among the genotypes analyzed, nine (15%) presented QTLs in two different LGs: QTLs SDS7-5 on LG C2, and SDS 7-2 and SDS 7-3 on LG G (Table 3). Together, these QTLs explain about 47% of the resistance. Of these, seven genotypes presented markers linked to QTL SDS7-5 on LG C2 (Satt371 and the additional SSR within the same genomic region, Satt357, Satt202, Satt316, and Satt307). In addition, SSR markers associated with SDS7-6 and SDS 15-9 (LG I) and SDS7-1 and SDS8-1 (LG G) (which explain about 35% of the resistance) were not amplified in any of the molecularly analyzed genotypes, except for the reference cv. Forrest (Table 3). According to the analysis, it was observed that three (SDS7-2, SDS7-3, and SDS7-5) of the five QTLs for SSD resistance mapped in cv. Forrest were found in the elite breeding germplasm panel from the EEAOC, with SDS7-5 being the most frequent. Meanwhile, cv. Forrest was the only one that presented all QTLs for SDS.

3.6. Stacking of Resistance Genes

Based on the genotyping survey, donor and recurrent parents carrying complementary resistance loci were selected to implement a marker-assisted backcrossing strategy (MABC).
Initial crosses were carried out between the cv. A8100RR (RP), carrying the alleles linked to the Rdm4 and Rcs3 resistance genes, and cv. Forrest (DP), carrying the QTLs for SDS resistance (Figure 1). After artificial crossing, 46 putative F1 hybrid seeds were obtained in 2016/17. Then, seeds were planted and the effective hybrids were molecularly verified using the specific foreground MM linked to the resistance genes/QTLs associated with the three diseases (SSR Sat_162, Satt233, Satt244). According to this analysis, 33 effective F1 hybrid plants were obtained (71% effectiveness). The selected F1 hybrids were backcrossed to the RP A8100RR, and the 31 BC1F1 putative lines were screened with the same specific foreground MM (Figure 1). According to the introgression of the target genes into the RP, a total of nine heterozygous BC1F1 lines were obtained. One line presented all desirable resistance alleles; four lines presented the resistances alleles for Rcs3, Rdm4, and SDS7-1, SDS7-2, and SDS7-3; and two lines showed resistance alleles Rcs3 and SDS7-1, SDS7-2 and SDS7-3 (Table S5).
These lines were selected and used to generate BC2 lines. Thirty-one putative BC2F2 lines were obtained. Nine lines were selected based on the presence of different combinations of resistance alleles in heterozygous condition and percentage of recovery of the genome of the RP. Among them, line R30 carried all six resistance loci, Rcs3, Rdm4, and the four SDS resistance QTLs (SDS7-1, SDS7-2, SDS7-3, SDS7-5, SDS15-9), and showed a 50% recovery of the recurrent parent genome (RPG). Four lines, R25, R27, R28, and R31, carried five of the six resistance loci (Rcs3, Rdm4, SDS7-1, SDS7-2, SDS7-3), with RPG similarities ranging from 70 to 91%. Line R26 carried Rdm4 and the three SDS QTLs (SDS7-1, SDS7-2, SDS7-3), lacking Rcs3, and presented 87% similarity to the RP. Line R8 carried Rcs3, Rdm4, and two SDS QTLs (SDS7-3 and SDS15-9), while line R1 carried Rcs3, Rdm4 and QTL SDS7-3; their RPG recoveries were 65%, respectively (Table 4).
All BC2F1 lines carrying combinations of three or more resistance loci were self-pollinated to produce BC2F2 progeny (Figure 1). Then, these lines were subjected to foreground selection with SSR markers linked to the resistance genes to confirm the presence of the target loci in heterozygous condition before fixation. Based on foreground selection, lines BC2F1 R30, which presented all the MMs linked to the resistance genes and exhibited the presence of 50% of the RPG, and R25, which presented all MMs, except for one SSR linked to QTL SDS7-3, and a highly significant similarity to the RPG, were selected for further development. Line BC2F1 R31 was not advanced further as it was lost during the trial.
In the 2019/20 season, BC2F2 lines were obtained, and foreground selection was carried out in these lines for identifying the lines carrying a combination of genes/QTLs using the respective foreground markers. Based on these results, four homozygous line BC2F3 were selected: R30-9, R30-6, R30-11, with all the MMs linked to the resistance genes for the three diseases, and R25-13, with three QTLs, SDS + Rcs3 + Rdm4 genes. The four lines were self-pollinated and advanced to BC2F5 in the 2022/2023 crop season, and molecular analysis showed that all SSRs linked to R genes and QTLS were present was performed with all the SSRs mentioned (Table 4).

