Evaluation and Application of Molecular Marker Sets for Developing PHS-Resistant Wheat in Korea

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To accelerate the development of PHS-resistant cultivars with improved agronomic performance, we employed a marker-assisted backcrossing (MABC) approach using molecular markers previ- ously reported in the literature. For foreground selection, 81 markers (74 KASP and 7 gel-based) targeting key genes related to PHS resistance, grain quality, and disease resistance were used. Genome-wide background selection was conducted with 547 KASP markers to monitor recovery of the recurrent parent genome. Genotyping of 94 wheat accessions revealed distinct genetic clustering among domestic cultivars, introduced germplasm, and 1BL.1RS rye translocation lines, demonstrating the effectiveness of the marker set in capturing genetic diversity. Using MABC combined with speed breeding, three elite lines were developed by pyramiding favorable alleles from ‘Joongmo2008’ and ‘Tapdong’. Among them, ‘Milyang55’ showed strong PHS resistance (germination rate: 21.1%), desirable agronomic traits, and flour quality comparable to ‘Joongmo2008’. Donor genome proportions ranged from 25.5% to 43.0%, confirming efficient recovery of the recurrent genome. These results demonstrate the utility of integrating molecu- lar markers with MABC and speed breeding for the rapid development of elite wheat cultivars with improved resistance and end-use quality. Triticum aestivum pre-harvest sprouting marker-assisted backcrossing KASP markers speed breeding Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Wheat ( Triticum aestivum L.) is a major cereal crop in Korea, with per capita consumption reaching 35.7 kg in 2023. Despite growing demand, wheat self-sufficiency was just 1.3% in 2022, reflecting heavy dependence on imports. This reliance exposes Korea to global market instability driven by extreme weather, geopolitical conflicts, and energy price fluctuations. To address this issue, the Korean government launched the Wheat Industry Promotion Implementation Plan in 2020. The plan includes the introduction of quality management standards, support for post-harvest infrastructure, and incentives for using domestic wheat in institutional food services. A key barrier to expanding domestic production has been the lack of local milling facilities. However, ongoing efforts—such as constructing four mills under the Wheat Industry Valley Demonstration Project—are expected to strengthen the wheat value chain and help raise self-sufficiency. Wheat yield and quality are highly sensitive to environmental stress. Abiotic factors can cause yield reductions of up to 82% (Boyer, 1982), and productivity varies widely by region and climate (Asseng et al., 2020). In Korea, yields have fluctuated from a high of 447 kg/10a in 2023 to a low of 325 kg/10a in 2020, with a five-year average of 403 kg/10a (KOSIS, 2023). Environmental conditions also play a dominant role in grain quality, accounting for greater variation than genotype (Fowler et al., 1990; Labuschagne et al., 2009). One of the most critical quality issues in Korean wheat production is pre-harvest sprouting (PHS), caused by rainfall during grain filling and ripening. Korea’s double-cropping system, where wheat is followed by rice, often results in overlap between the wheat ripening stage and the start of the rainy season, increasing the risk of sprouting. PHS reduces flour quality and market value, sometimes by 20–50%, and forces the downgraded use of wheat as feed grain (Jiang & Xiao, 2005; Sorenson & Wiersma, 2004). Even resistant varieties may sprout under prolonged wet conditions (Peery et al., 2023), making stronger resistance essential. PHS resistance in wheat is often associated with red grain color, largely due to the Myb10-D (PHS-3D) gene on chromosome 3D, which enhances abscisic acid (ABA) biosynthesis and limits water uptake during grain maturation (Lang et al., 2021). However, white-grained wheat is preferred by the milling industry for its bright flour, low ash content, and high extraction rate. For this reason, developing white-grained varieties with PHS resistance remains a major breeding goal. While cultivars such as ‘Rio Blanco’ show strong PHS resistance, they have proven unsuitable for Korean cultivation due to late heading that disrupts the cropping schedule. Although several PHS-resistant cultivars—such as ‘Goso’ and ‘Baekjoong’—have been developed in Korea, progress has been limited by the long breeding cycle, which typically exceeds 10 years. Additionally, the use of diverse genetic resources and structured pre-breeding strategies remains limited, slowing the development of high-performing cultivars. To overcome these limitations, efficient molecular breeding approaches are essential. Marker-assisted backcrossing (MABC) enables precise introgression of resistance genes into elite varieties, significantly accelerating the selection process. This strategy relies on informative marker sets that can efficiently distinguish polymorphisms between parental lines, enabling rapid recovery of the recurrent parent genotype with fewer backcross generations. By combining MABC with speed breeding, the timeline for developing PHS-resistant cultivars can be significantly reduced. In environments like Korea—where rainfall during ripening is a recurrent threat—varieties that delay moisture uptake or exhibit strong dormancy are essential for ensuring stable quality and yield. This study aims to establish a marker-assisted breeding framework for developing PHS-resistant wheat by identifying a marker set suitable for selecting resistant lines in domestic breeding programs. Ultimately, this approach supports the long-term goal of increasing wheat self-sufficiency in Korea. 2. Materials and Methods 2.1 Construction of marker set for wheat breeding 2.1.1. Plant materials preparation and DNA extraction To construct a marker set for marker-assisted selection (MAS) and MABC, ninety-four wheat cultivars and elite lines were selected for genotyping based on their frequency of use as crossing parents (S1) . Selected cultivars were sown in crossing block of NICS, in Miryang, Gyeongsangnam-do, Korea, on November 5, 2022, with a row spacing of 30 cm. Prior to sowing, fertilization was applied at a rate of N 2 : P 2 O 5 : K 2 O = 4.5 : 7.4 : 3.9 kg/10a as basal dressing, with an additional 4.5 kg/10a of nitrogen applied at the jointing stage. For genotyping, DNA was extracted from fresh seedling leaves of each line using the MagMAX DNA Multi-Sample Kit (Applied Biosystems, Woburn, MA, USA) and the KingFisher DNA extraction system (Thermo Fisher Scientific, Waltham, MA, USA), following the manufacturer's instructions. 2.1.2. Genotyping for marker selection in wheat cultivars To construct a marker set for foreground selection, 74 Kompetitive Allele Specific PCR (KASP) markers, and 7 gel-based markers from references were selected and validated across 94 wheat cultivars (S2, S3) . The markers were chosen for their relevance in selecting for biotic and abiotic resistance, as well as high-quality traits such as increased protein and gluten content. The PCR conditions for KASP markers were as follows: an initial denaturation at 95 °C for 15 minutes; 10 touchdown cycles of 95 °C for 20 seconds, with the annealing temperature starting at 65 °C and decreasing by 1 °C per cycle for 25 seconds; followed by 30 additional cycles at 95 °C for 10 seconds and 57 °C for 1 minute, with a final extension at 72 °C for 5 minutes. Fluorescence detection and data analysis were conducted using the QuantStudio3 real-time PCR system and QuantStudio Design & Analysis Software v.1.5.1 (Applied Biosystems, Carlsbad, CA, USA). The selected markers and their genotyping results were digitized, with favorable alleles (e.g., disease resistance) designated as ‘1’ and unfavorable alleles as ‘0.’ These data were uploaded to the ‘Crop Research Information System (CRIS)’ to facilitate efficient marker selection by breeders. The PCR conditions and gel electrophoresis for gel-based markers were performed according to previously published references (Himi et al., 2012; Lang et al., 2021; Nakamura et al., 2015; Saito & Nakamura, 2005; Saito et al., 2009; Shariflou et al., 2001; Su et al., 2018). For background selection in domestic wheat cultivars, a total of 731 KASP markers were utilized. This included 155 assays from the CIMMYT wheat molecular genetics study (Dreisigacker et al., 2016), 70 assays from Rasheed et al. (Rasheed et al., 2016), and the remaining markers from the CerealsDB marker set (Wilkinson et al., 2012). The PCR conditions were the same as those used in the previous experiment. 2.1.3. Phylogenetic analysis of genetic relationships among 94 wheat cultivars The phylogenetic analysis was conducted to evaluate the genetic relationships among 94 wheat cultivars, which were divided into domestic cultivars, introduced germplasms, and rye translocation lines. Out of 731 markers, 547 were successfully amplified, and genotyping was conducted using these markers. The resulting data were analyzed using the Molecular Evolutionary Genetics Analysis (MEGA) X software. Sequences of each cultivar were aligned, and a phylogenetic tree was constructed based on genetic distance using the neighbor-joining method with a bootstrap value of 1,000 replicates to assess the robustness of the tree. Cultivars were categorized into three major clusters, represented by green, red, and yellow color-coded branches. Green triangles, red circles, and yellow squares were used to indicate the distinct groups of domestic cultivars, introduced germplasms, and rye translocation lines, respectively. Branch lengths correspond to the genetic distance, and bootstrap values are presented for key nodes to indicate the confidence level of the grouping. 2.1.4. Calculation of genetic distances and distribution of markers on chromosomes To validate the suitability of these markers for a domestic wheat MABC system, selected markers were applied to 94 domestic cultivars and elite lines. The distribution of markers across chromosomes and distances between markers were analyzed. Genotyping data from the 94 cultivars were then used to construct physical maps using MapChart 2.2 software (Voorrips, 2002), and genetic distances between markers were calculated using MEGA X 5.1 according to the Kimura 2-parameter model (Tamura et al., 2011). Population structure analysis was conducted on 94 wheat cultivars using 547 KASP markers. STRUCTURE software (version 2.3.4., Stanford University, USA) was used to infer the population structure under an admixture model. A range of K values from K=1 to K=10 was tested with 10,000 burn-in iterations and 100,000 Markov Chain Monte Carlo (MCMC) repetitions. The optimal number of subpopulations (K) was determined by examining delta K values, following the method of Evanno et al. (2005). K=2 was selected based on the delta K values and biological relevance. The population structure results were visualized using bar plots, generated by STRUCTURE software. The genotypes of the 94 wheat cultivars were classified into four subpopulations, and the membership proportions of each individual cultivar in the various subpopulations were represented in the bar plots (Evanno et al., 2005). 2.2 Application of MABC in developing PHS resistant wheat 2.2.1 Parent selection To assess the effects of the selected markers and develop PHS-resistant lines, two domestic varieties, ‘Joongmo2008’ and ‘Tapdong’, were chosen as parents based on their agronomic traits and presence of PHS-resistant genes (S4) . ‘Joongmo2008’ is an intermediate wheat variety commonly used as a parent due to its high protein content, resistance to PHS (with a PHS rate of 9.8 %, likely associated with the presence of the MFT gene), and white seed coat (RDA, 2011). However, its susceptibility to lodging and waterlogging stress, along with its very low yield (378 kg/10a), makes it unsuitable for cultivation on its own. Instead, it is used as a crossing parent to enhance flour quality. To improve agronomic traits and perform gene stacking ( MFT + Myb10 ), ‘Joongmo2008’ was crossed with ‘Tapdong’, a red wheat variety known for its short stature, lodging resistance, and the presence of Myb10 , which confers strong PHS resistance (RDA). 2.2.2 Artificial crossing and generation advancement using speed-breeding ‘Joongmo2008’, as the female parent, was artificially crossed with ‘Tapdong’, the male parent, to develop F 1 plants under field conditions in 2020. The F 1 plants were then backcrossed to ‘Joongmo2008’ as the recurrent parent to generate BC 1 F 1 generations under speed-breeding conditions. 