Marker-assisted Pyramiding of Charcoal Rot Resistance Loci in Strawberry

Authors:
Elissar Alam Plant Breeding Graduate Program, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL 32611, USA

Search for other papers by Elissar Alam in
This Site
Google Scholar
Close
,
Seonghee Lee Plant Breeding Graduate Program, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL 32611, USA

Search for other papers by Seonghee Lee in
This Site
Google Scholar
Close
,
Natalia A. Peres Plant Pathology Department, University of Florida, Gulf Coast Research and Education Center, Wimauma, FL 33598, USA

Search for other papers by Natalia A. Peres in
This Site
Google Scholar
Close
, and
Vance M. Whitaker Plant Breeding Graduate Program, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL 32611, USA

Search for other papers by Vance M. Whitaker in
This Site
Google Scholar
Close

Click on author name to view affiliation information

Abstract

Macrophomina phaseolina, the causal agent of charcoal rot, is one of the most destructive soil-borne pathogens that affect the global strawberry industry. Resistant cultivars are critical for ensuring the profitability of strawberry production without the protection historically provided by methyl bromide. Previously, three loci, namely, FaRMp1, FaRMp2, and FaRMp3, associated with quantitative resistance to Macrophomina phaseolina have been identified and validated across diverse populations and environments. Among those, the locus with the largest effect, FaRMp3, was initially detected in crosses with an exotic Fragaria ×ananassa selection. We introgressed the favorable FaRMp3 allele into elite germplasm in the University of Florida strawberry breeding program already segregating for FaRMp1 and FaRMp2 and confirmed its phenotypic effects across various genetic backgrounds. Subsequently, we developed a high-throughput genotyping assay to facilitate the transfer and selection of FaRMp3 in breeding populations via marker-assisted selection. Given that three quantitative trait loci (QTL) contribute to partial resistance to Macrophomina phaseolina, stacking them within a single genotype presents a potential strategy for enhancing resistance. We screened 564 individuals that segregate for favorable alleles at all three QTL to assess their effects singly and in combination across multiple genetic backgrounds and production seasons. Inoculated field trials revealed that the three QTL cumulatively enhanced resistance levels, and that two-way QTL combinations including FaRMp3 provide increased protection against the pathogen. Pyramiding all three loci achieved the strongest resistance and could provide substantial economic value to the strawberry industry.

Soil-borne pathogens are a major constraint on strawberry (Fragaria ×ananassa Duchesne ex Rozier) production worldwide. Historically, many strawberry growers have relied on powerful soil fumigants, primarily preplant soil fumigation with methyl bromide (MeBr), to suppress pathogen populations in the soil (Holmes et al. 2020). However, MeBr was linked to ozone depletion and phased out for strawberry fruit production between 2005 and 2016 (Montzka et al. 2011; Velders et al. 2007). The subsequent shift to less effective chemicals has led to a surge in soil-borne disease epidemics in major strawberry industries, such as in Florida and California, which are the largest domestic sources of strawberries in the United States (Duniway 2002; Gordon et al. 2016; Rosskopf et al. 2005). One of the most destructive pathogens to emerge during this transition is the soil-borne fungus Macrophomina phaseolina, which is the causal agent of charcoal rot, a root and crown disease characterized by water stress symptoms such as stunting, wilting, and plant collapse (Avilés et al. 2008; Koike 2008; Mertely et al. 2005; Zveibil and Freeman 2005). In Florida, Macrophomina phaseolina had been recovered from necrotic crowns of wilting plants since 2001; however, infections were isolated to a few fields inadequately fumigated with MeBr (Mertely et al. 2005). Since then, the pathogen has become a major cause of plant death and yield loss across the state (Baggio et al. 2021) and other strawberry-producing regions such as Spain, Israel, and Egypt (Chamorro et al. 2016; Cohen et al. 2022; El-Marzoky et al. 2018). Macrophomina phaseolina thrives under high air and soil temperatures and in sandy soils, where infections are associated with heat and drought stress (Diourte et al. 1995; Kaur et al. 2012). For example, charcoal rot can cause severe losses of early season plantings under warm fall temperatures in Florida and can account for >80% plant mortality by the end of the season when favorable conditions persist (Baggio et al. 2019). The global trend toward an increase in climate change-associated heat and drought stress will likely aggravate the charcoal rot problem in strawberry production (Chaloner et al. 2021; Kaur et al. 2012; Leisner et al. 2023; Ristaino et al. 2021). Given the sensitivity of current disease management strategies to environmental conditions, high soil inoculum, and host susceptibility (Baggio et al. 2021b; Chamorro et al. 2016; Fennimore and Ajwa 2012), breeding resistant cultivars might be the only strategy that can provide growers with reliable and predictable control of Macrophomina phaseolina.

Breeding solutions emerged with the discovery of two loci, FaRMp1 and FaRMp2 (hereafter referred to as Mp1 and Mp2), associated with partial resistance to Macrophomina phaseolina in the elite breeding germplasm of University of Florida and were further strengthened with the discovery of FaRMp3 (hereafter referred to as Mp3), which is a large-effect locus that was traced to an introgression from FVC 11-58, an interspecific hybrid of Fragaria virginiana and Fragaria chiloensis ecotypes (Hancock et al. 2010; Nelson et al. 2021). Subsequently, all three quantitative trait loci (QTL) were found to be segregating in the University of California (Davis, CA, USA) breeding populations, where their phenotypic effects were proportional to those reported in the University of Florida study (Knapp et al. 2024; Nelson et al. 2021). Molecular markers that tag Mp1 and Mp2 have since been developed for marker-assisted selection (MAS) (Willborn 2022). More recently, the genome of a resistant descendant of FVC 11-58 was sequenced and assembled (deposited in the Genome Database for Rosacea; https://www.rosaceae.org/), thus enabling the development of a similar marker-based selection scheme for Mp3. These resources were critical for the present study that aimed to evaluate the phenotypic effect of Mp3 in elite University of Florida genetic backgrounds, develop high-throughput genotyping assays for single-nucleotide polymorphisms (SNPs) linked to Mp3, and experimentally investigate the impact of pyramiding favorable alleles at Mp1, Mp2, and Mp3 on resistance to Macrophomina phaseolina.

Our working hypothesis, built on findings in other plants, was that pyramiding favorable alleles at the three QTL will cumulatively increase resistance to the pathogen. The successful use of pyramids has been described for wheat (Triticum aestivum); three genes were found to combine additively to provide higher levels of resistance to crown rot caused by Fusarium pseudograminearum (Bovill et al. 2010). For cabbage (Brassica rapa), combining two QTL associated with resistance to clubroot disease caused by the soil-borne fungus Plasmodiophora brassicae significantly reduced disease severity (Li et al. 2022). Similarly, for potato (Solanum tuberosum), plants that express multiple late blight resistance genes exhibited improved resistance to the causal pathogen, Phythophtora infestans (Jo et al. 2014). More recently, for strawberry, Knapp et al. (2024) discovered that individuals exhibiting strong resistance to Macrophomina phaseolina accumulated favorable alleles at multiple small-effect and large-effect resistance loci, thus highlighting the merit of QTL stacking. In the present study, a total of 1115 individuals were evaluated for resistance to Macrophomina phaseolina in inoculated field trials, leading to the development of a DNA test to assist in the introgression and selection of favorable Mp3 alleles, as well as the identification of QTL pyramids that provide improved resistance.

Materials and Methods

Plant material and field experiments.

In 2020–21, a population of 499 individuals (hereafter referred to as C1) was developed by crossing FVC 11-58, a donor of favorable Mp3 alleles, to five highly susceptible University of Florida selections predicted to carry no resistance alleles. The objectives for the C1 population were to introgress Mp3 from an exotic source into elite University of Florida genetic backgrounds, develop a segregating population for testing Mp3-linked molecular markers, and identify prospective parents carrying favorable Mp3 alleles for QTL pyramiding.

In 2022–23, three full-sib families (N = 170; hereafter referred to as C2) were generated from crosses between two highly resistant progeny of FVC 11-58, identified in the C1 population, and two marginally resistant University of Florida selections that were commonly used elite parents. The purpose was to assess the combined effects of favorable alleles at Mp1 and Mp2, contributed by the University of Florida parents, and Mp3, contributed by the FVC 11-58 derived parents.

To further test the effects of QTL pyramiding observed in the C2 populations, six full-sib families (N = 392; hereafter referred to as C3) were developed in 2023–24 by crossing three C2 individuals carrying the resistant allele at Mp3 to three elite University of Florida selections carrying the resistant alleles at Mp1 and Mp2.

Seeds from the controlled crosses were germinated at the University of Florida Gulf Coast Research and Education Center (GCREC) in Wimauma, FL, USA, and seedlings were subsequently transported to the University of Florida summer breeding nursery in Malin, OR, USA, for propagation by stolons (runners). Four to eight clonal replicates (bare-root daughter plants) of each individual were harvested during mid-September of each year and transported to GCREC where they were stored at 3.5 °C until planting.