3.7. Phenotypic Validation of the R Gene-Pyramided Lines

Phenotypic Resistance to SSC. To validate the functionality of the Rdm4 gene in the genetic background of the four R gene-pyramided lines (BC2F5 lines), and, consequently, the resistance to D. aspalathi, artificial inoculation assays were conducted, testing the CCC123-09 isolate (Figure 2). At 42 and 49 dpi, the disease response of the pyramided lines showed significant differences among genotypes for severity (F = 10.28, p < 0.0001). The susceptible control cv. RA702 exhibited the highest severity and % of death plant (DP) values: 0.78 in severity and an average 70% mortality when inoculated. In contrast, the pyramided lines R30-11, R30-6, and R30-9, along with cv. Hutcheson (Rdm4) as the resistant control, formed a statistically homogeneous group with the lowest severity scores. Among them, R30-11 displayed the greatest resistance, with the lowest severity and 0% DP. The lines R30-6 and R30-9 also exhibited strong resistance with low severity levels and values of 5% DP and 0% DP with CCC123-09, respectively. Line R25-13 presented moderate resistance, severity and DP values (35% DP) (Figure 2).
Phenotypic Resistance to FLS: To validate the functionality of the Rcs3 gene in the genetic background of the R gene-pyramided lines (BC2F5 lines), and, consequently, the resistance to C. sojina, artificial inoculation assays were conducted (Figure 3). Genotype HO6620 was used as S control; meanwhile, cv. A8000RR (Rcs3) was used as R control.
The results showed significant differences among genotypes (F = 14.13, p < 0.0001), indicating a strong genotypic effect on foliar disease severity. The pyramided lines R30-9, R30-11, and R30-6 showed the lowest disease severity, being similar to the resistant cv. A8000RR (α = 0.05). In contrast, the genotype HO6620 showed the highest disease severity, and the line R25-13 presented significantly higher disease levels than the rest of the lines (Figure 3). These results suggest that the lines R30-9, R30-11, and R30-6 carrying the MM linked to the Rcs3 gene showed high resistance levels to C. sojina, demonstrating the functionality of the gene, while line R25-13 showed a moderately resistant (MR) phenotype under the evaluated conditions.
Phenotypic Resistance to SDS: To evaluate the phenotypic performance of the BC2F5 pyramided lines under the inoculation of a local isolate of F. virguliforme, three variables were analyzed: root dry weight (comparing inoculated and non-inoculated plants) and root and foliar disease severity index (DSI) (Figure 4).
Line R30-11 showed the most consistent resistance response, showing no differences when compared with cv. Forrest. It maintained a high root weight under pathogen inoculation (3.17 vs. 3.62 g in the control) and showed a low root and foliar DSI (0.30 and 0.25 respectively), indicating minor symptom development. Line R25-13 had one of the highest root weights in the non-inoculated condition (4.00 g) but exhibited a pronounced reduction under pathogen infection (1.03 g). Despite this, it maintained low root DSI (0.36) and the lowest foliar DSI among the pyramided lines (0.12), suggesting good symptom control but high sensitivity in terms of biomass loss. Line R30-6 showed a substantial reduction in root biomass under infection (from 4.30 to 1.23 g), accompanied by relatively high root DSI (0.56) and a moderate foliar DSI (0.36), indicating a more susceptible profile in both root damage and symptom expression. Similarly, line R30-9 showed a notable decrease in root weight (4.22 to 1.35 g) and presented the highest root DSI (0.57), although foliar DSI remained low (0.30), comparable to the other pyramided lines. Statistical analyses confirmed significant differences in root weight and root DSI among genotypes (p < 0.05), but no significant differences were found in foliar severity. Overall, line R30-11 displayed the best integrated resistance profile after F. virguliforme inoculation, combining reduced disease severity and minimal biomass loss.
Also, to evaluate the impact of F. tucumaniae inoculation, root dry weight and root and foliar DSI were analyzed across BC2F5 pyramided lines. Significant differences among genotypes were observed for all traits (p < 0.05), confirming differential responses to inoculation (Figure S4). Upon inoculation, all pyramided genotypes exhibited a significant reduction in root weight; however, no significant differences were observed among them when compared with the fungicide control (Figure S4). When analyzing foliar and root disease severity, the fungicide-treated control consistently exhibited the lowest severity levels in both tissues. Pyramided lines R30-11 and R25-13 showed similar severity values when measuring foliar and root severity. Line R30-6 also exhibited high severity values in both root and foliar tissues, clustering with more susceptible genotypes. R30-9 showed the highest root and foliar DSI among all lines, clustering separately in both traits, and indicating a more susceptible response. These results demonstrate that the pyramided line R30-11 expresses resistance responses against F. virguliforme and F. tucumaniae, indicating the effective incorporation of the QTLs.