13 BC 1 F 1 individuals were advanced in a speed-breeding system using 12 cm pots to obtain a sufficient number of grains. A total of 152 BC 1 F 2 seeds were sown in 72-cell trays and grown under a speed-breeding system, with a 22-hour light: 2-hour dark photoperiod cycle at 8 °C for 4 weeks (using LED lighting with a red: blue: white ratio of 8:3:2, at 100 μmol m –2 s –1 ), followed by a 22-hour light (22 °C): 2-hour dark (17 °C) photoperiod cycle until harvest (500 μmol m –2 s –1 ) (Cha et al., 2020; Cha et al., 2022). Visual phenotypic selection was conducted under speed-breeding conditions based on plant height, spike length, and days to heading. Selected plants were advanced for genetic fixation until the BC 1 F 4 generation ( Figure 1 ). 2.2.3 Foreground selection for PHS resistant wheat breeding materials For the foreground selection of PHS- resistant lines, three molecular markers were employed: one KASP marker (TaMFT_1617R) from the selected marker set, and two gel-based markers (MFT-3A, STS-Myb10). The PCR conditions for TaMFT_1617R involved an initial denaturation at 95 °C for 15 minutes, followed by 10 touchdown cycles of 95 °C for 20 seconds, with the annealing temperature starting at 65 °C and decreasing by 1 °C per cycle for 25 seconds. This was followed by 30 additional cycles at 95 °C for 10 seconds and 57 °C for 1 minute, with a final extension at 72 °C for 5 minutes. Fluorescence detection and data analysis were performed using the QuantStudio3 real-time PCR system and QuantStudio Design & Analysis Software v.1.5.1 (Applied Biosystems, Carlsbad, CA, USA). For MFT-3A, a CAPS marker, the conditions included an initial denaturation at 98 °C for 30 seconds, followed by 40 cycles of 10 seconds at 98 °C, 30 seconds at 60 °C, and 1 minute at 72 °C. A final extension of 7 minutes at 72 °C was applied. The amplified fragments (approximately 800 bp) were digested with the ClaI restriction enzyme (Takara Bio Inc.), and fragment sizes were analyzed by electrophoresis on a 3 % agarose gel at 200 V for 45 minutes (Chono et al., 2015). The STS-Myb10 marker’s conditions included an initial denaturation at 94 °C for 30 minutes, followed by 40 cycles of 20 seconds at 94 °C, 30 seconds at 60 °C, and 1 minute at 72 °C, with a final extension of 7 minutes at 72 °C. Fragment sizes for STS-Myb10 was analyzed using the same electrophoresis method as MFT-3A (Lang et al., 2021). 2.2.4 Background selection for recurrent recovery validation For background selection, a total of 547 KASP markers were used to assess the genetic background of the backcrossed lines. Since ‘Joongmo2008’ is known for its excellent bread-baking quality, the objective was to select lines that are suitable for bread-baking and also carry the PHS resistant gene. Of the 547 amplified markers, 515 were from the CerealsDB marker set (Wilkinson et al., 2012), and 63 were from CIMMYT wheat molecular genetics (Dreisigacker et al., 2016). The PCR conditions were as follows: initial denaturation at 95 °C for 15 minutes; 10 touchdown cycles at 95 °C for 20 seconds, with the annealing temperature starting at 65 °C and decreasing by 1 °C per cycle for 25 seconds; followed by an additional 30 cycles at 95 °C for 10 seconds and 57 °C for 1 minute; with a final extension at 72 °C for 5 minutes. Fluorescence detection and data analysis were conducted using a QuantStudio3 real-time PCR instrument and QuantStudio Design & Analysis Software v1.5.1 (Applied Biosystems, Carlsbad, CA, USA). 2.2.5 Field trials for the evaluation of agronomic traits in elite lines Among the BC 1 F 6 lines, the selected line named ‘Milyang55,’ which incorporates the PHS- resistant gene ‘ MFT ’ and the red seed coat color gene ‘ Myb10 ,’ was evaluated for field performance over two years at NICS in Miryang, Gyeongsangnam-do, Korea. Agronomic traits were assessed in observational yield trials (OYT) and advanced yield trials (AYT) starting in October 25, 2022, followed by a regional adaptability yield trial (RYT) in October 25, 2023. Prior to sowing, fertilization was applied at a rate of N 2 : P 2 O 5 : K 2 O = 4.5 : 7.4 : 3.9 kg/10a as basal dressing, with an additional 4.5 kg/10a of nitrogen applied at the jointing stage. In the AYT and OYT, seeds were sown with a spacing of 30 × 15 cm. In the RYT, seeds were sown using the broadcast method by hand, in a plot size of 150 × 120 m (1.8 m²), at a seeding rate of 16 kg/10a. Agronomic traits such as plant height, spike length, tiller number, and yield were measured in accordance with the RDA's Standards for Research and Analysis in Agricultural Technology (RDA, 2012). 2.2.6 Evaluation of PHS rates in selected elite line The germination index (GI) is widely used as an indicator of seed dormancy and PHS resistance due to its ability to provide more consistent evaluation results throughout the year compared to direct PHS assessments. PHS rates were determined following the method outlined by Li et al.(Li et al., 2021), with slight modifications. Heading dates were synchronized using speed breeding techniques, and at physiological maturity—45 days after heading, when the spikes had lost their green color—fifty seeds from the parent varieties and the elite line ‘Milyang55’ were selected. The seeds were placed in 90 × 15 mm petri dishes containing two filter papers moistened with 3 ml of distilled water and incubated at 20 °C for 7 days. Germinated seeds were counted and removed daily. The GI was calculated using the following formula, where N1, N2, …, N7 represent the number of seeds germinated on each day from the first to the seventh day (Li et al., 2021; Walker‐Simmons, 1988). 3. Results 3.1 Construction of a marker set for wheat MAS and MABC A total of 81 markers, including 74 KASP markers and 7 gel-based markers, were tested for their utility in MAS and MABC. Of the 74 KASP markers, 51 were successfully amplified and used for genotyping. Additionally, all 7 gel-based markers proved to be useful and applicable to domestic wheat cultivars. Among these, 2 gel-based markers were specifically applicable for PHS resistance ( Figure 2 ). MFT-3A and TaMFT_1617R refer to the same gene region associated with the MFT (MOTHER OF FT AND TFL1) gene located on chromosome 3A of wheat. This gene is linked to PHS resistance, and both the MFT-3A marker and the TaMFT_1617R marker target this specific region. In this study, the genotyping results were consistent for both markers, as expected (S4) . Based on these results, wheat varieties carrying the MFT and Tamyb10-D1 genes were classified according to their alleles. Eight varieties, including ‘Junggye4742’, possessed both MFT and Tamyb10-D1 ; however, these cultivars exhibited poor agronomic traits, such as excessive plant height, and were unsuitable for bread-baking quality, as determined by high molecular weight glutenin subunits and protein content. The majority of the cultivars (47) lacked both PHS resistance genes. Therefore, gene pyramiding for PHS resistance and quality improvement through breeding are urgently needed ( Table 1 ). Genotyping data from the KASP markers were digitized, with favorable alleles coded as ‘1’ and unfavorable alleles as ‘0’. The data from both KASP and gel-based markers were uploaded to the CRIS database, providing open access for breeders at the National Institute of Crop Science (NICS) to facilitate the selection of crossing parents. This marker set serves as a valuable tool for breeding programs aimed at improving agronomic traits, biotic/abiotic stress resistance, and flour quality in domestic wheat varieties ( Figure 3 ). Table 1. Classification and number of wheat varieties based on genotyping results ( MFT and Tamyb10-D1 ). Marker name (Gene) TaMFT-3A( MFT ) Resistant Susceptible STS-Myb10 ( Tamyb10-D1 ) Resistant 8 (Baekchal, Miryang54 etc.) 25 (Ariheukchal, Sugang etc.) Susceptible 14 (Baekjoong, Keumgang etc.) 47 (Joeunmil, FRONTANA etc.) Out of 731 KASP markers for background selection, 547 were successfully amplified. Markers with more than 20 % missing data were filtered out across all tested cultivars. The average p-distance, along with the standard error (SE), was calculated as 0.418 (SE = 0.020), indicating significant genetic divergence among the tested wheat varieties. The number of markers per chromosome ranged from 14 (on chromosomes 4D and 6D) to 44 (on chromosomes 5B and 7A), totaling 547 markers. The mean physical interval between markers was 6.4 Mb, with the maximum interval reaching 97.7 Mb on chromosome 2D. These markers were distributed across the wheat genome, efficiently capturing widely distributed polymorphisms within the chromosomes ( Figure 4 , Table 2 ) Table 2. Summary of the marker set for wheat MABC. Chromosome No. of marker Marker interval [Mb] Position [Mb] Max Mean Start End 1A 18 49.9 8.4 3.0 144.4 1B 32 18.5 5.4 9.7 177.6 1D 24 21.5 5.5 0.0 125.4 2A 28 22.5 6.2 0.0 167.9 2B 27 15.2 5.4 0.0 141.5 2D 22 97.7 9.3 0.0 194.3 3A 33 53.2 7.4 0.6 236.4 3B 34 39.7 7.0 4.6 234.4 3D 16 16.0 7.1 10.1 116.1 4A 24 23.2 7.1 1.3 164.1 4B 30 13.1 3.8 4.0 114.9 4D 14 23.2 8.1 0.0 105.9 5A 33 14.5 5.1 9.0 173.3 5B 44 41.8 6.0 6.1 264.8 5D 15 30.1 9.0 0.0 125.7 6A 31 19.3 5.3 0.0 159.8 6B 25 12.7 5.0 0.0 120.6 6D 14 19.5 6.0 0.0 78.5 7A 44 22.8 4.6 20.5 219.0 7B 20 42.0 5.9 0.5 113.0 7D 19 24.8 7.5 0.0 135.4 Total 547 97.7 6.4 3.2 Genetic diversity among wheat breeding materials To evaluate the genetic diversity among 94 wheat cultivars and germplasm, a phylogenetic tree was constructed using genotyped data. This analysis was based on data from 547 KASP markers, which were selected for background selection and distributed across the wheat genome, efficiently capturing widely distributed polymorphisms. The analysis identified three distinct clusters, demonstrating the effectiveness of the marker set in capturing the genetic diversity necessary for precision breeding. Cluster 1 (37 cultivars, represented by triangles) consists of domestic wheat cultivars, Cluster 2 (33 cultivars, represented by circles) comprises introduced germplasms from the RDA-GeneBank, and Cluster 3 (23 cultivars, represented by squares) includes 1BL.1RS wheat-rye translocation lines and intermediate crossing lines. These rye translocation lines incorporate the 1RS chromosome segment from winter rye cv. ‘Paldanghomil’ into soft red winter wheat cv. ‘Olmil’ (Ko et al., 2002). The phylogenetic analysis revealed clear clustering patterns based on genetic distance. Domestic cultivars, such as ‘Joongmo2008’, ‘Tapdong’, and ‘Jokyoung’ formed a tightly grouped cluster (green), indicating high genetic similarity. The introduced germplasms (red cluster), such as ‘Chinese Spring’ and ‘SANDO’ displayed greater genetic diversity. The rye translocation lines (yellow cluster) exhibited significant genetic divergence from both domestic and introduced cultivars, emphasizing their distinct genetic background ( Figure 5 ). The principal component analysis (PCA), also based on the 547 successfully amplified markers, effectively distinguished the 94 wheat cultivars into clear genetic groups. Domestic cultivars, including ‘Joongmo2008’ and ‘Hwanggeumal’, formed a unified cluster, illustrating their close genetic relationships ( Figure 6 ) . This tight clustering underscores the effectiveness of breeding programs in maintaining desired agronomic traits within domestic varieties. On the other hand, introduced germplasms and rye translocation lines exhibited broader genetic dispersion, indicating a higher degree of variability compared to domestic cultivars. This clear separation between domestic cultivars and introduced germplasms highlights the distinct genetic profiles of these groups. The observed genetic diversity within the rye translocation lines could be especially valuable for enhancing stress resistance and improving grain quality traits in future breeding efforts. These results validate the utility of the marker set in accurately capturing the genetic diversity within the population. The markers successfully highlighted the narrower genetic base of domestic cultivars, the broader diversity of introduced germplasms, and the distinct genetic profile of the rye translocation lines, emphasizing their potential to broaden the genetic base of domestic wheat breeding programs. 3.3 Population structure of wheat cultivars Population structure analysis of the 94 wheat cultivars, using genotyping data from 547 KASP markers, revealed the presence of four subpopulations ( Figure 7 ). STRUCTURE analysis at K=2 showed a clear genetic stratification, where individual cultivars displayed varying degrees of admixture. Each bar in the plot represents a cultivar, and the proportion of colors within a bar indicates the likelihood of belonging to specific subpopulations. The distinct patterns observed in the STRUCTURE barplot correspond well with the genetic diversity captured across the population. The varying membership proportions reflect the admixture between cultivars and align with the broader trends identified in the phylogenetic tree analysis, confirming the genetic differences among the breeding materials. 