Transplanting was performed on 25 Sep 2020, 4 Oct 2021, and 11 Oct 2023 using randomized complete block designs with four (C1 and C3) to eight (C2) single-plant replicates per entry. Each replicate block consisted of one (C2 and C3) or two (C1) raised beds planted in two staggered rows (11 inches apart) with 15-inch in-row spacing. Fields were fumigated via shank-injection of a 35:65 coformulation of chloropicrin:1,3-dichloropropene (Telone C-35) at 300 pounds per acre during bedding in mid-August of each year. Then, beds were completed with centrally placed irrigation drip tape and sealed with black polyethylene film. Field maintenance followed standard production practices for west-central Florida, USA. Fertilization, weed management, and pest control varied between seasons according to environmental conditions.

Inoculation and disease resistance phenotyping.

C1, C2, and C3 populations were evaluated for resistance to Macrophomina phaseolina using the same inoculation and disease rating protocols. Three pathogenic Macrophomina phaseolina isolates were revived from the culture collection at GCREC on potato dextrose agar plates (39 g PDA in 1 L of distilled water) and incubated in the dark at 30 °C for 14 d to produce microsclerotia. Inoculum suspensions were prepared by blending three plates of fungal culture with 200 mL of sterile deionized water and brought to a volume of 1.5 L with 0.15% agar broth. Microsclerotia were quantified with a hemocytometer and adjusted to a concentration of 1.2 × 104 microsclerotia per milliliter of suspension.

The roots of each clone (bare-root plant) were trimmed and submerged in the inoculum suspension for 5 s immediately before transplanting. Disease evaluations followed a binary format, where 0 = healthy and 1 = dead. Phenotyping was initiated with the onset of symptoms on susceptible checks and continued on a weekly or biweekly basis until 75% of the entire plant population had collapsed. This corresponded to nine, eight, and 10 evaluation timepoints between 16 Oct and 7 Dec, 4 Nov and 29 Dec, and 8 Nov and 21 Jan in 2020–21, 2022–23, and 2023–24, respectively. Re-isolations from the crowns of randomly selected symptomatic plants were performed multiple times each season to confirm Macrophomina phaseolina infection. The area under the disease progress stairs (AUDPS) (Simko and Piepho 2012) was calculated for each clonal replicate by considering the disease rating at all timepoints using the R package agricolae (de Mendiburu and Simon 2015).

High-resolution melting marker design.

High-resolution melting (HRM) markers were developed for SNPs located within the Mp3 locus on chromosome 4C [nomenclature described by Hardigan et al. (2021)] delimited by the FanaSNP array markers AX-184497781 and AX-184106040 (Hardigan et al. 2020; Nelson et al. 2021). The sequence in the Mp3 region was extracted from four Fragaria ×ananassa haplotype-resolved genome assemblies (eight haploid assemblies) downloaded from the Genome Database for Rosaceae (Supplemental Table 1) (Jung et al. 2019). The assembly representing the FVC 11-58 haplotype was the only carrier of favorable Mp3 alleles at the time of this analysis; therefore, it was used as the resistant reference for SNP detection. The six remaining genome assemblies corresponded to susceptible cultivars and elite selections from the University of Florida and the University of California (Davis, CA, USA) strawberry breeding programs (Supplemental Table 1). The Mp3 sequences were pairwise-aligned using MAFFT version 7.30 with default parameters in memory-saving mode (Katoh and Standley 2013). The alignments were visualized using Geneious Prime 2020.2.0 for SNP detection.

Twenty-one FanaSNP array markers spanning the 3.52 megabase pair (Mbp) region were selected for HRM marker development. These markers were polymorphic between the resistant and susceptible sequences and did not exhibit homology to sequences elsewhere in the genome. The HRM primers were designed using PolyOligo (https://github.com/MirkoLedda/polyoligo) with the ‘Camarosa’ reference genome (Hardigan et al. 2020) and default parameters. In addition, 23 primer pairs that interrogated 23 SNPs between resistant and susceptible sequences were manually designed using the PrimerQuest web tool from Integrated DNA Technologies Inc. Primer design parameters were set to minimum and maximum melting temperatures of 58 °C and 62 °C, respectively, and the amplicon size was set to 50 to 150 bp. A total of 44 SNP assays were developed and tested by screening up to 156 C1 individuals selected from the resistant and susceptible tails of the AUDPS distribution, along with their parents. Oligonucleotide primer sequences and other supporting data for HRM markers are available in Supplemental Tables 2 and 3.

DNA extraction and HRM analysis.

DNA was isolated from 50 mg of young leaf tissue harvested from field-grown plants, stored at −80 °C, and ground with a Qiagen TissueLyser II just before DNA extraction according to the modified CTAB method of Keb-Llanes et al. (2002).

Marker testing was performed using an HRM analysis of polymerase chain reaction (PCR) products. The reaction comprised 2 × AccuStartTM II PCR ToughMix® (Quantabio, Beverly, MA, USA) and 10 × LC Green® Plus Melting Dye (BioFire Defense, Salt Lake City, UT, USA) with 0.5 μM forward and reverse primers and 0.5 µL of DNA (10 ng/µL) in a 5 µL reaction volume. Thermocycling was performed in a LightCycler® 480 System II (Roche Life Science, Munich, Germany) using the following reaction conditions: initial denaturation at 95 °C for 30 s; 45 cycles of denaturation at 95 °C for 15 s; annealing at 62 °C for 15 s; and extension at 72 °C for 20 s. Melt curves were obtained with a melt gradient from 60 to 95 °C, with increases in increments of 1 °C per second and 25 acquisitions per degree. The HRM analyses were performed using the Melt Curve Genotyping and Gene Scanning software available in Roche LightCycler® (Roche Life Science). Genotypes were assigned by examining the differences in melt curves, based on relative fluorescence units as a function of the melting temperature. To assess the accuracy of HRM markers, we estimated the concordance between marker genotypes and their genotypes inferred from their phenotypes (A_ for resistant and aa for susceptible individuals). The C2 and C3 full-sib families, as well as their parents, were genotyped with Mp1, Mp2, and Mp3 markers via HRM analyses as described. The primer sequences for the QTL-specific markers are documented in Table 1.

Table 1.

Sequences of the forward and reverse oligonucleotide primers for the locus-specific high-resolution melting (HRM) assays.

Table 1.

Statistical analysis.

The AUDPS variable was analyzed within trials using the following linear mixed model:
y = 1µ + Xr + Z2g + e
where µ is the overall mean, r is a fixed replication effect, g is the total genetic value (i.e., clonal value) modeled as a random effect, and e is the residual error term. Clone-based broad-sense heritability was calculated from REML estimates of the genotype and residual variance components as follows:
H2 = σg2/σp2.

Phenotypic values corrected for experimental design factors were estimated for each genotype (individual) by fitting the linear mixed model previously described, but with blocks as random effects and genotypes as fixed effects (Supplemental File 1). Adjusted AUDPS values were used as the phenotypic input for all subsequent analyses.

The individual effects of QTL alleles and their significance levels were estimated within trials by regressing the adjusted phenotypes on the Mp1, Mp2, and Mp3 marker genotypes as follows:
y = 1µ + Xβ + e,

where β is a vector of marker effects and X is the n × 3 genotype matrix for the three QTL markers and n individuals per trial.

Differences in disease levels among individuals grouped by their multilocus genotype were compared with Tukey’s honestly significant difference (HSD) test at a significance level of P < 0.05 after fitting the following ANOVA model:
yij = 1µ + gj + e

where y is the adjusted AUDPS for ith genotype with the jth QTL combination, μ is the overall mean, g is the effect of the jth QTL combination, and e is the experimental error. Tukey’s test was performed using the HSD.test function in the R package agricolae (de Mendiburu and Simon 2015).

Results

Phenotypic variation.

Over the course of 3 years, 1115 individuals from 14 C1, C2, and C3 full-sib families were developed and screened for resistance to Macrophomina phaseolina in inoculated field trials in Wimauma, FL, USA. The AUDPS variable, a quantitative summary of disease progression over time, clearly differentiated the resistant from the susceptible checks in all years (Fig. 1A). Checks with the highest and lowest AUDPS values were consistent across years (Fig. 1A). The range of AUDPS values varied among the C1, C2, and C3 populations, as observed in Fig. 1B, which was partly attributed to differences in the “time” component of the AUDPS calculation rather than disease pressure, which did not vary substantially across years. Specifically, longer time intervals between consecutive evaluations and longer overall evaluation periods led to higher AUDPS values because they allowed for increased disease accumulation over time. For example, the biweekly evaluations conducted over a 10-week period from Nov to Jan 2023–24 yielded the highest AUDPS values. Estimates of broad-sense heritability on a clonal-mean basis were 0.486 for C1, 0.498 for C2, and 0.462 for C3, confirming that resistance phenotypes were highly heritable.