3.8. Evaluation of Agronomic Performance of Selected Pyramided Lines

The agronomic performance of the BC2F5 lines carrying stacked resistance genes was evaluated under field conditions to ensure that the incorporation of multiple disease resistance loci did not compromise key productive traits.
Herbicide Test: In herbicide tolerance assays, lines R30-6, R30-9, and R30-11 exhibited visible symptoms of susceptibility 10 days after application, whereas R25-13 demonstrated tolerance, consistent with the RP background, indicating compatibility with glyphosate-based weed management systems. This characteristic enabled its inclusion in field trials under traditional management practices, allowing for a reliable evaluation of agronomic traits such as plant height, architecture, and yield. Meanwhile, lines R30-6, R30-9, and R30-11 are conventional RR-free genotypes.
Yield Evaluation: Agronomic evaluation of the pyramided line R25-13 was carried out during the 2023/24 and 2024/25 growing seasons at three different locations. Across environments, R25-13 showed comparable plant height, depending on the environment (values between 60 and 97 cm), similar to the commercial standards (60–95 cm) and to the RP A8100RR (~90 cm) and competitive yield performance.
In the 2023/24 season, yields of R25-13 ranged from 2.962 to 3.510 kg/ha (average 3.217 kg/ha), close to the commercial standards (average 3.466 kg/ha). During the 2024/25 season, yields of R25-13 ranged from 2.324 to 3.390 kg/ha (average 2.744 kg/ha), values lower than the commercial standards (average 3.382 kg/ha).

