Fine Mapping and Identification of a Fusarium Wilt Resistance Gene FwS1 in Pea

Fine Mapping and Identification of a Fusarium Wilt Resistance Gene FwS1 in Pea | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Fine Mapping and Identification of a Fusarium Wilt Resistance Gene FwS1 in Pea Dong Deng, Suli Sun, Wenqi Wu, Canxing Duan, Xuehong Wu, Zhendong Zhu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4225694/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Jun, 2024 Read the published version in Theoretical and Applied Genetics → Version 1 posted 4 You are reading this latest preprint version Abstract Pea Fusarium wilt, incited by Fusarium oxysporum f. sp. pisi ( Fop ), has always been a devastating disease that causes severe yield losses and economic damage in pea-growing regions worldwide. The utilization of pea cultivars carrying resistance gene is the most efficient approach for managing this disease. In order to finely map resistance gene, a F 2 population was established through the cross between Shijiadacaiwan 1 (resistant) and Y4 (susceptible). The resistance genetic analysis indicated that the Fop resistance in Shijiadacaiwan 1 was governed by a single dominant gene, named as FwS1 . Based on the bulked segregant analysis sequencing (BSA-seq) analyses, the gene FwS1 was initially detected on chromosome 6 (i.e., linking group Ⅱ, chr6LG2), and subsequent linkage mapping with 589 F 2 individuals fine-mapped the gene FwS1 into a 91.4 kb region. The further functional annotation and haplotype analysis confirmed that the gene Psat6g003960 , characterized by a NB-ARC (nucleotide-binding adaptor shared by APAF-1, R proteins, and CED-4) domain, was considered as the most promising candidate gene. The encoding amino acids were altered by a “T/C” single-nucleotide polymorphism (SNP) in the first exon of the Psat6g003960 , resulting in the observed differences of Fop resistance in peas. Based on this SNP locus, the molecular marker A016180 was determined to be a diagnostic marker for FwS1 by validating its specificity in both pea accessions and genetic populations with different genetic backgrounds. The FwS1 with diagnostic KASP marker A016180 could facilitate marker-assisted selection in resistance pea breeding in pea. In addition, upon comparing the candidate gene Psat6g003960 of 74SN3B and SJ1, it was noted that their sequences are identical, suggesting that the FwS1 and Fwf may be the same resistance gene against Fusarium wilt. Pisum sativum Fusarium oxysporum f. sp. Pisi resistance gene bulked segregant analysis sequencing fine mapping Figures Figure 1 Figure 2 Figure 3 Figure 4 Key message A Fusarium wilt resistance gene FwS1 on pea chromosome 6 was identified and mapped to a 91.4 kb region by a comprehensive genomic-based approach，and the gene Psat6g003960 harboring NB-ARC domain was identified as the putative candidate gene. Introduction Pea ( Pisum sativum L.), including green pea and dry pea, is one of the most vital edible legumes and cultivated vegetable species globally (Cousin 1997 ; Toklu et al. 2021 ). Pea cultivation in China spans over 2000 years, and now China has been the leading producer of green peas and the third-largest producer of dry pea in the world (Li et al. 2017a , Wu et al. 2017 , FAOSTAT 2023). Fusarium wilt of peas, induced by Fusarium oxysporum f. sp. pisi ( Fop ), is a destructive disease and can cause severe yield loss in most pea-producing areas worldwide (Kraft and Pfleger 2001 ; Sharma et al. 2010 ; El-Sharkawy et al. 2021 ; Deng et al. 2022b ). Utilizing the disease resistance response of seven standard differential cultivars, Haglund and Kraft ( 1979 ) proposed the existence of four recognized races of Fop , namely races 1, 2, 5, and 6. The races 1 and 2 pose significant threats in numerous pea-producing regions worldwide, whereas races 5 and 6 only are epidemic in a few regions (Kraft and Pfleger 2001 ; Infantino et al. 2006 ; Shubha et al. 2016 ; Deng et al. 2022b ). The Fop is a soil-borne pathogen that can survive as thick-walled chlamydospores in the soil for over a decade in the absence of a host. Therefore, the Fop was difficult to be eradicated and can only be controlled with integrated management (Kraft and Pfleger 2001 ; Sharma et al. 2010 ). Among all management measures, deploying resistant pea cultivars is regarded as the most economical and ecofriendly approach for managing pea Fusarium wilt (Kraft and Pfleger 2001 ; Shubha et al. 2016 ; Jha et al. 2020 ; Sampaio et al. 2020 ). The resistance to Fop in peas exhibits two distinct types: complete resistance and partial resistance. Complete resistance is mediated by a single resistance gene that provides immunity or resistance to Fop , while partial resistance is governed by multiple genes and works to restrict pathogen colonization and spread within plant tissues (Wade 1929 ; Snyder 1933 ; Haglund and Kraft 1970 ; 1979 ; McPhee et al. 2012 ). In most cases, pea cultivars with complete resistance are preferred for breeding and cultivation to control Fusarium wilt, as it is difficult to breed cultivars with partial resistance with conventional methods, yet partial resistance is unstable and can be defeated in the face of high disease pressure (Infantino et al. 2006 ; Bani et al, 2012 ; Shubha et al, 2016 ). The breeding of pea resistance cultivars can be significantly facilitated through marker-assisted selection (MAS) (Ghafoor and McPhee 2012 ; Sharma et al. 2020 ). Currently, two complete resistance genes, Fw (Kwon et al. 2013 ; Jian et al. 2015) and Fwf (Coyne et al. 2000 ; Okubara et al. 2002 ), along with a putative major partial resistance gene, Fnw4.1 (McPhee et al. 2012 ), have been identified in pea. However, the three resistance genes have only been undergone preliminary mapping with a limited number of markers, and closely linked molecular markers only developed in gene Fw (McClendon et al. 2002 ; Okubara et al. 2002 ; McPhee et al. 2012 ; Kwon et al. 2013 ; Jian et al. 2015). The accuracy of MAS using these closely linked molecular markers could be affected by lack of polymorphism different genetic background and recombination (Loridon et al. 2005 ; Jian et al. 2015; Liu et al. 2020 ). Therefore, it is essential to construct high-resolution genetic or physical maps and develop effective diagnostic markers based on the differences in related gene-coding regions to identify pea genes resistant to Fusarium wilt. The traditional strategies for mapping resistant genes, which use DNA markers to amplify random genomic regions, typically require significant investments in time and resources (Jian et al. 2015; Wallace et al. 2017; Li et al. 2023 ). Some non-genic DNA markers linked with disease resistance genes could be useful in specific populations, but they may lack polymorphism in populations or cultivars with different genetic backgrounds (Loridon et al. 2005 ; Liu et al. 2020 ; Sawada et al. 2022). With the rapid development of next-generation sequencing (NGS) and third-generation sequencing (TGS), substantial progress has been made in the whole-genome sequencing of peas. The sequencing, assembly, and refined gene annotation of pea cultivars Caméor and Zhongwan 6 reference genomes have greatly contributed to develop additional molecular markers and explore potential resistant genes (Kreplak et al. 2019 ; Yang et al. 2022 ). The decline in sequencing costs and the progress of bioinfrmatics analysis and tools based on NGS technology have significantly accelerated the efficiency of target gene identification and mapping (Nguyen et al. 2018 ; Zhong et al. 2018 ; Luo et al. 2019 ; Zhang et al. 2022a ). The BSA-seq is a rapid technique for identifying markers linked to traits through the construction of DNA pools with extreme phenotypes, effectively pinpointing and characterizing resistance loci within specific genomic regions (Michelmore et al. 1991 ; Takagi et al. 2013 ). Compared to traditional mapping methods, the BSA-seq strategy can rapidly and precisely discover genome-wide polymorphic loci to confirm target candidate regions for interesting traits and eliminate the need for screening numerous molecular markers to save time and resource (Zhong et al. 2018 ; Zhang et al. 2022a ; Li et al. 2023 ; Wang et al. 2023 ). Furthermore, the BSA-seq strategy has proven effective in identifying both qualitative traits and quantitative traits and in developing specific diagnostic markers across various crops, such as pigeonpea (Singh et al., 2016), chickpea (Deokar et al. 2019 ), soybean (Li et al. 2017b ; Zhong et al. 2018 ), peanut (Pan et al. 2022 ; Zhang et al. 2022a ), cucumber (Njogu et al. 2020 ; Bartholomew et al. 2022 ), rice (Takagi et al. 2013 ; Zhang et al. 2020 ) and maize (Sun et al. 2021 ). In our previous research, a diverse collection of pea accessions resistant to Fusarium wilt was screened, but their specific resistance genes have not been cleared (Deng et al. 2022b ; 2022b ). In this study, we employed the outstanding Fop -resistant pea cultivar Shijiadacaiwan 1 (SJ1) to investigate the inheritance of the Fop resistance gene, which we refer to as FwS1 , to identify candidate genes of FwS1 by fine mapping and haplotype analysis, and to develop diagnostic marker with validate, laying the foundation for cloning pea resistant genes and breeding resistant varieties. Materials and methods Plant materials and pathogen inoculum The Fusarium wilt resistance pea cultivar Shijiadacaiwan 1 (SJ1) and susceptible line Y4 selected from SJ1 were crossed to construct mapping populations (Deng et al. 2022a ). The F 1 pea seeds was self-pollinated and conducted to obtain six F 2 populations (SJ1 × Y4 or Y4 × SJ1) comprising a total of 589 individual plants (Table 1 ) that were used for BSA-seq and the further fine mapping analysis. The extra three populations were generated through the crossing of SJ1 and its derivative lines Y3 (susceptible) and Y25 (susceptible), along with the cross of susceptible cultivar DDR11 and resistance cultivar Dingwan 4 (DW4). These populations contained 77 (Table S1 ), 73 (Table S2) and 70 F 2 (Table S3) individual plants, respectively, which were used to validate the accuracy of the diagnostic markers. Moreover, the seven pea differential cultivars, Litter Marvel, Darkskin Perfection, New Era, New Season, WSU 23, WSU 28, and WSU 31, were employed as control for phenotypic evaluation, and a set of 200 pea accessions, chosen from the previously screened 1304 pea accessions (Deng et al. 2022b ), were utilized for haplotype analysis (Table S4). The SJ1 and its derived lines, pea differential cultivars, and pea accessions, were acquired from Sichuan Academy of Agricultural Sciences (SAAS), the United States Department of Agriculture, Agricultural Research Service (USDA ARS), and National Crop Genebank and certain pea breeding units in China, respectively. Table 1 Genetic segregation in response to Fusarium oxysporum f. sp. pisi isolates PF22b in the F 2 population derived from Shijiadacaiwan 1 and Y4 Parent and the cross Generation Amount Observed number Chi - squared tests R S Expected ratio χ2 Ρ Shijiadacaiwan 1 (SJ1) P 1 30 30 – Y4 P 2 30 – 30 Y4 × SJ1 - A F 2 68 50 18 3:1 0.08 0.78 Y4 × SJ1 - B F 2 113 87 26 3:1 0.24 0.62 SJ1 × Y4 - C F 2 94 67 27 3:1 0.70 0.40 Y4 × SJ1 - D F 2 69 55 14 3:1 0.82 0.37 SJ1 × Y4 - E F 2 104 75 29 3:1 0.46 0.50 SJ1 × Y4 - F F 2 141 111 30 3:1 1.04 0.31 Total F 2 589 445 144 3:1 0.10 0.76 The Fop race 5 isolate PF22b, isolated from Sichuan Province was used for phenotypic evaluations (Deng et al. 2022b ). The isolate was stored at -80℃ and cultured at 27℃ on potato dextrose agar (PDA) medium. Phenotypic evaluation for Fusarium wilt Fifteen seeds of parents and 589 individual seeds of six F 2 populations were cultivated in paper cups (600 mL) filled with fresh vermiculite. Each paper cup was planted five seeds, and the planted cups placed in greenhouses 25 ± 1 ℃ for two weeks. Seedlings were inoculated by immersing the trimmed roots into the spore suspension in accordance with published procedures (Haglund 1989 ; Bani et al. 2012 ; Deng et al. 2022b ). The inoculated seedlings were continued to culture in greenhouses at 27 ± 1 ℃ under natural light for an additional 28 days. Watering was performed every two days, supplemented by nutrient solution on the 14th day. The percentage of leaves showing symptoms (PSL) for each individual plant was employed to evaluate the disease severity of the whole plant with a 0–5 scale: 0, PSL = 0; 1, 0 < PSL ≤ 25%; 2, 25% < PSL ≤ 50%; 3, 50% < PSL ≤ 75%; 4, 75% < PSL < 100%; 5, PSL = 100% (Bani et al. 2012 ; Deng et al. 2022b ). The individuals scoring 0–2 were categorized as resistant, while those scoring 3–5 were classified as susceptible (Bani et al. 2012 ; Deng et al. 2022b ; 2022b ) The phenotype data of F 2 populations were analyzed using the Chi-square ( χ 2 ) test to verify their compliance with the expected Mendelian segregation ratios. The additional F 2 populations, SJ1× Y3, SJ1 × Y25 and DDR11 × Dingwan 4 were subjected to the same strategies as the populations of Y4 and SJ1 for phenotypic evaluation. For 207 pea accessions including differential cultivars, planting and inoculation methods were the same as used in the population evaluations, except that 12 seeds from each pea accession were cultured in two replicate paper cups. The death rate (DR), disease index (DI), and PSL were employed to assess the disease severity of pea accessions following our previously established protocols, and the resistance of accessions and parents was categorized into five response types: highly resistant, resistant, intermediate, susceptible, and highly susceptible (Bani et al. 2012 ; Deng et al. 2022b ). Accessions classified as highly resistant or resistant to Fop were subjected to a repeat identification. Construction of sequencing libraries and whole genome sequencing The leaf tissues were collected from individual pea plants of parents, F 2 populations, and accessions in two days after inoculation. The genomic DNA were extracted from young leaves using the DNAsecure Plant Kit DP320-03 (Tiangen Biotech, Beijing, China), according to the manufacturer’s instruction. The DNA quality and concentration were assessed using 1.2% agarose gel electrophoresis and NanoDrop 2000/2000c (Thermo Fisher Scientific, Waltham, MA, USA). Based on the phenotype data, two DNA bulks created by combining equal amounts of DNA from 15 resistant individuals (Resistant-bulk) and 15 susceptible individuals (Susceptible-bulk) within the Y4 × SJ1 - A F 2 population. The Illumina libraries for whole-genome resequencing (WGRS) were prepared using the parents SJ1 and Y4 in 10× depth, as well as the Resistant-bulk and Susceptible-bulk