Fig. 1.
Fig. 1.

(A) The adjusted area under the disease progress stairs (AUDPS) value of each check across the C1, C2, and C3 trials; phenotypes are color-coded as susceptible (gray) or resistant (black). (B) Phenotypic distributions of the adjusted AUDPS values among C1 (N = 523), C2 (N = 185), and C3 (N = 407) individuals screened in 2020–21, 2022–23, and 2023–24, respectively. Phenotypes of the elite University of Florida (UF) parents are shown as pink arrows and those of FVC 11-58-derived parents are shown as green arrows.

Citation: HortScience 59, 9; 10.21273/HORTSCI17981-24

Our study was initiated by introgressing favorable Mp3 alleles from FVC 11-58 into five susceptible backgrounds. A total of 499 FVC 11-58 progeny from five C1 full-sib families were evaluated for resistance in an inoculated field trial in 2020–21. The AUDPS distribution was left-skewed (toward resistance), with a median of 35 and a mean of 30.71 (Fig. 1B). Symptomless individuals (AUDPS = 0) were identified within every C1 full-sib family, indicating that the phenotypic effects of Mp3 were expressed across the sampled genetic backgrounds.

Between 2022 and 2024, five resistant FVC 11-58 progeny were intercrossed with five marginally resistant University of Florida selections to generate nine C2 full-sib families. The former served as donors of resistance alleles at Mp2 and Mp3, whereas the latter contributed resistance alleles at Mp1 and Mp2. A total of 592 individuals were evaluated over two consecutive seasons to assess the impact of stacking favorable alleles among the three loci on the strength of resistance to Macrophomina phaseolina. The multimodal AUDPS distributions observed in C2 and C3 reflected the segregation of multiple loci with varying effects, as anticipated (Fig. 1B). Because our crosses were designed to generate heterozygous combinations of the three QTL rather than to increase resistant homozygotes, we did not observe a marked change in the frequency (f) of favorable alleles at any of the three loci between C2 and C3, where 0.3 < fMp1 < 0.4; 0.5 < fMp2 < 0.7; and 0.1 < fMp3 < 0.2. The mean AUDPS values for C2 and C3 were 34 and 38, with median values of 30 and 23, respectively. Symptomless individuals accounted for 7% of the C2 population and 15% of the C3 population. Notably, transgressive segregants, exceeding their parents in resistance, were observed in both populations and were likely driven by the net effects of favorable QTL alleles transmitted separately by the two parents (Fig. 1B).

HRM marker development.

To expedite the introduction and selection of Mp3 in breeding programs, we developed and tested HRM assays that interrogated sequence variation near the locus. A set of 29 subgenome-specific HRM assays were designed to target 21 FanaSNP array-based SNP marker loci and eight SNPs identified in silico (Supplemental Table 2) from alignments between eight F. ×ananassa sequences (Supplemental Table 1). These assays were tested in a panel of 68 C1 individuals selected from the tails of the AUDPS distribution to represent extreme resistance and extreme susceptibility, along with their parents. Of the 29 markers, 15 did not co-segregate with resistance, three were monomorphic in the tested samples, and four yielded additional variation beyond what was expected from the in silico alignments (Supplemental Table 2). This could be attributed to several reasons: additional variation may exist in the tested genotypes; the HRM assay may occasionally identify a nontarget polymorphism; or the original sequence data may contain errors. Seven markers produced biallelic HRM reactions that clearly distinguished the resistant from the susceptible phenotypes across 68 C1 individuals. The seven predictive markers colocalized to a 0.9-Mb segment on chromosome 4C delimited by the forward (5′ GGT GCT GCT CTC CGT ATC C 3′) and the reverse (5′ GCA ACT AAG ATG ATA TGG AGC TTT 3′) primers (Supplemental Table 2). This chromosomal segment was chosen to mine SNPs for a second round of marker development. Fifteen HRM assays that targeted 15 SNPs (on average, 40 kb apart) were subsequently designed and tested in the set of 68 C1 individuals (Supplemental Table 3). Of 12 markers that co-segregated with resistance, six were selected for validation in a larger set of 156 C1 individuals, where they accurately predicted the charcoal rot phenotype in 89% to 93% of the validation set. The most predictive SNP assay (Mp3-6.14-1) using the forward (5′ TTT CCA TCA TGA ACT CCT CCA G 3′) and reverse (5′ GGA TCA TGG ATT GCA TGT GAA G 3′) primers produced clear, biallelic segregation patterns and shows promise for MAS (Fig. 2A). The melt curves corresponded to the resistant A/G genotype, where A is the favorable allele, and the susceptible G/G genotype (Fig. 2A and B). As expected, resistant homozygotes (AA) were not observed in any populations because there were no backcrosses to FVC 11-58 or its progeny.

Fig. 2.
Fig. 2.

(A) High-resolution melting analysis (HRM) of 156 C1 individuals segregating for the Mp3-6.14-1 marker, an A/G variant associated with the FaRMp3 resistance locus, where A is the favorable allele. The resistant genotypes (A/G) are shown in green and the susceptible genotypes (GG) are shown in red. (B) Sequence alignment of Mp3-6.14-1 from a resistant individual and seven susceptible individuals. The target polymorphism is shaded in green for the favorable allele and in red for the susceptible allele. Boxed sequences correspond to the forward and reverse primers.

Citation: HortScience 59, 9; 10.21273/HORTSCI17981-24

Marker analysis.

The C2 and C3 parents and offspring were genotyped with HRM markers linked to all three loci and detailed in Table 1. The genotypic data are compiled in Supplemental File 2. Briefly, two-way and three-way QTL combinations accounted for 76% and 68% of the multilocus genotypes observed in C2 and C3, respectively.

The ANOVA revealed that each QTL had a significant individual effect in both populations, with the exception of Mp2 in C2 (Table 2). The Mp1, Mp2, and Mp3 markers reduced the adjusted AUDPS by 7.8 to 8.8, 3.1 to 6.2, and 22.3 to 22.2 units per dose of the resistance alleles in the C2 and C3 populations, respectively (Table 2).

Table 2.

Estimated effect sizes of the high-resolution melting (HRM) markers associated with FaRMp1, FaRMp2, and FaRMp3, the SE of the estimate, and its statistical significance as determined by the analysis of variance (ANOVA) in the C2 and C3 populations.

Table 2.

When two-way QTL interactions (i.e., epistasis) were considered in the model, the interaction terms were not significant (P < 0.05), negligibly decreased the unexplained error, and led to worse model fit according to likelihood ratio tests in both C2 and C3. Therefore, the interaction terms were dropped, and analyses rerun for main effect estimation. The absence of significant QTL interactions suggested a lack of epistasis or insufficient sample size and statistical power to identify epistasis in our study populations.

QTL pyramiding.

To compare the levels of disease reduction provided by different QTL combinations, C2 and C3 individuals were grouped into classes based on their multilocus genotypes (Fig. 3). Tukey’s honestly significant difference test was subsequently performed within trials after ANOVA F-tests indicated that the AUDPS means of different QTL classes were significantly different. Our field trials revealed that resistance conferred by each of the three QTL individually was incomplete and varied in magnitude between seasons (Fig. 3). Among the three loci, Mp3 had the greatest effect on resistance when present alone, and its combination with Mp1 and Mp2 cumulatively reduced AUDPS (Fig. 3). Within each multi-QTL combination (means plotted in Fig. 3), there were individuals whose resistance matched or surpassed that of the most resistant parent (individual values plotted in Fig. 3). Stacking favorable alleles at all three loci was slightly more effective at reducing disease compared with stacking Mp3 with Mp1 in C2 or with Mp2 in C3; however, this difference was not statistically significant (Fig. 3). To clarify, the mean AUDPS of the multilocus genotype corresponding to the three-way QTL pyramid was only 3% to 5% lower than that of individuals carrying favorable alleles at Mp3 and one other target locus in C2 and C3, respectively (Fig. 3). That said, the three-way QTL combination yielded lower individual variability in disease levels within C2 and C3, as inferred from SD estimates (SDA,C2 = SDA,C3 = 15), compared with stacking Mp3 with Mp1 (SDC,C2 = 19; SDC,C3 = 25) or with Mp2 (SDE,C2 = 20; SDE,C3 = 18). Additionally, the three-QTL pyramid had the most reproducible resistance effect among the three genotype classes when replicated in C2 and C3 (Fig. 3), indicating that QTL pyramiding both enhanced resistance and stabilized its expression across genetic backgrounds and seasons.

Fig. 3.
Fig. 3.

Comparisons of the area under the disease progress stairs (AUDPS) means among individuals grouped into QTL classes (x-axis) by their multilocus genotypes. Boxes represent favorable (black) and unfavorable (white) alleles for each QTL listed on the left. Means and standard errors (SEM; error bars) are plotted for each QTL class. Means labeled with the same letter do not differ significantly according to Tukey’s honestly significant difference (HSD) test at P < 0.05.