4. Discussion

One of the main factors that limit grain production and causes millions of dollars in economic losses is biotic stress caused by different pathogens. Specifically, SSC, FLS, and SDS are potentially very destructive fungal diseases, considered responsible for severe reductions in yield and grain quality in major production areas [3]. The use of resistant cultivars that carry either vertical resistance conferred by major R genes, horizontal or partial resistance conferred by QTLs, or both is considered the most effective and sustainable strategy for disease control. This reduces direct crop losses and minimizes the use of fungicides, and, consequently, their potential negative impact on health and the environment, as well as production costs. Therefore, the main objective of this work was to stack selected, validated, and accessible resistance genes and QTLs for SSC, FLS, and SDS into an agronomically competitive elite background. However, the introgression of multiple traits through conventional breeding is a time-consuming process. Meanwhile, marker-assisted selection (MAS) is a widely useful technique and has become a fundamental tool in soybean breeding programs, enabling the early and accurate identification of individuals carrying alleles of interest. This approach accelerates the breeding process and minimizes the need for constant phenotypic evaluations, which are often influenced by environmental variability, particularly for disease resistance, which is highly dependent on the plant and pathogen genotypes and the environment. This technology also allows pyramiding more than one resistance gene for the same disease or for different diseases in the same genotype. This has proven to be a powerful strategy to enhance multiple disease resistance, leading to stability and durability against the prevailing and emerging biotypes of pathogens [45]. Therefore, the combination of multiple genes in a single genotype may increase resistance due to the cumulative effects of combined genes [2]. This approach has been successfully implemented in other major crops where the combination of distinct resistance genes has proven to be an effective strategy for durable, broad-spectrum disease control. In rice, three bacterial blight (BB) resistance genes, along with a phosphorus starvation tolerance gene (OsPSTOL 1), were pyramided [46]. Moreover, the introgression of the Xa21, Bph14, and Bph15 genes in rice improves resistance to BB and BPH (brown planthopper) [47].
In soybean, previous pyramiding efforts have focused on combining resistance genes for a single pathogen, such as Rsv genes for Soybean Mosaic Virus (SMV) resistance [48,49], Rps genes against Phytophthora sojae [49] or Rpp genes for Asian soybean rust (ASR) [50,51,52,53,54]. Specifically, Wang et al. [55] combined three SMV resistance genes (Rsc4, Rsc8, and Rsc14Q) to develop lines highly resistant to multiple virus strains by using linked SSR and MAS. For ASR, pyramiding loci such as Rpp2, Rpp3, Rpp4, and Rpp5 has led to an enhanced and more stable resistance compared to single-gene lines [56]. For two diseases, Ramalingam et al. [57] reported the successful introgression of two major R-genes, including Rps2 (Phytophthora rot resistance) and Rmd-c (complete powdery mildew resistance). These reports clearly demonstrated the potential of genotypes with the combination of resistance genes to overcome the breakdown of single-gene resistance and provide a sustainable solution. To our knowledge, this study represents the first report of successful pyramiding of resistance loci (two major resistance genes and also QTLs) conferring resistance to three distinct fungal diseases in soybean: SSC (D. aspalathi), FLS (C. sojina), and SDS (F. virguliforme and F. tucumaniae).
In this work, the availability of SSR markers linked to the Rdm4 and Rcs3 genes and several QTLs for SDS resistance allowed the development of a strategy to identify and select genotypes resistant to SSC, FLS, and SDS [17,19,58]. These markers were used to screen an elite breeding germplasm bank and were highly associated with resistant genotypes tested under natural inoculum pressure. First, the markers Sat_162 and Satt244, associated with the Rdm4 and Rcs3 resistance genes (the most prevalent genes in the germplasm bank), respectively, were validated in a panel of soybean genotypes with known field resistance to SSC and FLS and also confirmed by phenotyping with representative isolates under semi-controlled conditions. As a result, their consistent association with resistant phenotypes validates their reliability for MAS. The validation of robust SSR markers linked to major resistance genes was a critical step in the pyramiding strategy implemented in this work. Thus, these diagnostic markers were subsequently used to screen genotypes carrying the target alleles and then applied in each backcross generation.
In soybean, MABC has been effectively used to introduce single as well as polygenic traits, demonstrating accelerated breeding cycles [49,58,59,60]. Furthermore, MABC has been proven to be effective in improving quantitative traits that contribute to soybean nutritional value, such as seed protein content and oil quality [61,62].
In this study, MABC with a two-step approach was used: gene pyramiding and gene fixation through backcrossing and selfing. This method enables the targeted introgression of multiple resistance alleles while simultaneously recovering the elite genetic background through background selection. The availability of tightly linked diagnostic SSR markers, Sat_162 for Rdm4, Satt244 for Rcs3, and additional markers for SDS QTLs (Satt163, Satt214, Satt270, Satt371, Satt570), enabled efficient MAS, accelerating the introgression and fixation of the genes in the desired genotypes. The stringent genotyping selection performed in this work at an earlier vegetative stage increased genotyping efficiency. Moreover, the selection based on genotyping data allowed us to identify the progenies with desired gene combination in the gene-stacked lines. This molecular-assisted approach not only reduced breeding time but also facilitated the introgression of QTLs and ensured precise selection of soybean lines with stable, multi-disease resistance.
In addition, the effectiveness of the pyramided resistances was confirmed through an extensive phenotypic validation under artificial inoculation with isolates of D. aspalathi, C. sojina, F. virguliforme, and F. tucumaniae. Given that it was previously demonstrated that the R genes/QTLs could exhibit differences in their expressivity depending on the genetic backgrounds [39], the disease severity and physiological parameters were evaluated under controlled conditions to assess the level of expression and functional stability of the stacked resistance loci. Nevertheless, as with any breeding material carrying multiple loci, recombination could theoretically separate these resistance loci in subsequent breeding cycles if these lines are used as parents in new crosses. For this reason, the use of diagnostic molecular markers remains an essential tool for maintaining the pyramided resistance combinations during future breeding efforts.
Among the BC2F5 lines developed, two exhibited the most consistent and broad-spectrum resistance across the three fungal diseases. Line R30-11 showed the lowest levels of DSI against FLS and plant mortality against SSC and maintained high root biomass and low symptom expression under F. virguliforme inoculation. Although more affected by F. tucumaniae, this line still outperformed the other pyramided lines, confirming the effective expression and compatibility of the Rdm4, Rcs3, and SDS resistance QTLs within an elite non-RR genetic background. Meanwhile, line R25-13 also combined resistance to FLS and SSC, with a resistance level to F. virguliforme similar to R30-11 but showing greater susceptibility to F. tucumaniae. Despite this, R25-13 maintained glyphosate tolerance and stable agronomic performance, representing a valuable source of stacked resistances and herbicide tolerance for breeding programs aiming to develop durable, multi-disease-resistant soybean cultivars. The agronomic performance and disease response of these advanced lines were evaluated in three representative environments of the soybean production region in northern Argentina, which differ in terms of rainfall regime and natural pathogen pressure, providing a relevant range of field conditions for assessing resistance stability. The variation in yield obtained in 2024/2025 compared with the 2023/2024 season could be explained because this latter season was characterized by a water deficit during the grain-filling period.
In this study, the pyramided lines carrying Rdm4 and Rcs3 showed a consistently resistant response to D. aspalathi and C. sojina, confirming the stable expression of these major resistance genes. This stability is possible because resistance is largely controlled by single dominant loci [17,19]. In contrast, resistance to both Fusarium spp. was more variable among the pyramided lines, likely due to the quantitative nature of SDS resistance, where multiple QTLs of small effect contribute to partial resistance. These findings support the importance of genetic background interactions and highlight the significance of maintaining the stable expression of pyramided resistance loci. In addition, the key QTLs mapped in cv. ‘Forrest’ were identified through inoculation with F. virguliforme [36]; however, their functionality against other Fusarium isolates was demonstrated in this work.
Therefore, our results demonstrate that lines R30-11 and R25-13 emerged as strong candidates for future cultivar development, combining broad-spectrum resistance to SSC, FLS, and SDS with stable agronomic performance, including yield potential and, in the case of R25-13, glyphosate tolerance.