3.4 Application of marker set in MABC for selecting PHS-resistant wheat lines From a total of 18 BC 1 F 1 plants, 7 were selected through foreground selection for pyramiding the MFT (MFT-3A) gene from ‘Joongmo2008’ and the Myb10 (Tamyb10-A1) gene from ‘Tapdong’. In the BC 1 F 2 generation, out of 152 plants, 39 were chosen using the same markers. These markers specifically targeted the regions associated with PHS resistance and red seed coat color. Among the 39 selected BC 1 F 2 individuals, phenotypic selection was conducted under speed-breeding conditions, identifying short-statured, ‘Tapdong-type’ plants that displayed favorable traits. Subsequently, 6 lines were evaluated in the observational yield trials (OYT), leading to the identification of 3 lines with high genetic recovery toward ‘Joongmo2008’, which is noted for its superior protein content among domestic wheat varieties. These three elite lines were further evaluated in advanced yield trials (AYT). To ensure comprehensive background selection, these lines were screened using 547 KASP markers, which were then used to construct a detailed physical map ( Figure 4 ). It is noteworthy that no markers were amplified on chromosome 3D, indicating no detectable substitution of chromosome segments from the donor parent, ‘Tapdong’ in this region. The polymorphism rate between ‘Joongmo2008’ and ‘Tapdong’ was 74.2 %, indicating considerable genetic diversity between the two parents. The percentage of donor parent chromosome substitution in each line was calculated at 33.0 %, 43.0 %, and 25.5 %. These percentages demonstrate higher proportions of recurrent parental genome recovery compared to previously reported values. This supports the efficiency of using MABC to expedite the recovery of the recurrent parent genotype ( Figure 8 ). According to Tanksley et al., for most crop breeding programs, over 90 % of the recurrent parental genotype can be recovered within two generations when a sufficient number of markers and an adequate number of progeny are utilized for background selection (Tanksley et al., 1989; Xu & Crouch, 2008). In this study, the selected marker set for MABC significantly accelerated the breeding process by enabling the precise recovery of the recurrent parent genome, saving time and resources compared to conventional backcrossing methods. The selection of these markers proves essential in improving breeding efficiency, especially for traits like PHS resistance, where rapid pyramiding of beneficial genes and high recovery of the recurrent parent genome are crucial. These results suggest that combining MFT and Myb10 through this enhanced MABC system can lead to the development of wheat lines with both agronomically important traits and quality characteristics desirable for domestic wheat production. 3.5 Evaluation of agronomic traits, PHS resistance, and flour quality in selected lines The agronomic traits of ‘Joongmo2008’, ‘Tapdong’, and the elite lines derived from their cross, including ‘Milyang55’, were evaluated in field trials conducted during 2022 and 2023. ‘Milyang55’ was developed using a newly constructed MABC system that involved 547 background selection markers, with the aim of applying this system to develop PHS-resistant wheat lines. The results indicate that ‘Milyang55’ successfully combines the traits of its parental varieties. Notably, ‘Milyang55’ exhibited remarkable PHS resistance, with a germination rate of 21.1 %, significantly lower than ‘Joongmo2008’s rate of 27.8 % and ‘Tapdong’s high rate of 94.4 %. This demonstrates the successful pyramiding of PHS-resistant traits from both parents into ‘Milyang55’. However, further research is required to confirm the gene pyramiding and validate its effects. In terms of other key agronomic traits, ‘Milyang55’ exhibited intermediate characteristics, such as moderate plant height and high spikelet number per square meter, placing it advantageously between its parents ( Table 3 , Figure 10 ). Additionally, ‘Milyang55’ maintained strong flour quality, comparable to ‘Joongmo2008’, further indicating its potential as a high-performing cultivar in various growing conditions ( Table 4 ). Among the sister lines, ‘YW3263-2B-32’ and ‘YW3263-2B-15’, ‘Milyang55’ was selected as the most superior line due to its exceptional performance in terms of agronomic traits, including its high spikelet density and superior flour quality. These attributes suggest that ‘Milyang55’ is well-positioned for further breeding and adaptation in diverse environments. In conclusion, ‘Milyang55’ not only inherits desirable traits from its parents but also enhances PHS resistance, which is critical for ensuring quality and yield stability in the face of environmental challenges. Table 3. Agronomic traits of ‘Joongmo2008’, ‘Tapdong’, and elite lines derived from these varieties, including ‘Milyang55’, evaluated in 2022 field trials. Variety Cross HD a (m.d) MD b (m.d.) Height (cm) SL c (cm) TN d (ea/㎡) GN e (ea/plant) LW f (g/L) TGW g (g) Yield (kg/10a) Joongmo2008 Eumpamil*2//SH3/CBRD/3/Keumgang 4.9 5.24 86.4 7.4 839 18.4 812 47.0 351 Tapdong Chugoku81//Suwon158/Toropi 4.15 5.30 65.0 8.9 528 38.9 801 40.9 537 Miryang55 Joongmo2008*2/Tapdong 4.7 5.24 74.8 7.5 1049 16.0 802 43.9 338 YW3263-2B-32 Joongmo2008*2/Tapdong 4.8 5.23 73.6 7.3 1006 24.2 804 39.0 410 YW3263-2B-15 Joongmo2008*2/Tapdong 4.8 5.25 73.4 7.5 890 17.3 792 43.0 277 a HD; heading date, b MD; maturity, c SL; spike length, d TL; tiller number, e GN; grain number, f LW; liter weight, g TGW; thousand grain weight Table 4. Composition of high molecular weight glutenin subunits (HMW-GS) and flour quality of ‘Joongmo2008’, ‘Tapdong’, and elite lines derived from these varieties, including ‘Milyang55’, evaluated in 2022 and 2023. HMW-GS Protein (%) Gluten SDSS a (mL) Glu-A1 Glu-B1 Glu-D1 Wet Gluten Content (%) Dry Gluten Content (%) Gluten Index Joongmo2008 N 17+18 5+10 13.4 36.9 13.7 83.0 71 Tapdong 2* 7+8 5+10 11.6 16.6 5.9 71.8 50 Miryang55 2* 17+18 5+10 13.3 38.1 13.6 81.5 82 YW3263-2B-32 N 17+18 5+10 11.0 29.5 11.1 81.9 64 YW3263-2B-15 N 17+18 5+10 13.9 40.9 14.9 73.8 75 a SDSS; Sodium dodecyl sulfate (SDS) Sedimentation 4. Discussion Using sequence information from references, a marker set of 547 markers was constructed to effectively capture genome-wide diversity among wheat varieties and germplasms commonly used as crossing parents in the NICS. Although the number of markers is relatively smaller compared to genome-wide genotyping, the mean interval between markers was 6.4 Mb, with markers evenly distributed across the chromosomes. This distribution demonstrates that the marker set can effectively evaluate polymorphism between crossing parents in the MABC process. The PCA revealed significant genetic relationships among varieties based on the type of breeding materials used. Additionally, the phylogenetic tree exhibited a similar grouping pattern, clustering the cultivars into three categories: domestic varieties, germplasms, and rye translocation lines. This classification underscores that the tested cultivars represent the genetic diversity necessary for effective wheat breeding programs. Plavšin et al. (Plavšin et al., 2021) indicated that the precision of population prediction depends on factors like population structure, trait heritability, and marker density, whereas the total number of markers has a limited effect on the accuracy of genomic selection. This study demonstrates the applicability of the constructed marker set in breeding programs, serving as an efficient tool to reduce both time and cost by minimizing the number of backcrossing steps. When combined with speed breeding methods, this approach significantly shortens the timeline for developing new lines. The application of this marker set in our breeding program for developing PHS-resistant, gene-stacked lines effectively demonstrated its utility for background selection. The set captures polymorphism between the two crossing parents, facilitating an easy calculation of polymorphism rates compared to genome-wide genotyping. The successful use of this marker set in enhancing PHS resistance illustrates its potential in accelerating the breeding process and improving the overall efficiency of wheat variety development. In conclusion, the integration of this marker set into breeding programs will greatly enhance the development of robust wheat varieties that can withstand PHS, ultimately contributing to the sustainability and resilience of wheat production in Korea. 5. Conclusions This study established a practical and effective framework for developing pre-harvest sprouting (PHS)-resistant wheat cultivars by integrating published molecular markers with marker-assisted backcrossing (MABC). A curated set of 51 markers for foreground selection and 547 genome-wide KASP markers for background selection were validated and shown to be effective for both genotyping and selection. These markers successfully captured the genetic diversity among domestic and introduced wheat lines, offering a valuable resource for informed breeding decisions. The implementation of this marker set in a breeding program targeting PHS resistance demonstrated its effectiveness in accelerating the recovery of the recurrent parent genome and in pyramiding key resistance alleles, including MFT and Tamyb10 . The resulting elite line, ‘Milyang55’, exhibited enhanced PHS resistance along with favorable agronomic and flour quality traits, indicating strong potential for deployment in wheat breeding programs. By combining marker-assisted selection with speed breeding, the approach significantly shortened the breeding cycle and improved selection efficiency. These results underscore the critical role of marker-based strategies in modern wheat improvement and highlight the potential of this system to support the development of high-quality, PHS-resistant cultivars tailored to domestic cultivation and processing needs. Ultimately, this work contributes to strengthening Korea’s wheat self-sufficiency and resilience in the face of environmental challenges. Abbreviations ABA – Abscisic acid AYT – Advanced yield trial BC – Backcross KASP – Kompet- itive allele-specific PCR MAS – Marker-assisted selection MABC – Marker-assisted backcrossing OYT – Observational yield trial PCA – Principal component analysis PHS – Pre-harvest sprouting Declarations Authors' contributions Conceptualization, J.H.L., and H.P.; software, Y.K., J.K.C., and S.M.L.; formal analysis, H.P.; writing—original draft preparation, H.P; supervision, J.H.L, and I.J.L. All authors have read and agreed to the published version of the manuscript. Fundin g This research was funded by Rural Development Administration, 'Rapid establishment of elite core resources for rice and wheat using digital breeding technology (grant number : PJ017212022025)'. Availability of data and materials Not applicable Competing interests The authors declare no conflict of interest. Author biography Hyeonjin Park National Institute of Crop and Food Science, Rural Development Administration Jin-Kyung Cha National Institute of Crop and Food Science, Rural Development Administration So-Myeong Lee National Institute of Crop and Food Science, Rural Development Administration Youngho Kwon National Institute of Crop and Food Science, Rural Development Administration Woo-Jae Kim National Institute of Crop and Food Science, Rural Development Administration Jong-Hee Lee National Institute of Crop and Food Science, Rural Development Administration In-Jung Lee Kyungpook National University References Asseng, S., Guarin, J. R., Raman, M., Monje, O., Kiss, G., Despommier, D. D., Meggers, F. M., & Gauthier, P. P. (2020). Wheat yield potential in controlled-environment vertical farms. Proceedings of the national academy of sciences , 117 (32), 19131-19135. Boyer, J. S. (1982). Plant productivity and environment. Science , 218 (4571), 443-448. 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11:09:01","extension":"html","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":136488,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-6951628/v1/0889c6faaf95ab7c6a16689d.html"},{"id":92405772,"identity":"626c6586-8d31-49a6-99ac-66450231f9c1","added_by":"auto","created_at":"2025-09-29 11:08:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":78471,"visible":true,"origin":"","legend":"\u003cp\u003eScheme for introducing PHS resistance using MABC and speed breeding. Donor: ‘Tapdong’ (\u003cem\u003eMyb10\u003c/em\u003e, red grain), Recurrent: ‘Joongmo2008’ (\u003cem\u003eMFT\u003c/em\u003e, white grain).