Genomics-guided landscape unlocks superior alleles and genes for yellow rust resistance in wheat

Genomics-guided landscape unlocks superior alleles and genes for yellow rust resistance in wheat | 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 Article Genomics-guided landscape unlocks superior alleles and genes for yellow rust resistance in wheat Jianhui Wu, Shengwei Ma, Jianqing Niu, Weihang Sun, Haitao Dong, and 19 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4257976/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Jul, 2025 Read the published version in Nature Genetics → Version 1 posted You are reading this latest preprint version Abstract Yellow rust (YR), caused by Puccinia striiformis f. sp. tritici ( Pst ), poses a significant threat to wheat production worldwide. Breeding resistant cultivar is crucial for managing this disease. However, understanding of the genetic mechanisms underlying YR resistance remains fragmented. To address this, we conducted a comprehensive analysis with variome data from 2,191 wheat accessions worldwide and over 47,000 YR response records across multiple environments and pathogen races. Through genome-wide association studies, we established a landscape for 431 YR resistance loci, providing a rich resource for resistance ( R ) gene deployment. Furthermore, we cloned genes corresponding to three resistance loci, namely Yr5x effective against multiple Pst races, Yr6/Pm5 that conferred resistance to two pathogen species, and YrKB ( TaEDR2-B ) conferring broad-spectrum rust resistance without yield penalty. These findings offer valuable insights into the genetic basis of YR resistance in wheat and lay the foundation for engineering wheat with durable disease resistance. Biological sciences/Genetics/Sequencing/DNA sequencing Biological sciences/Genetics/Sequencing/DNA sequencing Biological sciences/Plant sciences/Plant genetics Biological sciences/Plant sciences/Plant genetics Biological sciences/Plant sciences/Plant breeding Common wheat gene cloning genome-wide association study landscape of R genes yellow/stripe rust resistance Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Bread wheat ( Triticum aestivum L.) is a leading cereal grain crop globally, serving as the main food source for 30% of the human population 1 . However, wheat production faces numerous constraints, with an average 21% of the global wheat harvest estimated to be lost due to diseases and pests 2 . Yellow rust (YR) or stripe rust, caused by the fungal pathogen Puccinia striiformis Westend f. sp. tritici ( Pst ), remains a major problem in most wheat-producing regions 3 – 5 . In the last 60 years, recurrent Pst epidemics have caused substantial yield losses 6 , 7 . It is estimated that an annual yield reduction of 5.47 million tons of wheat is attributable to this disease, equivalent to an annual loss of US $ 979 million 8 . Chemical fungicides and resistant cultivars are the two most common strategies used to manage YR in wheat production. Fungicides are essential for controlling sudden YR outbreaks but with increased costs and a risk of environmental pollution. Resistant cultivars are more efficient, environmental-friendly and economic. With the use of high yielding clutivars and near mono-cropping in modern agriculture, the genetic diversity of a resistance ( R ) gene deployed at any time is minimal and the crop is challenged by rare or evolving virulent pathogen races 9 , 10 . This has necessitated a continuing search for new genetic resistance sources for deployment in future cultivars. So far, more than 200 new yellow rust genes ( Yr ) or quantitative trait loci (QTL) including 87 formally designated Yr genes have been reported 11 , 12 . Due to the complexity of the wheat genome, only 10 Yr genes have been cloned to date 12 , 13 . The lack of regular and extensive worldwide surveys on the genetic basis of YR resistance limits the global deployment of R genes based on pathogen-informed strategies 14 , 15 . With the current availability of high-quality wheat reference genomes, advanced sequencing technologies, and rapid gene isolation methods, identification and cloning of disease-resistance genes become more efficient and cost-effective 14 , 16 , 17 . These technologies provide opportunities for large-scale identification and cloning of novel disease-resistance genes and new alleles at known R gene loci, and for identification of favorable haplotypes from diverse sequence variations. In this study, we generated variome data for 2,191 global common wheat accessions and more than 47,000 yellow rust response data-points across 12 Pst races and 12 field environments, and systematically analyzed YR resistance loci/QTL by a genome-wide association study (GWAS) (Supplementary Fig. 1). Based on the GWAS results, we constructed a genome-wide landscape of YR resistance genes, providing a valuable resource for their future deployment. Furthermore, we cloned three resistance genes and assessed their effectiveness against multiple Pst races and even different pathogen species. This comprehensive landscape of YR resistance genes and alleles enhances our understanding of YR resistance on a genome-wide level, assisting in geographic deployment of R -gene diversity to match pathogen virulence profiles. The information and findings will help to develop wheat cultivars with durable YR resistance. Results Overview of a worldwide diverse panel of common wheat for YR resistance Pst urediniospores are windborne and can disperse at continental scales. Historically, based on the frequencies of widespread yellow rust epidemic occurrences and the worldwide population genetic structure of the pathogen, the global geography of wheat yellow rust can be divided into several main epidemic regions (ER) 6–8,18−20 , i.e., South and East Asia (ER1-1, and ER1-2), Europe and Africa (ER2-1 and ER2-2), North and South of America, and Oceania (ER3-1, ER-3-2, and ER3-3) (Fig. 1 a). Over past decades we collected 14,688 common wheat accessions covering most of the YR epidemic regions 21 – 27 . To investigate the genetic resistance against YR in global wheat, 1,629 of 14,688 wheat accessions were carefully chosen to cover most of the diversity of the worldwide wheat collection according to growth habitat, status (i.e., landrace or cultivar), geographical origin, and genetic and phenotypic diversity (Fig. 1 b and Supplementary Table 1). Among them, 666 accessions had published genomic resequencing data (see Methods), 349 were newly genotyped by whole genome re-sequencing (WGS) (Supplementary Table 2) and the genotypes of the remaining 614 accessions were analyzed by the Wheat660K array. In addition, 562 of 768 common wheat accessions that had undergone WGS by the German national GeneBank (henceforth, IPK collection) 28 were integrated into the study after removing redundancy. Finally, a panel of 2,191 common wheat accessions assembled from worldwide sources was constructed for dissecting the genetic variations and resistances involved in global YR epidemics. For characterization of genotypic variation, we aligned more than 1.83 trillion reads from 1,577 accessions to the most recent reference genome of Chinese Spring (CS) RefSeq v2.1 assembly 29 , and generated an extraordinarily abundant variation map comprising ~ 84 million high-quality variants (80 million single nucleotide polymorphisms, SNPs, and 4 million insertion and deletions, InDels) (Supplementary Fig. 2 and Supplementary Table 3). The variation density was, on average, 5.6 variants per kilobase (kb), and more than 98% of high confidence genes of common wheat were covered by potential functional variants. Among the deleterious variants, 51,970 non-synonymous SNPs and 4,071 frameshift InDels were in genes annotated nucleotide-binding leucine rich repeat (NLR) immune receptors and kinase domains and covered 97% (5,422/5,562) of these two classes genes in common wheat (Supplementary Tables 3 and 4), suggesting their potential impact on gene function. Based on the results of principle component analysis (PCA) and genetic diversity ( π ), the panel exhibited comparable genetic diversity and geographic representativeness that of a phylogeographical and historical panel 4,506 wheat accessions reported previously (Fig. 1 c) 30 . Principal component analysis (PCA), phylogenetic tree, and ADMIXTURE were conducted using 200,000 randomly selected variants to investigate the population structure present in our wheat panel (Figs. 1 d–f). The differentiation pattern of the population defined by ADMIXTURE with the number of ancestral populations ( K ) ranging from 2 to 9 (Fig. 1 f) was largely consistent with discrete clusters or branches in the PCA and phylogenetic tree (Fig. 1 d,e). At K = 2, there was a dichotomous pattern of population divergence among landraces and cultivars (Fig. 1 f). At K = 3, cultivars fell into two clusters corresponding to Asian and European origins (Fig. 1 f). Along with increased K , the entire panel gradually separated into nine subpopulations (hereafter named Sp1–Sp9). Landraces were separated into four subpopulations Sp1–Sp4 mainly comprised of landraces from Western and Central Asia, Europe, Southern Asia, and Eastern Asia, hence appearing geographically and historically relevant to their origins 30 , 31 . Cultivars separated into five subpopulations Sp5–Sp9, and genetic assignments were largely in accordance with geographical origins, breeding history, and YR epidemic region. For example, accessions in Sp5 were mainly from Eastern Asia, corresponding to the unique YR epidemic region ER1-1; Sp6 was largely composed of accessions from Southern Asia, Northern Africa, and Latin America, regions that widely used cultivars selected from materials distributed by the International Maize and Wheat Improvement Center (CIMMYT) and International Center for Agricultural Research in the Dry Areas (ICARDA); and Sp7 and Sp8 mainly comprised accessions collected from Canada, and United States of America and