5. Conclusions

To our knowledge, this is the first report of successful pyramiding of resistance genes and QTLs against three distinct fungal diseases (SSC, FLS, and SDS) in soybean, integrating a marker-assisted backcrossing strategy with validated SSR molecular markers. By stacking major resistance genes for soybean stem canker (Rdm4), frogeye leaf spot (Rcs3), and key quantitative trait loci associated with sudden death syndrome, we developed elite soybean lines with broad-spectrum disease resistance while maintaining agronomic performance. The resulting lines illustrate the versatility of resistance pyramiding, functioning either as robust donors of stacked resistance alleles or as agronomically competitive genotypes combining multi-disease resistance with glyphosate tolerance. Overall, the integration of validated diagnostic markers, robust geno-phenotyping, and molecular-assisted breeding provides a transferable framework for accelerating the development of elite soybean germplasm with broad-spectrum and durable resistance to diseases in complex scenarios.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy16070754/s1, Table S1. List of primers used in the study for gene detection and pyramiding of resistance genes to SSC, FLS and SDS. Table S2. List of isolates used in the phenotyping for resistance to SSC, FLS, and SDS. Table S3. Phenotypic response to Diaporthe aspalathi, causal agent of soybean stem canker (SSC), and presence of MM linked to resistance genes Rdm4 and Rdm5 in cultivars of the SBP-EEAOC, Tucumán, Argentina. Table S4. Phenotypic response to Cercospora sojina, causal agent of Frogeye leaf spot (FLS), and presence of MM Satt244 linked to Rcs3, RcsM.Rd and RscPeking genes in soybean cultivars of the SBP of EEAOC, Tucumán Argentina. Table S5. Analysis of the presence of the resistance-associated alleles in BC1F1 soybean lines. Figure S1. (a) Presence of molecular markers Sat_162 and Satt233 linked to the Rdm4 and Rdm5 resistance genes, respectively, among soybean genotypes classified as resistant (R) or moderately resistant (MR) and susceptible (S) to D. aspalathi under natural inoculum pressure. (b) Allelic frequency of molecular markers Sat_162 and Satt233, linked to the Rdm4 and Rdm5 resistance genes, respectively, in a panel of soybean cultivars evaluated under natural pressure of D. aspalathi. Figure S2. (a) Presence of molecular markers Satt244 linked to the Rsc3, RcsM.Rd and RcsPeking resistance genes, respectively, among soybean genotypes classified as resistant (R) or moderately resistant (MR) and susceptible (S) to C. sojina under natural inoculum pressure. (b) Allelic frequency of molecular markers Satt244 linked to the Rsc3, RcsM.Rd and RcsPeking resistance genes, respectively, in a panel of soybean cultivars evaluated under natural pressure of C. sojina. Figure S3. Genotyping of BC2F5 soybean lines using SSR markers linked to resistance genes and QTLs. (A) Molecular markers Sat_162 (linked to Rdm4), (B) Satt244 (linked to Rcs3), (C) markers associated with SDS resistance QTLs (SDS7-1, SDS8-1, SDS7-3, and SDS15-9) were used to identify lines with the stacked resistance alleles in the selected lines. Figure S4. Phenotypic evaluation of BC2F5 soybean pyramided lines under artificial inoculation with Fusarium tucumaniae at 30 days after sowing. Foliar disease severity index (DSI), root DSI and root dry weight were assessed to determine the resistance of the lines. Foliar and root disease severities were rated on a scale of 1–5, where 1 = no symptoms, 2 = slight symptoms, 3 = moderate symptoms, 4 = heavy symptoms, and 5 = severe symptoms development. Means with different letters were significantly different (p < 0.05).