in 20× depth, with the assistance of Novogene (Tianjin, China). Short-reads mapping of bulks and BSA-seq analysis The raw sequencing data were downloaded to the local server, and the fastp v0.23.4 software (Chen et al. 2018 ) was utilized to trim reads containing adapter sequences, and remove paired-end reads with low quality (Q < 15) or more than 5 undetermined bases. The retained clean reads were aligned to the “Caméor v1a” reference genome of pea (Kreplak et al. 2019 ) using genome-alignment software bwa-mem2 v2.2.1 software (Vasimuddin et al. 2019 ), and the alignment rate was evaluated to attain a 4× genome coverage, ensuring sufficient average depth. The duplicate reads were identified and marked using samtools v1.18 software (Danecek et al. 2021 ), and the GATK v4.4.0 software (Van der Auwera et al. 2020) was employed for local realignment, base recalibration, and other preprocessing steps to ensure the accuracy of different loci. The next filtering was performed using vcftools v0.1.16 software (Danecek et al. 2011 ), GATK v4.4.0 software (Van der Auwera et al. 2020), and Linux commands, applying the following criteria: 1) biallelic different loci; 2) homozygous loci with variations between parental samples; 3) different loci on the main chromosome with sequencing depth ≥ 4×. The SNP and insertion-deletion (InDel) sites obtained after filtering were annotated using SnpEff v5.2 software (Cingolani et al. 2012 ) with reference to the available genomic annotation file (gff/gtf), enabling the identification of their genomic regions and mutation types on the genome. The high-quality differential SNPs and InDels were discovered after filtering and annotation. The SNP-index, representing the variant proportion at each SNP and InDel sites, was calculated for the Resistant-bulk and Susceptible-bulk. The ΔSNP-index was employed to pinpoint potential candidate genomic regions containing resistance genes, relying on BSA-seq data derived from bulks and parental cultivars (Takagi et al. 2013 ). Therefore, to identify the associated candidate region for the FwS1 gene, the ΔSNP-index for each locus was determined by subtracting the SNP-index of the Resistant-bulk from that of the Susceptible-bulk and the formula was as follows: ΔSNP-index = AltR/(RefR + AltR)- AltS/(RefS + AltS) Where R = the Resistant-bulk, S = the Susceptible-bulk, Ref = the same read depth as the reference genome, and Alt = different read depth from the reference genome. The SNP-index and ΔSNP-index values was visualized using QTL-seq v2.2.3 software (Sugihara et al. 2022 ) and Adobe Illustrator 2023 ( http://www.adobe.com ). In order to define the candidate region, we posited the absence of a QTL at that particular position, which led to the random assignment of the locus genotyping to the two progenies during the genetic process. The greater the ΔSNP-index, the higher the likelihood that are associated with gene FwS1 . Genetic mapping of the candidate region using Kompetitive Allele-Specific PCR (KASP) markers Based on the gene annotation, the nonsignificant SNP and InDel loci such as the synonymous variant, intergenic region variant, and intron variant were filtered. The retained SNP and InDel loci, along with their 500 bp flanking sequences on both sides, were extracted using TBtools-II v2.027 software (Chen et al. 2023 ). Subsequently, a local BLAST was performed to align these sequences with the reference genome Caméor v1a, and sequences with more than four copies in the genome were excluded. The SNP and InDel loci located in the target genomic region were selected for conversion into KASP markers using the online platform Polymarker ( http://www.polymarker.info/ ). The 5’ end of both forward primers for every designed KASP assay was modified by the addition of the standard FAM or HEX fluorescent sequence (FAM tail: 5’- GAAGGTGACCAAGTTCATGCT-3’; HEX tail: 5’-GAAGGTCGGAGTCAACGGATT-3’). The KASP markers were amplified with a Douglas Scientific Array Tape Platform (China Golden Marker, Beijing, Biotech Co., Ltd.) in a 1.6 µL reaction volume including 0.8 µl DNA (~ 22 ng/µl), 0.022 µL primer mix, 0.4 µL KASP V4.0 2× Mastermix 1536 (LGC Biosearch Technologies, Shanghai, Biotech Co., Ltd.), and 0.4 µL ddH 2 O. PCR amplification on a Soellex PCR Thermal Cycler using the following program: 94 ℃ for 15 min, followed by 10 touchdown cycles of 95°C for 20 s, touchdown starting at 65 ℃ for 1 min (decreasing 0.6 ℃ per cycle), and then 26 cycles f 94 ℃ for 20 s, 55 ℃ for 1 min, and a final cooling to 4 ℃. A fluorescent end-point reading was completed with the Araya fluorescence detection system (part of the Douglas Scientific Array Tape Platform). Genotypes and clusters were visualized with Kraken 2 software (Wood et al. 2019 ). The six F 2 populations were sequentially evaluated using KASP markers. Genes containing co-segregating markers were prioritized as candidate genes, while markers were eliminated if the number of recombination events exceeded 5% within the same population. The linkage analysis was conducted based on the phenotypes and genotypes data of the KASP markers in the F 2 populations through MAPMAKER/EXP v3.0 software (Lincoln et al. 1992 ). Recombination frequencies were transformed into the genetic distances in centimorgans (cM) according to the Kosambi mapping function, and the linkage groups were determined using a minimum log-likelihood threshold of 3.0. The genetic linkage map and physical map depicting the KASP markers associated with the gene FwS1 in SJ1 was generated using Adobe Illustrator 2023. Haplotype analysis and diagnostic marker verification The candidate gene KASP markers of FwS1 gene were applied to genotype of 207 pea accessions. For the accuracy of detection, the individual plant samples were collected for analysis. The markers detecting genotypes in susceptible individual plants consistent with that of the resistant parent will be eliminated. The remaining markers were developed as diagnostic markers for detected the FwS1 gene in pea accessions. The candidate genes will be narrowed down to only encompass genes associated with these markers. The F 2 populations derived from, SJ1 × Y3, SJ1 × Y25, and DDR11 × DW 4, were used to verify the accuracy of the diagnostic markers. Results Phenotypic evaluation After 28 days of inoculation, all plants of the parent Y4 and the four differential cultivars, including Litter Marvel, Darkskin Perfection, New Era, and New Season, displayed wilt and dead. Conversely, each seedling of the parent SJ1 and the three differential cultivars, including WSU 23, WSU 28, and WSU 31 remained viable and showed no discernible disease symptoms (Fig. 1 A). Resistance reactions of seven differential cultivars confirmed race 5 identity of Fop isolate PF22b. Within the combined mapping population of 589 F 2 individuals, a total of 445 individuals were resistant (R) and 144 were susceptible (S), and the observed segregation ratio of R:S fit the expected 3:1 ratio ( χ 2 = 0.10, p = 0.76) (Table 1 ). For each F 2 populations, the observed segregation ratio was consisted with the expected 3:1 ratio as well (Table 1 ). The results indicated Fop resistance in SJ1 was controlled by a single dominant gene, and named FwS1 . Furthermore, the DW4 showed resistance to PF22b with symptoms similar to SJ1, while Y3, Y25, DDR11 was susceptible with the same symptoms as Y4 (Fig. 1 B-D). A total of 63 and 53 F 2 individuals were identified as resistant, and 14 and 20 as susceptible in the SJ1 × Y3 and SJ1 × Y25 population, respectively (Table S1 , Table S2), while in the DDR11 × D4 population, 51 individuals were resistant and 19 were susceptible (Table S3). The observed segregation ratio of R:S in the three populations were also the expected 3:1 ratio. For 200 pea accessions, 134 accessions exhibited high resistance or resistance to isolate PF22b, 29 accessions displayed intermediate and another 37 accessions were susceptible or high susceptible, and a collection of 155 resistant and 55 susceptible individual plants were obtained from the identified pea accessions and differential cultivars for subsequent haplotype analysis (Table S4). Reads mapping and BSA-seq analysis Leveraging the Illumina platform, approximately 45.29, 45.72, 95.63, and 95.36 Gb clean base data were generated for the Y4, SJ1, Susceptible-bulk, and Resistant-bulk, respectively (Table 2 ). Upon aligning these clean base data to the reference genome Caméor v1a, 300 million, 312 million, 611 million, and 635 million paired-end 150-bp clean reads were obtained from the Y4, SJ1, Susceptible-bulk, and Resistant-bulk, resulting in mean depths of 13.89×, 14.03×, 26.35×, and 26.81×, and the 4× genome coverage of 74.75%, 74.67%, 81.25%, and 81.54%, respectively (Table 2 ). Table 2 Whole-genome resequencing based on Illumina sequencing of parents and bulks Genotype sample Clean base (Gb) Number of clean reads Mapping rate (%) Mean depth (×) 4 × genome coverage rate (%) Y4 45.29 300,171,969 99.42 13.89 74.75 Shijiadacaiwan 1 45.72 302,904,242 99.38 14.03 74.67 Susceptible-bulk 95.63 611,740,352 99.46 26.35 81.25 Resistant-bulk 95.36 635,739,658 99.61 26.81 81.54 After mapping reads and filtering SNPs and InDels, a total of 4,119,468 reliable SNPs and InDels were identified across the seven chromosomes (Table S5). In this study, the InDels were treated as SNPs for calculating and analyzing the SNP-index. The analysis of SNP-index and ΔSNP-index of each SNPs in the Resistant-bulk and Susceptible-bulk revealed that a 10.4 Mb genomic region (1,169,564 − 11,563,817) at the top of chr6LG2 was significantly associated with pea resistance to Fop (Fig. 2 A). The SNP-index at the 10.4 Mb genomic region in Resistant-bulk exhibited a considerably higher value, while it was notably lower in Susceptible-bulk, and the ΔSNP-index was significantly higher than the random segregation scenario (Fig. 2 A). Thus, this genomic region is preliminary designated as the candidate region for the gene FwS1 . Validation of candidate region and fine mapping of FwS1 In order to validate the precision of the 10.4 Mb candidate genomic region harboring FwS1 , as pinpointed through WGRS and BSA-seq analysis, and to refine the target region, 23 candidate SNPs in this interval to were chosen to design KASP markers for sequential genotyping F 2 populations (Table S7). A total of 17 tested KASP markers were firstly genotyped 68 individuals of Y4 × SJ1 - A F 2 population (Fig. 2 B, Fig S1 , Fig S2A). Nine markers A015910, A015911, A016031, A015982, A016180, A016182, A016183, A015912, and A015985 displayed consistent linkage to FwS1 , and the interval harboring FwS1 was narrowed down to a 1.63 Mb region bordered by markers A015910 and A015985 (Fig. 2 B). To furtherly reduce the candidate region of the FwS1 gene, a larger segregating population composed of 589 F 2 individuals was genotyped using nine aforementioned full linkage markers, and the FwS1 was fine-mapped to a 91.4 kb region flanked by markers A015982 and A015912, with complete co-segregation with markers A016180, A016182, and A016183, revealing the absence of recombination events (Fig. 2 C). The 91.4 kb region contains 23 genes according to the annotation of Caméor reference genome (Fig. 2 D). For confirming putative genes, 10 additional KASP markers were developed using the non-candidate SNPs near the co-segregating markers (Table S6, Table S7), and six new KASP markers, A016299, A016557, A016558, A016559, A016561, and A016563, linked to FwS1 were employed to refine the candidate region (Fig. S1 ). The 589 F 2 individuals could be classified into 10 major haplotypes by the 11 markers (Fig. 2 E). The individuals containing SNPs corresponding to markers A016180, A016182, A016183, A016561, and A016563 consistently exhibited resistance to Fop , while individuals lacking these five SNPs displayed a susceptible phenotype (Fig. 2 E). This result suggested the gene FwS1 was situated between markers A016299 and A015912, co-segregating with markers A016180, A016182, A016183, A016561, and A016563. Haplotype analyses of FwS1 In order to ascertain SNPs associated with the resistance in the gene FwS1 and discover pea accessions containing the FwS1 unique haplotype, the eight co-segregated or linked KASP markers with the gene FwS1 were employed to detect the individuals of 207 pea accessions with a definite resistance phenotype (Fig. S2B). Twelve haplotypes were identified among 207 accessions, and a total of 88 accessions carrying marker A016180 displayed a resistant phenotype, whereas 55 accessions lacking marker A016180 exhibited a susceptible phenotype (Fig. 2 F, Table S4). Furthermore, 64 accessions lacking marker A016180 remained a resistant phenotype, suggesting the presence of other Fop resistant genes in them (Fig. 2 F, Table S4). The results showed that the pea accessions with resistant phenotype and carrying SNP marker A016180 genotype of “T/T” or “T/C” could harbor the gene FwS1 , while the accessions with susceptible or resistant genotype with “C/C” SNP genotype were lack of Fop resistance gene or possess other resistance genes. Some accessions containing other seven makers exhibited resistance or susceptible to Fop , suggesting that these SNPs were unrelated to the resistance gene FwS1 . Consequently, only the SNP corresponding to KASP marker A016180 displayed exclusive correlation with the resistance gene FwS1 . In accordance with the Caméor reference genome, the SNP, positioned at 2,600,848 bp in chr6LG2 or at 721 bp in Psat6g003960 , was a “T/C” substitution that caused the amino acid change from Aspartic (Asp) to Asparagine (Asn) (Fig. 2 G, Fig. 3 ). The annotation of Caméor reference genome suggested that Psat6g003960 was a potential disease resistance gene containing an NB-ARC domain. Specificity validation of the marker A016180 for FwS1 The Y3 and Y25 was selected from SJ1 and shared a similar genetic background with SJ1 and Y4, while DW4 and DDR11 were dry pea cultivars having large genetic difference with SJ1. Haplotype analysis showed Y3, Y25 and DW4 could carry the gene FwS1 . In order to demonstrate the marker A016180 is specific for the gene FwS1 , three F 2 populations derived from the crosses of SJ1 × Y3, SJ1 × Y25, and DDR11 × DW 4 were employed for mapping Fop resistance gene. As expected, co-segregated markers with the crosses of SJ1 × Y4, including the marker A016180, were also co-segregated with the crosses of SJ1 × Y3, SJ1 × Y25, and DDR11 × DW 4, respectively (Fig. 4 A-C). The results also indicated that the Fop resistance gene in Y3, Y25 and DW4 was the gene FwS1 . The marker A016180 could efficiently and accurately identify the individuals harboring the FwS1 gene within the three populations. The