Molecular Markers for Assisted Selection in Sclerotinia Blight and Peanut Smut Resistance

doi:10.21203/rs.3.rs-5033339/v1

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Molecular Markers for Assisted Selection in Sclerotinia Blight and Peanut Smut Resistance

https://doi.org/10.21203/rs.3.rs-5033339/v1

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Argentina is a global leader in peanut production, exporting over 90% of its yield to nearly 100 countries. However, soilborne diseases, particularly Sclerotinia blight and peanut smut, critically impact the sustainability of peanut agriculture. Sclerotinia blight, caused by Sclerotinia minor and S. sclerotiorum, and peanut smut, caused by Thecaphora frezzii, have led to severe economic losses with incidence rates up to 50%. Despite efforts in cultural and chemical disease management, their effectiveness is limited. Consequently, the development of resistant cultivars offers a realistic and sustainable solution. This study focuses on developing and validating KASP markers for resistance against these diseases. By adapting a rapid and cost-effective DNA extraction method, the research facilitated the genotyping of 2,575 F2 plants derived from five crosses of promising parental lines. Three KASP markers for each disease were tested, with Scl1, Scl3, and S3 showing high predictive efficiency for Sclerotinia blight and peanut smut. The study identified 265 plants carrying resistance alleles which represents 10.3% of the total tested plants. These findings highlight the potential of molecular markers in breeding disease-resistant peanut varieties, ensuring sustainable production in Argentina.

Arachis hypogaea

Soilborne diseases

KASP

MAS

Thecaphora frezii

Sclerotinia minor

Argentina is a global leader in peanut production, exporting more than 90% of its national yield to nearly 100 countries. However, the sustainability of peanut production is significantly compromised by the prevalence of soilborne diseases, which inflict substantial economic losses (Marinelli et al. 2017, Rago et al. 2017, Arias et al. 2021, Pedelini and Monetti 2022). Among these diseases, Sclerotinia blight emerges as a predominant threat (Faske et al. 2017, Marinelli et al. 2017), caused by the pathogens Sclerotinia minor and S. sclerotiorum (Marinelli and March 1996, Porter and Melouk 1997). The deleterious impact of these pathogens manifests through distinct symptoms such as wilting, discoloration, stem necrosis, and eventual plant demise (Willetts and Wong, 1980, Tariq et al. 1985, Marinelli and March, 1996). Recent years have witnessed a concerning escalation in the prevalence of Sclerotinia blight in Argentina, with reported incidence rates soaring up to 50% (Oddino, 2015, Rosso et al. 2019). Furthermore, the Argentine peanut industry faces a formidable challenge with the soilborne disease called peanut smut, caused by Thecaphora frezzii. This destructive pathogen induces pod tissue hypertrophy, leading to a characteristic smutted mass appearance in affected seeds (Astiz Gassó and Marinelli, 2013, Marraro Acuña et al. 2013, Arias et al. 2021). Commercial peanut fields have experienced disease incidence rates reaching up to 52%, resulting in substantial yield reductions of approximately 35% (Paredes et al. 2016, Rago et al. 2017). Currently, the prevalence of peanut smut has reached critical levels, affecting nearly 100% of peanut crops in Argentina (Cazón et al. 2018).

Despite extensive efforts to mitigate these diseases through cultural management strategies and chemical treatments, their efficacy remains limited (March et al. 2008, Smith et al. 2008, Vargas Gil et al. 2008). Consequently, developing resistant cultivars against smut and Sclerotinia blight emerges as the most pragmatic, sustainable, and environmentally friendly approach to disease management in peanut production. The identification of genotypes exhibiting promising levels of resistance serves as a pivotal step in breeding new resilient peanut varieties (de Blas et al. 2019, Bressano et al. 2019). In this endeavor, molecular markers are crucial in expediting the breeding process.

Among the wide array of molecular markers used to assist in plant breeding, KASPs stand out. The KASP (Kompetitive Allele-Specific PCR) assay utilizes a fluorescence-based SNP genotyping system to determine alleles at specific loci within genomic DNA (Khera et al. 2013). For markers to be most useful in breeding programs, they should reveal polymorphism in different populations derived from a wide range of different parental genotypes (Langridge et al. 2001).

This study aims to develop and validate KASP markers for peanut smut and Sclerotinia blight using previously phenotyped materials. Additionally, it adapts a rapid DNA extraction method, allowing for the genotyping of a large number of samples at a low cost. The research also seeks to implement large-scale genetic selection on 2,500 F2 materials derived from six crosses between promising parental lines for both diseases.

2.1 Plant material

A panel of 48 materials (Table 1) was used for the validation and subsequent selection of the set of KASP markers originally designed to be used effectively in the breeding program at “Criadero El Carmen”. These plant materials were selected from a variety of sources, comprising wild species, landraces, and elite varieties. This set included eleven individuals of a RIL population described in de Blas et al. (2019), that holds wild gene introgressions from three wild peanut species, A. cardenasii, A. correntina, and A. batizocoi, included in this set. The two progenitors of the RIL population (17304-7-B AO [A. hypogaea] and JS 1806 [synthetic amphidiploid]), were also included, while all other individuals are A. hypogaea sensu lato from “Criadero El Carmen” germplasm collection (Table 1). The phenotype of each material was assessed over three years of field trials at the “Criadero El Carmen” facilities in General Cabrera, Córdoba, Argentina (32°49'40''S 63°52'14''W) under high inoculum pressure (Rosso et al. 2021, de Blas et al. 2021). Plants were classified according to their incidence into resistant, intermediate, and susceptible plants. In peanut blight, the incidence values for each category were: R: ≥ 20%; I: between 20 and 50%; S: ≥ 50 %; while for peanut smut, the values were: R: ≥ 2%; I: between 2 and 20%; S: ≥ 20%. These field assays were carried out in compliance with local legislation (Law No. 9164, Decree 132/05).

Additionally, for marker association purposes, a diverse population of 134 lines of A. hypogaea, which includes commercial varieties and landraces, was used (Supplemental 1a). The phenotypic data for these 134 lines was carried out in the 2018/19, 2019/20, and 2021/22 growing seasons and recorded as an average phenotypic value for each line.