Citation: HortScience 59, 9; 10.21273/HORTSCI17981-24

Individuals harboring favorable alleles at Mp1 and Mp2, such as the C2 and C3 University of Florida parents, exhibited moderate resistance to charcoal rot, on average (Fig. 3), although some had AUDPS values higher than that of ‘Strawberry Festival’, the most susceptible check in our study (Supplemental File 1) (Baggio et al. 2021; Chandler et al. 2000).

Discussion

Genetic resistance to Macrophomina phaseolina that is resilient to projected increases in disease pressure due to the phase-out of MeBr and emerging climate patterns is a breeding priority for strawberry (Baggio et al. 2021; Chaloner et al. 2021; Holmes et al. 2020; Knapp et al. 2024; Nelson et al. 2021). Our study focused on three loci conferring quantitative resistance to Macrophomina phaseolina that have been identified and validated in multiple populations and seasons in Florida and California (Knapp et al. 2024; Nelson et al. 2021). The QTL with the largest effect, Mp3, was first reported in crosses with an exotic F. ×ananassa selection, FVC 11-58 (Hancock et al. 2010). In the present study, we introgressed Mp3 into 18 elite genetic backgrounds at the University of Florida strawberry breeding program over three cycles (C1, C2, and C3). The locus consistently achieved a 22-unit reduction in the AUDPS value, leading to a significant delay in the onset of symptoms, slower progression of symptoms, or both. Because the C1 crosses predated the development of Mp1, Mp2, and Mp3 markers, elite parents were selected solely based on their susceptible phenotype. When QTL markers later became available, the susceptible C1 parents were found to carry a dose of the resistance allele at Mp2 (Supplemental File 2). However, this discovery did not change our interpretation of the phenotypic effect of Mp3 in C1 because of the comparatively minor effect of Mp2, as evidenced by the susceptibility of the selected C1 parents and supported by Nelson et al. (2021) and Knapp et al. (2024).

We subsequently developed an HRM assay (Mp3-6.14-1) linked to Mp3 as a selectable tool to facilitate its transfer to breeding populations. The absence of Mp3 in University of Florida germplasm (Nelson et al. 2021) and its rarity in diverse germplasm (Knapp et al. 2024) suggest that it originates from a specific wild source; hence, this marker tagging the resistance allele transmitted by FVC 11-58 should aid Mp3 introgression efforts using this germplasm. Mp3-6.14-1 accounted for 30% and 17% of the phenotypic variance in C2 and C3, respectively, and accurately predicted charcoal rot phenotypes in 92% of the tested individuals (N = 156). This HRM assay combines commonly sought properties of DNA tests; it is sub-genome-specific, produces a biallelic segregation pattern, accurately predicts the charcoal rot phenotype, and facilitates high-throughput screening. Additionally, this assay promises to reduce errors compared with phenotypic selection and facilitate pyramiding via MAS. The success of this DNA-based approach in strawberry is exemplified by molecular markers tagging the FaRCa1 QTL for resistance to Colletotrichum acutatum, FaRXf1 for resistance to Xanthomonas fragariae and FaRPc2 for resistance to Phytophthora cactorum that are routinely used to select for these traits via MAS (Noh et al. 2018; Oh et al. 2020; Salinas et al. 2020). One caveat is that the causal mutation underlying Mp3 is not yet known and certainly not in perfect linkage disequilibrium with Mp3-6.14-1. As a result, the marker’s accuracy in a more diverse set of individuals might be reduced by recombination between the assayed and the causal polymorphisms. Here, we document all the assay details needed to validate the marker’s applicability beyond the University of Florida breeding program along with 12 other primer pairs that can also be tested for this purpose.

Because three loci associated with quantitative resistance to Macrophomina phaseolina have been identified, pyramiding was an obvious strategy to test. To this end, we generated heterozygotes and susceptible allele homozygotes at all three loci by crossing complementary University of Florida selections. This design enabled an accurate estimation and comparison of mean disease levels among these multilocus genotypes but did not permit statistical inferences about dominance or epistasis because of the absence or scarcity of resistant homozygotes in our unselected populations, as expected. Consistent with previous studies, favorable alleles at Mp3 resulted in substantial, albeit incomplete, disease reduction in both trials, whereas Mp1 and Mp2 provided lower levels of disease control (Knapp et al. 2024; Nelson et al. 2021). These results suggest that none of the QTL would provide sufficient protection if deployed alone, especially in environments highly conducive to disease. We caution that individuals that harbor favorable alleles at Mp3 alone constituted 2% of the C2 and C3 populations; hence, comparisons with this genotype were underpowered and should be made with caution (see error bars conveying the SEM in Fig. 3). Yet, QTL combinations lacking favorable alleles at Mp3 yielded lower levels of resistance than those including Mp3. For example, stacking Mp1 and Mp2 produced marginally resistant phenotypes at best; this was expected because the two QTL are already commonly stacked in commercial cultivars such as Florida Brilliance and Florida Pearl™, which are both classified as moderately resistant to Macrophomina phaseolina (Whitaker et al. 2019, 2023).

The strongest resistance was achieved by pyramiding favorable alleles at all three loci. In fact, 71% of the three-QTL individuals remained symptomless and healthy until at least eight of nine and nine of 10 evaluation timepoints, spanning most the Florida production window, in C2 and C3, respectively. Overall, our findings support QTL pyramiding as a means of attaining increased levels of resistance to Macrophomina phaseolina, with the potential of providing stable protection under different environmental conditions. Although our study was limited by the number of environments sampled (one location × 2 years), our conclusions are consistent with those reported by several other studies in which pyramiding QTL alleles led to strong and environmentally stable resistance (Fukuoka et al. 2015; Mundt 2018). An important consideration is that although the three-QTL pyramids developed in this study reduced disease levels below that of the best QTL alone, none achieved full immunity against the pathogen. Therefore, it may be necessary to introgress and accumulate additional loci associated with resistance in diverse germplasm (Knapp et al. 2024) to maximize resistance to Macrophomina phaseolina. That said, our inoculation protocols were much harsher than the disease pressure typically encountered in commercial settings. Therefore, the resistance levels (AUDPS) attained are still promising for commercial cultivar development.

Ultimately, a successful breeding strategy will build QTL pyramids while also achieving genetic gains for other traits. Only 149 of 592 (25%) individuals developed in this study harbored the favorable allele at the three loci, and the majority were heterozygous. Subjecting these individuals to selection for other commercially important traits, such as yield, fruit quality, and abiotic stress resistance, will further reduce the frequency of individuals with all desired QTL alleles because of unfavorable linkages, especially with Mp3, which was recently introduced from an exotic donor. As the numbers of traits and contributing QTL increase, synthesizing the ideal genotype becomes increasingly difficult (Bernardo 2008). Our study generated hybrids with resistance alleles at Mp3 and at least one other target locus that can serve as a starting point for a breeding scheme involving repeated cycles of outcrossing or selfing and marker selection, thus increasing the frequency of resistance alleles toward fixation. The HRM assays detailed here can be used for marker-based selection in all phases and generations of breeding (Bernardo 2008; Whitaker et al. 2020).

References Cited

  • Avilés M, Castillo S, Bascon J, Zea-Bonilla T, Martín-Sánchez PM, Pérez-Jiménez RM. 2008. First report of Macrophomina phaseolina causing crown and root rot of strawberry in Spain. Plant Pathol. 57(2):382382. https://doi.org/10.1111/j.1365-3059.2007.01717.x.

    • Search Google Scholar
    • Export Citation
  • Baggio JS, Cordova LG, Peres NA. 2019. Sources of inoculum and survival of Macrophomina phaseolina in Florida strawberry fields. Plant Dis. 103(9):24172424. https://doi.org/10.1094/PDIS-03-19-0510-RE.

    • Search Google Scholar
    • Export Citation
  • Baggio JS, Cordova LG, Seijo TE, Noling JW, Whitaker VM, Peres NA. 2021. Cultivar selection is an effective and economic strategy for managing charcoal rot of strawberry in Florida. Plant Dis. 105(8):20712077. https://doi.org/10.1094/PDIS-10-20-2250-RE.

    • Search Google Scholar
    • Export Citation
  • Bernardo R. 2008. Molecular markers and selection for complex traits in plants: Learning from the last 20 years. Crop Sci. 48(5):16491664. https://doi.org/10.2135/cropsci2008.03.0131.

    • Search Google Scholar
    • Export Citation
  • Bovill WD, Horne M, Herde D, Davis M, Wildermuth GB, Sutherland MW. 2010. Pyramiding QTL increases seedling resistance to crown rot (Fusarium pseudograminearum) of wheat (Triticum aestivum). Theor Appl Genet. 121(1):127136. https://doi.org/10.1007/s00122-010-1296-7.