genotype of the resistant individuals was “T/T” or “T/C”, and the susceptible individuals was “C/C” (Table S1 , S2, S3). This finding further confirmed the marker A016180 was specific for identification of the gene FwS1 in different genetic background. Integrating the findings of haplotype analysis and mapping Fop resistance gene in the crosses of SJ1 × Y3, SJ1 × Y25, and DDR11 × DW 4, the KASP marker A016180 could be used as a diagnostic marker for precise identification of the gene FwS1 in diverse pea populations and accessions and MAS for resistance breeding. The association between the resistance gene FwS1 and Fwf The candidate gene Psat6g003960 of FwS1 is situated within the potential interval of gene Fwf (Okubara et al. 2002 ). To test the relationship between gene FwS1 and Fwf , the markers utilized for constructing the genetic linkage map of Fwf were used to map the gene FwS1 with the SJ1 × Y4, SJ1 × Y3, SJ1 × Y25, and DDR11 × DW4. However, none of the constructing markers exhibited polymorphisms across these populations. Based on the Pulse Crop Database ( https://www.pulsedb.org/ ), the adjacent markers of these constructing markers in other genetic linkage map were identified and employed for screening across the aforementioned four populations. It is found that a gene marker RNApol2, closely linked to the constructing marker T03_650 of Fwf (0.8 cM) in a new pea consensus map (Bordat et al. 2011 ), exhibited polymorphism in the F 2 population derived from the cross of DDR11 × DW 4 and was mapped to the gene FwS1 region with a distance of 0.7 cM (Fig. 4 ). Therefore, we speculated that the genes FwS1 and Fwf were closely linked or identical. Subsequently, the FwS1 candidate gene Psat6g003960 from line 74SN3B, carrying Fwf , was cloned and compared with that from SJ1. The Psat6g003960 sequence of 74SN3B shared 100% homology with that of SJ1 (Fig. 3 ), which suggested that the FwS1 and Fwf may represent the same Fusarium wilt resistance gene. However, further confirmation through additional allelic tests is warranted. Discussion The Fusarium wilt, caused by Fop , is a persistent drawback leading to significant yield losses in many pea cultivation regions (Kraft et al. 2001; Sharma et al. 2010 ). Deployment of resistant cultivars remains the most efficient, cost-effective, and environmentally friendly strategy for disease control (Kraft et al. 2001; Bani et al. 2012 ; Shubha et al. 2016 ; Deng et al. 2022b ). It is crucial for this strategy of disease control to cultivate pea cultivars resistant to the prevalent race in specific production areas. In China, some cultivars or lines displaying resistance to Fop race 5 has been screened, which can effectively restrict the damage of Fusarium wilt in most pea-producing areas (Deng 2021 ). Nevertheless, the gene responsible for conferring resistance to race 5 in pea accessions has not been precisely located, and developing diagnostic molecular markers for MAS was still pending. Therefore, the SJ1, an elite vegetable pea cultivar resistant to race 5, was chosen for fine mapping resistance gene. The diagnostic marker for resistance gene were subsequently developed for MAS of pea Fusarium wilt resistance breeding. The peas have a notably low reproductive coefficient, as compared to the food crops, such as wheat and rice (Li et al. 2017a ; Rubiales et al 2019 ; Zhang et al. 2020 ; Wang et al. 2023 ). It is challenging for fine mapping the target genes or QTLs in peas through traditional genetic mapping methods, because of the need for large mapping population (Loridon et al. 2005 ; Liu et al. 2020 ). With the develop of technologies, the publication of the pea reference genome lays the foundation for identifying target genes. The BSA-seq provides an efficient strategy for rapid mapping of candidate regions by constructing, sequencing, and analyzing DNA pools with extreme phenotypes, offering a faster alternative to traditional BSA analysis that relies on screening of polymorphic markers by PCR amplification and large mapping population (Michelmore et al. 1991 ; Takagi et al. 2013 ; Sugihara et al. 2022 ). For BSA-seq, it is not necessary for large segregating populations to map target genes, and it has been successfully employed in peas to identify candidate regions and develop linked molecular markers for various traits, including leaf shape (Zheng et al. 2018 ), pod softness (Zhang et al. 2022b ), and disease resistance (Fondevilla et al. 2022 ; Wu et al. 2022 ). In this study, a 10.4 Mb candidate region in chr6LG2 associated with the FwS1 was pinpointed using the BSA-seq method. KASP markers within the candidate genomic region was further developed to construct the genetic and physical maps, and narrowed the candidate gene within a 91.4-kb interval. The combined strategy of BSA-seq and traditional mapping accelerated the identification of the candidate gene FwS1 , presenting an efficient strategy applicable for gene discovery in pea and other crops. Previous studies have identified three genes governing the Fop resistance in pea, including the gene Fw , Fwf , and the major-effect gene Fwn4.1 . The gene Fw , conferring resistance to race Fop race 1, is mapped between CASP (cleaved amplified polymorphic sequence) marker THO (0.9 cM) and SSR marker AD134 (3.4 cM) on chr5LG3 (Jian et al. 2015); the gene Fwf provides resistance to race 5 and is flanked by RAPD (Random Amplification Polymorphic DNA) marker U693a and isozyme marker Aat-p (aspartate aminotransferase, 9.1 cM) on chr6LG2 (Okubara et al. 2002 ); and the major-effect gene Fwn4.1 , offering resistance to race 2, is between SSR markers AC22 (159 cM on pea linkage group Ⅳ) and AD171(163 cM on pea linkage group Ⅳ) on chr4LG4, with LOD scores ranging from 40.0 to 65.6 (McPhee et al. 2012 ). In this study, the gene FwS1 , a dominant gene controlling resistance to race 5 in SJ1, was successfully mapped into a physical interval of 91.4 kb on chr6LG2 between KASP markers A016557 and A015912, and markers A016180, A016182, A016183, A016561, and A016563 were co-segregant with the gene FwS1 . Then, utilizing haplotype analysis with these eight linkage or co-segregation markers, and a functional SNP for FwS1 was identified, which corresponded to marker A016180 and positioned at 2600848 bp on chr6LG2, as well at 721 bp in the gene Psat6g003960 (Fig. 6). Therefore, the gene Psat6g003960 was designated as the candidate gene for FwS1 . The accuracy of molecular markers to identify resistance gene is crucial to achieve the objectives of MAS (Jian et al. 2015; Zhong et al. 2018 ; Zhang et al. 2022a ; Xu et al. 2023 ). Molecular markers linked to the Fusarium wilt resistance gene in peas have been identified (Okubara et al. 2002 ; McPhee et al. 2012 ; Kwon et al. 2013 ; Jian et al. 2015). However, these markers had limited efficiency in accurate identification of resistance genes, as all of them were linked markers and not specific for the putative candidate genes. For example, Jian et al (2015) developed a functional codominant marker THO of gene Fw from a putative functional gene coding THO complex subunit protein, and this marker was only able to select pea lines with 96 to 98% accuracy in mapping populations and 94% accuracy in advanced pea breeding lines. Among all types of molecular markers, genic molecular markers generated from candidate genes were superior for use in the marker-assisted selection (Varshney et al. 2007 ). Development and application of specific molecular markers from candidate genes of target gene has been extensively emphasized, because of their accuracy in MAS (Pandey et al. 2017 ; Zhong et al. 2018 ; Gangurde et al. 2023 ; Zhang et al. 2023 ). In this study, a KASP marker A016180 was developed from the candidate gene Psat6g003960 , and this marker was able to detect the gene FwS1 with 100% accuracy in mapping population derived from SJ1 and Y4. The marker was employed to genotype 207 pea accessions, 88 pea accessions which exhibited resistance to Fop race 5 carried the marker, and suggested presence of the gene FwS1 . Finally, the DW4 carrying the marker were employed to confirm presence of the gene FwS1 by gene mapping, as expected, the marker was co-segregant with the resistance gene of DW4. Therefore, the marker A016180 was specific for the gene FwS1 and could be used as a potential diagnostic marker for identification of the gene FwS1 in accessions and mapping populations with different genetic background, and MAS of pea resistance breeding. In order to explore the relationship between disease resistance genes FwS1 and Fwf located on chr6LG2. A pea consensus map, comprising 5460 pea Unigenes constructed by Bordat et al. ( 2011 ), served as a bridge to elucidate the relationship among different genetic linkage maps (Fig. 4 ). In this consensus map, the RAPD marker T03_650, associated with the gene Fwf (Fig. 4 E), was closely linked to gene RNApol2 encoding for an RNA polymerase with a 0.8 cM (Rameau et al. 1998 ; Okubara et al. 2002 ). In this study, the RAPD marker T03_650 and gene marker RNApol2 were also used to map the gene FwS1 , both markers were not polymorphic in the F 2 population produced from SJ1 and Y4, SJ1 and Y3, and SJ1 and Y24, but the marker RNApol2 exhibited linkage ro the gene FwS1 with a 0.7 cM distance in the DDR11 × DW4 F 2 population (Fig. 4 C). The sequence of gene marker RNApol2 was located at 3,661,380-3,661,608 bp on chr6LG2 and has 1,060,533 bp physical distance (~ 0.3 cM) from the FwS1 candidate gene Psat6g003960 (Fig. S3). Moreover, the line 74SN3B carrying Fwf was got to test the relationship between FwS1 and Fwf . Upon comparing the candidate gene Psat6g003960 of 74SN3B and SJ1, it was observed that their sequences are identical, implying that the FwS1 and Fwf may represent the same resistance gene against Fusarium wilt (Fig. 3 ). The NB-ARC domain serves as a functional ATPase domain, and the nucleotide-binding state of this domain is suggested to regulate the activity of the resistance protein (Tameling et al. 2006 ; Van Ooijen et al. 2008 ). The NB-ARC domain was consisted of three subdomains: a nucleotide-binding fold (NB), a four-helix bundle (ARC1) and a winged-helix fold (ARC2) (Tameling et al. 2006 ; Van Ooijen et al. 2008 ; Shokouhifar et al. 2016 ). In this functional framework, the NB subdomain operates as the catalytic core, the ARC1 subdomain functions as a scaffold facilitating intramolecular interactions with the LRR (Leucine Rich Repeat), and the ARC2 subdomain acts as the regulatory element transducing pathogen perception by the LRR into resistance-protein activation (Van Ooijen et al. 2008 ; Shokouhifar et al. 2016 ; Afzal et al. 2022 ). So far, the NB-ARC domain involving in resistance against Fusarium wilt has been documented in various plants such as melon (Shokouhifar et al. 2016 ), soybean (Afzal et al. 2022 ), chickpea (Chakraborty et al. 2018 ; 2020 ), banana (Chang et al. 2020 ), common bean (Paulino et al. 2021 ), radish (Ma et al. 2021 ), and rice (Wang et al. 2020). In this study, the sequence analysis and functional annotation of Psat6g003960 revealed the presence of an NB-ARC domain. A “T/C” SNP difference might be activated or inactivated the function of this domain. Therefore, we predicted that the Psat6g003960 might play a central role in governing the regulation of Fop resistance. However, given the presence of three homologous genes, Psat6g003880 , Psat6g004000 , and Psat6g006320 , adjacently the gene Psat6g003960 , with the lowest similarity exceeding 90%, the verification of the Psat6g003960 gene function remains a challenging and ongoing endeavor. In conclusion, we have identified and finely mapped a gene, FwS1 , that confers resistance against Fop race 5 in peas. The BSA-seq approach represent an accurate and efficient method for identifying candidate genes associated with specific traits. The FwS1 candidate gene, Psat6g003960 , were identified through fine mapping and haplotype analyses, which could potentially be useful for functional characterization in future cloning experiments of FwS1 . The diagnostic marker A016180 could potentially be applied to MAS in pea breeding programs to track the presence of FwS1 in populations and accessions. Declarations Acknowledgments We sincerely thank Dr. Guangqi Gao at Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China, for providing professional assistance in QTL-seq analyses; Professor Xuxiao Zong at Institute of Crop Sciences, Chinese Academy of Agricultural Sciences Beijing, China, and Professor Dongmei Yu at Sichuan Academy of Agricultural Sciences, Sichuan, China, for providing pea accessions; and Dr. Rebecca J. McGee at Grain Legume Genetics and Physiology Research Unit, USDA ARS, Pullman, WA 99164, USA, for providing the pea differential cultivars. Author contribution statement ZZ and XW conceived and designed the experiments DD, SS, WW, and CD performed the experiments DD and WW analyzed the data DD wrote the manuscript ZZ and SS revised the paper. 