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6951628/v1/c519dfc28008fdf0d7373fb9.png"},{"id":92405778,"identity":"5b5ddfaa-7706-44de-b001-e90724707b61","added_by":"auto","created_at":"2025-09-29 11:08:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":78471,"visible":true,"origin":"","legend":"\u003cp\u003eEvaluation of molecular markers for genotype analysis of PHS resistance. (A) Scatter plot for TaMFT_1617R. Blue indicates the CS-type (PHS susceptible) and red indicates the Zen-type (PHS resistant) for each variety. (B) TaMFT-3A and (C) STS-Myb10 were analyzed using agarose gel electrophoresis.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6951628/v1/dea68a9a207efe8012fbfc98.png"},{"id":92405769,"identity":"27db161c-7e10-421a-98c8-d700f5574c28","added_by":"auto","created_at":"2025-09-29 11:08:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":154445,"visible":true,"origin":"","legend":"\u003cp\u003eData analysis procedure (A), and digitized data available for wheat breeders (B).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6951628/v1/4ed6afe01cae609675a9535a.png"},{"id":92405786,"identity":"046b4bee-74ff-4f9f-beeb-3e2b395d70cf","added_by":"auto","created_at":"2025-09-29 11:09:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":454584,"visible":true,"origin":"","legend":"\u003cp\u003ePhysical map of the 94 analyzed wheat varieties using 547 KASP markers.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6951628/v1/f08d4bdd9d5d1c8719afbde0.png"},{"id":92405730,"identity":"d985a962-9b03-4ee7-a5a0-ea29c2537c89","added_by":"auto","created_at":"2025-09-29 11:08:52","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":192491,"visible":true,"origin":"","legend":"\u003cp\u003ePhylogenetic tree of the 94 analyzed wheat varieties using 547 KASP markers.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6951628/v1/9f89dc889e041462691b7217.png"},{"id":92405768,"identity":"aff1962f-b47e-4bc2-aac3-c84bd9dae8e7","added_by":"auto","created_at":"2025-09-29 11:08:57","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":134635,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal component analysis (PCA) based on genetic distance data among domestic wheat cultivars, introduced germplasms, and rye translocation lines.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6951628/v1/ede1c0b2e19347591b9c9e89.png"},{"id":92405780,"identity":"db6b86d8-61fc-4ddd-8954-7e5eef097f2a","added_by":"auto","created_at":"2025-09-29 11:09:00","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":67666,"visible":true,"origin":"","legend":"\u003cp\u003ePopulation structure analysis of 94 wheat cultivars assessed using 547 KASP markers. The barplot of the genotypes at K=2, generated by STRUCTURE software, identified 4 subpopulations.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6951628/v1/43ed07609e1fa6e5dcde21be.png"},{"id":92405773,"identity":"726a45f2-bf60-441e-b6bd-1787c379aed7","added_by":"auto","created_at":"2025-09-29 11:08:59","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":100698,"visible":true,"origin":"","legend":"\u003cp\u003eBackground selection of backcross breeding lines in ‘Joongmo2008’ genetic background. The gray box indicates substituted chromosome segments of the donor parent in ‘Miryang55’, and sister lines. J; Joongmo2008, T; Tapdong, M55; Miryang55, L1; YW3263-2B-32, L2; YW3263-2B-15.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6951628/v1/093e985cfdedc5b27b0eff16.png"},{"id":92406332,"identity":"a11a743f-dc7b-4d7a-8982-f42c111dbb48","added_by":"auto","created_at":"2025-09-29 11:17:01","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":326469,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure 10.\u003c/strong\u003e Field performance of ‘Milyang55’ at heading compared to its parental varieties, ‘Joongmo2008’ and ‘Tapdong’ (A), and results of PHS evaluation conducted under a speed-breeding system (B).\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-6951628/v1/bdc82ebf32352f34816d381a.png"},{"id":98243812,"identity":"6f3a50d0-d7f7-4d32-8eef-55204afff415","added_by":"auto","created_at":"2025-12-15 16:10:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2138144,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6951628/v1/57b365bb-a05f-4484-adfd-dc195a2d5b47.pdf"},{"id":92405791,"identity":"23bcff65-3470-4e11-8b97-8e17ae528dc0","added_by":"auto","created_at":"2025-09-29 11:09:01","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":31941,"visible":true,"origin":"","legend":"","description":"","filename":"EuphyticaSupplementaryPHS.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6951628/v1/da6f74b330cfbde602bcfd37.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluation and Application of Molecular Marker Sets for Developing PHS-Resistant Wheat in Korea","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.) is a major cereal crop in Korea, with per capita consumption reaching 35.7 kg in 2023. Despite growing demand, wheat self-sufficiency was just 1.3% in 2022, reflecting heavy dependence on imports. This reliance exposes Korea to global market instability driven by extreme weather, geopolitical conflicts, and energy price fluctuations.\u003c/p\u003e\n\u003cp\u003eTo address this issue, the Korean government launched the Wheat Industry Promotion Implementation Plan in 2020. The plan includes the introduction of quality management standards, support for post-harvest infrastructure, and incentives for using domestic wheat in institutional food services. A key barrier to expanding domestic production has been the lack of local milling facilities. However, ongoing efforts—such as constructing four mills under the Wheat Industry Valley Demonstration Project—are expected to strengthen the wheat value chain and help raise self-sufficiency.\u003c/p\u003e\n\u003cp\u003eWheat yield and quality are highly sensitive to environmental stress. Abiotic factors can cause yield reductions of up to 82% (Boyer, 1982), and productivity varies widely by region and climate (Asseng et al., 2020). In Korea, yields have fluctuated from a high of 447 kg/10a in 2023 to a low of 325 kg/10a in 2020, with a five-year average of 403 kg/10a\u0026nbsp;(KOSIS, 2023). Environmental conditions also play a dominant role in grain quality, accounting for greater variation than genotype\u0026nbsp;(Fowler et al., 1990; Labuschagne et al., 2009).\u003c/p\u003e\n\u003cp\u003eOne of the most critical quality issues in Korean wheat production is pre-harvest sprouting (PHS), caused by rainfall during grain filling and ripening. Korea’s double-cropping system, where wheat is followed by rice, often results in overlap between the wheat ripening stage and the start of the rainy season, increasing the risk of sprouting. PHS reduces flour quality and market value, sometimes by 20–50%, and forces the downgraded use of wheat as feed grain (Jiang \u0026amp; Xiao, 2005; Sorenson \u0026amp; Wiersma, 2004). Even resistant varieties may sprout under prolonged wet conditions (Peery et al., 2023), making stronger resistance essential.\u003c/p\u003e\n\u003cp\u003ePHS resistance in wheat is often associated with red grain color, largely due to the Myb10-D (PHS-3D) gene on chromosome 3D, which enhances abscisic acid (ABA) biosynthesis and limits water uptake during grain maturation (Lang et al., 2021). However, white-grained wheat is preferred by the milling industry for its bright flour, low ash content, and high extraction rate. For this reason, developing white-grained varieties with PHS resistance remains a major breeding goal. While cultivars such as ‘Rio Blanco’ show strong PHS resistance, they have proven unsuitable for Korean cultivation due to late heading that disrupts the cropping schedule.\u003c/p\u003e\n\u003cp\u003eAlthough several PHS-resistant cultivars—such as ‘Goso’ and ‘Baekjoong’—have been developed in Korea, progress has been limited by the long breeding cycle, which typically exceeds 10 years. Additionally, the use of diverse genetic resources and structured pre-breeding strategies remains limited, slowing the development of high-performing cultivars.\u003c/p\u003e\n\u003cp\u003eTo overcome these limitations, efficient molecular breeding approaches are essential. Marker-assisted backcrossing (MABC) enables precise introgression of resistance genes into elite varieties, significantly accelerating the selection process. This strategy relies on informative marker sets that can efficiently distinguish polymorphisms between parental lines, enabling rapid recovery of the recurrent parent genotype with fewer backcross generations.\u003c/p\u003e\n\u003cp\u003eBy combining MABC with speed breeding, the timeline for developing PHS-resistant cultivars can be significantly reduced. In environments like Korea—where rainfall during ripening is a recurrent threat—varieties that delay moisture uptake or exhibit strong dormancy are essential for ensuring stable quality and yield. This study aims to establish a marker-assisted breeding framework for developing PHS-resistant wheat by identifying a marker set suitable for selecting resistant lines in domestic breeding programs. Ultimately, this approach supports the long-term goal of increasing wheat self-sufficiency in Korea.\u003c/p\u003e"},{"header":"2.\tMaterials and Methods","content":"\u003cp\u003e\u003cspan id=\"_Toc179440172\"\u003e2.1 Construction of marker set for wheat breeding\u003c/span\u003e\u003c/p\u003e\n\u003cp id=\"_Toc179440173\"\u003e2.1.1. \u0026nbsp; \u0026nbsp; Plant materials preparation and DNA extraction\u003c/p\u003e\n\u003cp\u003eTo construct a marker set for marker-assisted selection (MAS) and MABC, ninety-four wheat cultivars and elite lines were selected for genotyping based on their frequency of use as crossing parents \u003cstrong\u003e(S1)\u003c/strong\u003e. Selected cultivars were sown in crossing block of NICS, in Miryang, Gyeongsangnam-do, Korea, on November 5, 2022, with a row spacing of 30 cm. Prior to sowing, fertilization was applied at a rate of N\u003csub\u003e2\u003c/sub\u003e : P\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e : K\u003csub\u003e2\u003c/sub\u003eO = 4.5 : 7.4 : 3.9 kg/10a as basal dressing, with an additional 4.5 kg/10a of nitrogen applied at the jointing stage.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor genotyping, DNA was extracted from fresh seedling leaves of each line using the MagMAX DNA Multi-Sample Kit (Applied Biosystems, Woburn, MA, USA) and the KingFisher DNA extraction system (Thermo Fisher Scientific, Waltham, MA, USA), following the manufacturer\u0026apos;s instructions.\u003c/p\u003e\n\u003cp id=\"_Toc179440174\"\u003e2.1.2. \u0026nbsp; \u0026nbsp; Genotyping for marker selection in wheat cultivars\u003c/p\u003e\n\u003cp\u003eTo construct a marker set for foreground selection, 74 Kompetitive Allele Specific PCR (KASP) markers, and 7 gel-based markers from references were selected and validated across 94 wheat cultivars \u003cstrong\u003e(S2, S3)\u003c/strong\u003e. The markers were chosen for their relevance in selecting for biotic and abiotic resistance, as well as high-quality traits such as increased protein and gluten content. The PCR conditions for KASP markers were as follows: an initial denaturation at 95 \u0026deg;C for 15 minutes; 10 touchdown cycles of 95 \u0026deg;C for 20 seconds, with the annealing temperature starting at 65 \u0026deg;C and decreasing by 1 \u0026deg;C per cycle for 25 seconds; followed by 30 additional cycles at 95 \u0026deg;C for 10 seconds and 57 \u0026deg;C for 1 minute, with a final extension at 72 \u0026deg;C for 5 minutes. Fluorescence detection and data analysis were conducted using the QuantStudio3 real-time PCR system and QuantStudio Design \u0026amp; Analysis Software v.1.5.1 (Applied Biosystems, Carlsbad, CA, USA). The selected markers and their genotyping results were digitized, with favorable alleles (e.g., disease resistance) designated as \u0026lsquo;1\u0026rsquo; and unfavorable alleles as \u0026lsquo;0.\u0026rsquo; These data were uploaded to the \u0026lsquo;Crop Research Information System (CRIS)\u0026rsquo; to facilitate efficient marker selection by breeders. The PCR conditions and gel electrophoresis for gel-based markers were performed according to previously published references (Himi et al., 2012; Lang et al., 2021; Nakamura et al., 2015; Saito \u0026amp; Nakamura, 2005; Saito et al., 2009; Shariflou et al., 2001; Su et al., 2018).