Oceania, respectively, with Pst populations ER3-1 and ER3-3 originating from the European epidemic region. Most accessions in cluster Sp9 were from Europe geographically coinciding with European YR epidemic region ER2-1. To gain a deeper insight into differences in yellow rust resistance between different wheat growing regions, we divided cultivars into four breeding groups (BG1 to BG4) based on genomic population architecture, breeding history and yellow rust epidemiology network. BG1, BG2, BG3 and BG4 comprised samples from East Asia (mainly Sp5); Latin America, Africa and South Asia (mainly Sp6); North America and Oceania (mainly Sp7 and Sp8), and Europe (mainly Sp9), respectively (Fig. 1 f and Supplementary Table 1). Landscape of YR resistance genes/loci and effective alleles To uncover the genetic basis and selection characteristics of YR resistance, we assessed the seedling YR responses of 1,629 wheat accessions using 12 historically important and current Pst races (see Methods; Supplementary Fig. 3a,b), and performed adult plant tests at five field locations in China over four years (Yangling, YL; Guiyang, GY; Jiangyou, JY; Chongqing, CQ; Tianshui, TS in years 2019, 2020, 2021 and 2022) (Fig. 1 g,h and Supplementary Table 5). More than 47,000 YR response datapoints evaluated with infection type (IT) and disease severity (DS) were obtained. As shown in Supplementary Fig. 3b, the proportions of accessions with seedling resistance declined with emergence of current Pst races, coinciding with accumulation of larger numbers of virulence factors without apparent loss of aggressiveness. Cluster analysis of YR indices (IT and DS) from field tests showed a complete range of response that was divided into three groups (resistant, intermediate and susceptible) (Fig. 1 i). Accessions with different YR responses were evenly distributed in each epidemic region indicating that the selected wheat accessions were representative in terms of phenotype (Fig. 1 a). The skewed distributions of YR indices were continuous in all environments with high correlation coefficients (Supplementary Fig. 3c), implying that resistance to yellow rust was conferred mainly by quantitative resistance. Then we performed analyses of variance for all environments except for GY and CQ as their single environment. Highly significant genotypic variation and high heritability (Supplementary Fig. 3d and Supplementary Table 6) suggested that the yellow rust responses were mainly controlled by genetic factors that conferred resistance across all environments. A large-scale genome-wide association analysis (GWAS) identified more than 800,000 marker-trait associations (MTAs) exceeding the suggested threshold ( P < 1.0e-06), and assigned them to 431 loci (Fig. 2 a, Supplementary Fig. 4a–f and Supplementary Table 7). For convenience, the lead SNP for each QTL was chosen based on its strongest association with the yellow rust response, coupled with the smallest associated P -value and the highest R 2 (phenotypic variance explained) value among the SNPs considered. To better characterize and compare the loci/genes identified in the present study, we compiled a reference dataset from 175 publications over the past three decades. This dataset comprises 1,125 QTL/genes for YR resistance, along with all cloned R genes or homologs conferring resistance to various diseases in the Triticeae tribe (Supplementary Table 8). These QTL/genes were positioned across the wheat 21 chromosomes using the IWGSC RefSeq v2.1 genome and were categorized into 217 independent QTL (iQTL) based on linkage disequilibrium (LD) blocks (see Methods). Next, we established a wheat genome landscape that provided a comprehensive summary of reported iQTL, cloned R genes and the 431 YR resistance-associated loci identified in this study. Nearly 60.1% (259/431) of the loci co-located with 182 reported iQTL, more than 80% of the total (Supplementary Table 8). These loci included well-characterized Yr genes/QTL or cloned resistance genes such as Yr18 , Yr29 and Yr46 , as well as well-known alien translocations such as 1BL.1RS (1RS introgression from Secale cereale carrying Yr9 ), 2NS-2AS.2AL (2NS introgression from Aegilops ventricosa carrying Yr17 ), and 5AS.5AL-5A m L (5A m L introgression from Triticum monococcum carrying Yr34 ) successfully used in wheat breeding historically, hence validating the effectiveness and robustness of our GWAS findings (Fig. 2 a and Supplementary Table 7). We further predicted 85 QTL-hotspot regions (QHRs) for YR resistance combined with meta-QTL (see Methods, Fig. 2 a and Supplementary Table 8). Notably, approximately 39.9% (172/431) of the identified loci were located in genomic regions distinct from those of reported YR resistance genes/QTL, suggesting that these loci may contain novel resistance genes with potential breeding value. Selection signatures for YR resistance and prioritization of candidate YR resistance genes To better understand genomic selection signatures and epidemiological characteristics underlying YR response to historic Pst races, we assessed changes in resistance allele frequencies (RAFs) represented by lead SNPs in the 431 loci both chronologically (from 1920s to 2020s spanning more than 100 years) and geographically. RAFs at 55 loci increased at specific times and then declined (Figs. 2 b,c), consistent with ‘boom and bust’ cycles associated with introduction of a single effective resistance gene into cultivars and then a decline following an increase in the frequency of virulent Pst races. Each resistance gene played a significant role in controlling YR during a specific period of effectiveness, and then failed due to the emergence of a new virulent race, for instance, Yr1 in the 1950s, Yr2 in the 1960s, YrA (later designated Yr73 and Yr74 ) in the 1970s, Yr7 in the 1980s, Yr9 and Yr17 in the 1990s, Yr4 and Yr27 in 2000s, and Yr24/26 and Yr34 in 2010s (Figs. 2 b,c) 6 , 32 – 36 . Especially, the RAFs for 71 loci showed major changes after 2000 (Figs. 2 c,d), allegedly related to the emergence of new, more aggressive pathotypes 37 , such as races within the molecular groupings PstS4, PstS7, PstS8 and PstS10 in Europe; PstS1 and PstS2 and derivatives in the United States and Australia; and the Yr26 -virulent pathotype group (V26) including race CYR34 in China 23 , 38 . In addition, alleles of 39 new loci identified in landraces have rarely been used in modern breeding (Fig. 2 e and Supplementary Table 7), indicating their potential for wheat improvement. On a geographical scale, we found that some resistance genes, such as Yr29 , Yr30 and Yr78 , located in the YR resistance hotspot regions tend to have a common existence in each epidemic region (Fig. 2 f and Supplementary Table 7), suggesting that they were widely used worldwide. In contrast, we also observed that genes such as Yr26 , Yr27 , Yr75 and YrJ22 tended to be region-specific or were preferentially selected by different breeding groups (Fig. 2 g and Supplementary Table 7). This is possibly attributable to widespread use of founder genotypes, for example, Frontana in the Americas, Cappelle Desprez in northwestern Europe, and Zhou 8425B and Jimai 22 in China 39 , 40 . The above results provide the first comprehensive analysis of genomic selection signatures and epidemiological characteristics of wheat- Pst interactions over the past century, revealing the co-evolutionary dynamics between resistance genes and pathogen races and offering critical insights for developing durable rust-resistant wheat cultivars. We implemented a prioritized candidate gene pipeline for 357 of 431 YR-related QTL (the 74 other QTL with linkage disequilibrium > 10 Mb were not considered). Briefly, this pipeline incorporated information of knowledge-based gene sets such as gene ontology (GO) category, homologous gene classification or related gene regulatory pathways, functional importance of variant effects in candidate gene regions, gene expression associated with the phenotype, association and causality degrees based on multi-model cross-validation (see Methods). After filtering with the pipeline, we identified 559 candidate genes in the top 30 GO terms that mainly belonged to eight pathways, i.e., immune response (P1), kinase signaling (P2), plant hormone (P3), transportation (P4), stress response (P5), nutrition (P6), ubiquitination (P7), and gene expression (P8) (Fig. 2 h and Supplementary Table 9). For each of these candidate genes, we performed haplotype-based association analysis and demonstrated that haplotypes carrying putative polymorphisms causing loss/gain-of-function mutations in the corresponding gene(s) were significantly associated with yellow rust resistance (Supplementary Table 9). Interestingly, these effective alleles of candidate genes could be divided into three major representative types (Fig. 2 i): 1) cloned Yr genes with novel allelic variants possibly conferring different resistance spectra against Pst ; 2) novel alleles of known R genes conferring resistance to other pathogens/pests could be associated with YR resistance; 3) promising broad-spectrum resistance (BSR) genes conferring quantitative resistance. Since the gene, or new allele at a particular locus could thus be viewed as a high-confidence candidate gene we selected one locus of each type for further characterization (Supplementary Table 9). Allelic variants of Yr5 enable resistance to multiple Pst races QYr.nwafu111 was identified in the GWAS as a major QTL on chromosome 2B conferring resistance to five Pst races (CYR23, CYR31, CYR32, CYR33 and TSA-V5) (Fig. 3 a, Supplementary Fig. 4a and Supplementary Table 7). The candidate region of QYr.nwafu111 contained two previously cloned NLR genes Yr7 and Yr5 , and the YR5 locus has two designated resistance alleles Yr5a and Yr5b (earlier designated as YrSp ) 41 . Distinct resistance patterns for these genes were revealed in seedling tests using near-isogenic lines. Yr7 was susceptible (S) to all tested Chinese Pst races and Yr5a displayed