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Crop J 9:1428-1437. https://doi.org/10.1016/j.cj.2020.11.011 Zhang C, Xie W, Fu H, Chen Y, Chen H, Cai T, Yang Q, Zhuang Y, Zhong X, Chen K, Gao M, Liu F, Wan Y, Pandey MK, Varshney RK, Zhuang W (2023) Whole genome resequencing identifies candidate genes and allelic diagnostic markers for resistance to Ralstonia solanacearum infection in cultivated peanut ( Arachis hypogaea L.). Front Plant Sci 13:1048168. https://doi.org/10.3389/fpls.2022.1048168 Zhang K, Yuan M, Xia H, He L, Ma J, Wang M, Zhao H, Hou L, Zhao S, Li P, Tian R, Pan J, Li G, Thudi M, Ma C, Wang X, Zhao C (2022a) BSA‑seq and genetic mapping reveals AhRt2 as a candidate gene responsible for red testa of peanut. Theor Appl Genet 135:1529-1540. https://doi.org/10.1007/s00122-022-04051-w Zhang P, Wang Y, Sun T, Wu X, Xia W, Fang P, Pandey A, Xu P (2022b) Fine mapping PsPS1 , a gene controlling pod softness that defines market type in pea ( Pisum sativum ). Plant Breed 141:418-428. https://doi.org/10.1111/pbr.13020 Zheng Y, Xu F, Li Q, Wang G, Liu N, Gong Y, Li L, Chen Z, Xu S (2018) QTL mapping combined with bulked segregant analysis identify SNP markers linked to leaf shape traits in Pisum sativum using SLAF sequencing. Front Genet 9:615. https://doi.org/10.3389/fgene.2018.00615 Zhong C, Sun S, Li Y, Duan C, Zhu Z (2018) Next-generation sequencing to identify candidate genes and develop diagnostic markers for a novel Phytophthora resistance gene, RpsHC18 , in soybean. Theor Appl Genet 131:525-538. https://doi.org/10.1007/s00122-017-3016-z Supplementary Files Supplementarymaterial.rar Supplementary material Fig. S1 The genetic maps of the FwS1 within one (a), three (b) and six (c) F2 populations derived from Shijiadacawan 1 and Y4. Fig. S2 The genotyping detection with representative marker A16180 in populations (A) and accessions (B). Fig. S3 Comparison of gene marker RNApol2 sequence from Dingwan 4, DDR 11, and the Caméor reference genome. Table S1 The detection of diagnostic marker A016180 in parents and F 2 individuals of the Shijiadacaiwan 1 × Y3 population. Table S2 The detection of diagnostic marker A016180 in parents and F 2 individuals of the Shijiadacaiwan 1 × Y25 population. Table S3 The detection of markers in parents and F 2 individuals of the DDR11 × Dingwan 4 population. Table S4 The detection of diagnostic marker A016180 in 7 pea differential cultivars and 200 accessions. Table S5 The SNPs and InDels distribution on the seven major chromosomes of pea. Table S6 The KASP primers for fine mapping Table S7 The differential SNPs between KASP markers A015910 and A015912. Cite Share Download PDF Status: Published Journal Publication published 26 Jun, 2024 Read the published version in Theoretical and Applied Genetics → Version 1 posted Reviewers agreed at journal 17 Apr, 2024 Reviewers invited by journal 15 Apr, 2024 Editor assigned by journal 08 Apr, 2024 First submitted to journal 05 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4225694","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":291295412,"identity":"b050395e-4b56-4d2c-bb5e-a1b4d3fcd1ff","order_by":0,"name":"Dong Deng","email":"","orcid":"","institution":"Chinese Academy of Agricultural Sciences Institute of Crop Sciences","correspondingAuthor":false,"prefix":"","firstName":"Dong","middleName":"","lastName":"Deng","suffix":""},{"id":291295413,"identity":"1e287db0-4740-48aa-91ac-8e81ce1f5448","order_by":1,"name":"Suli Sun","email":"","orcid":"","institution":"Chinese Academy of Agricultural Sciences Institute of Crop Sciences","correspondingAuthor":false,"prefix":"","firstName":"Suli","middleName":"","lastName":"Sun","suffix":""},{"id":291295414,"identity":"c080a558-277a-49e5-9057-252a10468859","order_by":2,"name":"Wenqi Wu","email":"","orcid":"","institution":"Chinese Academy of Agricultural Sciences Institute of Crop Sciences","correspondingAuthor":false,"prefix":"","firstName":"Wenqi","middleName":"","lastName":"Wu","suffix":""},{"id":291295415,"identity":"335189de-5d24-4cb2-91e9-554904b631de","order_by":3,"name":"Canxing Duan","email":"","orcid":"","institution":"Chinese Academy of Agricultural Sciences Institute of Crop Sciences","correspondingAuthor":false,"prefix":"","firstName":"Canxing","middleName":"","lastName":"Duan","suffix":""},{"id":291295416,"identity":"0d1435b6-da20-4bcc-afd3-864b212b080f","order_by":4,"name":"Xuehong Wu","email":"","orcid":"","institution":"China Agricultural University College of Plant Protection","correspondingAuthor":false,"prefix":"","firstName":"Xuehong","middleName":"","lastName":"Wu","suffix":""},{"id":291295417,"identity":"105cc778-6b84-454b-a25d-bd7d53b4cbfb","order_by":5,"name":"Zhendong Zhu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYDACCQYGZiDF2ADED0jWwmxAshY2CaJ0yM/uMf5cUHFHtl+6/VrFjxo7ewb2swfwamGcc8bAeMaZZ8Yz55wpu9lzLDmxgScvAa8WZokcg2TetsOJG27kpN1mYGNOYJDgwe8nNqCWw7z/IFqKGf7V2xPUwiORY9jM2wDSkn6MmbHtMGMDIS0SEmnFzDzHDhvPnJHDLNnbdzyxjScHvxb5GcmbP/PUHJbtl0h/+OHHt2p7fvYzRMYP0I0QlWzEqgcC9gckKB4Fo2AUjIKRBADOdkNhAQwgnQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-6867-0591","institution":"Chinese Academy of Agricultural Sciences Institute of Crop Sciences","correspondingAuthor":true,"prefix":"","firstName":"Zhendong","middleName":"","lastName":"Zhu","suffix":""}],"badges":[],"createdAt":"2024-04-06 05:02:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4225694/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4225694/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00122-024-04682-1","type":"published","date":"2024-06-26T06:06:54+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":55062354,"identity":"f6cde801-b5c0-4b50-8cbe-ce56f4f77a36","added_by":"auto","created_at":"2024-04-22 02:55:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":374394,"visible":true,"origin":"","legend":"\u003cp\u003eThe disease responsesof \u003cem\u003eFusarium\u003c/em\u003e \u003cem\u003eoxysporum\u003c/em\u003e f. sp. \u003cem\u003episi\u003c/em\u003e (Fop) resistance in the Shijiadacaiwan 1 and Y4 (A), Shijiadacaiwan 1 and Y3 (B), Shijiadacaiwan 1 and Y25 (C), and Dingwan 4 and DDR11 (D).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4225694/v1/f173ffd557b9d2da823608e7.png"},{"id":55062356,"identity":"3badd187-b1bd-478b-b8f9-e15cd7485174","added_by":"auto","created_at":"2024-04-22 02:55:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":138995,"visible":true,"origin":"","legend":"\u003cp\u003eBulked segregant analysis sequencing (BSA-seq), mapping and haplotype analyses of the Fusarium wilt resistance gene \u003cem\u003eFwS1\u003c/em\u003e. (A) The locus of \u003cem\u003eFwS1 \u003c/em\u003ethat determined resistance differences was genetically mapped through BSA-seq. The x-axis corresponds to the physical positions (in megabases, Mb) of the 7 pea chromosomes. The y-axis reflects the SNP-index or ΔSNP-index values. (B) The first map of \u003cem\u003eFwS1 \u003c/em\u003eusing 68 F\u003csub\u003e2\u003c/sub\u003e individuals derived from Shijiadacawan 1 (SJ1) and Y4. The numbers below the lines indicate the physical locations of the single nucleotide polymorphisms (SNPs). (C) The fine map of \u003cem\u003eFwS1 \u003c/em\u003eusing 589 F\u003csub\u003e2\u003c/sub\u003e individuals derived from SJ1 and Y4. The annotations below the lines indicate the mutation type of the SNPs. In B and C, the numbers of recombinants identified between the neighboring marker loci are indicated above the rectangle. (D) Several significant genes annotated within the candidate region. In A, B, C and D, the green region indicates the association locus of \u003cem\u003eFwS1\u003c/em\u003e. (E) The genotypes and phenotypes of significant recombinants detected in 589 F\u003csub\u003e2\u003c/sub\u003e\u0026nbsp;individuals. The markers highlighted in grey were developed using non-candidate SNPs. (F) The genotypes and phenotypes of significant recombinants detected in haplotype analysis of 200 accessions and 7 cultivars. In E and F, black rectangles represent the homozygous genotypes of SJ1, while white rectangles represent the homozygous genotypes of Y4. (G) The comparison of candidate SNP locus at KASP marker A016180 between SJ1 and Y4. The lettered vertical line represents the nonsynonymous SNP and amino acid, and the numbers above and below the rectangles represent the position of this locus on the chromosome and gene, respectively.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4225694/v1/927fd9c8ce136cc2e60564a3.png"},{"id":55063120,"identity":"82fecba2-7804-49ea-8d66-955d7f900443","added_by":"auto","created_at":"2024-04-22 03:03:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":378817,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the candidate gene \u003cem\u003ePsat6g003960\u003c/em\u003e sequence from Shijiadacaiwan 1 (\u003cem\u003eFwS1\u003c/em\u003e), Y4, 74SN3B (\u003cem\u003eFwf\u003c/em\u003e) and the Caméor reference genome. The red and blue rectangles represent the effective resistance SNP “A/G” in the 721 bp of \u003cem\u003ePsat6g003960\u003c/em\u003e, which corresponds to “T/C” in the 2600848 bp of chr6LG2 (chromosome 6, i.e., linking group Ⅱ), considering the reverse orientation of the \u003cem\u003ePsat6g003960\u003c/em\u003e gene in reference genome.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4225694/v1/96d52796fae873bb67a3a34b.png"},{"id":55062358,"identity":"0ffec355-2e02-48f0-be6d-78846f91c788","added_by":"auto","created_at":"2024-04-22 02:55:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":66878,"visible":true,"origin":"","legend":"\u003cp\u003eThe genetic maps of the Fusarium wilt resistance genes\u003cem\u003e \u003c/em\u003ewithin the crosses of Shijiadacaiwan 1 × Y3 (A), Shijiadacaiwan 1 × Y25 (B) and DDR11 × Dingwan 4 (C), the 10.4 cM genetic map of pea LG Ⅳ in Bordat et al. (2011) (D), and the genetic map of the resistance gene \u003cem\u003eFwf \u003c/em\u003eat the cross of 4SN3B × A83-22-4(e)-A in Okubata et al. (2002) (E). The dark grey rectangles signified the candidate genomic region of the resistance gene.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4225694/v1/b17f820e800b43aee45716df.png"},{"id":60279151,"identity":"00c94247-4c48-4421-b87e-abbb32e21f65","added_by":"auto","created_at":"2024-07-15 06:07:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1866607,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4225694/v1/db2fb331-fdff-48f9-ab42-f928cbe088a5.pdf"},{"id":55062355,"identity":"b5c128b3-45b7-4c2e-aac2-491fb234e2af","added_by":"auto","created_at":"2024-04-22 02:55:50","extension":"rar","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2888827,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig. S1\u003c/strong\u003e The genetic maps of the FwS1 within one (a), three (b) and six (c) F2 populations derived from Shijiadacawan 1 and Y4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig. S2\u003c/strong\u003e The genotyping detection with representative marker A16180 in populations (A) and accessions (B).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFig. S3\u003c/strong\u003e Comparison of gene marker RNApol2 sequence from Dingwan 4, DDR 11, and the Caméor reference genome.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S1\u003c/strong\u003e The detection of diagnostic marker A016180 in parents and F\u003csub\u003e2\u003c/sub\u003e individuals of the Shijiadacaiwan 1 × Y3 population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S2\u003c/strong\u003e The detection of diagnostic marker A016180 in parents and F\u003csub\u003e2\u003c/sub\u003e individuals of the Shijiadacaiwan 1 × Y25 population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S3\u003c/strong\u003e The detection of markers in parents and F\u003csub\u003e2\u003c/sub\u003e individuals of the DDR11 × Dingwan 4 population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S4\u003c/strong\u003e The detection of diagnostic marker A016180 in 7 pea differential cultivars and 200 accessions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S5\u003c/strong\u003e The SNPs and InDels distribution on the seven major chromosomes of pea.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S6 \u003c/strong\u003eThe KASP primers for fine mapping\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S7\u003c/strong\u003e The differential SNPs between KASP markers A015910 and A015912.\u003c/p\u003e","description":"","filename":"Supplementarymaterial.rar","url":"https://assets-eu.researchsquare.com/files/rs-4225694/v1/86aa489ef3194588ef5004e7.rar"}],"financialInterests":"","formattedTitle":"Fine Mapping and Identification of a Fusarium Wilt Resistance Gene FwS1 in Pea","fulltext":[{"header":"Key message ","content":"\u003cp\u003eA Fusarium wilt resistance gene \u003cem\u003eFwS1\u0026nbsp;\u003c/em\u003eon pea chromosome 6 was identified and mapped to\u0026nbsp;a 91.4 kb region by a comprehensive genomic-based approach，and the gene \u003cem\u003ePsat6g003960\u0026nbsp;\u003c/em\u003eharboring NB-ARC domain was identified as the putative candidate gene.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003ePea (\u003cem\u003ePisum sativum\u003c/em\u003e L.), including green pea and dry pea, is one of the most vital edible legumes and cultivated vegetable species globally (Cousin \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Toklu et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Pea cultivation in China spans over 2000 years, and now China has been the leading producer of green peas and the third-largest producer of dry pea in the world (Li et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e, Wu et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, FAOSTAT 2023).\u003c/p\u003e \u003cp\u003eFusarium wilt of peas, induced by \u003cem\u003eFusarium oxysporum\u003c/em\u003e f. sp. \u003cem\u003episi\u003c/em\u003e (\u003cem\u003eFop\u003c/em\u003e), is a destructive disease and can cause severe yield loss in most pea-producing areas worldwide (Kraft and Pfleger \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Sharma et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; El-Sharkawy et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Deng et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e). Utilizing the disease resistance response of seven standard differential cultivars, Haglund and Kraft (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1979\u003c/span\u003e) proposed the existence of four recognized races of \u003cem\u003eFop\u003c/em\u003e, namely races 1, 2, 5, and 6. The races 1 and 2 pose significant threats in numerous pea-producing regions worldwide, whereas races 5 and 6 only are epidemic in a few regions (Kraft and Pfleger \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Infantino et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Shubha et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Deng et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eFop\u003c/em\u003e is a soil-borne pathogen that can survive as thick-walled chlamydospores in the soil for over a decade in the absence of a host. Therefore, the \u003cem\u003eFop\u003c/em\u003e was difficult to be eradicated and can only be controlled with integrated management (Kraft and Pfleger \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Sharma et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Among all management measures, deploying resistant pea cultivars is regarded as the most economical and ecofriendly approach for managing pea Fusarium wilt (Kraft and Pfleger \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Shubha et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Jha et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sampaio et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The resistance to \u003cem\u003eFop\u003c/em\u003e in peas exhibits two distinct types: complete resistance and partial resistance. Complete resistance is mediated by a single resistance gene that provides immunity or resistance to \u003cem\u003eFop\u003c/em\u003e, while partial resistance is governed by multiple genes and works to restrict pathogen colonization and spread within plant tissues (Wade \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e1929\u003c/span\u003e; Snyder \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e1933\u003c/span\u003e; Haglund and Kraft \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1970\u003c/span\u003e; \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1979\u003c/span\u003e; McPhee