\u003c/p\u003e\n\u003cp\u003eFor background selection in domestic wheat cultivars, a total of 731 KASP markers were utilized. This included 155 assays from the CIMMYT wheat molecular genetics study (Dreisigacker et al., 2016), 70 assays from Rasheed et al. (Rasheed et al., 2016), and the remaining markers from the CerealsDB marker set (Wilkinson et al., 2012). The PCR conditions were the same as those used in the previous experiment.\u003c/p\u003e\n\u003cp id=\"_Toc179440175\"\u003e2.1.3. \u0026nbsp; \u0026nbsp; Phylogenetic analysis of genetic relationships among 94 wheat cultivars\u003c/p\u003e\n\u003cp\u003eThe phylogenetic analysis was conducted to evaluate the genetic relationships among 94 wheat cultivars, which were divided into domestic cultivars, introduced germplasms, and rye translocation lines. Out of 731 markers, 547 were successfully amplified, and genotyping was conducted using these markers. The resulting data were analyzed using the Molecular Evolutionary Genetics Analysis (MEGA) X software.\u003c/p\u003e\n\u003cp\u003eSequences of each cultivar were aligned, and a phylogenetic tree was constructed based on genetic distance using the neighbor-joining method with a bootstrap value of 1,000 replicates to assess the robustness of the tree. Cultivars were categorized into three major clusters, represented by green, red, and yellow color-coded branches. Green triangles, red circles, and yellow squares were used to indicate the distinct groups of domestic cultivars, introduced germplasms, and rye translocation lines, respectively. Branch lengths correspond to the genetic distance, and bootstrap values are presented for key nodes to indicate the confidence level of the grouping.\u003c/p\u003e\n\u003cp id=\"_Toc179440176\"\u003e2.1.4. \u0026nbsp; \u0026nbsp; Calculation of genetic distances and distribution of markers on chromosomes\u003c/p\u003e\n\u003cp\u003eTo validate the suitability of these markers for a domestic wheat MABC system, selected markers were applied to 94 domestic cultivars and elite lines. The distribution of markers across chromosomes and distances between markers were analyzed. Genotyping data from the 94 cultivars were then used to construct physical maps using MapChart 2.2 software (Voorrips, 2002), and genetic distances between markers were calculated using MEGA X 5.1 according to the Kimura 2-parameter model (Tamura et al., 2011).\u003c/p\u003e\n\u003cp\u003ePopulation structure analysis was conducted on 94 wheat cultivars using 547 KASP markers. STRUCTURE software (version 2.3.4., Stanford University, USA) was used to infer the population structure under an admixture model. A range of K values from K=1 to K=10 was tested with 10,000 burn-in iterations and 100,000 Markov Chain Monte Carlo (MCMC) repetitions. The optimal number of subpopulations (K) was determined by examining delta K values, following the method of Evanno et al. (2005). K=2 was selected based on the delta K values and biological relevance. The population structure results were visualized using bar plots, generated by STRUCTURE software. The genotypes of the 94 wheat cultivars were classified into four subpopulations, and the membership proportions of each individual cultivar in the various subpopulations were represented in the bar plots (Evanno et al., 2005).\u003c/p\u003e\n\u003cp\u003e\u003cspan id=\"_Toc179440177\"\u003e2.2 Application of MABC in developing PHS resistant wheat\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e2.2.1 Parent selection\u003c/p\u003e\n\u003cp\u003eTo assess the effects of the selected markers and develop PHS-resistant lines, two domestic varieties, \u0026lsquo;Joongmo2008\u0026rsquo; and \u0026lsquo;Tapdong\u0026rsquo;, were chosen as parents based on their agronomic traits and presence of PHS-resistant genes \u003cstrong\u003e(S4)\u003c/strong\u003e. \u0026lsquo;Joongmo2008\u0026rsquo; is an intermediate wheat variety commonly used as a parent due to its high protein content, resistance to PHS (with a PHS rate of 9.8 %, likely associated with the presence of the \u003cem\u003eMFT\u003c/em\u003e gene), and white seed coat (RDA, 2011). However, its susceptibility to lodging and waterlogging stress, along with its very low yield (378 kg/10a), makes it unsuitable for cultivation on its own. Instead, it is used as a crossing parent to enhance flour quality. To improve agronomic traits and perform gene stacking (\u003cem\u003eMFT\u003c/em\u003e + \u003cem\u003eMyb10\u003c/em\u003e), \u0026lsquo;Joongmo2008\u0026rsquo; was crossed with \u0026lsquo;Tapdong\u0026rsquo;, a red wheat variety known for its short stature, lodging resistance, and the presence of \u003cem\u003eMyb10\u003c/em\u003e, which confers strong PHS resistance (RDA).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.2.2 Artificial crossing and generation advancement using speed-breeding\u003c/p\u003e\n\u003cp\u003e\u0026lsquo;Joongmo2008\u0026rsquo;, as the female parent, was artificially crossed with \u0026lsquo;Tapdong\u0026rsquo;, the male parent, to develop F\u003csub\u003e1\u003c/sub\u003e plants under field conditions in 2020. The F\u003csub\u003e1\u003c/sub\u003e plants were then backcrossed to \u0026lsquo;Joongmo2008\u0026rsquo; as the recurrent parent to generate BC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e1\u003c/sub\u003e generations under speed-breeding conditions. 13 BC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e1\u003c/sub\u003e individuals were advanced in a speed-breeding system using 12 cm pots to obtain a sufficient number of grains. A total of 152 BC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e seeds were sown in 72-cell trays and grown under a speed-breeding system, with a 22-hour light: 2-hour dark photoperiod cycle at 8 \u0026deg;C for 4 weeks (using LED lighting with a red: blue: white ratio of 8:3:2, at 100 \u0026mu;mol m\u003csup\u003e\u0026ndash;2\u003c/sup\u003e s\u003csup\u003e\u0026ndash;1\u003c/sup\u003e), followed by a 22-hour light (22 \u0026deg;C): 2-hour dark (17 \u0026deg;C) photoperiod cycle until harvest (500 \u0026mu;mol m\u003csup\u003e\u0026ndash;2\u003c/sup\u003e s\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) (Cha et al., 2020; Cha et al., 2022). Visual phenotypic selection was conducted under speed-breeding conditions based on plant height, spike length, and days to heading. Selected plants were advanced for genetic fixation until the BC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e4\u003c/sub\u003e generation (\u003cstrong\u003eFigure 1\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.2.3 Foreground selection for PHS resistant wheat breeding materials\u003c/p\u003e\n\u003cp\u003eFor the foreground selection of PHS- resistant lines, three molecular markers were employed: one KASP marker (TaMFT_1617R) from the selected marker set, and two gel-based markers (MFT-3A, STS-Myb10). The PCR conditions for TaMFT_1617R involved an initial denaturation at 95 \u0026deg;C for 15 minutes, followed by 10 touchdown cycles of 95 \u0026deg;C for 20 seconds, with the annealing temperature starting at 65 \u0026deg;C and decreasing by 1 \u0026deg;C per cycle for 25 seconds. This was followed by 30 additional cycles at 95 \u0026deg;C for 10 seconds and 57 \u0026deg;C for 1 minute, with a final extension at 72 \u0026deg;C for 5 minutes. Fluorescence detection and data analysis were performed using the QuantStudio3 real-time PCR system and QuantStudio Design \u0026amp; Analysis Software v.1.5.1 (Applied Biosystems, Carlsbad, CA, USA).\u003c/p\u003e\n\u003cp\u003eFor MFT-3A, a CAPS marker, the conditions included an initial denaturation at 98 \u0026deg;C for 30 seconds, followed by 40 cycles of 10 seconds at 98 \u0026deg;C, 30 seconds at 60 \u0026deg;C, and 1 minute at 72 \u0026deg;C. A final extension of 7 minutes at 72 \u0026deg;C was applied. The amplified fragments (approximately 800 bp) were digested with the ClaI restriction enzyme (Takara Bio Inc.), and fragment sizes were analyzed by electrophoresis on a 3 % agarose gel at 200 V for 45 minutes (Chono et al., 2015). The STS-Myb10 marker\u0026rsquo;s conditions included an initial denaturation at 94 \u0026deg;C for 30 minutes, followed by 40 cycles of 20 seconds at 94 \u0026deg;C, 30 seconds at 60 \u0026deg;C, and 1 minute at 72 \u0026deg;C, with a final extension of 7 minutes at 72 \u0026deg;C. Fragment sizes for STS-Myb10 was analyzed using the same electrophoresis method as MFT-3A (Lang et al., 2021).\u003c/p\u003e\n\u003cp\u003e2.2.4 Background selection for recurrent recovery validation\u003c/p\u003e\n\u003cp\u003eFor background selection, a total of 547 KASP markers were used to assess the genetic background of the backcrossed lines. Since \u0026lsquo;Joongmo2008\u0026rsquo; is known for its excellent bread-baking quality, the objective was to select lines that are suitable for bread-baking and also carry the PHS resistant gene. Of the 547 amplified markers, 515 were from the CerealsDB marker set (Wilkinson et al., 2012), and 63 were from CIMMYT wheat molecular genetics (Dreisigacker et al., 2016). The PCR conditions were as follows: initial denaturation at 95 \u0026deg;C for 15 minutes; 10 touchdown cycles at 95 \u0026deg;C for 20 seconds, with the annealing temperature starting at 65 \u0026deg;C and decreasing by 1 \u0026deg;C per cycle for 25 seconds; followed by an additional 30 cycles at 95 \u0026deg;C for 10 seconds and 57 \u0026deg;C for 1 minute; with a final extension at 72 \u0026deg;C for 5 minutes. Fluorescence detection and data analysis were conducted using a QuantStudio3 real-time PCR instrument and QuantStudio Design \u0026amp; Analysis Software v1.5.1 (Applied Biosystems, Carlsbad, CA, USA).\u003c/p\u003e\n\u003cp\u003e2.2.5 Field trials for the evaluation of agronomic traits in elite lines\u003c/p\u003e\n\u003cp\u003eAmong the BC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e6\u003c/sub\u003e lines, the selected line named \u0026lsquo;Milyang55,\u0026rsquo; which incorporates the PHS- resistant gene \u0026lsquo;\u003cem\u003eMFT\u003c/em\u003e\u0026rsquo; and the red seed coat color gene \u0026lsquo;\u003cem\u003eMyb10\u003c/em\u003e,\u0026rsquo; was evaluated for field performance over two years at NICS in Miryang, Gyeongsangnam-do, Korea. Agronomic traits were assessed in observational yield trials (OYT) and advanced yield trials (AYT) starting in October 25, 2022, followed by a regional adaptability yield trial (RYT) in October 25, 2023.\u003c/p\u003e\n\u003cp\u003ePrior to sowing, fertilization was applied at a rate of N\u003csub\u003e2\u003c/sub\u003e : P\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e5\u003c/sub\u003e : K\u003csub\u003e2\u003c/sub\u003eO = 4.5 : 7.4 : 3.9 kg/10a as basal dressing, with an additional 4.5 kg/10a of nitrogen applied at the jointing stage. In the AYT and OYT, seeds were sown with a spacing of 30 \u0026times; 15 cm. In the RYT, seeds were sown using the broadcast method by hand, in a plot size of 150 \u0026times; 120 m (1.8 m\u0026sup2;), at a seeding rate of 16 kg/10a. Agronomic traits such as plant height, spike length, tiller number, and yield were measured in accordance with the RDA\u0026apos;s Standards for Research and Analysis in Agricultural Technology (RDA, 2012).\u003c/p\u003e\n\u003cp\u003e2.2.6 Evaluation of PHS rates in selected elite line\u003c/p\u003e\n\u003cp\u003eThe germination index (GI) is widely used as an indicator of seed dormancy and PHS resistance due to its ability to provide more consistent evaluation results throughout the year compared to direct PHS assessments. PHS rates were determined following the method outlined by Li et al.(Li et al., 2021), with slight modifications. Heading dates were synchronized using speed breeding techniques, and at physiological maturity\u0026mdash;45 days after heading, when the spikes had lost their green color\u0026mdash;fifty seeds from the parent varieties and the elite line \u0026lsquo;Milyang55\u0026rsquo; were selected. The seeds were placed in 90 \u0026times; 15 mm petri dishes containing two filter papers moistened with 3 ml of distilled water and incubated at 20 \u0026deg;C for 7 days. Germinated seeds were counted and removed daily. The GI was calculated using the following formula, where N1, N2, \u0026hellip;, N7 represent the number of seeds germinated on each day from the first to the seventh day (Li et al., 2021; Walker‐Simmons, 1988).\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e"},{"header":"3.