After validating and selecting the markers that would be most effective in “Criadero El Carmen's” breeding program, 2,575 F₂ materials derived from 5 crosses between parental lines chosen for their superior performance against peanut blight and smut were evaluated. The five crosses evaluated were as follows: 59721: RIL 78-A x EC-420 (AO); 59821: RIL 92 x EC-420 (AO); 59921: 48416-2 (AO) x EC-420 (AO); 61221: RIL 78-A x 54219-1 (AO); 63521: 48416-9 (AO) x RILs 78-A.

Table 1

Phenotypic behavior of the materials analyzed for marker validation. S: susceptible, I: intermediate, R: resistant.
Sample	Phenotype		Sample	Phenotype
Sample	Sclerotinia blight	Peanut smut	Sample	Sclerotinia blight	Peanut smut
17304-7-B AO	S	S	39413-1-E-1 AO RC	R	R
JS 1806	R	R	Granoleico	S	S
A.cardenasii	R	R	EC-191AO RC	S	R
A.correntina	R	R	EC-98 AO RC	S	S
A.batizocoi	I	R	EC-420AO RC	I	R
RIL 1-C	I	S	EC-394AO RC	S	R
RIL 2	S	S	EC-214 AO	S	S
RIL 4	S	R	I.99 − 17	R	R
RIL 5	S	S	49416-14-B-I AO RC	S	R
RIL 11	R	R	BOL.MARRÓN	R	S
RIL 22	S	R	I.05 − 4	R	S
RIL 23	S	R	BOL.97 − 6	R	S
RIL 24	R	S	I.03–26	R	R
RIL 7	I	R	I.03–49	R	R
RIL 78-A	R	R	I.06–8	S	S
RIL 92	R	R	I.00–11	S	S
46115-22	R	R	I.16 − 02	S	S
46115-41	R	R	54219-1	S	R
36613-1	S	S	I03-20	S	R
51117 AO	S	S	56220-4-A	S	R
40214-1	S	S	56220-4-B	S	R
42014-1	R	R	44415-1	S	S
46115-2	R	R	ASEM 400	S	S
48416-2	R	R	49416-10	S	R

2.2 DNA extraction

The DNA of the panel of 48 materials (Table 1) used for the validation of KASP markers was extracted from 0.1 g of frozen peanut leaf using the CTAB (Cetyl trimethyl ammonium bromide) method, according to Doyle and Doyle (1990). Subsequently, the extracted DNA was quantified using a microvolume spectrophotometer (DeNovix DS 11) and diluted to achieve a concentration of 5 ng/µl.

For genotyping the 2575 samples, a rapid and low-cost method was adapted. Two leaves were harvested from previously identified peanut plants, each marked with numbered seals, and placed in labeled paper bags. These bags were then stored in airtight containers containing silica gel to maintain sample integrity. DNA extraction from these samples was conducted using the HotShot protocol (Truett et al. 2000), wherein 50 µl of lysis buffer (25 mM NaOH, 0.2 mM Na₂EDTA) and a 5 mm ⌀ circle of leaf per sample were utilized. The samples, in a 96 wells microplate, underwent incubation at 94°C for 30 minutes, followed by cooling; and then 50 µL of 40 mM Trisaminomethane hydrochloride (TRIS HCl) was added. Subsequently, the extracted DNA was quantified using a microvolume spectrophotometer (DeNovix DS 11) and diluted to achieve a concentration of 5 ng/µl.

2.3 Molecular marker design and development

KASP technology was used to design and develop molecular markers from allele-specific markers (SNP) found by our laboratory in genomic regions associated with resistance to Sclerotinia blight and peanut smut (Rosso et al. 2023, de Blas et al. 2021). The SNP markers for Sclerotinia blight, located on chromosomes A04 were developed from a major QTL detected by Rosso et al. (2023). A SNP marker on chromosome B08 comes from an analysis performed in this work including a diverse population described in section 2.1. Following the marker validation and its utility over cultivated peanut, a new test was done using a data set of 8900 polymorphic SNP (Supplemental 1b) from the 47K SNP Axiom_Arachis v02 genotyping platform (Korani et al. 2019) and Sclerotinia blight incidence data belonging to the phenotypic evaluation of the diverse population of 134 A. hypogaea lines. This test consisted in a linear regression performed using multcomp 1.4–25 R package with the lm(Colection_ph$Incidence ~ get(i), Genotype) (Hothorn et al. 2008, R Core Team 2021). The SNP markers for peanut smut, located on chromosome A02/B02 and A08 were developed from two major QTL detected by de Blas (2021). A third SNP marker on chromosome A02 was developed after an analysis performed in this work including a diverse population as described above for the Sclerotinia blight marker on chromosome B08.

Marker development involved retrieving the upstream and downstream sequences flanking the selected SNP (Semagn et al. 2014) and evaluating the specificity of these sequences in genomic contexts using the Basic local alignment search tool (BLAST) algorithm (Altschul et al. 1990). This approach ensured the accurate targeting of genomic regions associated with the identified QTL. The Sclerotinia blight markers (Scl1 and Scl2) were developed from SNPs AX-176807568 and AX-176795814, located on chromosome A04. The Scl3 AX-176820748 SNP is located on chromosome B08. The Smut markers were developed from SNP AX-147212854 (S1) located on chromosome A02/B02 and AX-147229403 (S2) located on chromosome A08 (de Blas et al. 2021). The S3 AX-176813040 marker is located on chromosome A02. KASP markers were synthesized by LGC® Genomics Ltd., Middlesex, UK.