    • Search Google Scholar
    • Export Citation
  • Chaloner TM, Gurr SJ, Bebber DP. 2021. Plant pathogen infection risk tracks global crop yields under climate change. Nat Clim Chang. 11(8):710715. https://doi.org/10.1038/s41558-021-01104-8.

    • Search Google Scholar
    • Export Citation
  • Chamorro M, Seijo TE, Noling JC, de los Santos B, Peres NA. 2016. Efficacy of fumigant treatments and inoculum placement on control of Macrophomina phaseolina in strawberry beds. Crop Prot. 90:163169. https://doi.org/10.1016/j.cropro.2016.08.020.

    • Search Google Scholar
    • Export Citation
  • Chandler CK, Legard DE, Dunigan DD, Crocker TE, Sims CA. 2000. ‘Strawberry Festival’ strawberry. HortScience. 35(7):13661367. https://doi.org/10.21273/HORTSCI.35.7.1366.

    • Search Google Scholar
    • Export Citation
  • Cohen R, Elkabetz M, Paris HS, Gur A, Dai N, Rabinovitz O, Freeman S. 2022. Occurrence of Macrophomina phaseolina in Israel: Challenges for disease management and crop germplasm enhancement. Plant Dis. 106(1):1525. https://doi.org/10.1094/PDIS-07-21-1390-FE.

    • Search Google Scholar
    • Export Citation
  • Diourte M, Starr JL, Jeger MJ, Stack JP, Rosenow DT. 1995. Charcoal rot (Macrophomina phaseolina) resistance and the effects of water stress on disease development in sorghum. Plant Pathol. 44(1):196202. https://doi.org/10.1111/j.1365-3059.1995.tb02729.x.

    • Search Google Scholar
    • Export Citation
  • Duniway JM. 2002. Status of chemical alternatives to methyl bromide for pre-plant fumigation of soil. Phytopathology. 92(12):13371343. https://doi.org/10.1094/PHYTO.2002.92.12.1337.

    • Search Google Scholar
    • Export Citation
  • El-Marzoky H, Abdalla M, Abdel-Sattar M, Abid M. 2018. Management of crown and root rot diseases in strawberry commercial fields in Egypt. J Plant Prot Pathol. 9(7):399404. https://doi.org/10.21608/jppp.2018.42184.

    • Search Google Scholar
    • Export Citation
  • Fennimore S, Ajwa H. 2012. Totally impermeable film retains fumigants, allowing lower application rates in strawberry. Calif Agric. 65(4):211215.

    • Search Google Scholar
    • Export Citation
  • Fukuoka S, Saka N, Mizukami Y, Koga H, Yamanouchi U, Yoshioka Y, Hayashi N, Ebana K, Mizobuchi R, Yano M. 2015. Gene pyramiding enhances durable blast disease resistance in rice. Sci Rep. 5(1):7773. https://doi.org/10.1038/srep07773.

    • Search Google Scholar
    • Export Citation
  • Gordon TR, Daugovish O, Koike ST, Islas CM, Kirkpatrick SC, Yoshisato JA, Shaw DV. 2016. Options for management of fusarium wilt of strawberry in California. Int J Fruit Sci. 16(Suppl 1):160168. https://doi.org/10.1080/15538362.2016.1219294.

    • Search Google Scholar
    • Export Citation
  • Hancock JF, Finn CE, Luby JJ, Dale A, Callow PW, Serçe S. 2010. Reconstruction of the Strawberry, Fragaria ×ananassa, using genotypes of F. virginiana and F. chiloensis. HortScience. 45(7):10061013. https://doi.org/10.21273/HORTSCI.45.7.1006.

    • Search Google Scholar
    • Export Citation
  • Hardigan MA, Feldmann MJ, Lorant A, Bird KA, Famula R, Acharya C, Cole G, Edger PP, Knapp SJ. 2020. Genome synteny has been conserved among the octoploid progenitors of cultivated strawberry over millions of years of evolution. Front Plant Sci. 10:1789. https://doi.org/10.3389/fpls.2019.01789.

    • Search Google Scholar
    • Export Citation
  • Hardigan MA, Feldmann MJ, Pincot DDA, Famula RA, Vachev MV, Madera MA, Zerbe P, Mars K, Peluso P, Rank D, Ou S, Saski CA, Acharya CB, Cole GS, Yocca AE, Platts AE, Edger PP, Knapp SJ. 2021. Blueprint for phasing and assembling the genomes of heterozygous polyploids: Application to the octoploid genome of strawberry. bioRxiv. 2021.11.03.467115. https://doi.org/10.1101/2021.11.03.467115.

    • Search Google Scholar
    • Export Citation
  • Holmes GJ, Mansouripour SM, Hewavitharana SS. 2020. Strawberries at the crossroads: management of soilborne diseases in California without methyl bromide. Phytopathology. 110(5):956968. https://doi.org/10.1094/PHYTO-11-19-0406-IA.

    • Search Google Scholar
    • Export Citation
  • Jo K-R, Kim C-J, Kim S-J, Kim T-Y, Bergervoet M, Jongsma MA, Visser RG, Jacobsen E, Vossen JH. 2014. Development of late blight resistant potatoes by cisgene stacking. BMC Biotechnol. 14(1):50. https://doi.org/10.1186/1472-6750-14-50.

    • Search Google Scholar
    • Export Citation
  • Jung S, Lee T, Cheng C-H, Buble K, Zheng P, Yu J, Humann J, Ficklin SP, Gasic K, Scott K, Frank M, Ru S, Hough H, Evans K, Peace C, Olmstead M, DeVetter LW, McFerson J, Coe M, Wegrzyn JL, Staton ME, Abbott AG, Main D. 2019. 15 years of GDR: New data and functionality in the Genome Database for Rosaceae. Nucleic Acids Res. 47(D1):D1137D1145. https://doi.org/10.1093/nar/gky1000.

    • Search Google Scholar
    • Export Citation
  • Katoh K, Standley DM. 2013. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 30(4):772780. https://doi.org/10.1093/molbev/mst010.

    • Search Google Scholar
    • Export Citation
  • Kaur S, Dhillon GS, Brar SK, Vallad GE, Chand R, Chauhan VB. 2012. Emerging phytopathogen Macrophomina phaseolina: Biology, economic importance and current diagnostic trends. Crit Rev Microbiol. 38(2):136151. https://doi.org/10.3109/1040841X.2011.640977.

    • Search Google Scholar
    • Export Citation
  • Keb-Llanes M, González G, Chi-Manzanero B, Infante D. 2002. A rapid and simple method for small-scale DNA extraction in Agavaceae and other tropical plants. Plant Mol Biol Rep. 20(3):299299. https://doi.org/10.1007/BF02782465.

    • Search Google Scholar
    • Export Citation
  • Knapp SJ, Cole GS, Pincot DDA, Dilla-Ermita CJ, Bjornson M, Famula RA, Gordon TR, Harshman JM, Henry PM, Feldmann MJ. 2024. Transgressive segregation, hopeful monsters, and phenotypic selection drove rapid genetic gains and breakthroughs in predictive breeding for quantitative resistance to Macrophomina in strawberry. Hortic Res. 11(2):uhad289. https://doi.org/10.1093/hr/uhad289.

    • Search Google Scholar
    • Export Citation
  • Koike ST. 2008. Crown rot of strawberry caused by Macrophomina phaseolina in California. Plant Dis. 92(8):1253. https://doi.org/10.1094/PDIS-92-8-1253B.

    • Search Google Scholar
    • Export Citation
  • Leisner CP, Potnis N, Sanz-Saez A. 2023. Crosstalk and trade-offs: Plant responses to climate change-associated abiotic and biotic stresses. Plant Cell Environ. 46(10):29462963. https://doi.org/10.1111/pce.14532.

    • Search Google Scholar
    • Export Citation
  • Li X, Wei Y, Ma Y, Cao G, Ma S, Zhang T, Zhan Z, Piao Z. 2022. Marker-assisted pyramiding of CRa and CRd genes to improve the clubroot resistance of Brassica rapa. Genes. 13(12):2414. https://doi.org/10.3390/genes13122414.

    • Search Google Scholar
    • Export Citation
  • Mendiburu FD, Simon R. 2015. Agricolae - Ten years of an open source statistical tool for experiments in breeding, agriculture and biology. PeerJ Inc. https://doi.org/10.7287/peerj.preprints.1404v1.

    • Search Google Scholar
    • Export Citation
  • Mertely J, Seijo T, Peres N. 2005. First report of Macrophomina phaseolina causing a crown rot of strawberry in Florida. Plant Dis. 89(4):434434. https://doi.org/10.1094/PD-89-0434A.