\tResults","content":"\u003cp\u003e3.1 Construction of a marker set for wheat MAS and MABC\u003c/p\u003e\n\u003cp\u003eA total of 81 markers, including 74 KASP markers and 7 gel-based markers, were tested for their utility in MAS and MABC. Of the 74 KASP markers, 51 were successfully amplified and used for genotyping. Additionally, all 7 gel-based markers proved to be useful and applicable to domestic wheat cultivars. Among these, 2 gel-based markers were specifically applicable for PHS resistance (\u003cstrong\u003eFigure 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eMFT-3A and TaMFT_1617R refer to the same gene region associated with the \u003cem\u003eMFT\u003c/em\u003e (MOTHER OF FT AND TFL1) gene located on chromosome 3A of wheat. This gene is linked to PHS resistance, and both the MFT-3A marker and the TaMFT_1617R marker target this specific region. In this study, the genotyping results were consistent for both markers, as expected \u003cstrong\u003e(S4)\u003c/strong\u003e. Based on these results, wheat varieties carrying the \u003cem\u003eMFT\u003c/em\u003e and \u003cem\u003eTamyb10-D1\u003c/em\u003e genes were classified according to their alleles. Eight varieties, including \u0026lsquo;Junggye4742\u0026rsquo;, possessed both \u003cem\u003eMFT\u003c/em\u003e and \u003cem\u003eTamyb10-D1\u003c/em\u003e; however, these cultivars exhibited poor agronomic traits, such as excessive plant height, and were unsuitable for bread-baking quality, as determined by high molecular weight glutenin subunits and protein content. The majority of the cultivars (47) lacked both PHS resistance genes. Therefore, gene pyramiding for PHS resistance and quality improvement through breeding are urgently needed (\u003cstrong\u003eTable 1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eGenotyping data from the KASP markers were digitized, with favorable alleles coded as \u0026lsquo;1\u0026rsquo; and unfavorable alleles as \u0026lsquo;0\u0026rsquo;. The data from both KASP and gel-based markers were uploaded to the CRIS database, providing open access for breeders at the National Institute of Crop Science (NICS) to facilitate the selection of crossing parents. This marker set serves as a valuable tool for breeding programs aimed at improving agronomic traits, biotic/abiotic stress resistance, and flour quality in domestic wheat varieties (\u003cstrong\u003eFigure 3\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;1. Classification and number of wheat varieties based on genotyping results (\u003cem\u003eMFT\u003c/em\u003e and \u003cem\u003eTamyb10-D1\u003c/em\u003e).\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"566\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" style=\"width: 176px;\"\u003e\n \u003cp\u003eMarker name\u003c/p\u003e\n \u003cp\u003e(Gene)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 390px;\"\u003e\n \u003cp\u003eTaMFT-3A(\u003cem\u003eMFT\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 195px;\"\u003e\n \u003cp\u003eResistant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 195px;\"\u003e\n \u003cp\u003eSusceptible\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 97px;\"\u003e\n \u003cp\u003eSTS-Myb10\u003c/p\u003e\n \u003cp\u003e(\u003cem\u003eTamyb10-D1\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eResistant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 195px;\"\u003e\n \u003cp\u003e8\u003cbr\u003e\u0026nbsp;(Baekchal, Miryang54 etc.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 195px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003cp\u003e(Ariheukchal, Sugang etc.)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003eSusceptible\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 195px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003cp\u003e(Baekjoong, Keumgang etc.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 195px;\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003cp\u003e(Joeunmil, FRONTANA etc.)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eOut of 731 KASP markers for background selection, 547 were successfully amplified. Markers with more than 20 % missing data were filtered out across all tested cultivars. The average p-distance, along with the standard error (SE), was calculated as 0.418 (SE = 0.020), indicating significant genetic divergence among the tested wheat varieties. The number of markers per chromosome ranged from 14 (on chromosomes 4D and 6D) to 44 (on chromosomes 5B and 7A), totaling 547 markers. The mean physical interval between markers was 6.4 Mb, with the maximum interval reaching 97.7 Mb on chromosome 2D. These markers were distributed across the wheat genome, efficiently capturing widely distributed polymorphisms within the chromosomes (\u003cstrong\u003eFigure 4\u003c/strong\u003e\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e)\u003c/p\u003e\n\u003cp\u003eTable 2. Summary of the marker set for wheat MABC.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"565\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 94px;\"\u003e\n \u003cp\u003eChromosome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 94px;\"\u003e\n \u003cp\u003eNo. of\u003cbr\u003e\u0026nbsp;marker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 188px;\"\u003e\n \u003cp\u003eMarker interval [Mb]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 188px;\"\u003e\n \u003cp\u003ePosition [Mb]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eStart\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eEnd\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e49.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e144.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e9.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e177.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e21.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e125.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e22.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e167.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e15.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e141.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e97.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e9.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e194.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e3A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e53.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e7.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e236.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e3B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e39.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e7.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e234.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e3D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e16.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e10.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e116.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e4A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e23.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e164.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e4B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e13.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e114.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e4D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e23.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e8.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e105.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e5A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e14.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e9.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e173.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e5B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e41.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e264.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e5D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e30.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e9.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e125.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e6A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e19.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e159.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e6B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e12.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e120.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e6D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e19.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e78.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e7A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e22.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e20.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e219.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e7B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e42.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e113.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e7D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e24.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e135.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e97.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e3.2 Genetic diversity among wheat breeding materials\u003c/p\u003e\n\u003cp\u003eTo evaluate the genetic diversity among 94 wheat cultivars and germplasm, a phylogenetic tree was constructed using genotyped data. This analysis was based on data from 547 KASP markers, which were selected for background selection and distributed across the wheat genome, efficiently capturing widely distributed polymorphisms. The analysis identified three distinct clusters, demonstrating the effectiveness of the marker set in capturing the genetic diversity necessary for precision breeding. Cluster 1 (37 cultivars, represented by triangles) consists of domestic wheat cultivars, Cluster 2 (33 cultivars, represented by circles) comprises introduced germplasms from the RDA-GeneBank, and Cluster 3 (23 cultivars, represented by squares) includes 1BL.1RS wheat-rye translocation lines and intermediate crossing lines. These rye translocation lines incorporate the 1RS chromosome segment from winter rye cv. \u0026lsquo;Paldanghomil\u0026rsquo; into soft red winter wheat cv. \u0026lsquo;Olmil\u0026rsquo; (Ko et al., 2002). The phylogenetic analysis revealed clear clustering patterns based on genetic distance. Domestic cultivars, such as \u0026lsquo;Joongmo2008\u0026rsquo;, \u0026lsquo;Tapdong\u0026rsquo;, and \u0026lsquo;Jokyoung\u0026rsquo; formed a tightly grouped cluster (green), indicating high genetic similarity. The introduced germplasms (red cluster), such as \u0026lsquo;Chinese Spring\u0026rsquo; and \u0026lsquo;SANDO\u0026rsquo; displayed greater genetic diversity. The rye translocation lines (yellow cluster) exhibited significant genetic divergence from both domestic and introduced cultivars, emphasizing their distinct genetic background (\u003cstrong\u003eFigure 5\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eThe principal component analysis (PCA), also based on the 547 successfully amplified markers, effectively distinguished the 94 wheat cultivars into clear genetic groups. Domestic cultivars, including \u0026lsquo;Joongmo2008\u0026rsquo; and \u0026lsquo;Hwanggeumal\u0026rsquo;, formed a unified cluster, illustrating their close genetic relationships (\u003cstrong\u003eFigure 6\u003c/strong\u003e\u003cstrong\u003e)\u003c/strong\u003e. This tight clustering underscores the effectiveness of breeding programs in maintaining desired agronomic traits within domestic varieties. On the other hand, introduced germplasms and rye translocation lines exhibited broader genetic dispersion, indicating a higher degree of variability compared to domestic cultivars. This clear separation between domestic cultivars and introduced germplasms highlights the distinct genetic profiles of these groups. The observed genetic diversity within the rye translocation lines could be especially valuable for enhancing stress resistance and improving grain quality traits in future breeding efforts.\u003c/p\u003e\n\u003cp\u003eThese results validate the utility of the marker set in accurately capturing the genetic diversity within the population. The markers successfully highlighted the narrower genetic base of domestic cultivars, the broader diversity of introduced germplasms, and the distinct genetic profile of the rye translocation lines, emphasizing their potential to broaden the genetic base of domestic wheat breeding programs.\u003c/p\u003e\n\u003cp\u003e3.3 Population structure of wheat cultivars\u003c/p\u003e\n\u003cp\u003ePopulation structure analysis of the 94 wheat cultivars, using genotyping data from 547 KASP markers, revealed the presence of four subpopulations (\u003cstrong\u003eFigure 7\u003c/strong\u003e). STRUCTURE analysis at K=2 showed a clear genetic stratification, where individual cultivars displayed varying degrees of admixture. Each bar in the plot represents a cultivar, and the proportion of colors within a bar indicates the likelihood of belonging to specific subpopulations.