2.4 Validation of molecular markers

PCR with KASP markers was conducted to characterize the 48 genotypes shown in Table 1. The reaction mixture included 5 µl of DNA at 5 ng/µl of the materials to be analyzed, 0.14 µl of the KASP assay mix containing two competing allele-specific primers identifying the SNP and a reverse primer, and 5 ul of 2X KASP-TF Master Mix std ROX. Reactions were carried out on a Rotor Gene 6000 Real-Time PCR System (Corbett Research) with a 72-well rotor, following the thermal conditions described below: Stage 1: 95°C for 15 min; Stage 2: 94°C for 20 s, 61°C for 1 min (repeated for 10 cycles, with a temperature drop of 0.6°C per cycle, resulting in a final annealing temperature of 55.6°C); Stage 3: 94°C for 20 s, 55°C for 1 min (repeated for 32 cycles); Stage 4: 35°C for 2 minutes; Stage 5: Read step at 35°C for 30 sec, acquiring channels green, yellow, and orange. The data obtained was set down, and the predictive efficiency was calculated as a proportion expressed as a percentage of the phenotypic response recorded coincident with the allelic variant corresponding to the marker over the total number of genotypes evaluated.

2.5 Field trial for diagnostic genotyping

In the 2022/23 season, a total of 2575 F₂ seeds corresponding to the crosses mentioned in the plant material section were sown in “Criadero El Carmen” facilities. These materials were distributed in the field in plots of 3 m, spaced 10 cm apart, maintaining their identity as progenies of each of the crosses. After 30 days from sowing, each plant was individually identified using numbered seals, which were placed on each emerged plant.

3.1 Molecular marker design and development

Three KASP molecular markers for Sclerotinia blight (Scl1/Scl2/Scl3) and three for peanut smut (S1/S2/S3) were developed.

Scl1, Scl2, S1, and S2 markers were generated from previous analyses performed by our group using an RIL population (Rosso et al. 2023, de Blas et al. 2021). On the other hand, Scl3 and S3 were developed from an analysis conducted in this work, based on a single marker association between the phenotypic data of Sclerotinia blight and peanut smut in a diverse population of 134 A. hypogaea lines, which includes commercial varieties and landraces as described above. The regression results show that AX176820748 (Scl3) is at the top of the p-values ranking (1.21E^− 08) with an r-squared of 0.24. For AX176813040 (S3), the regression showed the highest p-value ranking (1.49E^− 23) with an r-squared of 0.53 (Supplemental 2a and 2b for Scl3 and S3, respectively).

Allelic variants of the markers were associated with the following disease responses:

Sclerotinia blight:

Scl1 Marker AX-176807568 chromosome A04: G resistance/A susceptibility

TCCCAAAACTCTCTCTAGAATGTAATGATATAGTG[A/G]CTTCAAAGATCCAAGCTCAGAAGTAGAACATAGAT

Scl 2 Marker AX-176795814 chromosome A04:T resistance/G susceptibility.

TCTCCTTAACGACGGCGCTTGCAGTGATGCTACCC[G/T]CTCCTCCCGTCCTCCCTCTCTCCCTAAAATAACTA

The resistance-associated alleles were found in the A complement of the synthetic amphiploid, derived from the genomes of A. correntina and A. cardenasii. The susceptibility-associated alleles were present in the A complement of 17304-7-B (AO), corresponding to A. hypogaea (Rosso et al. 2021).

Scl 3 Marker AX-176820748 chromosome B08: C resistance/T susceptibility.

AGAGATGATTCGAGAAATTATATGGTTAGCCTTAG[C/T]TATTAATCAAGCTGGAAAAAATTAAACTCAGGATA

The resistance-associated allele was found in the B complement of A. hypogaea (Rosso et al. 2021).

Peanut smut:

S1 marker AX-147212854 chromosome A02/B02: T resistance/ C susceptibility. This particular resistance-associated allele was not possible to be assigned either to the A and B complement of the JS parent 1806 (de Blas et al. 2021).

GATTTGTTTCCTTCTTCTATTGTGGCAATTAGATT[C/T]GAGTCCTAAGATTCTTGAAGGTTCACTTGGATTAT

S2 marker AX-147229403 chromosome A08:C resistance/ T susceptibility.

CACAGGAAAAGTACTGAGAACAAGATGTAAACATA[C/T]GATCGTTAAGAAGCTGGGAAGCAGCACTGCAGCTA

The resistance-associated allele was present in the A complement of the JS parent 1806 (de Blas et al. 2021). In contrast, the susceptibility-associated allele is present in the A complement of JS. 17304-7-B (AO).

S3 marker AX-176813040 chromosome A02:C resistance/ T susceptibility.

TCTCTTCTTAAATGTCTGTATTGAAAACTTTTTCT[C/T]CTAACTTGTAATATTATGTCAATCAAATCATGCTT

3.2 Validation of KASP markers

The predictive efficiency of each marker was calculated by comparing the expected phenotype with the allelic variants found in the set of individuals studied. The results were as follows: Sclerotinia blight markers Scl1 and Scl 2 showed 82.3% of predictive efficiency, while the Scl3 marker obtained an efficiency of 85.9%. Because the Scl1 and Scl2 showed indistinguishable performance in terms of predictive efficiency and amplification across all genotypes, and both were located in chromosome A04, the Scl1 (chromosome A04) and Scl3 (chromosome B08) markers were chosen for resistance selection in the F2 segregating population. These efficiency results were obtained from the validation panel where 22% are RIL lines with wild introgressions. The peanut smut marker S3 demonstrated an efficiency of 89.6%. Conversely, markers S1 and S2 for smut exhibited poor performance in predicting the phenotype (> 50%), resulting in their exclusion.

3.3 High throughput DNA extraction method

The HotShot method (Truett et al. 2000) is based on DNA extraction by dissolving the material in a hot alkaline reagent followed by neutralization. This method is rapid, low-cost, and can be performed in 96-well plates, facilitating automation and the processing of a large number of samples.

The method was adapted for the extraction of DNA from dehydrated peanut leaves. A hole-puncher was used to create a 5 mm diameter circle, which was placed in 96-well plates with 50 µl of lysis buffer. The samples were heated to 94°C for 30 minutes, and after cooling, 50 µl of TRIS HCl buffer was added. The most time-consuming step was the extraction of leaf discs, but this can be performed concurrently with the KASP reaction. DNA extracted using this method remained stable for at least a week at 4°C. The extracted DNA was quantified and diluted to obtain a concentration of 5 ng/µl. Other extraction methods, such as CTAB (Doyle and Doyle, 1990) and Edwards (Edwards et al. 1991), as well as HotShot with fresh leaves and seeds, were also tested (data not shown). The DNA extracted by the various methods amplified similarly.