    • Search Google Scholar
    • Export Citation
  • Montzka SA, Reimann S, Engel A, Kruger K, Sturges WT, Blake D, Dorf M, Fraser P, Froidevaux L, Jucks K, Kreher K, Kurylo MJ, Mellouki A, Miller J, Nielsen O-J, Orkin VL, Prinn RG, Rhew R, Santee ML, Stohl A, Verdonik D, Krummel PB, Atlas E, Bernath P, Blumenstock T, Butler JH, Butz A, Connor B, Duchatelet P, Dutton G, Hendrick F, Krummel PB, Kuijpers LJM, Mahieu E, Manning A, Muhle J, Pfeilsticker K, Quack B, Ross M, Salavitch RJ, Schauffler S, Simpson IJ, Toohey D, Vollmer MK, Wallington TJ, Wang HJR, Weiss RF, Yamabe M, Yokouchi Y, Yvon-Lewis S. 2011. Scientific assessment of ozone depletion: 2010. Glob Ozone Res Monit Proj - Rep No 51.

    • Search Google Scholar
    • Export Citation
  • Mundt CC. 2018. Pyramiding for resistance durability: theory and practice. Phytopathology. 108(7):792802. https://doi.org/10.1094/PHYTO-12-17-0426-RVW.

    • Search Google Scholar
    • Export Citation
  • Nelson JR, Verma S, Bassil NV, Finn CE, Hancock JF, Cole GS, Knapp SJ, Whitaker VM. 2021. Discovery of three loci increasing resistance to charcoal rot caused by Macrophomina phaseolina in octoploid strawberry. G3 GenesGenomesGenetics. 11(3):jkab037. https://doi.org/10.1093/g3journal/jkab037.

    • Search Google Scholar
    • Export Citation
  • Noh Y-H, Oh Y, Mangandi J, Verma S, Zurn JD, Lu Y-T, Fan Z, Bassil N, Peres N, Cole G, Acharya C, Famula R, Knapp S, Whitaker VM, Lee S. 2018. High-throughput marker assays for FaRPc2-mediated resistance to Phytophthora crown rot in octoploid strawberry. Mol Breeding. 38(8):104. https://doi.org/10.1007/s11032-018-0861-7.

    • Search Google Scholar
    • Export Citation
  • Oh Y, Chandra S, Lee S. 2020. Development of subgenome-specific markers for FaRXf1 conferring resistance to bacterial angular leaf spot in allo-octoploid strawberry. Int J Fruit Sci. 20(Suppl 2):S198S210. https://doi.org/10.1080/15538362.2019.1709116.

    • Search Google Scholar
    • Export Citation
  • Ristaino JB, Anderson PK, Bebber DP, Brauman KA, Cunniffe NJ, Fedoroff NV, Finegold C, Garrett KA, Gilligan CA, Jones CM, Martin MD, MacDonald GK, Neenan P, Records A, Schmale DG, Tateosian L, Wei Q. 2021. The persistent threat of emerging plant disease pandemics to global food security. Proc Natl Acad Sci USA. 118(23):e2022239118. https://doi.org/10.1073/pnas.2022239118.

    • Search Google Scholar
    • Export Citation
  • Rosskopf EN, Chellemi DO, Kokalis-Burelle N, Church GT. 2005. Alternatives to methyl bromide: a Florida perspective. Plant Health Prog. 6(1):19. https://doi.org/10.1094/PHP-2005-1027-01-RV.

    • Search Google Scholar
    • Export Citation
  • Salinas N, Fan Z, Peres N, Lee S, Whitaker VM. 2020. FaRCa1 confers moderate resistance to the root necrosis form of strawberry anthracnose caused by Colletotrichum acutatum. HortScience. 55(5):693698. https://doi.org/10.21273/HORTSCI14807-20.

    • Search Google Scholar
    • Export Citation
  • Simko I, Piepho H-P. 2012. The area under the disease progress stairs: Calculation, advantage, and application. Phytopathology. 102(4):381389. https://doi.org/10.1094/PHYTO-07-11-0216.

    • Search Google Scholar
    • Export Citation
  • Velders GJM, Andersen SO, Daniel JS, Fahey DW, McFarland M. 2007. The importance of the Montreal Protocol in protecting climate. Proc Natl Acad Sci USA. 104(12):48144819. https://doi.org/10.1073/pnas.0610328104.

    • Search Google Scholar
    • Export Citation
  • Whitaker VM, Dalid C, Osorio LF, Peres NA, Verma S, Lee S, Plotto A. 2023. Florida Pearl® ‘FL 16.78-109’ pineberry. HortScience. 58(1):143146. https://doi.org/10.21273/HORTSCI16951-22.

    • Search Google Scholar
    • Export Citation
  • Whitaker VM, Knapp SJ, Hardigan MA, Edger PP, Slovin JP, Bassil NV, Hytönen T, Mackenzie KK, Lee S, Jung S, Main D, Barbey CR, Verma S. 2020. A roadmap for research in octoploid strawberry. Hortic Res. 7:33. https://doi.org/10.1038/s41438-020-0252-1.

    • Search Google Scholar
    • Export Citation
  • Whitaker VM, Peres NA, Osorio LF, Fan Z, do Nascimento Nunes MC, Plotto A, Sims CA. 2019. ‘Florida Brilliance’ strawberry. HortScience. 54(11):20732077. https://doi.org/10.21273/HORTSCI14327-19.

    • Search Google Scholar
    • Export Citation
  • Willborn WC. 2022. Fine mapping, marker development, and candidate gene discovery for two quantitative trait loci, FaRMp1 and FaRMp2, conferring resistance to charcoal rot in strawberry (F. ×ananassa) [Master’s thesis, University of Florida].

    • Search Google Scholar
    • Export Citation
  • Zveibil A, Freeman S. 2005. First report of crown and root rot in strawberry caused by Macrophomina phaseolina in Israel. Plant Dis. 89(9):1014. https://doi.org/10.1094/PD-89-1014C.

    • Search Google Scholar
    • Export Citation
  • Fig. 1.

    (A) The adjusted area under the disease progress stairs (AUDPS) value of each check across the C1, C2, and C3 trials; phenotypes are color-coded as susceptible (gray) or resistant (black). (B) Phenotypic distributions of the adjusted AUDPS values among C1 (N = 523), C2 (N = 185), and C3 (N = 407) individuals screened in 2020–21, 2022–23, and 2023–24, respectively. Phenotypes of the elite University of Florida (UF) parents are shown as pink arrows and those of FVC 11-58-derived parents are shown as green arrows.

  • Fig. 2.

    (A) High-resolution melting analysis (HRM) of 156 C1 individuals segregating for the Mp3-6.14-1 marker, an A/G variant associated with the FaRMp3 resistance locus, where A is the favorable allele. The resistant genotypes (A/G) are shown in green and the susceptible genotypes (GG) are shown in red. (B) Sequence alignment of Mp3-6.14-1 from a resistant individual and seven susceptible individuals. The target polymorphism is shaded in green for the favorable allele and in red for the susceptible allele. Boxed sequences correspond to the forward and reverse primers.

  • Fig. 3.

    Comparisons of the area under the disease progress stairs (AUDPS) means among individuals grouped into QTL classes (x-axis) by their multilocus genotypes. Boxes represent favorable (black) and unfavorable (white) alleles for each QTL listed on the left. Means and standard errors (SEM; error bars) are plotted for each QTL class. Means labeled with the same letter do not differ significantly according to Tukey’s honestly significant difference (HSD) test at P < 0.05.

  • Avilés M, Castillo S, Bascon J, Zea-Bonilla T, Martín-Sánchez PM, Pérez-Jiménez RM. 2008. First report of Macrophomina phaseolina causing crown and root rot of strawberry in Spain. Plant Pathol. 57(2):382382. https://doi.org/10.1111/j.1365-3059.2007.01717.x.

    • Search Google Scholar
    • Export Citation
  • Baggio JS, Cordova LG, Peres NA. 2019. Sources of inoculum and survival of Macrophomina phaseolina in Florida strawberry fields. Plant Dis. 103(9):24172424. https://doi.org/10.1094/PDIS-03-19-0510-RE.

    • Search Google Scholar
    • Export Citation
  • Baggio JS, Cordova LG, Seijo TE, Noling JW, Whitaker VM, Peres NA. 2021. Cultivar selection is an effective and economic strategy for managing charcoal rot of strawberry in Florida. Plant Dis. 105(8):20712077. https://doi.org/10.1094/PDIS-10-20-2250-RE.

    • Search Google Scholar
    • Export Citation
  • Bernardo R. 2008. Molecular markers and selection for complex traits in plants: Learning from the last 20 years. Crop Sci. 48(5):16491664. https://doi.org/10.2135/cropsci2008.03.0131.

    • Search Google Scholar
    • Export Citation
  • Bovill WD, Horne M, Herde D, Davis M, Wildermuth GB, Sutherland MW. 2010. Pyramiding QTL increases seedling resistance to crown rot (Fusarium pseudograminearum) of wheat (Triticum aestivum). Theor Appl Genet. 121(1):127136. https://doi.org/10.1007/s00122-010-1296-7.