Author Contributions

C.M.L.R.: Visualization, Investigation, Methodology, Formal analysis, Writing—original draft. M.G.G.: Investigation, Methodology. J.R.S.: Investigation, Resources. A.P.C.: Conceptualization, Funding acquisition, Supervision. E.M.P.: Conceptualization, Writing—review and editing, Project administration. M.A.C.: Conceptualization, Writing—review and editing, Funding acquisition. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Estación Experimental Agroindustrial Obispo Colombres, by Instituto de Tecnologia Agroindustrial del Noroeste Argentino-Consejo Nacional de Investigaciones Científicas y Técnicas (EEAOC, ITANOA-CONICET) as part of the “Evaluation and characterization of soybean genotypes and related pathogens for the identification of DNA segments associated with agronomic important traits (GrB2)”; PDTS 0590: “Genetic improvement in important crops in the NOA region” and through PUE 22920160100043CO (IICAR-CONICET/UNR). C.M.L. Rocha was awarded a scholarship by CONICET. A.P. Castagnaro and M.A. Chiesa are CONICET Career Researchers.

Data Availability Statement

The data supporting the findings of this study are available from the corresponding author upon reasonable request. No large-scale datasets were generated or analyzed during the current study that require deposition in a public repository.

Acknowledgments

We would like to express our gratitude to Francisca Perera and Daniel Ploper for kindly reading the manuscript and to Andrea Peña Malavera for her invaluable contribution to the statistical analysis of the data. We would also like to express our gratitude to Agustin Padilla and Nahuel Ruiz de Huidobro for been responsible for the agronomic management of field assays.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
SSCSoybean stem canker
FLSFrogeye leaf spot
SDSSudden death syndrome
MABCMarker-assisted backcrossing
MMMolecular markers
MASMarker-assisted selection
BCBackcross
QTLsQuantitative trait loci
RPRecurrent parent
DPDonor parent