resistance (R) to most races except TSA-V5, while Yr5b showed specific effectiveness against CYR23 and CYR32 (Fig. 3 a). Given that Yr5a originated from spelt wheat ( T. spelta ) and has not been widely used in common wheat cultivars, it was unlikely to be the candidate gene. Therefore, based on the resistance patterns, Yr5b emerged as the most likely candidate gene for resistance to both CYR23 and CYR32. To validate this hypothesis, we conducted a detailed analysis of the GWAS results for CYR32. There was a peak for CYR32 harboring 79 significant variants in the 693.32–694.09 Mb region based on IWGSC RefSeq v2.1 (Fig. 3 b, Supplementary Fig. 5a and Supplementary Table 7), and the lead SNP s2B_693786097 significantly distinguished two sets of wheat accessions ( P = 1.2e-18, Fig. 3 c). To verify the gene detected by GWAS, we performed bi-parental genetic mapping in a recombinant inbred line (RIL) population ABM6 (cross of susceptible Avocet S (AvS) × resistant cultivar Baomai 6 (BM6)) and their heterozygous inbred families (HIFs). Linkage analysis showed that a major locus was present in the same region as detected by GWAS, explaining 87.2% of the phenotypic variance explained (PVE) (Supplementary Fig. 5b) was present in the same region as detected by GWAS. The underlying resistance gene was delimited to a 1,078.27 kb physical interval flanked by markers M9374 and M8998 in IWGSC RefSeq v2.1, encompassing nine high-confidence genes (Fig. 3 e,f and Supplementary Tables 10 and 11). According to functional annotations, RNA-seq data from Pst -infected seedling leaves of BM6 and AvS, and DNA variations between AvS and BM6 by genome resequencing, only NLR gene TraesCS2B03G1231900 ( Ta12319 in Fig. 3 e) was expressed (Supplementary Table 11) and showed nonsynonymous variations between the two parents (Supplementary Table 12). We cloned the gene by PCR and found that its sequence was identical to Yr5b , confirming that Yr5b in wheat cultivar BM6 should confer the resistance to CYR32. Interestingly, we detected another GWAS signal in this region associated with resistance to Pst race TSA-V5 (Fig. 3 b,d and Supplementary Fig. 5c). Given that TSA-V5 is virulent to all known alleles ( Yr5a , Yr5b , and Yr7 ) in this region (Fig. 3 a), this signal suggested that the presence of either a novel resistance gene or a previously uncharacterized Yr5 allele. To verify the hypothesis, we performed fine mapping of the locus using a RIL population AXN3517, derived from a cross between AvS (susceptible) and Xinong 3517 (XN3517, resistant) that showed contrasting responses to Pst race TSA-V5, along with their HIFs. A major locus with PVE 57.7% (Supplementary Fig. 5d) was mapped to a 604.22 kb region containing five high-confidence genes flanked by markers M4345 and M8998 (Fig. 3 e,f and Supplementary Tables 10 and 11). Only Yr5 in the candidate region was expressed and was predicted as the underlying causative gene (Supplementary Table 12). To test whether Yr5 was responsible for resistance to Pst race TSA-V5, we performed virus-induced gene silencing (VIGS). We isolated the open reading frame (ORF) of Yr5 from XN3517 and designed specific RNA interference (RNAi) fragments based on sequence differences among published sequences of Yr5 41 . Barley stripe mosaic virus (BSMV)-induced gene silencing of Yr5 in XN3517 suppressed its resistance to Pst race TSA-V5 (Fig. 3 g and Supplementary Fig. 6a). Further sequence analysis revealed that Yr5 in XN3517 was a novel functional allele, hereafter named as Yr5x (Fig. 3 f). To further validate the function of Yr5x in resistance to yellow rust, we overexpressed (OE) it in cultivar Fielder (wild type, WT) which is susceptible to Pst race TSA-V5 and generated Yr5x -OE transgenic wheat plants (Supplementary Fig. 6b). Transgenic T 1 lines derived from nine independent T 0 individuals that were confirmed to have Yr5x by PCR and RT-qPCR conferred resistance to Pst race TSA-V5 (Fig. 3 h and Supplementary Fig. 6b-d). We also investigated the disease phenotype in infected leaves of Fielder(WT) and Fielder(OE) through histological observations. No significant histological differences were observed at 1 day post inoculation (dpi) but colonization and development of secondary hyphae were strongly restricted in the Fielder(OE) compared with Fielder(WT) starting from 2 dpi (Fig. 3 i), indicating that overexpression of Yr5x leads to restricted fungal development in the wheat -Pst interaction. Furthermore, RT-qPCR estimates of Yr5x transcript levels relative to ACTIN in XN3517 showed notable differences between plants inoculated with Pst race TSA-V5 at 0, 2, 4, 5, 7, 10, and 14 dpi (Supplementary Fig. 6e), indicating that Yr5x was induced by the presence of the pathogen. The above results indicate that Yr5b and Yr5x are causal genes for resistance to CYR32 and that Yr5x additionally confers resistance to race TSA-V5. To investigate variations in Yr5x , we collected 60 publicly available wheat genomes, including chromosome-level and scaffold-level assemblies from diploid, tetraploid, and hexaploid wheat and related species (see Methods). Sequence alignment identified 23 homologs in different genomes, including Yr5a , Yr5b , and Yr5x (Source Data for Yr5 ). Phylogenetic analysis of these homologs resulted in their classification into nine distinct clusters based on sequence similarity (Supplementary Fig. 7). Among them, the homologous gene in Shi4185, which also conferred resistance to CYR32, matched Yr5b (Supplementary Table 1); the homologous genes in Cadenza, Paragon and Robigus were the same as Yr5x and Cadenza also conferred resistance to TSA-V5 (Supplementary Fig. 6c). No homolog was identical to Yr5a ; the other homologs were classified into six types, but it remains uncertain whether they have functional roles. This points to complexity and diversity of the YR5 locus, and the functionality of other types that needs to be further verified. In summary, our results show that different alleles of a single NLR gene can confer different resistance spectra against Pst , and pave an alternative route to combat plant diseases by identifying or engineering novel alleles of NLR genes. Allele-specific recognition of different pathogens in NLR locus YR6 / PM5 Although numerous NLRs are characterized as conferring resistance to a single plant pathogen/pest, rare NLRs recognize effectors in taxonomically unrelated pathogens. Our GWAS results showed an extremely significant peak ( QYr.nwafu405 ) located in a QHR associated with resistance to Pst race V26/GS (Fig. 4 a–c) and co-located with previously reported Yr6 gene on chromosome 7B 42 . To verify this GWAS signal, we performed map-based cloning of Yr6 using the AAK58 RIL population and selected HIFs derived from a cross between AvS and resistant cultivar Aikang 58 (AK58). The causal gene was delimited to a 0.08 cM interval corresponding to a physical 205.3 kb region (716.10–716.30 Mb) in the AK58 genome 43 . The region contained six annotated genes (Fig. 4 d and Supplementary Tables 13 and 14). Based on RNA-seq and genome resequencing data, only NLR gene TraesAK58CH7B01G485100 (hereafter abbreviated as TaAK4851 ) both showed high expression in response to Pst infection (Supplementary Table 14) and contained nonsynonymous SNP variations between the two parents (Supplementary Table 15). Hence, TaAK4851 was predicted as the most promising candidate underlying Yr6 . Interestingly, a previous study reported that Pm5-AK58 ( TaAK4851 ) in AK58 is allelic to Pm5e , which was identified in cultivar Fuzhuang 30 and conferred resistance to powdery mildew caused by Blumeria graminis f. sp. tritici ( Bgt ) 44 . We hypothesized that the YR6 locus was responsible for resistance to both powdery mildew and yellow rust. To test this hypothesis, we transiently silenced TaAK4851 in AK58 by VIGS, and the silenced lines were susceptible to yellow rust (Supplementary Fig. 8a,b). We also generated knockout mutants by CRISPR–Cas9 in the cultivar Fielder, which carries Yr6 . Three independent yr6 knockout mutant lines (KO#20, KO#43 and KO#47) containing frameshift mutations (Fig. 4 e) were obtained and showed susceptibility to Pst race V26/GS in contrast to the Fielder control in both microscopic and visual assessments (Fig. 4 f–h). Furthermore, RT-qPCR analysis revealed that TaAK4851 was strongly induced upon inoculation with Pst race V26/GS (Supplementary Fig. 8c), indicating that TaAK4851 was upregulated in response to YR infection. Subcellular localization analysis demonstrated that TaAK4851 is localized to the cytoplasm (Supplementary Fig. 8d). Ultimately, the comprehensive genetic, molecular and functional analyses indicated that TaAK4851 , the Pm5 allele, corresponds to Yr6 and confers resistance to Pst V26/GS. With Yr6 cloned, we compared its sequence to that of Pm5 . Amino acid sequence analysis showed that YR6 protein was 94.37% identical to PM5E protein, with differences in only 60 of the 1,061 amino acids (Fig. 4 i and Supplementary Table 16). According to the degree of association between amino acid variation and phenotype, we identified two amino acid sites that possibly recognized the different pathogens (Fig. 4 i). One, I366T located in the NBS domain, was essential for Pst resistance, and the other, M1011I, at the end of the carboxyl terminal (intrinsically disordered region) was required for Bgt resistance. To validate and expand upon these initial observations, we performed haplotype analysis in the present wheat accessions. Using 212 nonsynonymous variants of Yr6 / Pm5 , we removed samples with a missing rate more than 20% and classified 1,449 of 2,191 wheat accessions into 16 haplotypes (Fig. 4 j and Supplementary Fig. 9a and Supplementary Table 17). Plants with haplotype Yr6_h8 in the representative cultivar ‘Fielder’ conferred resistance to Pst , but were susceptible to Bgt ; whereas those with Pm5_h4 (corresponding to Pm5e ) in the representative cultivar ‘Fuzhuang 30’ had Bgt resistance, but were susceptible to Pst ; the