    • Search Google Scholar
    • Export Citation
  • Chaloner TM, Gurr SJ, Bebber DP. 2021. Plant pathogen infection risk tracks global crop yields under climate change. Nat Clim Chang. 11(8):710715. https://doi.org/10.1038/s41558-021-01104-8.

    • Search Google Scholar
    • Export Citation
  • Chamorro M, Seijo TE, Noling JC, de los Santos B, Peres NA. 2016. Efficacy of fumigant treatments and inoculum placement on control of Macrophomina phaseolina in strawberry beds. Crop Prot. 90:163169. https://doi.org/10.1016/j.cropro.2016.08.020.

    • Search Google Scholar
    • Export Citation
  • Chandler CK, Legard DE, Dunigan DD, Crocker TE, Sims CA. 2000. ‘Strawberry Festival’ strawberry. HortScience. 35(7):13661367. https://doi.org/10.21273/HORTSCI.35.7.1366.

    • Search Google Scholar
    • Export Citation
  • Cohen R, Elkabetz M, Paris HS, Gur A, Dai N, Rabinovitz O, Freeman S. 2022. Occurrence of Macrophomina phaseolina in Israel: Challenges for disease management and crop germplasm enhancement. Plant Dis. 106(1):1525. https://doi.org/10.1094/PDIS-07-21-1390-FE.

    • Search Google Scholar
    • Export Citation
  • Diourte M, Starr JL, Jeger MJ, Stack JP, Rosenow DT. 1995. Charcoal rot (Macrophomina phaseolina) resistance and the effects of water stress on disease development in sorghum. Plant Pathol. 44(1):196202. https://doi.org/10.1111/j.1365-3059.1995.tb02729.x.

    • Search Google Scholar
    • Export Citation
  • Duniway JM. 2002. Status of chemical alternatives to methyl bromide for pre-plant fumigation of soil. Phytopathology. 92(12):13371343. https://doi.org/10.1094/PHYTO.2002.92.12.1337.

    • Search Google Scholar
    • Export Citation
  • El-Marzoky H, Abdalla M, Abdel-Sattar M, Abid M. 2018. Management of crown and root rot diseases in strawberry commercial fields in Egypt. J Plant Prot Pathol. 9(7):399404. https://doi.org/10.21608/jppp.2018.42184.

    • Search Google Scholar
    • Export Citation
  • Fennimore S, Ajwa H. 2012. Totally impermeable film retains fumigants, allowing lower application rates in strawberry. Calif Agric. 65(4):211215.

    • Search Google Scholar
    • Export Citation
  • Fukuoka S, Saka N, Mizukami Y, Koga H, Yamanouchi U, Yoshioka Y, Hayashi N, Ebana K, Mizobuchi R, Yano M. 2015. Gene pyramiding enhances durable blast disease resistance in rice. Sci Rep. 5(1):7773. https://doi.org/10.1038/srep07773.

    • Search Google Scholar
    • Export Citation
  • Gordon TR, Daugovish O, Koike ST, Islas CM, Kirkpatrick SC, Yoshisato JA, Shaw DV. 2016. Options for management of fusarium wilt of strawberry in California. Int J Fruit Sci. 16(Suppl 1):160168. https://doi.org/10.1080/15538362.2016.1219294.

    • Search Google Scholar
    • Export Citation
  • Hancock JF, Finn CE, Luby JJ, Dale A, Callow PW, Serçe S. 2010. Reconstruction of the Strawberry, Fragaria ×ananassa, using genotypes of F. virginiana and F. chiloensis. HortScience. 45(7):10061013. https://doi.org/10.21273/HORTSCI.45.7.1006.

    • Search Google Scholar
    • Export Citation
  • Hardigan MA, Feldmann MJ, Lorant A, Bird KA, Famula R, Acharya C, Cole G, Edger PP, Knapp SJ. 2020. Genome synteny has been conserved among the octoploid progenitors of cultivated strawberry over millions of years of evolution. Front Plant Sci. 10:1789. https://doi.org/10.3389/fpls.2019.01789.

    • Search Google Scholar
    • Export Citation
  • Hardigan MA, Feldmann MJ, Pincot DDA, Famula RA, Vachev MV, Madera MA, Zerbe P, Mars K, Peluso P, Rank D, Ou S, Saski CA, Acharya CB, Cole GS, Yocca AE, Platts AE, Edger PP, Knapp SJ. 2021. Blueprint for phasing and assembling the genomes of heterozygous polyploids: Application to the octoploid genome of strawberry. bioRxiv. 2021.11.03.467115. https://doi.org/10.1101/2021.11.03.467115.

    • Search Google Scholar
    • Export Citation
  • Holmes GJ, Mansouripour SM, Hewavitharana SS. 2020. Strawberries at the crossroads: management of soilborne diseases in California without methyl bromide. Phytopathology. 110(5):956968. https://doi.org/10.1094/PHYTO-11-19-0406-IA.

    • Search Google Scholar
    • Export Citation
  • Jo K-R, Kim C-J, Kim S-J, Kim T-Y, Bergervoet M, Jongsma MA, Visser RG, Jacobsen E, Vossen JH. 2014. Development of late blight resistant potatoes by cisgene stacking. BMC Biotechnol. 14(1):50. https://doi.org/10.1186/1472-6750-14-50.

    • Search Google Scholar
    • Export Citation
  • Jung S, Lee T, Cheng C-H, Buble K, Zheng P, Yu J, Humann J, Ficklin SP, Gasic K, Scott K, Frank M, Ru S, Hough H, Evans K, Peace C, Olmstead M, DeVetter LW, McFerson J, Coe M, Wegrzyn JL, Staton ME, Abbott AG, Main D. 2019. 15 years of GDR: New data and functionality in the Genome Database for Rosaceae. Nucleic Acids Res. 47(D1):D1137D1145. https://doi.org/10.1093/nar/gky1000.

    • Search Google Scholar
    • Export Citation
  • Katoh K, Standley DM. 2013. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 30(4):772780. https://doi.org/10.1093/molbev/mst010.

    • Search Google Scholar
    • Export Citation
  • Kaur S, Dhillon GS, Brar SK, Vallad GE, Chand R, Chauhan VB. 2012. Emerging phytopathogen Macrophomina phaseolina: Biology, economic importance and current diagnostic trends. Crit Rev Microbiol. 38(2):136151. https://doi.org/10.3109/1040841X.2011.640977.

    • Search Google Scholar
    • Export Citation
  • Keb-Llanes M, González G, Chi-Manzanero B, Infante D. 2002. A rapid and simple method for small-scale DNA extraction in Agavaceae and other tropical plants. Plant Mol Biol Rep. 20(3):299299. https://doi.org/10.1007/BF02782465.

    • Search Google Scholar
    • Export Citation
  • Knapp SJ, Cole GS, Pincot DDA, Dilla-Ermita CJ, Bjornson M, Famula RA, Gordon TR, Harshman JM, Henry PM, Feldmann MJ. 2024. Transgressive segregation, hopeful monsters, and phenotypic selection drove rapid genetic gains and breakthroughs in predictive breeding for quantitative resistance to Macrophomina in strawberry. Hortic Res. 11(2):uhad289. https://doi.org/10.1093/hr/uhad289.

    • Search Google Scholar
    • Export Citation
  • Koike ST. 2008. Crown rot of strawberry caused by Macrophomina phaseolina in California. Plant Dis. 92(8):1253. https://doi.org/10.1094/PDIS-92-8-1253B.

    • Search Google Scholar
    • Export Citation
  • Leisner CP, Potnis N, Sanz-Saez A. 2023. Crosstalk and trade-offs: Plant responses to climate change-associated abiotic and biotic stresses. Plant Cell Environ. 46(10):29462963. https://doi.org/10.1111/pce.14532.

    • Search Google Scholar
    • Export Citation
  • Li X, Wei Y, Ma Y, Cao G, Ma S, Zhang T, Zhan Z, Piao Z. 2022. Marker-assisted pyramiding of CRa and CRd genes to improve the clubroot resistance of Brassica rapa. Genes. 13(12):2414. https://doi.org/10.3390/genes13122414.

    • Search Google Scholar
    • Export Citation
  • Mendiburu FD, Simon R. 2015. Agricolae - Ten years of an open source statistical tool for experiments in breeding, agriculture and biology. PeerJ Inc. https://doi.org/10.7287/peerj.preprints.1404v1.

    • Search Google Scholar
    • Export Citation
  • Mertely J, Seijo T, Peres N. 2005. First report of Macrophomina phaseolina causing a crown rot of strawberry in Florida. Plant Dis. 89(4):434434. https://doi.org/10.1094/PD-89-0434A.