et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In most cases, pea cultivars with complete resistance are preferred for breeding and cultivation to control Fusarium wilt, as it is difficult to breed cultivars with partial resistance with conventional methods, yet partial resistance is unstable and can be defeated in the face of high disease pressure (Infantino et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Bani et al, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Shubha et al, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe breeding of pea resistance cultivars can be significantly facilitated through marker-assisted selection (MAS) (Ghafoor and McPhee \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Sharma et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Currently, two complete resistance genes, \u003cem\u003eFw\u003c/em\u003e (Kwon et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Jian et al. 2015) and \u003cem\u003eFwf\u003c/em\u003e (Coyne et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Okubara et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), along with a putative major partial resistance gene, \u003cem\u003eFnw4.1\u003c/em\u003e (McPhee et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), have been identified in pea. However, the three resistance genes have only been undergone preliminary mapping with a limited number of markers, and closely linked molecular markers only developed in gene \u003cem\u003eFw\u003c/em\u003e (McClendon et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Okubara et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; McPhee et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Kwon et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Jian et al. 2015). The accuracy of MAS using these closely linked molecular markers could be affected by lack of polymorphism different genetic background and recombination (Loridon et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Jian et al. 2015; Liu et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Therefore, it is essential to construct high-resolution genetic or physical maps and develop effective diagnostic markers based on the differences in related gene-coding regions to identify pea genes resistant to Fusarium wilt.\u003c/p\u003e \u003cp\u003eThe traditional strategies for mapping resistant genes, which use DNA markers to amplify random genomic regions, typically require significant investments in time and resources (Jian et al. 2015; Wallace et al. 2017; Li et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Some non-genic DNA markers linked with disease resistance genes could be useful in specific populations, but they may lack polymorphism in populations or cultivars with different genetic backgrounds (Loridon et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sawada et al. 2022). With the rapid development of next-generation sequencing (NGS) and third-generation sequencing (TGS), substantial progress has been made in the whole-genome sequencing of peas. The sequencing, assembly, and refined gene annotation of pea cultivars Cam\u0026eacute;or and Zhongwan 6 reference genomes have greatly contributed to develop additional molecular markers and explore potential resistant genes (Kreplak et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The decline in sequencing costs and the progress of bioinfrmatics analysis and tools based on NGS technology have significantly accelerated the efficiency of target gene identification and mapping (Nguyen et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zhong et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Luo et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e). The BSA-seq is a rapid technique for identifying markers linked to traits through the construction of DNA pools with extreme phenotypes, effectively pinpointing and characterizing resistance loci within specific genomic regions (Michelmore et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Takagi et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Compared to traditional mapping methods, the BSA-seq strategy can rapidly and precisely discover genome-wide polymorphic loci to confirm target candidate regions for interesting traits and eliminate the need for screening numerous molecular markers to save time and resource (Zhong et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Furthermore, the BSA-seq strategy has proven effective in identifying both qualitative traits and quantitative traits and in developing specific diagnostic markers across various crops, such as pigeonpea (Singh et al., 2016), chickpea (Deokar et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), soybean (Li et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017b\u003c/span\u003e; Zhong et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), peanut (Pan et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e), cucumber (Njogu et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Bartholomew et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), rice (Takagi et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and maize (Sun et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn our previous research, a diverse collection of pea accessions resistant to Fusarium wilt was screened, but their specific resistance genes have not been cleared (Deng et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e; \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e). In this study, we employed the outstanding \u003cem\u003eFop\u003c/em\u003e-resistant pea cultivar Shijiadacaiwan 1 (SJ1) to investigate the inheritance of the \u003cem\u003eFop\u003c/em\u003e resistance gene, which we refer to as \u003cem\u003eFwS1\u003c/em\u003e, to identify candidate genes of \u003cem\u003eFwS1\u003c/em\u003e by fine mapping and haplotype analysis, and to develop diagnostic marker with validate, laying the foundation for cloning pea resistant genes and breeding resistant varieties.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlant materials and pathogen inoculum\u003c/h2\u003e \u003cp\u003eThe Fusarium wilt resistance pea cultivar Shijiadacaiwan 1 (SJ1) and susceptible line Y4 selected from SJ1 were crossed to construct mapping populations (Deng et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e). The F\u003csub\u003e1\u003c/sub\u003e pea seeds was self-pollinated and conducted to obtain six F\u003csub\u003e2\u003c/sub\u003e populations (SJ1 \u0026times; Y4 or Y4 \u0026times; SJ1) comprising a total of 589 individual plants (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) that were used for BSA-seq and the further fine mapping analysis. The extra three populations were generated through the crossing of SJ1 and its derivative lines Y3 (susceptible) and Y25 (susceptible), along with the cross of susceptible cultivar DDR11 and resistance cultivar Dingwan 4 (DW4). These populations contained 77 (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), 73 (Table S2) and 70 F\u003csub\u003e2\u003c/sub\u003e (Table S3) individual plants, respectively, which were used to validate the accuracy of the diagnostic markers. Moreover, the seven pea differential cultivars, Litter Marvel, Darkskin Perfection, New Era, New Season, WSU 23, WSU 28, and WSU 31, were employed as control for phenotypic evaluation, and a set of 200 pea accessions, chosen from the previously screened 1304 pea accessions (Deng et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e), were utilized for haplotype analysis (Table S4). The SJ1 and its derived lines, pea differential cultivars, and pea accessions, were acquired from Sichuan Academy of Agricultural Sciences (SAAS), the United States Department of Agriculture, Agricultural Research Service (USDA ARS), and National Crop Genebank and certain pea breeding units in China, respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGenetic segregation in response to \u003cem\u003eFusarium oxysporum\u003c/em\u003e f. sp. \u003cem\u003episi\u003c/em\u003e isolates PF22b in the F\u003csub\u003e2\u003c/sub\u003e population derived from Shijiadacaiwan 1 and Y4\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParent and the cross\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGeneration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAmount\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eObserved number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eChi - squared tests\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eExpected ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eχ2\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eΡ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShijiadacaiwan 1 (SJ1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eY4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eY4 \u0026times; SJ1 - A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3:1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eY4 \u0026times; SJ1 - B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3:1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSJ1 \u0026times; Y4 - C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3:1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eY4 \u0026times; SJ1 - D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3:1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSJ1 \u0026times; Y4 - E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3:1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSJ1 \u0026times; Y4 - F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3:1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3:1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe \u003cem\u003eFop\u003c/em\u003e race 5 isolate PF22b, isolated from Sichuan Province was used for phenotypic evaluations (Deng et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e). The isolate was stored at -80℃ and cultured at 27℃ on potato dextrose agar (PDA) medium.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003ePhenotypic evaluation for Fusarium wilt\u003c/h2\u003e \u003cp\u003eFifteen seeds of parents and 589 individual seeds of six F\u003csub\u003e2\u003c/sub\u003e populations were cultivated in paper cups (600 mL) filled with fresh vermiculite. Each paper cup was planted five seeds, and the planted cups placed in greenhouses 25\u0026thinsp;\u0026plusmn;\u0026thinsp;1 ℃ for two weeks. Seedlings were inoculated by immersing the trimmed roots into the spore suspension in accordance with published procedures (Haglund \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Bani et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Deng et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e). The inoculated seedlings were continued to culture in greenhouses at 27\u0026thinsp;\u0026plusmn;\u0026thinsp;1 ℃ under natural light for an additional 28 days. Watering was performed every two days, supplemented by nutrient solution on the 14th day. The percentage of leaves showing symptoms (PSL) for each individual plant was employed to evaluate the disease severity of the whole plant with a 0\u0026ndash;5 scale: 0, PSL\u0026thinsp;=\u0026thinsp;0; 1, 0\u0026thinsp;\u0026lt;\u0026thinsp;PSL\u0026thinsp;\u0026le;\u0026thinsp;25%; 2, 25% \u0026lt; PSL\u0026thinsp;\u0026le;\u0026thinsp;50%; 3, 50% \u0026lt; PSL\u0026thinsp;\u0026le;\u0026thinsp;75%; 4, 75% \u0026lt; PSL\u0026thinsp;\u0026lt;\u0026thinsp;100%; 5, PSL\u0026thinsp;=\u0026thinsp;100% (Bani et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Deng et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e). The individuals scoring 0\u0026ndash;2 were categorized as resistant, while those scoring 3\u0026ndash;5 were classified as susceptible (Bani et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Deng et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e; \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e) The phenotype data of F\u003csub\u003e2\u003c/sub\u003e populations were analyzed using the Chi-square (\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e) test to verify their compliance with the expected Mendelian segregation ratios. The additional F\u003csub\u003e2\u003c/sub\u003e populations, SJ1\u0026times; Y3, SJ1 \u0026times; Y25 and DDR11 \u0026times; Dingwan 4 were subjected to the same strategies as the populations of Y4 and SJ1 for phenotypic evaluation.\u003c/p\u003e \u003cp\u003eFor 207 pea accessions including differential cultivars, planting and inoculation methods were the same as used in the population evaluations, except that 12 seeds from each pea accession were cultured in two replicate paper cups. The death rate (DR), disease index (DI), and PSL were employed to assess the disease severity of pea accessions following our previously established protocols, and the resistance of accessions and parents was categorized into five response types: highly resistant, resistant, intermediate, susceptible, and highly susceptible (Bani et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Deng et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e). Accessions classified as highly resistant or resistant to \u003cem\u003eFop\u003c/em\u003e were subjected to a repeat identification.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of sequencing libraries and whole genome sequencing\u003c/h2\u003e \u003cp\u003eThe leaf tissues were collected from individual pea plants of parents, F\u003csub\u003e2\u003c/sub\u003e populations, and accessions in two days after inoculation. The genomic DNA were extracted from young leaves using the DNAsecure Plant Kit DP320-03 (Tiangen Biotech, Beijing, China), according to the manufacturer\u0026rsquo;s instruction. The DNA quality and concentration were assessed using 1.2% agarose gel electrophoresis and NanoDrop 2000/2000c (Thermo Fisher Scientific, Waltham, MA, USA). Based on the phenotype data, two DNA bulks created by combining equal amounts of DNA from 15 resistant individuals (Resistant-bulk) and 15 susceptible individuals (Susceptible-bulk) within the Y4 \u0026times; SJ1 - A F\u003csub\u003e2\u003c/sub\u003e population. The Illumina libraries for whole-genome resequencing (WGRS) were prepared using the parents SJ1 and Y4 in 10\u0026times; depth, as well as the Resistant-bulk and Susceptible-bulk in 20\u0026times; depth, with the assistance of Novogene (Tianjin, China).