other haplotypes seemed to be susceptible to both pathogens (Fig. 4 k,l and Supplementary Fig. 9b,c and Supplementary Table 17). Furthermore, we conducted a comprehensive search for homologs of Yr6/Pm5 across 36 publicly available wheat genomes that include either B or S subgenomes (Source Data for Yr6 ). We classified these 36 homologs into twelve clusters based on sequence similarity (Supplementary Fig. 10). Notably, Yr6 was specifically localized within cluster C1, carrying the key variant C1098T; while Pm5e was absent from all examined published wheat genome sequences, confirming previous reports of its limited distribution 44 . Numerous variations in the promoter region and the whole gene coding region indicate the wide diversity of the YR6 / PM5 locus and underscore the need for verification of the functionality of other haplotypes in the future. Our findings indicate that different haplotypes of a single-copy NLR gene can confer resistance to taxonomically unrelated pathogens, and the two amino acid sites I366T and M1011I are potential targets for engineering Yr6 / Pm5 alleles with multiple recognition specificity in the future. TaEDR2-B confers broad-spectrum rust resistance without substantial yield penalty Broad-spectrum rust resistance (BSR) is a desirable trait conferring resistance to multiple isolates or pathogens. In this study, we identified a QTL ( QYr.nwafu406 ) stably expressed in all field environments, and which exhibited BSR characteristics (Supplementary Fig. 4b). A meta-analysis 45 showed that QYr.nwafu406 co-located with the previously reported YrKB ( QYr.nwafu-7BL.1 ) on chromosome arm 7BL 46 , 47 , and considered to be a QHR 48 . LD analysis on the peak region of QYr.nwafu406 ( YrKB ) showed that the significant SNPs were located between 726.56 Mb and 727.35 Mb in IWGSC RefSeq v2.1 (Fig. 5 a and Supplementary Fig. 11a–c). To further verify the GWAS signal and narrow down the candidate region, we fine-mapped YrKB using the AFLA RIL population developed from a cross of AvS and resistant cultivar Flanders (FLA) and selected HIFs, and KJ RIL population from a cross of susceptible cultivar Kenong 9204 (KN9204) and resistant cultivar Jing 411 (J411) (see Methods). QYr.nwafu-7BL.1 ( YrKB ) was identified with PVE ranging from 18.2 to 40.5% (Supplementary Fig. 11d), and YrKB was mapped to a 0.09 cM genetic interval corresponding to 266.2 kb with 24 high-confidence genes in IWGSC RefSeq v2.1 (Fig. 5 b and Supplementary Table 18). RNA-seq and genome resequencing data revealed that these genes except for TraesCS7B03G1236800 (hereafter abbreviated as Ta12368 ) were neither expressed nor had nonsynonymous variations between the two pairs of parents (Supplementary Tables 19 and 20), indicating that Ta12368 was the most likely candidate for YrKB . Gene-based association analysis with 40 variants identified three significant variants in Ta12368 between the parents. Two variants s7B_726855255 and s7B_726862747 caused G to T and C to G changes, resulting in a splice region variant and an arginine to glycine substitution, respectively. The third variation, s7B_726852804 , in the promoter region caused an A to G change that possibly affected regulation of the gene expression (Fig. 5 c–e and Supplementary Table 21). Ta12368 encodes a protein containing a steroidogenic acute regulatory protein-related lipid transfer (START) domain, a putative pleckstrin homology (PH) domain and an ENHANCED DISEASE RESISTANCE 2 domain (EDR2) and is a homolog of Arabidopsis thaliana AtEDR2 (henceforth tentatively named as TaEDR2-B in wheat). Phylogenetic analysis based on the START domain sequence revealed a close evolutionary relationship between TaEDR2-B and the BSR gene Yr36 49 (Supplementary Fig. 12 and Supplementary Table 22). These findings imply that TaEDR2-B could be associated with a broad-spectrum source of yellow rust resistance. To confirm the causal gene underlying YrKB -mediated resistance, we performed a forward genetic screen using an exome-sequenced mutant population of J411 50 , which harbors the same TaEDR2-B allele as in FLA (see Methods, Supplementary Table 21). Five mutant lines based on predicted functional impact and nucleotide base or amino acid change were obtained (231delATC_splice, 232delTCT_splice, G1882A_splice, G644D, and G665Q) (Fig. 5 e and Supplementary Table 23). Following infection with a mixture Pst races in the field, all five mutant lines (M1–M5) exhibited higher disease severities than Jing 411 ( P < 1.3e-05) (Fig. 5 f,g). We confirmed their YR responses at both the seedling and adult plant stages in the greenhouse. All five mutant lines and wild type Jing 411 were susceptible as seedlings. However, these mutants displayed higher susceptibility than Jing 411 at the adult plant stage (Supplementary Fig. 13a,b). These results indicated that TaEDR2-B was necessary for YrKB -mediated broad-spectrum resistance. We further transformed TaEDR2-B into the Pst -susceptible accession Zhengmai 7698 (ZM7698) that harbors a susceptible haplotype (Supplementary Table 21). Eleven positive T 3 transgenic overexpression (OE) lines were obtained and their responses to Pst were assessed. Histological observations on infected leaves of both seedlings and adult plants indicated that TaEDR2 -OE seedlings exhibited abundant hyphal growth like ZM7698 whereas the fungal growth was significantly restricted in adult plants (Fig. 5 h). These observations were consistent with visible YR symptoms on the leaves in greenhouse (Supplementary Fig. 14). All 11 OE lines were much more resistant (mean disease severity, 28.8%) to yellow rust than ZM7698 (83.4%) when tested in the field (Fig. 5 i–k). Transcription of TaEDR2-B in adult FLA plants was induced more than 7-fold at 5 dpi (Supplementary Fig. 15a) indicating that TaEDR2 - B expression is induced by YR infection. Subcellular localization analysis demonstrated that TaEDR2-B was diffusely distributed in the cytoplasm (Supplementary Fig. 15b). These results showed that TaEDR2-B was sufficient to confer YrKB -mediated broad-spectrum rust resistance and contribute to our understanding the role of TaEDR2-B in plant defense. Agronomic assessment of TaEDR2-B OE lines along with ZM7698 in the field under rust-free and rust-affected conditions showed no significant difference in spike length, spikelet number per spike, kernel number per spike, heading date (Supplementary Fig. 16a,b). The mean grain yields of TaEDR2-B OE lines was equal to ZM7698 under rust-free conditions (Supplementary Fig. 16c,d) and there was a significant increase (9.09%) in grain yield compared to ZM7698 in the presence of yellow rust (Supplementary Fig. 16d). These results indicate that TaEDR2-B has no negative effect on agronomic traits under rust-free conditions, and it could potentially boost grain yield under rust-affected conditions by enhancing resistance to yellow rust. Further haplotype analysis with 40 variants based on WGS data covering the promoter and coding region of TaEDR2-B identified eight haplotypes ( TaEDR2-B_h1 ~_ h8 ) among 2,183 wheat accessions (Supplementary Fig. 17a,b and Supplementary Table 21). Lines with TaEDR2-B_h8 ( n = 166, 7.4%), had the largest mean effect on resistance to yellow rust (1.1e-08 ≤ P ≤ 3.5e-04), and additionally showed resistance to leaf rust (Supplementary Fig. 17c,d and Supplementary Table 21). To fully resolve DNA sequence variations in TaEDR2-B , a survey of sequences covering the promoter and gene coding regions of 60 genomes (Source Data for TaEDR2-B ) identified several new variations in the coding region and numerous structural variations in the promoter region (Supplementary Fig. 18), indicating that future studies should also focus on the promoter region. All the above findings reveal that TaEDR2-B_h8 is an elite haplotype conferring resistance to yellow rust and probably leaf rust without yield penalty and has significant potential for targeting in breeding. Discussion The continuous emergence of new races of rust fungi enforces ongoing exploration for new resistance genes for use in breeding for resistance. The importance of incorporating diversity in breeding programs is widely acknowledged, but if diversity does not provide selectable allelic variation in key traits implementation may be questioned. In this study, high-throughput genome analysis, multirace/multi-environment phenotyping of yellow rust response, and GWAS analyses of large populations enabled us to decipher the global genetic basis of YR resistance. Using the prioritized candidate gene pipeline combining omics datasets and multiple bioinformatics methods, we identified candidate genes underlying resistance to yellow rust. The allele distributions of these genes in different populations constitute a valuable resource that can be used in crop improvement, such as trait identification and alteration, breeding programs and allele optimization. Moreover, it will be intriguing to investigate whether convergent gene networks or distinct genetic mechanisms underlie domestication and improvement of wheat and other grain crop species. Functional allele mining is a research field that investigates allelic variation for significant traits within genetic resource collections, especially regarding resistance genes of known function. We identified a substantial set of potential functional alleles of established R genes, specifically confirming the presence of Yr5x and Yr6/Pm5. For locus YR5 , five haplotypes were previously identified in sequenced wheat cultivars and alleles Yr5a and Yr5b were functionally validated 41 . In this study, we confirmed that the allele Yr5x in cultivar Xinong 3517 conferred resistance to Chinese Pst race TSA-V5. Due to an incomplete sequence of the YR5 locus in IWGSC RefSeq v2.1 51 , we could not explore YR5 diversity using the WGS data alone. We therefore carried out multiple alignment of YR5 homologs to find additional variations. The identified multiple Yr5 alleles will serve as valuable tools for identifying their