\u003c/p\u003e\n\u003cp\u003eThe distinct patterns observed in the STRUCTURE barplot correspond well with the genetic diversity captured across the population. The varying membership proportions reflect the admixture between cultivars and align with the broader trends identified in the phylogenetic tree analysis, confirming the genetic differences among the breeding materials.\u003c/p\u003e\n\u003cp\u003e3.4 Application of marker set in MABC for selecting PHS-resistant wheat lines\u003c/p\u003e\n\u003cp\u003eFrom a total of 18 BC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e1\u003c/sub\u003e plants, 7 were selected through foreground selection for pyramiding the \u003cem\u003eMFT\u003c/em\u003e (MFT-3A) gene from \u0026lsquo;Joongmo2008\u0026rsquo; and the\u003cem\u003e\u0026nbsp;Myb10\u003c/em\u003e (Tamyb10-A1) gene from \u0026lsquo;Tapdong\u0026rsquo;. In the BC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e generation, out of 152 plants, 39 were chosen using the same markers. These markers specifically targeted the regions associated with PHS resistance and red seed coat color.\u003c/p\u003e\n\u003cp\u003eAmong the 39 selected BC\u003csub\u003e1\u003c/sub\u003eF\u003csub\u003e2\u003c/sub\u003e individuals, phenotypic selection was conducted under speed-breeding conditions, identifying short-statured, \u0026lsquo;Tapdong-type\u0026rsquo; plants that displayed favorable traits. Subsequently, 6 lines were evaluated in the observational yield trials (OYT), leading to the identification of 3 lines with high genetic recovery toward \u0026lsquo;Joongmo2008\u0026rsquo;, which is noted for its superior protein content among domestic wheat varieties. These three elite lines were further evaluated in advanced yield trials (AYT).\u003c/p\u003e\n\u003cp\u003eTo ensure comprehensive background selection, these lines were screened using 547 KASP markers, which were then used to construct a detailed physical map (\u003cstrong\u003eFigure 4\u003c/strong\u003e). It is noteworthy that no markers were amplified on chromosome 3D, indicating no detectable substitution of chromosome segments from the donor parent, \u0026lsquo;Tapdong\u0026rsquo; in this region. The polymorphism rate between \u0026lsquo;Joongmo2008\u0026rsquo; and \u0026lsquo;Tapdong\u0026rsquo; was 74.2 %, indicating considerable genetic diversity between the two parents.\u003c/p\u003e\n\u003cp\u003eThe percentage of donor parent chromosome substitution in each line was calculated at 33.0 %, 43.0 %, and 25.5 %. These percentages demonstrate higher proportions of recurrent parental genome recovery compared to previously reported values. This supports the efficiency of using MABC to expedite the recovery of the recurrent parent genotype \u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003eFigure 8\u003c/strong\u003e\u003cstrong\u003e).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to Tanksley et al., for most crop breeding programs, over 90 % of the recurrent parental genotype can be recovered within two generations when a sufficient number of markers and an adequate number of progeny are utilized for background selection (Tanksley et al., 1989; Xu \u0026amp; Crouch, 2008). In this study, the selected marker set for MABC significantly accelerated the breeding process by enabling the precise recovery of the recurrent parent genome, saving time and resources compared to conventional backcrossing methods.\u003c/p\u003e\n\u003cp\u003eThe selection of these markers proves essential in improving breeding efficiency, especially for traits like PHS resistance, where rapid pyramiding of beneficial genes and high recovery of the recurrent parent genome are crucial. These results suggest that combining \u003cem\u003eMFT\u003c/em\u003e and \u003cem\u003eMyb10\u003c/em\u003e through this enhanced MABC system can lead to the development of wheat lines with both agronomically important traits and quality characteristics desirable for domestic wheat production.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3.5 Evaluation of agronomic traits, PHS resistance, and flour quality in selected lines\u003c/p\u003e\n\u003cp\u003eThe agronomic traits of \u0026lsquo;Joongmo2008\u0026rsquo;, \u0026lsquo;Tapdong\u0026rsquo;, and the elite lines derived from their cross, including \u0026lsquo;Milyang55\u0026rsquo;, were evaluated in field trials conducted during 2022 and 2023. \u0026lsquo;Milyang55\u0026rsquo; was developed using a newly constructed MABC system that involved 547 background selection markers, with the aim of applying this system to develop PHS-resistant wheat lines.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe results indicate that \u0026lsquo;Milyang55\u0026rsquo; successfully combines the traits of its parental varieties. Notably, \u0026lsquo;Milyang55\u0026rsquo; exhibited remarkable PHS resistance, with a germination rate of 21.1 %, significantly lower than \u0026lsquo;Joongmo2008\u0026rsquo;s rate of 27.8 % and \u0026lsquo;Tapdong\u0026rsquo;s high rate of 94.4 %. This demonstrates the successful pyramiding of PHS-resistant traits from both parents into \u0026lsquo;Milyang55\u0026rsquo;. However, further research is required to confirm the gene pyramiding and validate its effects. In terms of other key agronomic traits, \u0026lsquo;Milyang55\u0026rsquo; exhibited intermediate characteristics, such as moderate plant height and high spikelet number per square meter, placing it advantageously between its parents (\u003cstrong\u003eTable 3\u003c/strong\u003e\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFigure 10\u003c/strong\u003e). Additionally, \u0026lsquo;Milyang55\u0026rsquo; maintained strong flour quality, comparable to \u0026lsquo;Joongmo2008\u0026rsquo;, further indicating its potential as a high-performing cultivar in various growing conditions (\u003cstrong\u003eTable 4\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eAmong the sister lines, \u0026lsquo;YW3263-2B-32\u0026rsquo; and \u0026lsquo;YW3263-2B-15\u0026rsquo;, \u0026lsquo;Milyang55\u0026rsquo; was selected as the most superior line due to its exceptional performance in terms of agronomic traits, including its high spikelet density and superior flour quality. These attributes suggest that \u0026lsquo;Milyang55\u0026rsquo; is well-positioned for further breeding and adaptation in diverse environments.\u003c/p\u003e\n\u003cp\u003eIn conclusion, \u0026lsquo;Milyang55\u0026rsquo; not only inherits desirable traits from its parents but also enhances PHS resistance, which is critical for ensuring quality and yield stability in the face of environmental challenges.\u003c/p\u003e\n\u003cp\u003eTable 3. Agronomic traits of \u0026lsquo;Joongmo2008\u0026rsquo;, \u0026lsquo;Tapdong\u0026rsquo;, and elite lines derived from these varieties, including \u0026lsquo;Milyang55\u0026rsquo;, evaluated in 2022 field trials.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"751\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eVariety\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eCross\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eHD \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(m.d)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eMD \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(m.d.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eHeight\u003c/p\u003e\n \u003cp\u003e(cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eSL\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eTN \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(ea/㎡)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eGN \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(ea/plant)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eLW \u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eTGW \u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eYield\u003c/p\u003e\n \u003cp\u003e(kg/10a)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eJoongmo2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eEumpamil*2//SH3/CBRD/3/Keumgang\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e4.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e5.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e86.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e7.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e18.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e47.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e351\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eTapdong\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eChugoku81//Suwon158/Toropi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e4.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e5.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e65.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e38.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e40.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e537\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eMiryang55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eJoongmo2008*2/Tapdong\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e5.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e74.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e16.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e43.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e338\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eYW3263-2B-32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eJoongmo2008*2/Tapdong\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e5.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e73.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e1006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e24.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e39.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e410\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99px;\"\u003e\n \u003cp\u003eYW3263-2B-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 191px;\"\u003e\n \u003cp\u003eJoongmo2008*2/Tapdong\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e5.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e73.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e17.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e43.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e277\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003ea\u0026nbsp;\u003c/sup\u003eHD; heading date, \u003csup\u003eb\u0026nbsp;\u003c/sup\u003eMD; maturity, \u003csup\u003ec\u0026nbsp;\u003c/sup\u003eSL; spike length, \u003csup\u003ed\u0026nbsp;\u003c/sup\u003eTL; tiller number, \u003csup\u003ee\u0026nbsp;\u003c/sup\u003eGN; grain number,\u0026nbsp;\u003csup\u003ef\u0026nbsp;\u003c/sup\u003eLW; liter weight,\u003cbr\u003e\u003csup\u003eg\u0026nbsp;\u003c/sup\u003eTGW; thousand grain weight\u003c/p\u003e\n\u003cp\u003eTable 4. Composition of high molecular weight glutenin subunits (HMW-GS) and flour quality of \u0026lsquo;Joongmo2008\u0026rsquo;, \u0026lsquo;Tapdong\u0026rsquo;, and elite lines derived from these varieties, including \u0026lsquo;Milyang55\u0026rsquo;, evaluated in 2022 and 2023.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"562\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 170px;\"\u003e\n \u003cp\u003eHMW-GS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 63px;\"\u003e\n \u003cp\u003eProtein\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 176px;\"\u003e\n \u003cp\u003eGluten\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 58px;\"\u003e\n \u003cp\u003eSDSS\u0026nbsp;\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003e(mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cem\u003eGlu-A1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cem\u003eGlu-B1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cem\u003eGlu-D1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003eWet \u0026nbsp; Gluten Content\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003eDry Gluten Content\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003eGluten Index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eJoongmo2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e17+18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e5+10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e13.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e36.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e13.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e83.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eTapdong\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e7+8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e5+10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e11.