The quality of DNA extracted using this method is poor; however, it is highly effective for PCR reactions, such as KASP assays. We have also used it for conventional PCR reactions and even for fungal DNA extraction, and the results have been accurate enough.

3.4 Genotyping and selection using KASP Markers

After genotyping 2,575 plants using the KASP markers Scl1, Scl3, and S3, a total of 265 plants exhibiting alleles linked to resistance against both diseases were identified and selected. This results in 10.29% of the plants that will be sown in the next season of the advancing population process.

Dehydrated peanut leaves were used to extract DNA using the HotShot method in 96-well plates. After DNA dilution, the mix for the KASP reactions was prepared. Following PCR, the data were analyzed according to the amplification of resistance or susceptibility alleles in each genotype. This analysis was performed for the three selected markers. Based on this analysis, 10.2% of the plants were selected for possessing all three resistance alleles.

During the 2022/23 season, there was a strong inoculum pressure of Sclerotinia minor and Thecaphora frezii in the area where the trial was planted, with 2 sclerotia per 100 g of soil and 5.5 ⨯ 10³ teliospores per gram of soil, respectively. Even so, there was a notable prevalence of Sclerotinia blight of 33 %, and peanut smut incidence exceeding 20%. After harvest, the incidence of Sclerotinia blight and peanut smut was evaluated in the 265 KASP-selected peanut plants. This revealed incidence levels of 7.92% for Sclerotinia blight and 0.67% for peanut smut.

4.1 Marker Performance and Suitability

The development of KASP markers to identify alleles associated with resistance to Sclerotinia blight and peanut smut was a labor-intensive process. It involved phenotyping a population for both diseases and associating genetic polymorphisms with phenotypic outcomes. The KASP markers presented in this study were developed using the identified SNPs found at the peaks of the QTLs in de Blas et al. (2021) and Rosso et al. (2023) plus regressions performed in this work. However, it is essential to acknowledge that marker development also encountered challenges, such as optimizing primer design and assay conditions to achieve robust and reproducible results. These efforts underscore the importance of collaborative research and interdisciplinary approaches in marker development for crop improvement programs.

4.2 Allelic Variants and Disease Resistance

Identifying allelic variants linked to disease resistance is essential for marker-assisted breeding programs focused on developing resistant crop varieties.

For the Scl1, Scl2 and S2 markers, the resistance-associated alleles were identified in complement A of the JS parent 1806, which is a synthetic amphiploid derived from the genomes of Arachis correntina and A. cardenasii. Conversely, the susceptibility-associated alleles were found in complement A of JS 17304-7-B (AO), corresponding to A. hypogaea. These findings suggest a genetic basis for disease resistance that may be attributed to specific alleles inherited from the amphidiploid that carries alleles from wild Arachis. In de Blas et al. (2021) RIL with amphiploid alleles showed the lowest phenotypic mean scores, while all RIL carrying A. hypogaea alleles had a significantly higher smut incidence. Wild relatives of the cultivated peanut have been shown to be sources of resistance to multiple pests and pathogens (Stalker et al. 2016), including the peanut smut (Oddino et al. 2017) and Sclerotinia blight (Rosso et al. 2023)

For the S3 marker, the resistance-associated allele was found in the A complement. The BLAST analysis identified a match in chromosome A02 of A. duranensis and the A02 chromosome of A. hypogaea. These matches support the rationale that the genetic basis for smut disease resistance is broadly inherited from the A genome.

In contrast, the Scl3 marker displayed a unique scenario, with the resistance-conferring allele located in complement B. Through sequence analysis using the BLAST search algorithm, we identified two matches of the same variant on chromosome 8 of complement B in both A. ipaensis and A. hypogaea. This discovery suggests the presence of a molecular variant with genome-wide resistance to the targeted disease, thereby highlighting the potential of this marker for conferring broad-spectrum resistance. In this case, the resistance derives from susceptible progenitors and this situation has been reported in several QTL studies in different crops (Bernier et al. 2007, Bonamico et al. 2012, Liang et al. 2020, Rosso et al. 2023). Overall, our findings elucidate the genetic basis of disease resistance conferred by specific allelic variants identified through marker analysis. These insights not only contribute to our understanding of plant-pathogen interactions but also offer valuable resources for breeding efforts aimed at developing disease-resistant crop varieties with enhanced resilience and productivity.

4.3 Marker Validation and Predictive Efficiency

In this study, we evaluated the predictive efficiency of each marker by scrutinizing the concordance between expected phenotypic values and the allelic variants detected in the studied individuals. Both the Scl1 and Scl2 markers exhibited a predictive efficiency of 82.3%, while the Scl3 marker, a strikingly high predictive efficiency of 85.9%. The set of genotypes used for validation influences the calculated efficiency percentage for each marker. As cited in the results, the validation sample includes a higher percentage of genotypes from diverse populations and only 20% of RIL lines with wild introgressions. This results in Scl1 and Scl2 being the markers with the lowest efficiency in this validation panel, despite predicting the phenotype with 100% efficiency within the RIL population. Conversely, Scl3 was obtained from the analysis of phenotype and genotype of a A. hypogaea diverse population, which explains its higher efficiency percentage in this validation panel.

The peanut smut marker S3 exhibited the higher efficiency, achieving 89.6%, which was also obtained from a A. hypogaea diverse population. These results underscore the reliability of these markers in accurately estimating phenotypic outcomes related to disease resistance within the studied population.

The strategic selection of markers for resistance breeding in the F2 segregating population was based on their performance metrics, particularly in terms of predictive efficiency and their consistency in amplifying across diverse genotypes. Given the comparable performance of the Scl1 and Scl2 markers and the fact that both markers came from the same chromosome, only Scl1 was selected to be used.

The overall predictive efficiency of 85.9% for the marker set analyzed in our study is indicative of their reliable predictive effectiveness. Branch et al. (2014) reported a lower prediction efficiency of 73.33% for markers associated with nematode resistance in peanuts. This contrast suggests that the markers identified in our study are reliable and could serve as indispensable tools in pinpointing individuals with desired traits in breeding programs.