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eShort-reads mapping of bulks and BSA-seq analysis\u003c/h2\u003e \u003cp\u003eThe raw sequencing data were downloaded to the local server, and the fastp v0.23.4 software (Chen et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) was utilized to trim reads containing adapter sequences, and remove paired-end reads with low quality (Q\u0026thinsp;\u0026lt;\u0026thinsp;15) or more than 5 undetermined bases. The retained clean reads were aligned to the \u0026ldquo;Cam\u0026eacute;or v1a\u0026rdquo; reference genome of pea (Kreplak et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) using genome-alignment software bwa-mem2 v2.2.1 software (Vasimuddin et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and the alignment rate was evaluated to attain a 4\u0026times; genome coverage, ensuring sufficient average depth. The duplicate reads were identified and marked using samtools v1.18 software (Danecek et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and the GATK v4.4.0 software (Van der Auwera et al. 2020) was employed for local realignment, base recalibration, and other preprocessing steps to ensure the accuracy of different loci. The next filtering was performed using vcftools v0.1.16 software (Danecek et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), GATK v4.4.0 software (Van der Auwera et al. 2020), and Linux commands, applying the following criteria: 1) biallelic different loci; 2) homozygous loci with variations between parental samples; 3) different loci on the main chromosome with sequencing depth\u0026thinsp;\u0026ge;\u0026thinsp;4\u0026times;. The SNP and insertion-deletion (InDel) sites obtained after filtering were annotated using SnpEff v5.2 software (Cingolani et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) with reference to the available genomic annotation file (gff/gtf), enabling the identification of their genomic regions and mutation types on the genome.\u003c/p\u003e \u003cp\u003eThe high-quality differential SNPs and InDels were discovered after filtering and annotation. The SNP-index, representing the variant proportion at each SNP and InDel sites, was calculated for the Resistant-bulk and Susceptible-bulk. The ΔSNP-index was employed to pinpoint potential candidate genomic regions containing resistance genes, relying on BSA-seq data derived from bulks and parental cultivars (Takagi et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Therefore, to identify the associated candidate region for the \u003cem\u003eFwS1\u003c/em\u003e gene, the ΔSNP-index for each locus was determined by subtracting the SNP-index of the Resistant-bulk from that of the Susceptible-bulk and the formula was as follows:\u003c/p\u003e \u003cp\u003eΔSNP-index\u0026thinsp;=\u0026thinsp;AltR/(RefR\u0026thinsp;+\u0026thinsp;AltR)- AltS/(RefS\u0026thinsp;+\u0026thinsp;AltS)\u003c/p\u003e \u003cp\u003eWhere R\u0026thinsp;=\u0026thinsp;the Resistant-bulk, S\u0026thinsp;=\u0026thinsp;the Susceptible-bulk, Ref\u0026thinsp;=\u0026thinsp;the same read depth as the reference genome, and Alt\u0026thinsp;=\u0026thinsp;different read depth from the reference genome. The SNP-index and ΔSNP-index values was visualized using QTL-seq v2.2.3 software (Sugihara et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Adobe Illustrator 2023 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.adobe.com\u003c/span\u003e\u003cspan address=\"http://www.adobe.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn order to define the candidate region, we posited the absence of a QTL at that particular position, which led to the random assignment of the locus genotyping to the two progenies during the genetic process. The greater the ΔSNP-index, the higher the likelihood that are associated with gene \u003cem\u003eFwS1\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eGenetic mapping of the candidate region using Kompetitive Allele-Specific PCR (KASP) markers\u003c/h2\u003e \u003cp\u003eBased on the gene annotation, the nonsignificant SNP and InDel loci such as the synonymous variant, intergenic region variant, and intron variant were filtered. The retained SNP and InDel loci, along with their 500 bp flanking sequences on both sides, were extracted using TBtools-II v2.027 software (Chen et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Subsequently, a local BLAST was performed to align these sequences with the reference genome Cam\u0026eacute;or v1a, and sequences with more than four copies in the genome were excluded. The SNP and InDel loci located in the target genomic region were selected for conversion into KASP markers using the online platform Polymarker (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.polymarker.info/\u003c/span\u003e\u003cspan address=\"http://www.polymarker.info/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe 5\u0026rsquo; end of both forward primers for every designed KASP assay was modified by the addition of the standard FAM or HEX fluorescent sequence (FAM tail: 5\u0026rsquo;- GAAGGTGACCAAGTTCATGCT-3\u0026rsquo;; HEX tail: 5\u0026rsquo;-GAAGGTCGGAGTCAACGGATT-3\u0026rsquo;). The KASP markers were amplified with a Douglas Scientific Array Tape Platform (China Golden Marker, Beijing, Biotech Co., Ltd.) in a 1.6 \u0026micro;L reaction volume including 0.8 \u0026micro;l DNA (~\u0026thinsp;22 ng/\u0026micro;l), 0.022 \u0026micro;L primer mix, 0.4 \u0026micro;L KASP V4.0 2\u0026times; Mastermix 1536 (LGC Biosearch Technologies, Shanghai, Biotech Co., Ltd.), and 0.4 \u0026micro;L ddH\u003csub\u003e2\u003c/sub\u003eO. PCR amplification on a Soellex PCR Thermal Cycler using the following program: 94 ℃ for 15 min, followed by 10 touchdown cycles of 95\u0026deg;C for 20 s, touchdown starting at 65 ℃ for 1 min (decreasing 0.6 ℃ per cycle), and then 26 cycles f 94 ℃ for 20 s, 55 ℃ for 1 min, and a final cooling to 4 ℃. A fluorescent end-point reading was completed with the Araya fluorescence detection system (part of the Douglas Scientific Array Tape Platform). Genotypes and clusters were visualized with Kraken 2 software (Wood et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The six F\u003csub\u003e2\u003c/sub\u003e populations were sequentially evaluated using KASP markers. Genes containing co-segregating markers were prioritized as candidate genes, while markers were eliminated if the number of recombination events exceeded 5% within the same population.\u003c/p\u003e \u003cp\u003eThe linkage analysis was conducted based on the phenotypes and genotypes data of the KASP markers in the F\u003csub\u003e2\u003c/sub\u003e populations through MAPMAKER/EXP v3.0 software (Lincoln et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). Recombination frequencies were transformed into the genetic distances in centimorgans (cM) according to the Kosambi mapping function, and the linkage groups were determined using a minimum log-likelihood threshold of 3.0. The genetic linkage map and physical map depicting the KASP markers associated with the gene \u003cem\u003eFwS1\u003c/em\u003e in SJ1 was generated using Adobe Illustrator 2023.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eHaplotype analysis and diagnostic marker verification\u003c/h2\u003e \u003cp\u003eThe candidate gene KASP markers of \u003cem\u003eFwS1\u003c/em\u003e gene were applied to genotype of 207 pea accessions. For the accuracy of detection, the individual plant samples were collected for analysis. The markers detecting genotypes in susceptible individual plants consistent with that of the resistant parent will be eliminated. The remaining markers were developed as diagnostic markers for detected the \u003cem\u003eFwS1\u003c/em\u003e gene in pea accessions. The candidate genes will be narrowed down to only encompass genes associated with these markers. The F\u003csub\u003e2\u003c/sub\u003e populations derived from, SJ1 \u0026times; Y3, SJ1 \u0026times; Y25, and DDR11 \u0026times; DW 4, were used to verify the accuracy of the diagnostic markers.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003ePhenotypic evaluation\u003c/h2\u003e\n \u003cp\u003eAfter 28 days of inoculation, all plants of the parent Y4 and the four differential cultivars, including Litter Marvel, Darkskin Perfection, New Era, and New Season, displayed wilt and dead. Conversely, each seedling of the parent SJ1 and the three differential cultivars, including WSU 23, WSU 28, and WSU 31 remained viable and showed no discernible disease symptoms (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA). Resistance reactions of seven differential cultivars confirmed race 5 identity of \u003cem\u003eFop\u003c/em\u003e isolate PF22b.\u003c/p\u003e\n \u003cp\u003eWithin the combined mapping population of 589 F\u003csub\u003e2\u003c/sub\u003e individuals, a total of 445 individuals were resistant (R) and 144 were susceptible (S), and the observed segregation ratio of R:S fit the expected 3:1 ratio (\u003cem\u003e\u0026chi;\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.10, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.76) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). For each F\u003csub\u003e2\u003c/sub\u003e populations, the observed segregation ratio was consisted with the expected 3:1 ratio as well (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The results indicated \u003cem\u003eFop\u003c/em\u003e resistance in SJ1 was controlled by a single dominant gene, and named \u003cem\u003eFwS1\u003c/em\u003e.\u003c/p\u003e\n \u003cp\u003eFurthermore, the DW4 showed resistance to PF22b with symptoms similar to SJ1, while Y3, Y25, DDR11 was susceptible with the same symptoms as Y4 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB-D). A total of 63 and 53 F\u003csub\u003e2\u003c/sub\u003e individuals were identified as resistant, and 14 and 20 as susceptible in the SJ1 \u0026times; Y3 and SJ1 \u0026times; Y25 population, respectively (Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e, Table S2), while in the DDR11 \u0026times; D4 population, 51 individuals were resistant and 19 were susceptible (Table S3). The observed segregation ratio of R:S in the three populations were also the expected 3:1 ratio. For 200 pea accessions, 134 accessions exhibited high resistance or resistance to isolate PF22b, 29 accessions displayed intermediate and another 37 accessions were susceptible or high susceptible, and a collection of 155 resistant and 55 susceptible individual plants were obtained from the identified pea accessions and differential cultivars for subsequent haplotype analysis (Table S4).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eReads mapping and BSA-seq analysis\u003c/h2\u003e\n \u003cp\u003eLeveraging the Illumina platform, approximately 45.29, 45.72, 95.63, and 95.36 Gb clean base data were generated for the Y4, SJ1, Susceptible-bulk, and Resistant-bulk, respectively (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Upon aligning these clean base data to the reference genome Cam\u0026eacute;or v1a, 300 million, 312 million, 611 million, and 635 million paired-end 150-bp clean reads were obtained from the Y4, SJ1, Susceptible-bulk, and Resistant-bulk, resulting in mean depths of 13.89\u0026times;, 14.03\u0026times;, 26.35\u0026times;, and 26.81\u0026times;, and the 4\u0026times; genome coverage of 74.75%, 74.67%, 81.25%, and 81.54%, respectively (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eWhole-genome resequencing based on Illumina sequencing of parents and bulks\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGenotype sample\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClean base (Gb)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of clean reads\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMapping rate (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean depth (\u0026times;)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e4 \u0026times; genome coverage rate (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eY4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e300,171,969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e99.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShijiadacaiwan 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e302,904,242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e99.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSusceptible-bulk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e95.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e611,740,352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e99.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResistant-bulk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e95.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e635,739,658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e99.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eAfter mapping reads and filtering SNPs and InDels, a total of 4,119,468 reliable SNPs and InDels were identified across the seven chromosomes (Table S5). In this study, the InDels were treated as SNPs for calculating and analyzing the SNP-index. The analysis of SNP-index and \u0026Delta;SNP-index of each SNPs in the Resistant-bulk and Susceptible-bulk revealed that a 10.4 Mb genomic region (1,169,564\u0026thinsp;\u0026minus;\u0026thinsp;11,563,817) at the top of chr6LG2 was significantly associated with pea resistance to \u003cem\u003eFop\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA). The SNP-index at the 10.4 Mb genomic region in Resistant-bulk exhibited a considerably higher value, while it was notably lower in Susceptible-bulk, and the \u0026Delta;SNP-index was significantly higher than the random segregation scenario (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA). Thus, this genomic region is preliminary designated as the candidate region for the gene \u003cem\u003eFwS1\u003c/em\u003e.