corresponding Avr genes and resistance mechanisms. It is common to find allelic series of NLR receptors that serve as significant sources of genetic variation in wheat, for instance, 17 functional alleles of PM3 52 ; six verified stem rust resistance alleles of SR9 53 ; and four validated alleles of each of SR13 54 and LR21 55 , respectively. These multiallelic resistance loci are thought to have evolved under strong diversifying selection to recognize different specific avirulence ( Avr ) effectors secreted by the respective pathogens. More importantly, Yr6 and Pm5 conferring immunity to two different fungal diseases were found to be different haplotypes at a single R locus. To our knowledge, this is the first report of NLR variation in a wheat R gene conferring resistance to yellow rust and powdery mildew. Other similar examples in the Triticeae include Yr27 / Lr13 , with 97% amino acid homology and conferring resistance yellow rust and leaf rust respectively 56 ; a single NLR, Lr85 / Yr87 , also confers resistance to both leaf rust and yellow rust 12 ; Mla7 and Mla8 in Hordeum vulgare confer resistance to both barley powdery mildew and wheat stripe rust 57 ; Pm4f functions against powdery mildew and wheat blast 58 , 59 ; a pair of tandem kinase (WTK) genes Pm24 / Rwt4 have functional haplotypes for resistance to wheat blast and powdery mildew, respectively 60 , 61 . As indicated above, these findings give a deeper comprehension of the evolutionary trajectory of allelic series of disease resistance genes. Encouragingly, in this study a considerable number of promising alleles of known R genes having significant association with YR resistance were uncovered using large-scale genomic variation data coupled with multirace phenotype data. Obtaining the molecular identity of these genes by identification of key amino acids determining pathogen specificity will provide prospects for engineering novel variations conferring new specificities to single pathogens or possibly multiple pathogens 62 . Consequently, by altering the adaptive landscape, breeders could impose evolutionary constraints on pathogen populations, leading to prolonged resistance durability 63 . Although utilization of highly resistant cultivars offers an effective approach for controlling crop disease, genetic immunity often carries an unintended reduction in growth and yield 64 , 65 . Some resistance genes lead to substantial fitness penalties such as mlo in wheat and SWEET genes in rice 66 , 67 . There have been many recent advances in understanding the molecular mechanism underlying the trade-off between resistance and crop yield 68 . Here, we isolated TaEDR2-B , which conferred partial resistance to multiple Pst races with no evidence of yield penalty. Phylogenetic analysis of the gene indicated high similarity to Yr36 . START domains, possessing a hydrophobic ligand binding pocket, have a role in lipid/sterol binding, transport, and signaling across animal and plant species 69 . Therefore, it will be of interest to investigate the lipid ligands associated with the START domain in TaEDR2-B and further balancing of resistance and yield during Pst infection. The understanding will provide clues for engineering favorable crop cultivars carrying enhanced disease resistance without yield penalty. In addition, we developed a series of genetically diagnostic markers for detection of Yr5b / 5x , Yr6 and TaEDR2-B , and validated them in a panel of 576 wheat cultivars and breeding lines (Supplementary Tables 24 and 25). In summary, our investigation highlights the utility of whole-genome resequencing in large populations to provide a resource to improve understanding of the genetics of wheat disease resistance and to inform future studies on allelic variation of relevant traits within resource collections, thereby facilitating wheat improvement. The alarm bell is always rung to signify the message that "rust never sleeps", nevertheless, R gene never dies and will be revived like the phoenix rising from the ashes. Methods Plant and pathogen materials To represent the widest genetic diversity in common wheat we collected 14,688 common wheat accessions from worldwide resources, including modern cultivars, advanced breeding lines, core germplasm collections, founder parents, and landraces 21 – 27 . From these, 1,629 accessions were selected for GWAS covering most of the worldwide diversity, including 666 with accessible genome sequence information (29 from Cheng et al. 70 , 85 from Hao et al. 71 , 105 from Zhou et al. 72 , 92 from Guo et al. 73 , and 355 from Niu et al. 74 ), 349 newly sequenced accessions using WGS, and 614 accessions genotyped using 660 K SNP array in the present study. Five hundred and sixty-two among 768 publicly published genome re-sequences from the Gatersleben Genebank of Leibniz Institute of Plant Genetics and Crop Plant Research 28 were also integrated to generate a high-density genome variation map and validate the frequencies of favorable haplotypes. Additionally, 280 K SNP genotyping data for 4,506 wheat accessions in the INRAE global collection were also used in evaluating genetic diversity 30 . Integrated geographic and yellow rust epidemiological information identified 8 macrogeographic regions (Fig. 1 b) 6 , 8 , 18 – 20 . The final panel included 2,191 accessions from the 8 macrogeographic regions: 174 from West and Central Asia, 97 from South Asia, 272 from Africa, 658 from East Asia, 106 from Latin America, 639 from Europe, 193 from North America, and 52 from Oceania. There were 1,382 (63.07%) winter, 739 (33.73%) spring, and 34 (1.55%) facultative wheat types; 684 (31.22%) were landraces, 135 (6.16%) were pre-Green Revolution before 1960, 608 (27.75%) registered 1960–2000, and 764 (34.87%) registered after 2000. The plant materials from which information was extracted are available at the Chinese Crop Germplasm Resources Information System (CGRIS), the USA National Small Grains Collection (NSGC), Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) GeneBank, International Centre for Agricultural Research in Dry Areas (ICARDA) GeneBank, and the International Maize and Wheat Improvement Center (CIMMYT) Wheat Germplasm Bank. Passport data including year of registration or naming, geographic origin, growth habit, and pedigree where available are listed in Supplementary Table 1. Genetic mapping populations recombinant inbred lines (RILs) and selected segregating RILs (heterozygous inbred families, HIFs) from five bi-parental crosses were used for validation of GWAS signals. They were Avocet S (AvS)/Baomai 6 (BM6) containing 142 ABM6-RILs and 3,645 HIFs, AvS/Xinong 3517 (XN3517) containing 162 AXN3517-RILs and 3,586 HIFs, AvS/Aikang 58 (AK58) containing 128 AAK58-RILs and 4,452 HIFs, Kenong 9204 (KN9204)/Jing 411 (J411) containing 188 KJ-RILs, and AvS/Flanders (FLA) containing 184 AFLA-RILs and 5,426 HIFs. We integrated seedling stage resistance spectrum signatures of the GWAS panel for a total of 12 historically widespread and emerging Pst races in China under controlled greenhouse. Although these Pst races were from China, they shared virulence factors with the races from other countries 75 . The avirulence/virulence arrays and their distributions should be useful in breeding stripe-rust-resistant wheat cultivars. Among of them, CYR17, CYR23, CYR29, CYR31, Sull-4 and Sull-5 were predominant races pre-2000, whereas CYR32, CYR33, CYR34 predominated post-2000 and races V26/GS, V26/SC and TSA-V5 are currently widespread. The races were collected from Gansu, Shaanxi and Sichuan provinces and they are maintained by the Institute of Plant Pathology, Northwest A&F University. The virulence/avirulence patterns of the races were confirmed by testing near-isogenic lines of AvS, Chinese differentials, and specific Yr gene donor lines 23 , 27 . Puccinia triticina f. sp. tritici ( Pt ) race PHQS (provided by Prof. Shisheng Chen, Peking University Institute of Advanced Agricultural Sciences, Beijing) and Blumeria graminis f. sp. tritici ( Bgt ) race Bgt21-2 (provided by Dr. Lijun Yang, Hubei Academy of Agricultural Sciences, Wuhan) were used in leaf rust and powdery mildew phenotyping. Planting and evaluation of disease response Greenhouse trials Tests were conducted under controlled conditions to characterize seedling responses of the GWAS panel, genetic populations and transgenic lines. Generally, 10–15 seedlings were grown in 9 × 9 × 9 cm pots, or three plants were grown in 20 × 20 × 15 cm pots for adult-plant tests. For Pst tests, seedlings at the two-leaf stage (14 days after planting) and adult-plants at booting were separately inoculated with Pst urediniospores of each race mixed with talc (approximately 1:20). Inoculated plants were incubated at 10°C in a dew chamber in darkness for 24 h, and then transferred to a greenhouse at 17 ± 2°C with 14 h of light (22,000 lx) daily. Infection types (IT) were recorded 18–21 days post inoculation (dpi) using a 0–9 scale of increasing reaction 76 . For Pt tests, seedlings at the three-leaf stage were inoculated with fresh Pt urediniospores mixed with talcum powder at a ratio of 1:20. The inoculated plants were placed in a dark dew chamber set at 22°C for approximately 24 h and then maintained at 22–24°C with a 16 h photoperiod. ITs of plants were scored at ~ 12 dpi using a 0–4 scale 77 . For Bgt tests, seedlings at the two-leaf stage were inoculated with Bgt isolates and ITs for each line were scored on a scale of 0–4 at 7 dpi when susceptible control plants displayed severe symptoms 61 . Field trials The GWAS panel of 1,629 wheat accessions, genetic populations and transgenic plants were grown at 12 locations across four years (2019–2022): Yangling (YL, 2019–2022) in Shaanxi province; Jiangyou (JY, 2019–2021) in Sichuan province; Tianshui (TS, 2019–2021) in Gansu province; Chongqing (CQ, 2021); and Guiyang (GY, 2021) in Guizhou province. All field management activities were conducted according to local cultivation standards. All trials were