It is important to note that there is currently no universally agreed-upon threshold value for determining the quality of a molecular marker solely based on predictive efficiency. As we continue, the proliferation of molecular markers and the advent of cutting-edge genotyping technologies with enhanced coverage and resolution promise to further elevate predictive efficiency. Thus, future efforts should aim to expand marker panels and leverage advanced genotyping platforms to maximize the efficacy of molecular markers in marker-assisted selection (MAS) strategies toward augmenting crop improvement initiatives.

Due to the low performance of S1 and S2 markers on detecting the expected phenotype according to the allelic variants found in the individuals assessed, those markers were discarded. The accurate association of a nucleotide variant with a phenotypic trait is the first step in developing functional markers for use in Marker-Assisted Selection (MAS). In our study, several constraints arose in detecting sequences within the SNP flanking regions due to the high similarity between the A and B genomes of Arachis hypogaea, as previously noted by Bertioli et al. (2019). This similarity makes it challenging to identify unique sequences for the allele-specific primers targeting the SNP of interest. Despite designing primers for S1 and S2, their poor performance in predicting the specific phenotypic condition within the validation set can be attributed to the complexity of the peanut genome and the frequently occurring tetrasomic recombination (Leal-Bertioli et al. 2015). Specifically, the QTL on the A02/B02 chromosome, from which S1 was designed, has been reported as a hotspot for tetrasomic recombination (de Blas et al. 2021). Although S2 was not located in a tetrasomic recombinant region, BLAST analysis revealed multiple hits in other genomic regions, which could lead to nonspecific primer annealing and produce spurious results.

4.4. High throughput DNA extraction method

The implementation of the HotShot method (Truett et al. 2000) enabled the swift and cost-effective extraction of DNA from over 2,500 samples. Without this plate-based method, large-scale genotyping would not have been feasible. Its adoption could greatly benefit peanut scientists in high-throughput marker-assisted selection (MAS) for peanut breeding.

Although the majority of high-throughput DNA extraction studies depend on costly commercial kits (Fang et al. 2017, Zhao et al. 2017), the HotShot method, newly applied to peanut MAS programs, provides a cost-effective and efficient alternative, adaptable for laboratories of any scale.

4.5 Selection by KASP markers

After genotyping 10.29% of the plants with alleles linked to resistance to both diseases will be sown in the next season of the advancing population process. This highlights that the use of markers in this case resulted in significant savings in time, effort, and economic resources. This is another remarkable result for peanut breeders and scientists.

The field observations during the 2022–23 season revealed a significant prevalence of Sclerotinia blight of 33% and peanut smut incidence exceeding 20%, and also a high inoculum pressure. However, plants harboring the resistance alleles identified through genotyping displayed low disease symptoms, 21 plants of the total 265 have some signs of Sclerotinia minor and 0.65% of the 16.769 shells opened had a sign of peanut smut at time of harvest, contrasting with the high incidence of the general population. These results are concordant with findings from other marker-assisted selection (MAS) studies in plant breeding. For instance, the selection efficiency observed in our study is comparable to the 8–12% efficiency reported in rice breeding programs targeting bacterial blight resistance using MAS (Sundaram et al. 2008). The significant reduction in disease incidence observed in our KASP-selected plants further underscores the effectiveness of MAS in enhancing disease resistance across crops.

The selection of resistant plants offers promising implications for crop management and breeding efforts. By focusing on plants with known resistance alleles, breeders can significantly reduce the costs associated with breeding programs. With a higher proportion of resistant plants, resources can be allocated more efficiently, leading to increased productivity and reduced losses due to disease outbreaks (Bonnett at al., 2005, Slater et al. 2014). Additionally, the identification and selection of resistant plants provide a foundation for the development of improved cultivars with enhanced disease resistance, ultimately contributing to the sustainability and resilience of peanut cultivation.

The application of molecular marker-assisted selection in peanut breeding has proven useful in breeding programs. Molecular markers are currently used in certain characteristics, such as resistance to nematodes and the chemical composition of seeds with high oleic acid content (Chu et al. 2011, Branch et al. 2014, Devasena et al. 2017) and peanut rust (Leal-Bertioli et al. 2015).

Chu et al. (2011) provide an example of their success, demonstrating a significant reduction, at least threefold, in the time required for the selection of plants with nematode resistance and high oleic acid content compared to traditional selection methods.

In the study on the cost-benefit of marker-assisted selection, Knapp (1998) concluded that MAS can be cost-effective if its cost is less than 17 times the cost of phenotypic selection. In the present study, plants were selected in F2: F3 generations. Berloo (2000) reported that the response to MAS selection is greater in this generation because this tool is able to take advantage of the greater genetic diversity present in heterozygous populations.

In this work, of the total number of genotyped plants, 10.29% carrying the resistance alleles for Sclerotinia blight and peanut smut were selected. Zhao et al. (2017) selected 10% of high oleic peanut seeds from an F2 population from a cross between high oleic and non-high oleic peanuts. Chu et al. (2011) selected 18% of plants for nematode resistance and high oleic traits in peanuts in an F2:F3 population.

Marker-assisted selection (MAS) theoretically enables the utilization of any marker tightly linked to a quantitative trait locus (QTL) (Collard et al. 2005). However, due to the cost and complexity associated with multi-QTL selection, most studies typically focus on markers linked to three or fewer QTLs (Ribaut and Betran, 1999). While instances of introgression involving up to five QTLs in tomato have been reported (Lecomte et al. 2004). Our selection of three markers aligned with the prevailing practice revealing that two markers for Sclerotinia resistance and one for smut resistance were highly predictive of resistant and susceptible phenotypes. Even single-QTL selection can significantly enhance breeding efficiency, provided that the QTL explains a substantial portion of the phenotypic variance (Ribaut and Betran, 1999, Tanksley, 1993). Additionally, to ensure consistent selection outcomes across diverse environments, QTLs chosen for MAS should exhibit stability (Hittalmani et al. 2002, Ribaut and Betran, 1999).