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Agronomy 16 00754 g001
Figure 1. Marker-assisted backcross (MABC) breeding scheme performed for developing new soybean pyramided lines carrying molecular markers Sat_162 (linked to Rdm4) for resistance to SSC, Satt244 (linked to Rcs3) for resistance to FLS and QTLs (SDS7-1, SDS7-2, SDS7-3, SDS7-5, SDS715-9) associated with SDS resistance. Symbols: X indicates a cross between genotypes; ⓧ indicates selfing.
Figure 1. Marker-assisted backcross (MABC) breeding scheme performed for developing new soybean pyramided lines carrying molecular markers Sat_162 (linked to Rdm4) for resistance to SSC, Satt244 (linked to Rcs3) for resistance to FLS and QTLs (SDS7-1, SDS7-2, SDS7-3, SDS7-5, SDS715-9) associated with SDS resistance. Symbols: X indicates a cross between genotypes; ⓧ indicates selfing.
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Figure 2. Phenotypic validation of the R gene-pyramided lines under artificial inoculation with Diaphorte aspalathi, the causal agent of SSC. At 49 days post-inoculation, pyramided lines were evaluated by disease severity index (DSI) and % of dead plants (DP). Cultivar RA702 was used as a susceptible control and Hutcheson (Rdm4) as resistance control. Values are expressed as mean ± SE. Different letters indicate significant differences according to LSD with Fisher’s test, p < 0.05.
Figure 2. Phenotypic validation of the R gene-pyramided lines under artificial inoculation with Diaphorte aspalathi, the causal agent of SSC. At 49 days post-inoculation, pyramided lines were evaluated by disease severity index (DSI) and % of dead plants (DP). Cultivar RA702 was used as a susceptible control and Hutcheson (Rdm4) as resistance control. Values are expressed as mean ± SE. Different letters indicate significant differences according to LSD with Fisher’s test, p < 0.05.
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Figure 3. Phenotypic evaluation of the R gene-pyramided lines under artificial inoculation with Cercospora sojina, causal agent of FLS. At 30 days post-inoculation, the disease severity index (DSI) of the pyramided lines was evaluated. Genotype HO6620 was used as susceptible control; meanwhile, A8000 (Rcs3) was used as resistant control. Values are expressed as mean ± SE. Different letters indicate significant differences according to LSD with Fisher’s test, p < 0.05.
Figure 3. Phenotypic evaluation of the R gene-pyramided lines under artificial inoculation with Cercospora sojina, causal agent of FLS. At 30 days post-inoculation, the disease severity index (DSI) of the pyramided lines was evaluated. Genotype HO6620 was used as susceptible control; meanwhile, A8000 (Rcs3) was used as resistant control. Values are expressed as mean ± SE. Different letters indicate significant differences according to LSD with Fisher’s test, p < 0.05.
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Figure 4. Phenotypic evaluation of soybean pyramided lines under artificial inoculation with Fusarium virguliforme at 30 days after sowing. Foliar disease severity index (DSI), root DSI and root dry weight between inoculated (light gray bars) and non-inoculated (dark gray bars) plants were assessed to determine the resistance of the lines. Values are expressed as mean ± SE. Different letters indicate significant differences according to the DGC test, p < 0.05.
Figure 4. Phenotypic evaluation of soybean pyramided lines under artificial inoculation with Fusarium virguliforme at 30 days after sowing. Foliar disease severity index (DSI), root DSI and root dry weight between inoculated (light gray bars) and non-inoculated (dark gray bars) plants were assessed to determine the resistance of the lines. Values are expressed as mean ± SE. Different letters indicate significant differences according to the DGC test, p < 0.05.
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Table 1. Phenotypic response of a set of genotypes inoculated with Diaphorte aspalathi, the causal agent of soybean stem canker (SSC), under semi-controlled conditions to validate the association between SSR-MM and the resistance conferred by the Rdm genes in cultivars of SBP-EEAOC, Tucumán, Argentina.
Table 1. Phenotypic response of a set of genotypes inoculated with Diaphorte aspalathi, the causal agent of soybean stem canker (SSC), under semi-controlled conditions to validate the association between SSR-MM and the resistance conferred by the Rdm genes in cultivars of SBP-EEAOC, Tucumán, Argentina.
CultivarsPhenotype aGenesControlled Conditions c
Rdm5 (Satt233)Rdm4 (Sat_162) bDP d (%)Phenotype e
A8100 RGR-+7.4R
Stonewall CMR+-44.4MS
A 6401 RRR++7.4R
DM 8002R-+11.1R
Munasqa RRR++7.5R
NA 6126 RRR++3.7R
Qaylla RRR++7.9R
LaxS--100S
A8000 RRR-+13.9R
YanasuR-+6.3R
DM 6200R-+4.2R
HuayraR--6R
LaxR-+3.7R
RA702 *S--80.5S
J77-339 *S--60.6S
Dowling **R-+0R
Hutcheson **R++0R