arranged in single randomized complete blocks. Each line was grown in 100-cm, 2-row plots with 30 cm between rows. Xiaoyan 22 as a susceptible check was planted after every 20 rows. Inoculum spreader rows containing a mixture Mingxian 169 and Avocet S were planted around the plot areas. Tianshui, Jiangyou, Chongqing and Guiyang are hotspot regions for natural yellow rust development and nurseries regularly become infected without artificial inoculation. Trials at Yangling were inoculated with a mixture of Pst races CYR32, CYR33, CYR34 suspended in liquid parafin (1:300) sprayed onto the spreaders at flag leaf emergence. Test rows were visually rated for infection type (IT) and disease severity (DS) 18–20 days post-flowering when severity levels on the susceptible checks reached maximum levels of 90–100% (For Pt tests, only DS was recorded). Disease severity was assessed visually using percentage diseased leaf area based on the modified Cobb scale 78 . Analyses of phenotypic data For each environment, the maximum phenotypic score was used as a phenotypic measure. Genotypes (1,629 accessions) and environments were treated as random effects in a linear mixed model to estimate best linear unbiased estimators (BLUEs) using the lme4 package in R 3.5.3 79 . The yellow rust response data for each environment were subjected to analysis of variance (ANOVA) along with the mean data across environments (BLUE). Broad-sense heritability ( H 2 ) estimates were calculated using the lme4 package with the formula H 2 = V G /(V G + V E + V G×E ), where V G, V E and V G×E represent the genotypic, environmental and their interaction variances, respectively. Pearson’s correlation coefficients ( r ) of pairwise environments were computed using the Hmisc package to determine the consistency of yellow rust response across environments. Sample preparation and sequencing Genomic DNA was extracted from seedlings of 349 wheat accessions using a Plant Genomic DNA Kit (Tiangen, Beijing) according to the manufacturer’s instructions. Each sample with approximately 10 mg of DNA was used to construction a 350-bp paired-end library with the Bioruptor Pico Sonication System (diagenode). Then, the libraries were sequenced with the DNBSEQ platform of BGI-Shenzhen, generating 150-bp paired-end reads by whole genome resequencing producing ~ 4.9 × 10 12 100-bp paired-end reads and average sequencing coverage depth of ~ 11× for each accession. Total RNA with three biological replicates was isolated using a RNeasy Plant mini kit from seedling or flag leaves of 8 accessions (parents of genetic mapping populations, and susceptible controls Mingxian 169 and Xiaoyan 22) at 48 h post inoculation with Pst urediniospores or water, respectively. Prior to RNA isolation, the leaf tissues from the three replicates were pooled in equal proportions. As described above, RNA-seq libraries were constructed and sequenced using BGISEQ500 on the DNBSEQ platform to generate 150-bp paired-end reads. Transcripts Per Million reads (TPM) values were calculated for each gene with Salmon (v.1.9.0) 80 , with TPM less than one considered as no expression. Sequencing data integration, read mapping and variant calling WGS reads from 1,577 accessions were mapped to the Chinese Spring (CS) Refseq v2.1 reference assembly using BWA mem (v.0.7.17) 81 . The mapped reads were subsequently sorted according to genomic position using the SAMtools command sort (v.1.11) 82 . Duplicated reads were marked and read groups were flagged using the Picard tools ( http://broadinstitute.github.io/picard/ ). HaplotypeCaller from GATK (v.4.1.8.0) 83 was utilized to detect variants and generate individual specific.gvcf files, which were then followed by a joint variant calling process conducted by GenotypeGVCF. SNPs were hard filtered using SnpSift Filter 84 removing putative variants according to the following criteria: ‘(!(exists SNP) & (QD > 2.0) & (FS < 200.0) & (SOR 2.0) & (MQ > 40.0) & (FS < 60.0) & (SOR -12.5) & (ReadPosRankSum > -8.0))’. These variants were annotated using snpEff (v.5.1d) 85 . Mapping statistics were calculated from the BAM files using SAMtools (v.1.11; option ‘flag-stat’) to obtain the number of mapped reads, and the mapping rate was then calculated as follows: (the number of mapped reads/the total number of reads) × 100. The variant density was calculated using bin sizes of 1 Mb, and the nucleotide diversity was calculated in sliding windows of 10,000 bp per chromosome using VCFtools (v.0.1.17) 86 . Genotype imputation Genotype data from the 1,577 sequenced accessions was used as a reference panel for imputation. An integrated VCF file was created, including 614 accessions genotyped by the wheat660K SNP array and the reference panel. Beagle v.5.4 (beagle.22Jul22.46e.jar) 87 was then used to impute missing genotype calls with the following settings: ‘gp = true window = 40 ne = 2000’. Genotype calls with probability (GP) 30% heterozygous genotype calls or > 30% missing data were removed, resulting in a set of about 84,855,324 variants. Population structure and linkage disequilibrium Using 200,000 randomly selected variants, we performed PCA to identify genetic relationships between accessions with PLINK (v.1.90) 88 ; an unrooted neighbour-joining phylogenetic tree was generated using FastTree (v 2.1.11) 89 with the following settings: ‘-nt -gtr’; the population structure was assessed using ADMIXTURE version 1.3.0 90 . Linkage disequilibrium (LD) decay was computed for WGS data as the squared correlation coefficient ( r 2 ) with plink (v.1.90) using the methods described by Wu et al. 25 . LD blocks were defined as groups of SNPs meeting a threshold of 0.8, with block size determined by the physical distance between the outermost flanking SNPs. Adjacent blocks that might still maintain strong linkage disequilibrium were merged into larger ones using the method described by Cheng et al. 91 . The average linkage disequilibrium (LD) decay distance for the whole genome was approximately 3.2 Mb (Supplementary Fig. 19a). The LD decay of all chromosomes ranged from 0.62 Mb to over 100 Mb, indicating that different genomic regions were subjected to artificial selection and the haplotype diversity is extensive in this diversity panel (Supplementary Table 26 and Supplementary Fig. 19b–d). Therefore, establishment of confidence intervals for QTL-harboring regions was based on the independent LD block size at each side of the peak of significant associations. Meta-analysis and GWAS To provide a more detailed characterization and comparison of the identified loci/genes in this study, we collected information on 1,125 QTL/genes for yellow rust resistance and all cloned R genes or homologs for resistance to various diseases in the Triticeae as well as QTL/gene mapping data from more recent GWAS, QTL mapping and meta-QTL studies 92 , 93 from 175 publications over the past three decades. With information for individual QTL/genes, such as: (i) type and size of the mapping population, (ii) flanking markers and their physical positions on the map, (iii) peak positions, (iv) phenotypic variance explained (PVE or R 2 value), and (v) logarithm of the odds (LOD) score (QTL mapping) or p -value (GWAS), we compiled an integrated reference physical map of YR resistance loci for ease of later comparison. A total of 1,125 QTL/genes and over 40 cloned resistance genes/alleles or homologous genes were positioned across the 21 wheat chromosomes based on reference genome IWGSC RefSeq v2.1 as follows: (1) the sequences of the closest linked markers or two flanking markers of the QTL confidence interval were retrieved from various databases, including WheatOmics ( http://wheatomics.sdau.edu.cn ), CerealsDB ( https://www.cerealsdb.uk.net ), GrainGenes ( https://wheat.pw.usda.gov/GG3 ), DArT ( https://www.diversityarrays.com ), and URGI ( https://wheat-urgi.versailles.inra.fr/ ); (2) physical positions of the above marker sequences obtained from the RefSeq v2.1 assembly using BLASTN; (3) physical locations of each QTL were determined by calculating the confidence intervals defined by flanking or closest linked marker positions. If the flanking interval was less than 10 Mb, the midpoint was used. Otherwise, the entire flanking interval was used to display the QTL. Presence of one independent QTL (iQTL) on the same chromosome was indicated if the QTL was in a different LD block, while QTL hotspot regions were classified if the distance was less than 10 Mb or within the Meta-QTL (MQTL) region. This distance was chosen as a conservative estimation that mirrored the scope of previous QTL mapping studies and meta-analysis studies 92 , 93 . We performed multiple GWAS analyses at different levels using both the FarmCPU and MLM models 94 : that is, the entire panel of 1,629 accessions using all the genotypic data (614 accessions with imputation data); 614 accessions genotyped by the 660K SNP array; 1,015 accessions genotyped by WGS; landraces (n = 384, WGS), and modern cultivars (n = 637, WGS). In addition, to avoid detection of QTL for partial resistance masked by the presence of major resistance genes, accessions with high resistance (IT ≤ 4, DS ≤ 30) were removed and the remaining accessions (n = 484, WGS) comprised a new panel for moderate resistance. Association tests were carried out for: (1) all single Pst race data sets, (2) all single environment data sets, (3) best linear unbiased estimation (BLUEs) across experiments (years) for each location, and (4) BLUEs across all 12 environments. High-confidence marker–trait associations (MTAs) were filtered by the criteria: 1) significant DNA variations (SNPs and InDels) mapped within the interval of a meta-QTL associated with YR resistance; 2) significant DNA variations yielded by both the FarmCPU and MLM models; 3) significant DNA variations with large effect (-log 10 ( P ) > 6.00) in seedling tests; and 4) significant DNA variations simultaneously identified at least three field environments. QTL detected in at least six environments