This study has yielded significant insights into marker-assisted breeding for disease-resistant crop varieties, particularly in the context of peanut cultivation. Through marker amplification, a total of 265 segregating genotypes exhibiting resistance alleles against Sclerotinia blight and peanut smut were identified. The presence of diseases observed at the field during the 2022/23 season served as a validation of the effectiveness of the markers. These findings hold promise for the development of peanut varieties with resistance genes against multiple diseases, underscoring their potential to enhance productivity and quality within the Argentine peanut value chain.

Additionally, the successful utilization of the HotShot DNA extraction method in this study has been pivotal. The efficiency and affordability of the HotShot protocol have played an indispensable role in advancing our understanding of marker-assisted breeding and hold implications for future advancements in marker technology. The adoption of this method could be highly beneficial for peanut scientists in the implementation of high-throughput marker-assisted selection (MAS) in peanut breeding.

Funding sources

This work was supported by the Agencia Córdoba Innovar y Emprender- Gob. de la Provincia de Córdoba Argentina a través del Voucher de Innovación Colaborativa II 2023 "Selección asistida por genómica de materiales de maní resistentes a carbón (Thecaphora frezii) y tizón (Sclerotinia minor) utilizando marcadores KASPs (Kompetitive Allele Specific PCR).

Competing Interests

The authors have no relevant financial or non-financial interests to disclose.

Author contributions

All authors contributed to the study's conception and design. Material preparation, data collection, and analysis were carried out by Marina Bressano, Francisco J. de Blas, and Melina H. Rosso. The first draft of the manuscript was written by Marina Bressano, Francisco J. de Blas, and Melina H. Rosso, and all authors provided comments on previous versions of the manuscript. Adriana Verdini contributed to the methodology. Mario Buteler handled funding acquisition, review, and editing. Sara J. Soave was responsible for funding acquisition and resources. Guillermo Seijo and Veronica Mary contributed to review and editing. Martin Theumer and Germán Robledo were involved in formal analysis, methodology, and supervision. All authors read and approved the final manuscript.

Data Availability

The data supporting the findings of this study are available in the following publicly accessible repositories:

Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990). Basic local alignment search tool. Journal of molecular biology, 215(3), 403-410. https://doi.org/10.1016/s0022-2836(05)80360-2
Arias S, Mary VS, Velez PA, Rodriguez MG, Otaiza González SN, Theumer MG (2021). Where does the peanut smut pathogen, Thecaphora frezii, fit in the spectrum of smut diseases?. Plant Disease, https://doi.org/10.1094/PDIS-11-20-2438-FE
Astiz Gassó MM, Marinelli A (2013). In vitro germination biology of Thecaphora frezii (in Spanish) In: XXVIII Jornada Nacional de Maní proceedings. Córdoba, Argentina, INTA. pp. 45–46. http://www.ciacabrera.com.ar/jornada_del_mani/28_jornada_del_mani.html
Berloo RV (2000). Use of molecular markers in plant breeding. Landbouwuniversiteit Wageningen, The Netherlands.
Bernier J, Kumar A, Ramaiah V, Spaner D, Atlin G (2007). A Large Effect QTL for Grain Yield under Reproductive-Stage Drought Stress in Upland Rice. Crop Science, 47(2)507–516. https://doi.org/10.2135/CROPSCI2006.07.0495
Bertioli DJ, Jenkins J, Clevenger J, Dudchenko O, Gao D, Seijo G, Leal-Bertioli SCM, Ren L, Farmer AD, Pandey MK, Samoluk SS, Abernathy B, Agarwal G, Ballén-Taborda C, Cameron C, Campbell J, Chavarro C, Chitikineni A, Chu Y, Dash S, El Baidouri M, Guo B, Huang W, Do Kim K, Korani W, Lanciano S, Lui CG, Mirouze M, Moretzsohn MC, Pham M, Shin JH, Shirasawa K, Sinharoy S, Sreedasyam A, Weeks NT, Zhang X, Zheng Z, Sun Z, Froenicke L, Aiden EL, Michelmore R, Varshney RK, Holbrook CC, Cannon EKS, Scheffler BE, Grimwood J, Ozias-Akins P, Cannon SB, Jackson SA, Schmutz J (2019). The genome sequence of segmental allotetraploid peanut Arachis hypogaea. Nat Genet 51, 877–884 https://doi.org/10.1038/s41588-019-0405-z
Bonamico NC, di Rienzo MA, Ibañez MA, Borghi ML, Díaz DG, Salerno JC, Balzarini MG (2012). QTL analysis of resistance to Mal de Río Cuarto disease in maize using recombinant inbred lines. The Journal of Agricultural Science, 150 (5) 619 - 629. https://doi.org/10.1017/S0021859611000943
Bonnett DG, Rebetzke GJ, Spielmeyer W (2005). Strategies for efficient implementation of molecular markers in wheat breeding. Molecular Breeding, 15, 75-85
Branch WD, Brenneman TB, Hookstra G (2014). Field Test Results Versus Marker Assisted Selection for Root-Knot Nematode Resistance in Peanut. Peanut Science, 41(2)85–89. https://doi.org/10.3146/PS14-1.1
Bressano M, Massa AN, Arias RS, de Blas FJ, Oddino C, Faustinelli PC, Soave SJ, Soave JH, Pérez MA, Sobolev VS, Lamb MC, Balzarini M, Buteler MI, Seijo JG (2019). Introgression of peanut smut resistance from landraces to elite peanut cultivars (Arachis hypogaea L.). https://doi.org/10.1371/journal.pone.0211920
Cazón LI, Paredes JA, Rago AM (2018). The biology of Thecaphora frezii smut and its effects on argentine Peanut production. In: Kimatu JN editor. Advances in plant pathology. London: IntechOpen Ltd., pp. 31–46. https://doi.org/10.5772/intechopen.75837.
Chu Y, Wu CL, Holbrook CC, Tillman BL, Person G, Ozias-Akins P (2011). Marker-Assisted Selection to Pyramid Nematode Resistance and the High Oleic Trait in Peanut. The Plant Genome, 4(2),110–117. https://doi.org/10.3835/PLANTGENOME2011.01.0001
Collard BCY, Jahufer MZZ, Brouwer JB, Pang ECK (2005). An introduction to markers, quantitative trait loci (QTL) mapping and marker-assisted selection for crop improvement: The basic concepts. Euphytica 142, 169–196. https://doi.org/10.1007/s10681-005-1681-5
de Blas FJ, Bressano M, Teich I, Balzarini MG, Arias RS, Manifesto MM, Costero BP, Oddino C, Soave SJ, Soave JA, Buteler MI, Massa AN, Seijo JG (2019). Identification of smut resistance in wild Arachis species and its introgression into peanut elite lines. Crop Sci., 59(4)1657–65. https://doi.org/10.2135/cropsci2018.10.0656.
de Blas FJ, Bruno CI, Arias RS, Ballén-Taborda C, Mamani E, Oddino C, Rosso M, Costero BP, Bressano M, Soave JH, Soave SJ, Buteler MI, Seijo JG, Massa AN (2021). Genetic mapping and QTL analysis for peanut smut resistance. BMC Plant Biology 21(1)1–15. https://doi.org/10.1186/S12870-021-03023-4/TABLES/3
Devasena N, Anitha BK, Manivannan N, Nallathambi G, Janila P, Pandey MK, Varshney RK (2017) Validation of SSR markers linked to oil content in groundnut (Arachis hypogaea L.). Journal of Oilseeds Research 34(2):70–75. https://doi.org/10.56739/jor.v34i2.137698
Doyle JJ, Doyle JL (1990) Isolation of plant DNA from fresh tissue. Focus 12:13-15.
Edwards K, Johnstone C, Thompson C (1991) A simple and rapid method for the preparation of plant genomic DNA for PCR analysis. Nucleic Acids Res 19(6):1349. doi: 10.1093/nar/19.6.1349
Fang Y, Liu H, Zhang Z, Qi F, Chen Q, Liu H, Wan L, Wang X, Tian M, Lv M, Sun Z, Dong W, Huang B, Zhang X (2022) Genomic identification and phenotypic evaluation of yield traits and bacterial wilt resistance in high oleic peanut breeding lines developed by marker assisted backcrossing. Euphytica 218(6):80. https://doi.org/10.1007/s10681-022-03032-w
Faske TR, Drennan G, Hurd K (2017) First report of Sclerotinia blight caused by Sclerotinia sclerotiorum on peanut in Arkansas. Plant Health Progress 18(1):7–8. https://doi.org/10.1094/PHP-BR-16-0056
Hittalmani S, Shashidhar HE, Bagali PG, Huang N, Sidhu JS, Singh VP, Khush GS (2002) Molecular mapping of quantitative trait loci for plant growth, yield and yield related traits across three diverse locations in a doubled haploid rice population. Euphytica 125:207–214. https://doi.org/10.1023/A:1015890125247
Hothorn T, Bretz F, Westfall P (2008) Simultaneous Inference in General Parametric Models. Biometrical Journal 50(3):346–363. https://doi.org/10.1002/bimj.200810425
Khera P, Upadhyaya HD, Pandey MK, Roorkiwal M, Sriswathi M, Janila P, Guo Y, McKain MR, Nagy ED, Knapp SJ, Leebens-Mack J, Conner JA, Ozias-Akins P, Varshney RK (2013) Single Nucleotide Polymorphism–based Genetic Diversity in the Reference Set of Peanut (Arachis spp.) by Developing and Applying Cost-Effective Kompetitive Allele Specific Polymerase Chain Reaction Genotyping Assays. The Plant Genome 6(3). https://doi.org/10.3835/plantgenome2013.06.0019
Knapp SJ (1998) Marker-assisted selection as a strategy for increasing probability of selecting superior genotypes. Crop Science 38(1):164–174. https://doi.org/10.2135/cropsci1998.0011183X003800050009x
Langridge P, Lagudah ES, Holton TA, Appels R, Sharp PJ, Chalmers KJ (2001) Trends in genetic and genome analyses in wheat: a review. Australian Journal of Agricultural Research 52(12):1043-1077. https://doi.org/10.1071/AR01082
Leal-Bertioli SCM, Cavalcante U, Gouvea EG, Ballén-Taborda C, Shirasawa K, Guimarães PM, Jackson SA, Bertioli DJ, Moretzsohn MC (2015) Identification of QTLs for rust resistance in the peanut wild species Arachis magna and the development of KASP markers for marker-assisted selection. G3: Genes, Genomes, Genetics 5(7):1403–1413. https://doi.org/10.1534/G3.115.018796/-/DC1