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eValidation of candidate region and fine mapping of\u003c/strong\u003e \u003cstrong\u003eFwS1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIn order to validate the precision of the 10.4 Mb candidate genomic region harboring \u003cem\u003eFwS1\u003c/em\u003e, as pinpointed through WGRS and BSA-seq analysis, and to refine the target region, 23 candidate SNPs in this interval to were chosen to design KASP markers for sequential genotyping F\u003csub\u003e2\u003c/sub\u003e populations (Table S7). A total of 17 tested KASP markers were firstly genotyped 68 individuals of Y4 \u0026times; SJ1 - A F\u003csub\u003e2\u003c/sub\u003e population (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB, Fig \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e, Fig S2A). Nine markers A015910, A015911, A016031, A015982, A016180, A016182, A016183, A015912, and A015985 displayed consistent linkage to \u003cem\u003eFwS1\u003c/em\u003e, and the interval harboring \u003cem\u003eFwS1\u003c/em\u003e was narrowed down to a 1.63 Mb region bordered by markers A015910 and A015985 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e\n \u003cp\u003eTo furtherly reduce the candidate region of the \u003cem\u003eFwS1\u003c/em\u003e gene, a larger segregating population composed of 589 F\u003csub\u003e2\u003c/sub\u003e individuals was genotyped using nine aforementioned full linkage markers, and the \u003cem\u003eFwS1\u003c/em\u003e was fine-mapped to a 91.4 kb region flanked by markers A015982 and A015912, with complete co-segregation with markers A016180, A016182, and A016183, revealing the absence of recombination events (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC). The 91.4 kb region contains 23 genes according to the annotation of Cam\u0026eacute;or reference genome (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD). For confirming putative genes, 10 additional KASP markers were developed using the non-candidate SNPs near the co-segregating markers (Table S6, Table S7), and six new KASP markers, A016299, A016557, A016558, A016559, A016561, and A016563, linked to \u003cem\u003eFwS1\u003c/em\u003e were employed to refine the candidate region (Fig. \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). The 589 F\u003csub\u003e2\u003c/sub\u003e individuals could be classified into 10 major haplotypes by the 11 markers (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eE). The individuals containing SNPs corresponding to markers A016180, A016182, A016183, A016561, and A016563 consistently exhibited resistance to \u003cem\u003eFop\u003c/em\u003e, while individuals lacking these five SNPs displayed a susceptible phenotype (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eE). This result suggested the gene \u003cem\u003eFwS1\u003c/em\u003e was situated between markers A016299 and A015912, co-segregating with markers A016180, A016182, A016183, A016561, and A016563.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eHaplotype analyses of\u003c/strong\u003e \u003cstrong\u003eFwS1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIn order to ascertain SNPs associated with the resistance in the gene \u003cem\u003eFwS1\u003c/em\u003e and discover pea accessions containing the \u003cem\u003eFwS1\u003c/em\u003e unique haplotype, the eight co-segregated or linked KASP markers with the gene \u003cem\u003eFwS1\u003c/em\u003e were employed to detect the individuals of 207 pea accessions with a definite resistance phenotype (Fig. S2B). Twelve haplotypes were identified among 207 accessions, and a total of 88 accessions carrying marker A016180 displayed a resistant phenotype, whereas 55 accessions lacking marker A016180 exhibited a susceptible phenotype (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eF, Table S4). Furthermore, 64 accessions lacking marker A016180 remained a resistant phenotype, suggesting the presence of other \u003cem\u003eFop\u003c/em\u003e resistant genes in them (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eF, Table S4). The results showed that the pea accessions with resistant phenotype and carrying SNP marker A016180 genotype of \u0026ldquo;T/T\u0026rdquo; or \u0026ldquo;T/C\u0026rdquo; could harbor the gene \u003cem\u003eFwS1\u003c/em\u003e, while the accessions with susceptible or resistant genotype with \u0026ldquo;C/C\u0026rdquo; SNP genotype were lack of \u003cem\u003eFop\u003c/em\u003e resistance gene or possess other resistance genes. Some accessions containing other seven makers exhibited resistance or susceptible to \u003cem\u003eFop\u003c/em\u003e, suggesting that these SNPs were unrelated to the resistance gene \u003cem\u003eFwS1\u003c/em\u003e.\u003c/p\u003e\n \u003cp\u003eConsequently, only the SNP corresponding to KASP marker A016180 displayed exclusive correlation with the resistance gene \u003cem\u003eFwS1\u003c/em\u003e. In accordance with the Cam\u0026eacute;or reference genome, the SNP, positioned at 2,600,848 bp in chr6LG2 or at 721 bp in \u003cem\u003ePsat6g003960\u003c/em\u003e, was a \u0026ldquo;T/C\u0026rdquo; substitution that caused the amino acid change from Aspartic (Asp) to Asparagine (Asn) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eG, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The annotation of Cam\u0026eacute;or reference genome suggested that \u003cem\u003ePsat6g003960\u003c/em\u003e was a potential disease resistance gene containing an NB-ARC domain.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity validation of the marker A016180 for\u003c/strong\u003e \u003cstrong\u003eFwS1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe Y3 and Y25 was selected from SJ1 and shared a similar genetic background with SJ1 and Y4, while DW4 and DDR11 were dry pea cultivars having large genetic difference with SJ1. Haplotype analysis showed Y3, Y25 and DW4 could carry the gene \u003cem\u003eFwS1\u003c/em\u003e. In order to demonstrate the marker A016180 is specific for the gene \u003cem\u003eFwS1\u003c/em\u003e, three F\u003csub\u003e2\u003c/sub\u003e populations derived from the crosses of SJ1 \u0026times; Y3, SJ1 \u0026times; Y25, and DDR11 \u0026times; DW 4 were employed for mapping \u003cem\u003eFop\u003c/em\u003e resistance gene. As expected, co-segregated markers with the crosses of SJ1 \u0026times; Y4, including the marker A016180, were also co-segregated with the crosses of SJ1 \u0026times; Y3, SJ1 \u0026times; Y25, and DDR11 \u0026times; DW 4, respectively (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA-C). The results also indicated that the \u003cem\u003eFop\u003c/em\u003e resistance gene in Y3, Y25 and DW4 was the gene \u003cem\u003eFwS1\u003c/em\u003e. The marker A016180 could efficiently and accurately identify the individuals harboring the \u003cem\u003eFwS1\u003c/em\u003e gene within the three populations. The genotype of the resistant individuals was \u0026ldquo;T/T\u0026rdquo; or \u0026ldquo;T/C\u0026rdquo;, and the susceptible individuals was \u0026ldquo;C/C\u0026rdquo; (Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e, S2, S3). This finding further confirmed the marker A016180 was specific for identification of the gene \u003cem\u003eFwS1\u003c/em\u003e in different genetic background.\u003c/p\u003e\n \u003cp\u003eIntegrating the findings of haplotype analysis and mapping \u003cem\u003eFop\u003c/em\u003e resistance gene in the crosses of SJ1 \u0026times; Y3, SJ1 \u0026times; Y25, and DDR11 \u0026times; DW 4, the KASP marker A016180 could be used as a diagnostic marker for precise identification of the gene \u003cem\u003eFwS1\u003c/em\u003e in diverse pea populations and accessions and MAS for resistance breeding.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eThe association between the resistance gene\u003c/strong\u003e \u003cstrong\u003eFwS1\u003c/strong\u003e \u003cstrong\u003eand\u003c/strong\u003e \u003cstrong\u003eFwf\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThe candidate gene \u003cem\u003ePsat6g003960\u003c/em\u003e of \u003cem\u003eFwS1\u003c/em\u003e is situated within the potential interval of gene \u003cem\u003eFwf\u003c/em\u003e (Okubara et al. \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e). To test the relationship between gene \u003cem\u003eFwS1\u003c/em\u003e and \u003cem\u003eFwf\u003c/em\u003e, the markers utilized for constructing the genetic linkage map of \u003cem\u003eFwf\u003c/em\u003e were used to map the gene \u003cem\u003eFwS1\u003c/em\u003e with the SJ1 \u0026times; Y4, SJ1 \u0026times; Y3, SJ1 \u0026times; Y25, and DDR11 \u0026times; DW4. However, none of the constructing markers exhibited polymorphisms across these populations.\u003c/p\u003e\n \u003cp\u003eBased on the Pulse Crop Database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.pulsedb.org/\u003c/span\u003e\u003c/span\u003e), the adjacent markers of these constructing markers in other genetic linkage map were identified and employed for screening across the aforementioned four populations. It is found that a gene marker RNApol2, closely linked to the constructing marker T03_650 of \u003cem\u003eFwf\u003c/em\u003e (0.8 cM) in a new pea consensus map (Bordat et al. \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e), exhibited polymorphism in the F\u003csub\u003e2\u003c/sub\u003e population derived from the cross of DDR11 \u0026times; DW 4 and was mapped to the gene \u003cem\u003eFwS1\u003c/em\u003e region with a distance of 0.7 cM (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Therefore, we speculated that the genes \u003cem\u003eFwS1\u003c/em\u003e and \u003cem\u003eFwf\u003c/em\u003e were closely linked or identical. Subsequently, the \u003cem\u003eFwS1\u003c/em\u003e candidate gene \u003cem\u003ePsat6g003960\u003c/em\u003e from line 74SN3B, carrying \u003cem\u003eFwf\u003c/em\u003e, was cloned and compared with that from SJ1. The \u003cem\u003ePsat6g003960\u003c/em\u003e sequence of 74SN3B shared 100% homology with that of SJ1 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e), which suggested that the \u003cem\u003eFwS1\u003c/em\u003e and \u003cem\u003eFwf\u003c/em\u003e may represent the same Fusarium wilt resistance gene. However, further confirmation through additional allelic tests is warranted.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe Fusarium wilt, caused by \u003cem\u003eFop\u003c/em\u003e, is a persistent drawback leading to significant yield losses in many pea cultivation regions (Kraft et al. 2001; Sharma et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Deployment of resistant cultivars remains the most efficient, cost-effective, and environmentally friendly strategy for disease control (Kraft et al. 2001; Bani et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Shubha et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Deng et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e). It is crucial for this strategy of disease control to cultivate pea cultivars resistant to the prevalent race in specific production areas. In China, some cultivars or lines displaying resistance to \u003cem\u003eFop\u003c/em\u003e race 5 has been screened, which can effectively restrict the damage of Fusarium wilt in most pea-producing areas (Deng \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Nevertheless, the gene responsible for conferring resistance to race 5 in pea accessions has not been precisely located, and developing diagnostic molecular markers for MAS was still pending. Therefore, the SJ1, an elite vegetable pea cultivar resistant to race 5, was chosen for fine mapping resistance gene. The diagnostic marker for resistance gene were subsequently developed for MAS of pea Fusarium wilt resistance breeding.\u003c/p\u003e \u003cp\u003eThe peas have a notably low reproductive coefficient, as compared to the food crops, such as wheat and rice (Li et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017a\u003c/span\u003e; Rubiales et al \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). It is challenging for fine mapping the target genes or QTLs in peas through traditional genetic mapping methods, because of the need for large mapping population (Loridon et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). With the develop of technologies, the publication of the pea reference genome lays the foundation for identifying target genes. The BSA-seq provides an efficient strategy for rapid mapping of candidate regions by constructing, sequencing, and analyzing DNA pools with extreme phenotypes, offering a faster alternative to traditional BSA analysis that relies on screening of polymorphic markers by PCR amplification and large mapping population (Michelmore et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Takagi et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Sugihara et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For BSA-seq, it is not necessary for large segregating populations to map target genes, and it has been successfully employed in peas to identify candidate regions and develop linked molecular markers for various traits, including leaf shape (Zheng et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), pod softness (Zhang et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e), and disease resistance (Fondevilla et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wu et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In this study, a 10.4 Mb candidate region in chr6LG2 associated with the \u003cem\u003eFwS1\u003c/em\u003e was pinpointed using the BSA-seq method. KASP markers within the candidate genomic region was further developed to construct the genetic and physical maps, and narrowed the candidate gene within a 91.4-kb interval. The combined strategy of BSA-seq and traditional mapping accelerated the identification of the candidate gene \u003cem\u003eFwS1\u003c/em\u003e, presenting an efficient strategy applicable for gene discovery in pea and other crops.\u003c/p\u003e \u003cp\u003ePrevious studies have identified three genes governing the \u003cem\u003eFop\u003c/em\u003e resistance in pea, including the gene \u003cem\u003eFw\u003c/em\u003e, \u003cem\u003eFwf\u003c/em\u003e, and the major-effect gene \u003cem\u003eFwn4.1\u003c/em\u003e. The gene \u003cem\u003eFw\u003c/em\u003e, conferring resistance to race \u003cem\u003eFop\u003c/em\u003e race 1, is mapped between CASP (cleaved amplified polymorphic sequence) marker THO (0.9 cM) and SSR marker AD134 (3.4 cM) on chr5LG3 (Jian et al. 2015); the gene \u003cem\u003eFwf\u003c/em\u003e provides resistance to race 5 and is flanked by RAPD (Random Amplification Polymorphic DNA) marker U693a and isozyme marker \u003cem\u003eAat-p\u003c/em\u003e (aspartate aminotransferase, 9.1 cM) on chr6LG2 (Okubara et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2002\u003c/span\u003e); and the major-effect gene \u003cem\u003eFwn4.1\u003c/em\u003e, offering resistance to race 2, is between SSR markers AC22 (159 cM on pea linkage group Ⅳ) and AD171(163 cM on pea linkage group Ⅳ) on chr4LG4, with LOD scores ranging from 40.0 to 65.6 (McPhee et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In this study, the gene \u003cem\u003eFwS1\u003c/em\u003e, a dominant gene controlling resistance to race 5 in SJ1, was successfully mapped into a physical interval of 91.4 kb on chr6LG2 between KASP markers A016557 and A015912, and markers A016180, A016182, A016183, A016561, and A016563 were co-segregant with the gene \u003cem\u003eFwS1\u003c/em\u003e. Then, utilizing haplotype analysis with these eight linkage or co-segregation markers, and a functional SNP for \u003cem\u003eFwS1\u003c/em\u003e was identified, which corresponded to marker A016180 and positioned at 2600848 bp on chr6LG2, as well at 721 bp in the gene \u003cem\u003ePsat6g003960\u003c/em\u003e (Fig.