a Results of field evaluations carried out in the 2010 and 2011 crop seasons in macro plots located in different locations in northwest (NW) Argentina with natural inoculum. R: resistant; MR: moderately resistant; S: susceptible. b The diagnostic MMs Sat_162 and Satt233 were used to detect the presence of Rdm4 and Rdm5 genes, respectively, (+) presence/(-) absence of the allele of Hutcheson genotype of reference. c Phenotypic response to isolate CCC123-09 of D. aspalathi under semi-controlled conditions. d R: resistant; MS: moderately susceptible; S: susceptible. e Percentage of dead plants (% DP). * Reference susceptible genotypes for SSC. ** Reference resistant genotypes for SSC carrying Rdm4/5 genes.
Table 2. Results of phenotypic response of a set of genotypes inoculated with Cercospora sojina, the causal agent of frogeye leaf spot (FLS), under semi-controlled conditions, to validate the association between MM and Rcs3, RcsMte.Red and RcsPekin genes.
Table 2. Results of phenotypic response of a set of genotypes inoculated with Cercospora sojina, the causal agent of frogeye leaf spot (FLS), under semi-controlled conditions, to validate the association between MM and Rcs3, RcsMte.Red and RcsPekin genes.
CultivarPhenotype aGenes bControlled Conditions c
RcsPekingRcsMte.RedRcs3Phenotype dDSI
A8100 RRR--+R0.05
A8000 RRR--+R0.06
Carver CND--+R0.09
A 6401 RRR---R0.13
A 6411 RRR--+R0.11
Munasqa RRR---R0.09
NA 6126 RRR---R0.12
Qaylla RRR---R0.08
Maxcy CND--+R0.06
YanasuMR--+R0.12
Davis **R--+R0.07
Mte Red **R-+-R0.1
Peking **R+--R0.05
Anta 8.2 *S---S0.7
a Results of field evaluations carried out in the 2010 and 2011 crop seasons in macro plots located in different locations in NW Argentina with natural inoculum. R: resistant; MR: moderately resistant; S: susceptible. b The diagnostic MM Satt244 detected the presence of Rcs3, RcsMte.Red and RcsPekin. c Phenotypic response to isolate CCC232-109 of C. sojina under semi-controlled conditions. d Disease severity index (DSI). * Reference susceptible genotypes for FLS. ** Reference genotypes for FLS resistance genes.
Table 3. Results of genotyping performed with MM SSRs associated with the QTLs for SDS resistance in soybean genotypes of the SBP of EEAOC, Tucumán, Argentina.
Table 3. Results of genotyping performed with MM SSRs associated with the QTLs for SDS resistance in soybean genotypes of the SBP of EEAOC, Tucumán, Argentina.
SDS
CultivarPhenotype *QTLs
SDS7-1SDS7-2SDS7-3SDS7-5SDS7-6SDS8-1SDS15-9
SattSattSattSattSattSattSattSattSattSattSatt
214 a309 a570 a371 b354 c163 a270 c307 b316 b202 b357 b
Forrest **R+++++++++++
A8100 RGMR + ++ +
Agustina 49 RRND + ++++
AW 5581R + ++++
DM 4800 RRR + +
Hartwing CR + + +
Munasqa RRMR ++ +
Nueva Andrea 66MR +++ + +
Qaylla RRND +++ +
A8000MR + ++
* Results of field evaluations carried out in two different crop seasons in macro plots located in different regions of NW Argentina, under natural inoculum conditions. R: resistant; S: susceptible; MR: moderately resistant. ND: not determined. (+) Indicates the presence of the SSRs associated with the different QTLs in the genotypes analyzed. ** Reference resistant genotype for SDS. ND, no phenotypic data available; a, QTLs in LG- G; b, QTLs in LG C2; c, QTLs in LG I.
Table 4. Summary of the presence of the MM SSRs associated with the resistance genes for the three diseases under study in nine BC2F1 lines.
Table 4. Summary of the presence of the MM SSRs associated with the resistance genes for the three diseases under study in nine BC2F1 lines.
BC2F1Satt244 (Rcs3)Sat_162 (Rdm4)Satt270 SDS15-9 (12% *)Satt371 SDS7-5 (12% *)Satt570 SDS7-3 (18.1% *)Satt309 SDS7-2 (19.6% *)Satt214 SDS7-1 (23.6% *)Similarity RP (%)
R1++ + 65
R8+++ + 65
R25 b++ ++++65
R26 + +++87
R27++ +++60
R28++ +++91
R29 + +++44
R30 a+++++++50
R31++ +++70
* % resistance phenotype explained by the QTL. a: BC2F1 line with all the MM-SSR associated with the pyramided resistance genes. b: BC2F1 line with six out of seven MM-SSR associated with the pyramided resistance genes.
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Rocha, C.M.L.; García, M.G.; Pardo, E.M.; Sánchez, J.R.; Castagnaro, A.P.; Chiesa, M.A. Marker-Assisted Breeding for Pyramiding Multiple Resistance to Soybean Fungal Diseases. Agronomy 2026, 16, 754. https://doi.org/10.3390/agronomy16070754

AMA Style

Rocha CML, García MG, Pardo EM, Sánchez JR, Castagnaro AP, Chiesa MA. Marker-Assisted Breeding for Pyramiding Multiple Resistance to Soybean Fungal Diseases. Agronomy. 2026; 16(7):754. https://doi.org/10.3390/agronomy16070754

Chicago/Turabian Style

Rocha, Carla María Lourdes, María Gabriela García, Esteban Mariano Pardo, José Ramón Sánchez, Atilio Pedro Castagnaro, and María Amalia Chiesa. 2026. "Marker-Assisted Breeding for Pyramiding Multiple Resistance to Soybean Fungal Diseases" Agronomy 16, no. 7: 754. https://doi.org/10.3390/agronomy16070754

APA Style

Rocha, C. M. L., García, M. G., Pardo, E. M., Sánchez, J. R., Castagnaro, A. P., & Chiesa, M. A. (2026). Marker-Assisted Breeding for Pyramiding Multiple Resistance to Soybean Fungal Diseases. Agronomy, 16(7), 754. https://doi.org/10.3390/agronomy16070754

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