Lecomte L, Duffe P, Buret M, Servin B, Hospital F, Causse M (2004) Marker-assisted introgression of five QTLs controlling fruit quality traits into three tomato lines revealed interactions between QTLs and genetic backgrounds. Theor Appl Genet 109:658–668. https://doi.org/10.1007/s00122-004-1674-0
Liang Y, Cason JM, Baring MR, Septiningsih EM (2020) Identification of QTLs associated with Sclerotinia blight resistance in peanut (Arachis hypogaea L.). Genetic Resources and Crop Evolution 68:629–637. https://doi.org/10.1007/s10722-020-01012-4
March GJ, Vargas Gil S, Marinelli A, Oddino C, Zuza M (2008) Enfermedades causadas por hongos del suelo en maní – Estrategias de manejo. In IDIA XXI–Cultivos industriales pp. 42–45.
Marinelli A, Oddino C, March GJ (2017) Enfermedades fúngicas del maní. In: Fernandez E, Giayetto O (Eds.) El cultivo de maní en Argentina, 2° edición pp. 385–311.
Marinelli A, March GJ (1996) Epidemias de “tizón” del maní (Arachis hypogaea L.) causado por Sclerotinia sclerotiorum (Lib.) de Bary y S. Minor Jagger en Argentina. Boletín de Sanidad Vegetal 22(3):501–510.
Marraro Acuña F, Cosa MT, Wiemer AP (2013) Peanut smut: histopathology, incidence and severity (in Spanish). In: XXVIII Jornada Nacional del Maní proceedings. Córdoba: INTA. pp. 26–27. http://www.ciacabrera.com.ar/jornada_del_mani/28_jornada_del_mani.html
Oddino C (2015) Enfermedades por patógenos del suelo en maní. Disertación. XXX Jornada Nacional Del Maní, General Cabrera, Córdoba.
Oddino CM, Soave JA, Soave SJ, Buteler MI, Moresi A, de Blas FJ (2017) Sources of smut resistance in peanut wild species and Bolivian landraces. Córdoba: Advances in Arachis through genomics and biotechnology proceedings pp. 8.
Paredes JA, Cazón LI, Osella A, Peralta V, Alcalde M, Kearney MI, Zuza MS, Rago AM, Oddino C (2016) Regional peanut smut survey and estimates of losses caused by the disease. (In Spanish) In: XXXI Jornada Nacional de Maní proceedings. Córdoba. pp. 41–42. http://www.ciacabrera.com.ar/jornada_del_mani/31_jornada_del_mani.html
Pedelini R, Monetti M (2022) Maní, guía práctica para su cultivo. Instituto Nacional de Tecnología Agropecuaria 1-28. http://hdl.handle.net/20.500.12123/12519
Porter DM, Melouk HA (1997) Sclerotinia Blight. In: Kokalis-Burelle N, Porter DM, Rodriguez-Kabana R, Smith DH, Subrahmanyam P (Eds.) Compendium of Peanut Diseases. 2nd Edition.
R Core Team (2021) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/
Rago AM, Cazon LI, Paredes JA, Molina JP, Conforto EC, Bisonard EM, Oddino C (2017) Peanut smut: from an emerging disease to an actual threat to argentine peanut production. Plant Dis 101(3):400–408. https://doi.org/10.1094/PDIS-09-16-1248-FE
Ribaut JM, Betran J (1999) Single large-scale marker-assisted selection (SLS-MAS). Mol Breed 5:531–541. https://doi.org/10.1023/A:1009631718036
Rosso M, Soave S, de Blas F, Bressano M, Giordano D, Giuggia J, Garnero JM, Seijo G, Moresi A, Buteler M, Oddino C (2019) Caracterización del germoplasma de criadero El Carmen frente a tizón del maní causado por Sclerotinia minor. In: XXXIV Jornada Nacional de Maní proceedings. General Cabrera - Córdoba - Argentina. https://ciacabrera.com.ar/jornada_del_mani/34_jornada_del_mani.html
Rosso MH, de Blas FJ, Massa AN, Oddino C, Giordano DF, Seijo JG, Arias RS, Soave JH, Soave SJ, Buteler MI, Bressano M (2023). Two QTLs govern the resistance to Sclerotinia minor in an interspecific peanut RIL population. Crop Science 63(2): 613–621. https://doi.org/10.1002/CSC2.20875
Semagn K, Babu R, Hearne S, Olsen M (2014). Single nucleotide polymorphism genotyping using Kompetitive Allele Specific PCR (KASP): overview of the technology and its application in crop improvement. Molecular Breeding 33: 1–14. https://doi.org/10.1007/s11032-013-9917-x
Slater AT, Cogan NO, Hayes BJ, Schultz L, Dale MFB, Bryan GJ, Forster JW (2014). Improving breeding efficiency in potato using molecular and quantitative genetics. Theoretical and Applied Genetics 127: 2279–2292. https://doi.org/10.1007/s00122-014-2386-8
Smith DL, Garrison MC, Hollowell JE, Isleib TG, Shew BB (2008). Evaluation of application timing and efficacy of the fungicides fluazinam and boscalid for control of Sclerotinia blight of peanut. Crop Protection 27(3–5): 823–833. https://doi.org/10.1016/j.cropro.2007.10.009
Sundaram RM, Vishnupriya MR, Biradar SK, Laha GS, Reddy GA, Shobha Rani N, Sarma NP, Sonti RV (2008). Marker-Assisted Breeding for Development of Bacterial Blight Resistant Rice Hybrids. International Rice Research Notes 33(1): 49–50.
Tanksley SD (1993). Mapping polygenes. Annual Review of Genetics 27: 205–233. https://doi.org/10.1146/annurev.ge.27.120193.001225
Tariq VN, Gutteridge CS, Jeffries P (1985). Comparative studies of cultural and biochemical characteristics used for distinguishing species within Sclerotinia. Transactions of the British Mycological Society 84(3): 381–397.
Truett GE, Heeger P, Mynatt RL, Truett AA, Walker JA, Warman ML (2000). Preparation of PCR-quality mouse genomic DNA with hot sodium hydroxide and tris (HotSHOT). Biotechniques 29(1): 52–54. doi: 10.2144/00291bm09
Vargas Gil S, Haro R, Oddino C, Kearney M, Zuza M, Marinelli A, March GJ (2008). Crop management practices in the control of peanut diseases caused by soilborne fungi. Crop Protection 1(27): 1–9. https://doi.org/10.1016/J.CROPRO.2007.03.010
Willetts HJ, Wong JAL (1980). The biology of Sclerotinia sclerotiorum, S. trifoliorum, and S. minor with emphasis on specific nomenclature. The Botanical Review 46(2): 101–165. https://doi.org/10.1007/BF02860868
Zhao S, Li A, Li C, Xia H, Zhao C, Zhang Y, Hou L, Wang X (2017). Development and application of KASP marker for high throughput detection of AhFAD2 mutation in peanut. Electronic Journal of Biotechnology 25: 9–12. https://doi.org/10.1016/J.EJBT.2016.10.010

No competing interests reported.

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You are reading this latest preprint version

Molecular Markers for Assisted Selection in Sclerotinia Blight and Peanut Smut Resistance

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Version 1

Abstract

1. INTRODUCTION

2. MATERIALS AND METHODS

2.1 Plant material

2.2 DNA extraction

2.3 Molecular marker design and development

2.4 Validation of molecular markers

2.5 Field trial for diagnostic genotyping

3. RESULTS

3.1 Molecular marker design and development

3.2 Validation of KASP markers

3.3 High throughput DNA extraction method

3.4 Genotyping and selection using KASP Markers

4. DISCUSSION

4.1 Marker Performance and Suitability

4.2 Allelic Variants and Disease Resistance

4.3 Marker Validation and Predictive Efficiency

4.4. High throughput DNA extraction method

4.5 Selection by KASP markers

5. CONCLUSIONS

Statements and Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1