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e16.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e71.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eMiryang55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e17+18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e5+10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e13.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e38.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e13.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e81.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eYW3263-2B-32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e17+18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e5+10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e29.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e11.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e81.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eYW3263-2B-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e17+18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e5+10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e13.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e40.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e14.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e73.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e SDSS; Sodium dodecyl sulfate (SDS) Sedimentation\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"4.\tDiscussion","content":"\u003cp\u003eUsing sequence information from references, a marker set of 547 markers was constructed to effectively capture genome-wide diversity among wheat varieties and germplasms commonly used as crossing parents in the NICS. Although the number of markers is relatively smaller compared to genome-wide genotyping, the mean interval between markers was 6.4 Mb, with markers evenly distributed across the chromosomes. This distribution demonstrates that the marker set can effectively evaluate polymorphism between crossing parents in the MABC process.\u003c/p\u003e\n\u003cp\u003eThe PCA revealed significant genetic relationships among varieties based on the type of breeding materials used. Additionally, the phylogenetic tree exhibited a similar grouping pattern, clustering the cultivars into three categories: domestic varieties, germplasms, and rye translocation lines. This classification underscores that the tested cultivars represent the genetic diversity necessary for effective wheat breeding programs.\u003c/p\u003e\n\u003cp\u003ePlav\u0026scaron;in et al. (Plav\u0026scaron;in et al., 2021) indicated that the precision of population prediction depends on factors like population structure, trait heritability, and marker density, whereas the total number of markers has a limited effect on the accuracy of genomic selection. This study demonstrates the applicability of the constructed marker set in breeding programs, serving as an efficient tool to reduce both time and cost by minimizing the number of backcrossing steps. When combined with speed breeding methods, this approach significantly shortens the timeline for developing new lines.\u003c/p\u003e\n\u003cp\u003eThe application of this marker set in our breeding program for developing PHS-resistant, gene-stacked lines effectively demonstrated its utility for background selection. The set captures polymorphism between the two crossing parents, facilitating an easy calculation of polymorphism rates compared to genome-wide genotyping. The successful use of this marker set in enhancing PHS resistance illustrates its potential in accelerating the breeding process and improving the overall efficiency of wheat variety development.\u003c/p\u003e\n\u003cp\u003eIn conclusion, the integration of this marker set into breeding programs will greatly enhance the development of robust wheat varieties that can withstand PHS, ultimately contributing to the sustainability and resilience of wheat production in Korea.\u003c/p\u003e"},{"header":"5.\tConclusions","content":"\u003cp\u003eThis study established a practical and effective framework for developing pre-harvest sprouting (PHS)-resistant wheat cultivars by integrating published molecular markers with marker-assisted backcrossing (MABC). A curated set of 51 markers for foreground selection and 547 genome-wide KASP markers for background selection were validated and shown to be effective for both genotyping and selection. These markers successfully captured the genetic diversity among domestic and introduced wheat lines, offering a valuable resource for informed breeding decisions.\u003c/p\u003e\n\u003cp\u003eThe implementation of this marker set in a breeding program targeting PHS resistance demonstrated its effectiveness in accelerating the recovery of the recurrent parent genome and in pyramiding key resistance alleles, including \u003cem\u003eMFT\u003c/em\u003e and \u003cem\u003eTamyb10\u003c/em\u003e. The resulting elite line, ‘Milyang55’, exhibited enhanced PHS resistance along with favorable agronomic and flour quality traits, indicating strong potential for deployment in wheat breeding programs.\u003c/p\u003e\n\u003cp\u003eBy combining marker-assisted selection with speed breeding, the approach significantly shortened the breeding cycle and improved selection efficiency. These results underscore the critical role of marker-based strategies in modern wheat improvement and highlight the potential of this system to support the development of high-quality, PHS-resistant cultivars tailored to domestic cultivation and processing needs. Ultimately, this work contributes to strengthening Korea’s wheat self-sufficiency and resilience in the face of environmental challenges.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eABA – Abscisic acid AYT – Advanced yield trial BC – Backcross KASP – Kompet- itive allele-specific PCR MAS – Marker-assisted selection MABC – Marker-assisted backcrossing OYT – Observational yield trial \u0026nbsp;PCA \u0026nbsp;– \u0026nbsp; Principal \u0026nbsp;component \u0026nbsp;analysis PHS – Pre-harvest sprouting\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAuthors' contributions\u003c/p\u003e\n\u003cp\u003eConceptualization, J.H.L., and H.P.; software, Y.K., J.K.C., and S.M.L.; formal analysis, H.P.; writing—original draft preparation, H.P; supervision, J.H.L, and I.J.L. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFundin\u003c/strong\u003e\u003cstrong\u003eg\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by Rural Development Administration, 'Rapid establishment of elite core resources for rice and wheat using digital breeding technology (grant number : PJ017212022025)'.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The authors declare no conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor biography\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHyeonjin Park National Institute of Crop and Food Science, Rural Development Administration\u003c/p\u003e\n\u003cp\u003eJin-Kyung Cha National Institute of Crop and Food Science, Rural Development Administration\u003c/p\u003e\n\u003cp\u003eSo-Myeong Lee National Institute of Crop and Food Science, Rural Development Administration\u003c/p\u003e\n\u003cp\u003eYoungho Kwon National Institute of Crop and Food Science, Rural Development Administration\u003c/p\u003e\n\u003cp\u003eWoo-Jae Kim National Institute of Crop and Food Science, Rural Development Administration\u003c/p\u003e\n\u003cp\u003eJong-Hee Lee National Institute of Crop and Food Science, Rural Development Administration\u003c/p\u003e\n\u003cp\u003eIn-Jung Lee Kyungpook National University\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAsseng, S., Guarin, J. 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CerealsDB 2.0: an integrated resource for plant breeders and scientists. \u003cem\u003eBMC bioinformatics\u003c/em\u003e,\u003cem\u003e\u0026nbsp;13\u003c/em\u003e, 1-6.\u003c/li\u003e\n \u003cli\u003eXu, Y., \u0026amp; Crouch, J. H. (2008). Marker‐assisted selection in plant breeding: From publications to practice. \u003cem\u003eCrop Science\u003c/em\u003e,\u003cem\u003e\u0026nbsp;48\u003c/em\u003e(2), 391-407.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"euphytica","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"euph","sideBox":"Learn more about [Euphytica](https://www.springer.com/journal/10681)","snPcode":"10681","submissionUrl":"https://submission.springernature.com/new-submission/10681/3","title":"Euphytica","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Triticum aestivum, pre-harvest sprouting, marker-assisted backcrossing, KASP markers, speed breeding","lastPublishedDoi":"10.21203/rs.3.rs-6951628/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6951628/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePre-harvest sprouting (PHS) is a major constraint on wheat production in Korea, affecting grain quality and end-use value. To accelerate the development of PHS-resistant cultivars with improved agronomic performance, we employed a marker-assisted backcrossing (MABC) approach using molecular markers previ- ously reported in the literature. For foreground selection, 81 markers (74 KASP and 7 gel-based) targeting key genes related to PHS resistance, grain quality, and disease resistance were used. Genome-wide background selection was conducted with 547 KASP markers to monitor recovery of the recurrent parent genome. Genotyping of 94 wheat accessions revealed distinct genetic clustering among domestic cultivars, introduced germplasm, and 1BL.1RS rye translocation lines, demonstrating the effectiveness of the marker set in capturing genetic diversity. Using MABC combined with speed breeding, three elite lines were developed by pyramiding favorable alleles from ‘Joongmo2008’ and ‘Tapdong’. Among them, ‘Milyang55’ showed strong PHS resistance (germination rate: 21.1%), desirable agronomic traits, and flour quality comparable to ‘Joongmo2008’. Donor genome proportions ranged from 25.5% to 43.0%, confirming efficient recovery of the recurrent genome. These results demonstrate the utility of integrating molecu- lar markers with MABC and speed breeding for the rapid development of elite wheat cultivars with improved resistance and end-use quality.\u003c/p\u003e","manuscriptTitle":"Evaluation and Application of Molecular Marker Sets for Developing PHS-Resistant Wheat in Korea","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-29 11:08:40","doi":"10.21203/rs.3.rs-6951628/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-29T09:24:38+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-29T03:12:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-28T11:21:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-27T16:31:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-26T07:59:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"309021751640539350368403886421442035634","date":"2025-09-21T21:00:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"177379766282944554597760645133861507826","date":"2025-09-19T02:46:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"257597428003651082967669872802631305125","date":"2025-09-18T23:01:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"202100054238872211866014628792401753296","date":"2025-09-18T10:29:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-18T08:26:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-23T13:04:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-23T13:01:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"Euphytica","date":"2025-06-22T23:51:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"euphytica","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"euph","sideBox":"Learn more about [Euphytica](https://www.springer.com/journal/10681)","snPcode":"10681","submissionUrl":"https://submission.springernature.com/new-submission/10681/3","title":"Euphytica","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"af8dc642-3079-4375-9a5f-047af347f7d4","owner":[],"postedDate":"September 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-15T16:02:47+00:00","versionOfRecord":{"articleIdentity":"rs-6951628","link":"https://doi.org/10.1007/s10681-025-03649-7","journal":{"identity":"euphytica","isVorOnly":false,"title":"Euphytica"},"publishedOn":"2025-12-09 15:58:07","publishedOnDateReadable":"December 9th, 2025"},"versionCreatedAt":"2025-09-29 11:08:40","video":"","vorDoi":"10.1007/s10681-025-03649-7","vorDoiUrl":"https://doi.org/10.1007/s10681-025-03649-7","workflowStages":[]},"version":"v1","identity":"rs-6951628","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6951628","identity":"rs-6951628","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}