for IT and/or DS, or at least three environments when mapped within the interval of a meta-QTL were chosen to identify resistance genes with broad-spectrum resistance. Identification and prioritization of YR candidate genes Identification of causal genes that underlie complicated agronomic traits directly from GWAS results remains difficult. Taking into consideration false-positive and false-negative issues associated with GWAS 95 , we employed a comprehensive analysis pipeline using multi-omics datasets and multiple bioinformatics methods to prioritize candidate genes in QTL regions identified by GWAS. The filtering procedure was: 1) search for high-confidence genes located within the LD block and evaluate the gene expression to predict whether genes were associated with phenotypes using transcriptome datasets; 2) perform GO analysis with agriGO (v.2.0) of candidate genes and homologous genes with known function that were involved in biological pathways of plant defense responses. Enrichment significance was analyzed by Fisher’s exact test. Enrichment results with more than 5 annotations and FDR < 0.05 were plotted with the R package clusterProfiler (v.3.10.0); 3) using a five-level grouping method (referred to as G1–G5) to evaluate variant effects in gene regions based on the estimated functional importance of each nucleotide polymorphism as described by Yano et al. 96 . These levels included: G1, significant MTAs in the GWAS (-log 10 P ≥ the threshold value in a particular chromosome) that putatively caused amino acid conversion and alternative splicing; G2, significant MTAs in the 5′ flanking sequences (≤ 2 kb from the first ATG), which were considered to be promoter regions; G3, significant MTAs within the coding region but belonging to synonymous mutations, introns or 3′ noncoding sequences; G4, significant MTAs outside coding regions; and G5, polymorphic but MTA not significant; 4) calculate the degree of haplotype-based association for each potential gene using GAPIT 97 . Genetic mapping and positional cloning The four bi-parental RIL populations, i.e. ABM6, AAK58, AXN3517, and AFLA were genotyped using the wheat 16K SNP array. SNP marker filtering and evaluation was described in Wu et al. 98 . QTL detection was carried out using IciMapping 4.1 software 99 with default parameters and the PVE was used to evaluate the genetic effects of identified QTL. SNPs flanking target loci were converted to allele-specific quantitative PCR (AQP) markers to screen recombinants for fine mapping in the corresponding HIF populations. χ 2 tests were used to determine agreement of observed segregation and theoretically expected ratios. Linkage analysis and high-density genetic map construction was established using JoinMap v.4.0 100 with default parameters. Linkage to target loci was estimated with the Kosambi mapping function 101 and a LOD score of 3.0 as a threshold. The genetic linkage map was drawn using Mapchart v.2.3 102 . All parents were re-sequenced, and their genomic sequences at target loci regions were compared to identify sequence polymorphisms. Key polymorphic SNPs in candidate genes were also converted to diagnostic AQP markers 103 . Primers used for position cloning are listed in Supplementary Table 24. Virus-induced gene silencing (VIGS) We utilized siRNA-Finder (siFi21) software to search the predicted coding sequences of the Yr5x and Yr6 / Pm5 candidate genes to create candidate gene-specific probes for VIGS. Two fragments with a higher number of efficient and fewer off-targets to design silencing probes were selected and two probes designed for each candidate gene were flanked by PacI and NotI , then synthesized at Tsingke Biotech and subsequently cloned into the BSMV:γ vector. Each of the BSMV constructs (BSMV: Yr5x -1as and BSMV: Yr5x -2as for silencing Yr5x , BSMV:γ as control, and BSMV: TaPDS (phytoene desaturase-antisense) as a virus-positive control) was inoculated into the second seedling leaves. Mock control plants were treated with 1xFes buffer as per the protocol described in our previous work 104 . The treated seedlings were exposed to 100% relative humidity in darkness for 24 h and then transferred to an incubator at 25℃ for 9 days before phenotypic analysis. When TaPDS -silenced infection sites showed photobleaching, 4-leaf plants were infected with freshly harvested urediniospores of the Pst race TSA-V5. The inoculated leaves were sampled at 0 and 24 h for silencing efficiency assessment by Quantitative reverse transcription PCR (RT-qPCR). The images of yellow rust responses of the gene-silenced plants were recorded at 14 dpi using an Olympus BX-63 fluorescence microscope. BSMV infection and Pst race V26/GS inoculation for the Yr6/Pm5 candidate gene were performed as described above. All inoculation experiments were repeated as three biological replicates. Mutagenesis analysis of TaEDR2-B A forward genetic screen was conducted to validate TaEDR2-B as the candidate gene responsible for YrKB -mediated resistance. This was achieved using an exome-sequenced mutant population in the reference Chinese wheat cultivar Jing 411 (J411), which has the same TaEDR2-B haplotype as Flanders (Supplementary Table 21). J411 has displayed durable adult plant YR resistance since its release in 1987 and likely carries the multipathogen resistance locus Yr29 / Lr46 and other resistance QTL, including YrKB 105 . We searched for information about mutations relevant to TaEDR2-B using gene ID TraesCS7B03G1236800 on the public database ( http://jing411.molbreeding.com/#/ ) 50 and selected target mutant lines based on predicted functional impacts and nucleotide base or amino acid conversions. Wheat transformation To produce transgenic wheat plants overexpressing Yr5x and TaEDR2-B , the full-length coding sequences (CDS) from Xinong 3517 and Flanders were cloned behind the ubiquitin promoter using homologous recombination in a pCub vector (with the bar gene conferring basta resistance) to produce pUbi:Yr5x and pUbi:TaEDR2-B, respectively. Agrobacterium-mediated transformation generated independent transgenic lines of each construct in wheat cv. Fielder and cv. Zhengmai 7698, respectively. The presence of transgenes in T 0 to T 1 /T 2 generation plants was confirmed by PCR or RT-qPCR amplification using specific primers designed to detect the transgene from vectors. The CRISPR-Cas9 genome editing system was used to knock out Yr6 / Pm5 and generate yr6 mutant lines. The target sequences for the single guide RNA (sgRNA) were designed based on the exon sequences of Yr6 using online website WheatOmics ( http://wheatomics.sdau.edu.cn ) 106 . The sgRNA was constructed in a pENTR:gRNA4 vector, and converted to the final vector Cas9-PCL4 by Gateway technology and transformed into cv. Fielder using Agrobacterium. Positive transgenic plants were screened with hygromycin (100 mg/L) and genomic DNA was extracted to detect the Cas9 gene fragment and the hygromycin B phosphotransferase gene by PCR. The specific primers are listed in Supplementary Table 24. Microscopy of Pst –wheat interactions within infected leaves To observe the development of Pst on different genotypes, we collected Pst -infected leaf samples at 1, 2, 5, and 16 dpi. Pst hyphae were stained with wheat germ agglutinin conjugated to Alexa-488 (Invitrogen, USA) as described previously 107 with slight modifications. Briefly, the decolorized leaves were cut into 2 cm pieces, placed in a 10 mL centrifuge tube, washed twice with 5 mL of 50% ethanol, washed twice with distilled water for 10 min, boiled in water for 10 min, washed twice with distilled water, and then soaked in 5 mL of 50 mM Tris (pH 7.5) for 30 min before staining with 20 µg/mL WGA in darkness. The tissues were stained for 15 min and then washed with distilled water. WGA-stained tissues were examined under blue light excitation using a Zeiss LSM 880 confocal microscope. RNA extraction and RT-qPCR analysis Fresh leaves from plant materials were frozen in liquid nitrogen, and total RNA from inoculated leaves was extracted using TRIzol reagent (Takara, Dalian). We synthesized 1 µg of complementary DNA (cDNA) using TransScript® Uni One-Step gDNA Removal and cDNA Synthesis SuperMix (TransGen Biotech, Beijing). The resulting reverse transcript was diluted 1-fold and used for quantitative real-time PCR (RT-qPCR). A 20 µL RT-qPCR mixture was used, including 10 µL 2× ChamQ Blue Universal SYBR qPCR Master Mix (Vazyme, Nanjing), 0.4 µL 10 mM forward and reverse primers, and 3 µL diluted cDNA. PCR amplification started at 95°C for 30 sec, followed by 40 cycles of 95°C for 10 sec and 60°C for 30 sec. The default melting curve acquisition program of the CFX Connect Real-Time Instrument (Bio-Rad, Hercules, USA) was then used. Wheat genome TaActin was used as an internal control. Primers used for VIGS and RT-qPCR are listed in Supplementary Table 24. Subcellular location in N. benthamiana To determine the subcellular localization of YR6, and TaEDR2-B, the full-length CDS of the three genes were constructed into pCAMBIA1305-GFP vector to generate the corresponding GFP fusion vectors. All constructs were individually transformed into A. tumefaciens cells GV3101(pSoup-p19) (Weidibio, Shanghai). Agrobacterium cultures held overnight were collected by centrifugation, resuspended in MMA induction buffer, and incubated at room temperature for 2 h. For co-expression assays, two A. tumefaciens strains (e.g., strains carrying eGFP-tagged and mCherry-tagged constructs) were mixed in a 1:1 ratio. The mixture was infiltrated into N. benthamiana leaves and held in darkness for about 24 h. Leaf samples were collected after 72 h, and GFP and mCherry fluorescence was observed with a Zeiss LSM880 confocal laser microscope. Phylogenetic analysis The protein sequences of START and its orthologues from different plant species were extracted from the EnsemblPlants database ( http://plants.ensembl.org/index.html ). A phylogenetic tree was constructed using the maximum-likelihood method in the