    • Search Google Scholar
    • Export Citation
  • Montzka SA, Reimann S, Engel A, Kruger K, Sturges WT, Blake D, Dorf M, Fraser P, Froidevaux L, Jucks K, Kreher K, Kurylo MJ, Mellouki A, Miller J, Nielsen O-J, Orkin VL, Prinn RG, Rhew R, Santee ML, Stohl A, Verdonik D, Krummel PB, Atlas E, Bernath P, Blumenstock T, Butler JH, Butz A, Connor B, Duchatelet P, Dutton G, Hendrick F, Krummel PB, Kuijpers LJM, Mahieu E, Manning A, Muhle J, Pfeilsticker K, Quack B, Ross M, Salavitch RJ, Schauffler S, Simpson IJ, Toohey D, Vollmer MK, Wallington TJ, Wang HJR, Weiss RF, Yamabe M, Yokouchi Y, Yvon-Lewis S. 2011. Scientific assessment of ozone depletion: 2010. Glob Ozone Res Monit Proj - Rep No 51.

    • Search Google Scholar
    • Export Citation
  • Mundt CC. 2018. Pyramiding for resistance durability: theory and practice. Phytopathology. 108(7):792802. https://doi.org/10.1094/PHYTO-12-17-0426-RVW.

    • Search Google Scholar
    • Export Citation
  • Nelson JR, Verma S, Bassil NV, Finn CE, Hancock JF, Cole GS, Knapp SJ, Whitaker VM. 2021. Discovery of three loci increasing resistance to charcoal rot caused by Macrophomina phaseolina in octoploid strawberry. G3 GenesGenomesGenetics. 11(3):jkab037. https://doi.org/10.1093/g3journal/jkab037.

    • Search Google Scholar
    • Export Citation
  • Noh Y-H, Oh Y, Mangandi J, Verma S, Zurn JD, Lu Y-T, Fan Z, Bassil N, Peres N, Cole G, Acharya C, Famula R, Knapp S, Whitaker VM, Lee S. 2018. High-throughput marker assays for FaRPc2-mediated resistance to Phytophthora crown rot in octoploid strawberry. Mol Breeding. 38(8):104. https://doi.org/10.1007/s11032-018-0861-7.

    • Search Google Scholar
    • Export Citation
  • Oh Y, Chandra S, Lee S. 2020. Development of subgenome-specific markers for FaRXf1 conferring resistance to bacterial angular leaf spot in allo-octoploid strawberry. Int J Fruit Sci. 20(Suppl 2):S198S210. https://doi.org/10.1080/15538362.2019.1709116.

    • Search Google Scholar
    • Export Citation
  • Ristaino JB, Anderson PK, Bebber DP, Brauman KA, Cunniffe NJ, Fedoroff NV, Finegold C, Garrett KA, Gilligan CA, Jones CM, Martin MD, MacDonald GK, Neenan P, Records A, Schmale DG, Tateosian L, Wei Q. 2021. The persistent threat of emerging plant disease pandemics to global food security. Proc Natl Acad Sci USA. 118(23):e2022239118. https://doi.org/10.1073/pnas.2022239118.

    • Search Google Scholar
    • Export Citation
  • Rosskopf EN, Chellemi DO, Kokalis-Burelle N, Church GT. 2005. Alternatives to methyl bromide: a Florida perspective. Plant Health Prog. 6(1):19. https://doi.org/10.1094/PHP-2005-1027-01-RV.

    • Search Google Scholar
    • Export Citation
  • Salinas N, Fan Z, Peres N, Lee S, Whitaker VM. 2020. FaRCa1 confers moderate resistance to the root necrosis form of strawberry anthracnose caused by Colletotrichum acutatum. HortScience. 55(5):693698. https://doi.org/10.21273/HORTSCI14807-20.

    • Search Google Scholar
    • Export Citation
  • Simko I, Piepho H-P. 2012. The area under the disease progress stairs: Calculation, advantage, and application. Phytopathology. 102(4):381389. https://doi.org/10.1094/PHYTO-07-11-0216.

    • Search Google Scholar
    • Export Citation
  • Velders GJM, Andersen SO, Daniel JS, Fahey DW, McFarland M. 2007. The importance of the Montreal Protocol in protecting climate. Proc Natl Acad Sci USA. 104(12):48144819. https://doi.org/10.1073/pnas.0610328104.

    • Search Google Scholar
    • Export Citation
  • Whitaker VM, Dalid C, Osorio LF, Peres NA, Verma S, Lee S, Plotto A. 2023. Florida Pearl® ‘FL 16.78-109’ pineberry. HortScience. 58(1):143146. https://doi.org/10.21273/HORTSCI16951-22.

    • Search Google Scholar
    • Export Citation
  • Whitaker VM, Knapp SJ, Hardigan MA, Edger PP, Slovin JP, Bassil NV, Hytönen T, Mackenzie KK, Lee S, Jung S, Main D, Barbey CR, Verma S. 2020. A roadmap for research in octoploid strawberry. Hortic Res. 7:33. https://doi.org/10.1038/s41438-020-0252-1.

    • Search Google Scholar
    • Export Citation
  • Whitaker VM, Peres NA, Osorio LF, Fan Z, do Nascimento Nunes MC, Plotto A, Sims CA. 2019. ‘Florida Brilliance’ strawberry. HortScience. 54(11):20732077. https://doi.org/10.21273/HORTSCI14327-19.

    • Search Google Scholar
    • Export Citation
  • Willborn WC. 2022. Fine mapping, marker development, and candidate gene discovery for two quantitative trait loci, FaRMp1 and FaRMp2, conferring resistance to charcoal rot in strawberry (F. ×ananassa) [Master’s thesis, University of Florida].

    • Search Google Scholar
    • Export Citation
  • Zveibil A, Freeman S. 2005. First report of crown and root rot in strawberry caused by Macrophomina phaseolina in Israel. Plant Dis. 89(9):1014. https://doi.org/10.1094/PD-89-1014C.

    • Search Google Scholar
    • Export Citation

Supplementary Materials

Elissar Alam Plant Breeding Graduate Program, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL 32611, USA

Search for other papers by Elissar Alam in
Google Scholar
Close
,
Seonghee Lee Plant Breeding Graduate Program, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL 32611, USA

Search for other papers by Seonghee Lee in
Google Scholar
Close
,
Natalia A. Peres Plant Pathology Department, University of Florida, Gulf Coast Research and Education Center, Wimauma, FL 33598, USA

Search for other papers by Natalia A. Peres in
Google Scholar
Close
, and
Vance M. Whitaker Plant Breeding Graduate Program, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, FL 32611, USA

Search for other papers by Vance M. Whitaker in
Google Scholar
Close

Contributor Notes

We thank the members of the University of Florida GCREC strawberry breeding, genomics, and pathology laboratories for their work in trial maintenance and the Florida strawberry industry for continued and generous support. This research was supported by grants awarded to Vance M. Whitaker from the USDA National Institute of Food and Agriculture (NIFA) Specialty Crops Research Initiative (awards 2017-51181-26833 and 2022-51181-38328) and the Florida Strawberry Research and Education Foundation (FSREF).

On behalf of all authors, the corresponding author states that there is no conflict of interest.

V.M.W. is the corresponding author. E-mail: vwhitaker@ufl.edu.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 324 324 80
PDF Downloads 326 326 57
  • Fig. 1.

    (A) The adjusted area under the disease progress stairs (AUDPS) value of each check across the C1, C2, and C3 trials; phenotypes are color-coded as susceptible (gray) or resistant (black). (B) Phenotypic distributions of the adjusted AUDPS values among C1 (N = 523), C2 (N = 185), and C3 (N = 407) individuals screened in 2020–21, 2022–23, and 2023–24, respectively. Phenotypes of the elite University of Florida (UF) parents are shown as pink arrows and those of FVC 11-58-derived parents are shown as green arrows.

  • Fig. 2.

    (A) High-resolution melting analysis (HRM) of 156 C1 individuals segregating for the Mp3-6.14-1 marker, an A/G variant associated with the FaRMp3 resistance locus, where A is the favorable allele. The resistant genotypes (A/G) are shown in green and the susceptible genotypes (GG) are shown in red. (B) Sequence alignment of Mp3-6.14-1 from a resistant individual and seven susceptible individuals. The target polymorphism is shaded in green for the favorable allele and in red for the susceptible allele. Boxed sequences correspond to the forward and reverse primers.

  • Fig. 3.

    Comparisons of the area under the disease progress stairs (AUDPS) means among individuals grouped into QTL classes (x-axis) by their multilocus genotypes. Boxes represent favorable (black) and unfavorable (white) alleles for each QTL listed on the left. Means and standard errors (SEM; error bars) are plotted for each QTL class. Means labeled with the same letter do not differ significantly according to Tukey’s honestly significant difference (HSD) test at P < 0.05.

Advertisement
Advertisement
Save