\u0026nbsp;6). Therefore, the gene \u003cem\u003ePsat6g003960\u003c/em\u003e was designated as the candidate gene for \u003cem\u003eFwS1\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eThe accuracy of molecular markers to identify resistance gene is crucial to achieve the objectives of MAS (Jian et al. 2015; Zhong et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e; Xu et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Molecular markers linked to the Fusarium wilt resistance gene in peas have been identified (Okubara et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; McPhee et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Kwon et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Jian et al. 2015). However, these markers had limited efficiency in accurate identification of resistance genes, as all of them were linked markers and not specific for the putative candidate genes. For example, Jian et al (2015) developed a functional codominant marker THO of gene \u003cem\u003eFw\u003c/em\u003e from a putative functional gene coding THO complex subunit protein, and this marker was only able to select pea lines with 96 to 98% accuracy in mapping populations and 94% accuracy in advanced pea breeding lines. Among all types of molecular markers, genic molecular markers generated from candidate genes were superior for use in the marker-assisted selection (Varshney et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Development and application of specific molecular markers from candidate genes of target gene has been extensively emphasized, because of their accuracy in MAS (Pandey et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zhong et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Gangurde et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In this study, a KASP marker A016180 was developed from the candidate gene \u003cem\u003ePsat6g003960\u003c/em\u003e, and this marker was able to detect the gene \u003cem\u003eFwS1\u003c/em\u003e with 100% accuracy in mapping population derived from SJ1 and Y4. The marker was employed to genotype 207 pea accessions, 88 pea accessions which exhibited resistance to \u003cem\u003eFop\u003c/em\u003e race 5 carried the marker, and suggested presence of the gene \u003cem\u003eFwS1\u003c/em\u003e. Finally, the DW4 carrying the marker were employed to confirm presence of the gene \u003cem\u003eFwS1\u003c/em\u003e by gene mapping, as expected, the marker was co-segregant with the resistance gene of DW4. Therefore, the marker A016180 was specific for the gene \u003cem\u003eFwS1\u003c/em\u003e and could be used as a potential diagnostic marker for identification of the gene \u003cem\u003eFwS1\u003c/em\u003e in accessions and mapping populations with different genetic background, and MAS of pea resistance breeding.\u003c/p\u003e \u003cp\u003eIn order to explore the relationship between disease resistance genes \u003cem\u003eFwS1\u003c/em\u003e and \u003cem\u003eFwf\u003c/em\u003e located on chr6LG2. A pea consensus map, comprising 5460 pea Unigenes constructed by Bordat et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), served as a bridge to elucidate the relationship among different genetic linkage maps (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In this consensus map, the RAPD marker T03_650, associated with the gene \u003cem\u003eFwf\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE), was closely linked to gene \u003cem\u003eRNApol2\u003c/em\u003e encoding for an RNA polymerase with a 0.8 cM (Rameau et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Okubara et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). In this study, the RAPD marker T03_650 and gene marker RNApol2 were also used to map the gene \u003cem\u003eFwS1\u003c/em\u003e, both markers were not polymorphic in the F\u003csub\u003e2\u003c/sub\u003e population produced from SJ1 and Y4, SJ1 and Y3, and SJ1 and Y24, but the marker RNApol2 exhibited linkage ro the gene \u003cem\u003eFwS1\u003c/em\u003e with a 0.7 cM distance in the DDR11 \u0026times; DW4 F\u003csub\u003e2\u003c/sub\u003e population (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). The sequence of gene marker RNApol2 was located at 3,661,380-3,661,608 bp on chr6LG2 and has 1,060,533 bp physical distance (~\u0026thinsp;0.3 cM) from the \u003cem\u003eFwS1\u003c/em\u003e candidate gene \u003cem\u003ePsat6g003960\u003c/em\u003e (Fig. S3). Moreover, the line 74SN3B carrying \u003cem\u003eFwf\u003c/em\u003e was got to test the relationship between \u003cem\u003eFwS1\u003c/em\u003e and \u003cem\u003eFwf\u003c/em\u003e. Upon comparing the candidate gene \u003cem\u003ePsat6g003960\u003c/em\u003e of 74SN3B and SJ1, it was observed that their sequences are identical, implying that the \u003cem\u003eFwS1\u003c/em\u003e and \u003cem\u003eFwf\u003c/em\u003e may represent the same resistance gene against Fusarium wilt (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe NB-ARC domain serves as a functional ATPase domain, and the nucleotide-binding state of this domain is suggested to regulate the activity of the resistance protein (Tameling et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Van Ooijen et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The NB-ARC domain was consisted of three subdomains: a nucleotide-binding fold (NB), a four-helix bundle (ARC1) and a winged-helix fold (ARC2) (Tameling et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Van Ooijen et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Shokouhifar et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In this functional framework, the NB subdomain operates as the catalytic core, the ARC1 subdomain functions as a scaffold facilitating intramolecular interactions with the LRR (Leucine Rich Repeat), and the ARC2 subdomain acts as the regulatory element transducing pathogen perception by the LRR into resistance-protein activation (Van Ooijen et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Shokouhifar et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Afzal et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). So far, the NB-ARC domain involving in resistance against Fusarium wilt has been documented in various plants such as melon (Shokouhifar et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), soybean (Afzal et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), chickpea (Chakraborty et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), banana (Chang et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), common bean (Paulino et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), radish (Ma et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and rice (Wang et al. 2020). In this study, the sequence analysis and functional annotation of \u003cem\u003ePsat6g003960\u003c/em\u003e revealed the presence of an NB-ARC domain. A \u0026ldquo;T/C\u0026rdquo; SNP difference might be activated or inactivated the function of this domain. Therefore, we predicted that the \u003cem\u003ePsat6g003960\u003c/em\u003e might play a central role in governing the regulation of \u003cem\u003eFop\u003c/em\u003e resistance. However, given the presence of three homologous genes, \u003cem\u003ePsat6g003880\u003c/em\u003e, \u003cem\u003ePsat6g004000\u003c/em\u003e, and \u003cem\u003ePsat6g006320\u003c/em\u003e, adjacently the gene \u003cem\u003ePsat6g003960\u003c/em\u003e, with the lowest similarity exceeding 90%, the verification of the \u003cem\u003ePsat6g003960\u003c/em\u003e gene function remains a challenging and ongoing endeavor.\u003c/p\u003e \u003cp\u003eIn conclusion, we have identified and finely mapped a gene, \u003cem\u003eFwS1\u003c/em\u003e, that confers resistance against \u003cem\u003eFop\u003c/em\u003e race 5 in peas. The BSA-seq approach represent an accurate and efficient method for identifying candidate genes associated with specific traits. The \u003cem\u003eFwS1\u003c/em\u003e candidate gene, \u003cem\u003ePsat6g003960\u003c/em\u003e, were identified through fine mapping and haplotype analyses, which could potentially be useful for functional characterization in future cloning experiments of \u003cem\u003eFwS1\u003c/em\u003e. The diagnostic marker A016180 could potentially be applied to MAS in pea breeding programs to track the presence of \u003cem\u003eFwS1\u003c/em\u003e in populations and accessions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely thank Dr. Guangqi Gao at Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China, for providing professional assistance in QTL-seq analyses; Professor Xuxiao Zong at Institute of Crop Sciences, Chinese Academy of Agricultural Sciences Beijing, China, and Professor Dongmei Yu at Sichuan Academy of Agricultural Sciences, Sichuan, China, for providing pea accessions; and Dr. Rebecca J. McGee at Grain Legume Genetics and Physiology Research Unit, USDA ARS, Pullman, WA 99164, USA, for providing the pea differential cultivars.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZZ and XW conceived and designed the experiments DD, SS, WW, and CD performed the experiments DD and WW analyzed the data DD wrote the manuscript ZZ and SS revised the paper. All authors read and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Key R\u0026amp;D Program of China (2019YFD1001300, 2019YFD1001301), the China Agriculture Research System of MOF and MARA (CARS-08), the National Crop Germplasm Resources Center (NCGRC-2022-09), and the Scientific Innovation Program of the Chinese Academy of Agricultural Sciences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e The authors declare that they have no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAfzal M, Alghamdi SS, Nawaz H, Migdadi HH, Altaf M, El-Harty E, Al-Fifi SA, Sohaib M (2022) Genome-wide identification and expression analysis of CC-NB-ARC-LRR (NB-ARC) disease-resistant family members from soybean (\u003cem\u003eGlycine max\u003c/em\u003e L.) reveal their response to biotic stress. 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Theor Appl Genet 131:525-538. https://doi.org/10.1007/s00122-017-3016-z\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":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taag","sideBox":"Learn more about [Theoretical and Applied Genetics](https://www.springer.com/journal/122)","snPcode":"122","submissionUrl":"https://submission.nature.com/new-submission/122/3","title":"Theoretical and Applied Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Pisum sativum, Fusarium oxysporum f. sp. Pisi, resistance gene, bulked segregant analysis sequencing, fine mapping","lastPublishedDoi":"10.21203/rs.3.rs-4225694/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4225694/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePea Fusarium wilt, incited by \u003cem\u003eFusarium oxysporum\u003c/em\u003e f. sp.\u003cem\u003e pisi\u003c/em\u003e (\u003cem\u003eFop\u003c/em\u003e), has always been a devastating disease that causes severe yield losses and economic damage in pea-growing regions worldwide. The utilization of pea cultivars carrying resistance gene is the most efficient approach for managing this disease. In order to finely map resistance gene, a F\u003csub\u003e2\u003c/sub\u003e population was established through the cross between Shijiadacaiwan 1 (resistant) and Y4 (susceptible). The resistance genetic analysis indicated that the \u003cem\u003eFop\u003c/em\u003e resistance in Shijiadacaiwan 1 was governed by a single dominant gene, named as \u003cem\u003eFwS1\u003c/em\u003e. Based on the bulked segregant analysis sequencing (BSA-seq) analyses, the gene \u003cem\u003eFwS1\u003c/em\u003e was initially detected on chromosome 6 (i.e., linking group Ⅱ, chr6LG2), and subsequent linkage mapping with 589 F\u003csub\u003e2\u003c/sub\u003e individuals fine-mapped the gene \u003cem\u003eFwS1 \u003c/em\u003einto a 91.4 kb region. The further functional annotation and haplotype analysis confirmed that the gene \u003cem\u003ePsat6g003960\u003c/em\u003e, characterized by a NB-ARC (nucleotide-binding adaptor shared by APAF-1, R proteins, and CED-4) domain, was considered as the most promising candidate gene. The encoding amino acids were altered by a “T/C” single-nucleotide polymorphism (SNP) in the first exon of the \u003cem\u003ePsat6g003960\u003c/em\u003e, resulting in the observed differences of \u003cem\u003eFop\u003c/em\u003e resistance in peas. Based on this SNP locus, the molecular marker A016180 was determined to be a diagnostic marker for \u003cem\u003eFwS1\u003c/em\u003e by validating its specificity in both pea accessions and genetic populations with different genetic backgrounds. The \u003cem\u003eFwS1 \u003c/em\u003ewith diagnostic KASP marker A016180 could facilitate marker-assisted selection in resistance pea breeding in pea. In addition, upon comparing the candidate gene\u003cem\u003e Psat6g003960\u003c/em\u003e of 74SN3B and SJ1, it was noted that their sequences are identical, suggesting that the \u003cem\u003eFwS1\u003c/em\u003e and \u003cem\u003eFwf \u003c/em\u003emay be the same resistance gene against Fusarium wilt.\u003c/p\u003e","manuscriptTitle":"Fine Mapping and Identification of a Fusarium Wilt Resistance Gene FwS1 in Pea","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-22 02:55:41","doi":"10.21203/rs.3.rs-4225694/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-04-17T07:12:46+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-15T09:30:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-08T13:43:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"Theoretical and Applied Genetics","date":"2024-04-06T01:01:59+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taag","sideBox":"Learn more about [Theoretical and Applied Genetics](https://www.springer.com/journal/122)","snPcode":"122","submissionUrl":"https://submission.nature.com/new-submission/122/3","title":"Theoretical and Applied Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4afea69d-8382-47c1-be0f-2d0cf501d370","owner":[],"postedDate":"April 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-07-15T06:06:54+00:00","versionOfRecord":{"articleIdentity":"rs-4225694","link":"https://doi.org/10.1007/s00122-024-04682-1","journal":{"identity":"theoretical-and-applied-genetics","isVorOnly":false,"title":"Theoretical and Applied Genetics"},"publishedOn":"2024-06-26 06:06:54","publishedOnDateReadable":"June 26th, 2024"},"versionCreatedAt":"2024-04-22 02:55:41","video":"","vorDoi":"10.1007/s00122-024-04682-1","vorDoiUrl":"https://doi.org/10.1007/s00122-024-04682-1","workflowStages":[]},"version":"v1","identity":"rs-4225694","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4225694","identity":"rs-4225694","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}