MEGA7.0 program 108 with bootstrap (1,000 replicates) and complete deletion. Alignments of Yr5x, Yr6 and TaEDR2-B homologs from different genomes We collected 60 complete and scaffolded genomes from diploid, tetraploid and hexaploid wheat as well as its related species (WheatOmics, http://wheatomics.sdau.edu.cn ; Supplementary Table 27) 109 – 112 . First, we employed the full-length target genes or additional promoter regions as reference sequence to align with each genome using BLASTN 113 and then performed multiple sequence alignments using Muscle 114 before presenting the results visually to deliver a detailed analysis of the evolutionary conservation and variability of homologous genes across different genomes. Throughout this process, we checked for the presence of the start “ATG” codon and stop codons to ensure completeness of the gene sequences. Finally, we determined the classification of the homologous gene sequences based on similarity clustering in the multiple sequence alignments. Declarations Data availability The raw sequence data for WGS and RNA-seq reported in this paper have been deposited in the Genome Sequence Archive 115 in National Genomics Data Center 116 , China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences (GSA: CRA004322, CRA014998, CRA005878 and CRA021484) that are publicly accessible at https://ngdc.cncb.ac.cn/gsa. The genotype data of the variant call format (VCF) was available in NGDC under accession number GVM000830 (https://ngdc.cncb.ac.cn/gvm/getProjectDetail?Project=GVM000830). To facilitate easier access and accelerate stripe rust resistance breeding, we have developed a dedicated database (https://wheat.dftianyi.com). All cloned genes PQ112656 ( Yr5x , original name Yr5c ), PQ112657 ( Yr6 ) and PQ112658 ( TaEDR2-B ) were available in NCBI GeneBank. Funding This work was supported by the National Key R&D Program of China (2021YFD1401000, 2021YFD1200600 and 2023YFD1200400), National Natural Science Foundation of China (Grant No. 31961143019, 32272088, 32201745, 32372138, 32372562, 32302377), Key R&D Program of Qinghai Province (2022-NK-125), Natural Science Basic Research Plan in Shaanxi Province of China (2019JCW-18, 2020JCW-16). Author Contributions J.W. designed the experiments, managed the project, performed most of the experiments, analyzed the data and wrote the manuscript. D.H., Q.Z., Z.K., and H.L. conceived and supervised the project and revised the manuscript. S.M. performed most of the genotype data analysis and GWAS results and wrote the manuscript. J.N. analyzed the data and wrote the manuscript. W.S., W.Z., Y.L., L.C., Y.W., and J.H. performed most of the experiments related to gene cloning and functional validation. H.D., J.Z., C.Z., T.C., and B.D. participated in data analysis and revised the manuscript. S.Z., R.Y., S.L., W.X., W.Z., and C.L. contributed to the field trails and conducted some phenotypic assays. All authors read and approved the final manuscript. Acknowledgements We take this opportunity to pay deep memory of Prof. Changfa Wang for his contributions in our group’s resistance breeding. The authors thank all members of the plant immunity research team in the State Key Laboratory of Crop Stress Resistance and High-Efficiency Production at Northwest A&F University for helpful comments; Drs. Meng Wang, Gang Li, Weilong Guo, Guangwei Li and Cong Jiang for their helpful suggestions and discussions; Prof. R.A. McIntosh for language editing and proofreading of the draft manuscript; Prof. Jun Guo, Drs. Jia Guo, Xingxuan Bai, Xueling Huang for assistance with genetic transformation; Drs. Xueling Huang, Qiong Zhang and Ms. Xiaona Zhou for RT-qPCR assays and AQP genotyping; Drs. Hua Zhao and Fengping Yuan for confocal experimental assistance; Ms. Haiying Wang for construction of a genomic DNA library; Dr. Guohao Han for assistance with provision of the pCAMBIA1305-GFP vector; Dr. Jingyang Tong for sharing most of the YR resistance QTL information; Drs. Jiuyuan Du, Yunliang Peng, Liyi Zhang, Wentao Zhang, Zhi Xu, Yuheng Yang, Bin Cheng and Qiang Yao for help in phenotyping. Competing Interests The authors declare no competing interests. Correspondence and requests for materials should be addressed to Jianhui Wu, Dejun Han, Qingdong Zeng, Zhensheng Kang or Hong-Qing Ling. References FAO. Food and Agriculture Organization of the United Nations (FAO) (2022). FAOSTAT. Crops. Latest update: December 27, 2023. Available online at: https://www.fao.org/faostat/en/#data/QC (accessed January 12, 2024) (2022). Savary, S. et al. The global burden of pathogens and pests on major food crops. Nat Ecol Evol 3, 430–439 (2019). Hafeez, A.N. et al. Creation and judicious application of a wheat resistance gene atlas. Mol Plant 14, 1053–1070 (2021). Hovmøller, M.S., Walter, S. & Justesen, A.F. Escalating threat of wheat rusts. Science 329, 369–369 (2010). Hovmøller, M.S., Thach, T. & Justesen, A.F. Global dispersal and diversity of rust fungi in the context of plant health. Curr Opin Microbiol 71, 102243–102243 (2023). Wellings, C.R. Global status of stripe rust: a review of historical and current threats. Euphytica 179, 129–141 (2011). Chen, X. Pathogens which threaten food security: Puccinia striiformis , the wheat stripe rust pathogen. Food Secur 12, 239–251 (2020). Beddow, J.M. et al. Research investment implications of shifts in the global geography of wheat stripe rust. Nat Plants 1, 15132 (2015). Bouvet, L. et al. The evolving battle between yellow rust and wheat: implications for global food security. Theor Appl Genet 135, 741–753 (2022). McIntosh, R., Mu, J., Han, D. & Kang, Z. Wheat stripe rust resistance gene Yr24/Yr26 : A retrospective review. Crop J 6, 321–329 (2018). McIntosh, R.A. Catalogue of gene symbols for wheat: 2024 supplement. https://wheat.pw.usda.gov/GG3/WGC (2024). Sharma, D. et al. A single NLR gene confers resistance to leaf and stripe rust in wheat. Nat Commun 15, 9925 (2024). Chen, R., Gajendiran, K. & Wulff, B.B.H. R we there yet? Advances in cloning resistance genes for engineering immunity in crop plants. Curr Opin Plant Biol 77, 102489 (2024). Dracatos, P.M., Lu, J., Sánchez Martín, J. & Wulff, B.B.H. Resistance that stacks up: engineering rust and mildew disease control in the cereal crops wheat and barley. Plant Biotechnol J 21, 1938–1951 (2023). Keller, B., Wicker, T. & Krattinger, S.G. Advances in wheat and pathogen genomics: Implications for disease control. Annu Rev Phytopathol 56, 67–87 (2018). Jaganathan, D., Bohra, A., Thudi, M. & Varshney, R.K. Fine mapping and gene cloning in the post-NGS era: advances and prospects. Theor Appl Genet 133, 1791–1810 (2020). McGuire, A.L. et al. The road ahead in genetics and genomics. Nat Rev Genet 21, 581–596 (2020). Ali, S. et al. Origin, migration routes and worldwide population genetic structure of the wheat yellow rust pathogen Puccinia striif ormis f. sp. tritici . PLoS Pathog 10, e1003903 (2014). Li, Z. & Zeng, S. Wheat rusts in China , (Chinese Agricultural Press (In Chinese), Beijing, 2002). Stubbs, R. Stripe rust. In: Roelfs AP, Bushnell WR, editors. The cereal rusts., Vol. Vol. II61-101 (New York (NY): Academic Press, 1985). Han, D. et al. Identification and evaluation of resistance to stripe rust in 1980 wheat landraces and abroad germplasm. Scientia Agricultura Sinica [In Chinese with English summary] 45, 5013–5023 (2012). Mu, J. et al. Identification of sources of resistance in geographically diverse wheat accessions to stripe rust pathogen in China. Crop Prot 122, 1–8 (2019). Wang, M. et al. Variation in TaSPL6-D confers salinity tolerance in bread wheat by activating TaHKT1;5-D while preserving yield-related traits. Nat Genet, 56, 1257–1269 (2024). Wu, J. et al. Association analysis identifies new loci for resistance to Chinese Yr26 -virulent races of the stripe rust pathogen in a diverse panel of wheat germplasm. Plant Dis 104, 1751–1762 (2020). Wu, J. et al. A large-scale genomic association analysis identifies the candidate causal genes conferring stripe rust resistance under multiple field environments. Plant Biotechnol J 19, 177–191 (2021). Zeng, Q. et al. Stripe rust resistance and genes in Chinese wheat cultivars and breeding lines. Euphytica 196, 271–284 (2014). Zhang, G. et al. Evaluation of the potential risk of the emerging Yr5 -virulent races of Puccinia striiformis f. sp. tritici to 165 Chinese wheat cultivars. Plant Disease 106, 1867–1874 (2022). Schulthess, A.W. et al. Genomics-informed prebreeding unlocks the diversity in genebanks for wheat improvement. Nat Genet 54, 1544–1552 (2022). Zhu, T. et al. Optical maps refine the bread wheat Triticum aestivum cv. Chinese Spring genome assembly. Plant J 107, 303–314 (2021). Balfourier, F. et al. Worldwide phylogeography and history of wheat genetic diversity. Sci Adv 5, eaav0536-eaav0536 (2019). Zhao, X. et al. Population genomics unravels the Holocene history of bread wheat and its relatives. Nat Plants 9, 403–419 (2023). Brar, G.S., Fetch, T., McCallum, B.D., Hucl, P.J. & Kutcher, H.R. Virulence dynamics and breeding for resistance to stripe, stem, and leaf rust in Canada since 2000. Plant Dis 103, 2981–2995 (2019). Hovmøller, M.S. et al. Replacement of the European wheat yellow rust population by new races from the centre of diversity in the near-Himalayan region. Plant Pathol 65, 402–411 (2016). Liu, T., Wan, A., Liu, D. & Chen, X. Changes of races and virulence genes in Puccinia striiformis f. sp. tritici , the wheat stripe rust pathogen, in the United States from 1968 to 2009. Plant Dis 101, 1522–1532 (2017). Milus, E.A., Kristensen, K. & Hovmøller, M.S. Evidence for increased aggressiveness in a recent widespread strain of Puccinia striiformis f. sp. tritici causing stripe rust of wheat. Phytopathology 99, 89–94 (2009). Wellings, C.R. & McIntosh, R.A. Puccinia striiformis f. sp. tritici in Australasia: Pathogenic changes during the first 10 years. Plant pathol 39, 316–325 (1990). Chen, X. & Kang, Z. Stripe Rust , (Springer, Netherlands, 2017). Ali, S. et al. Yellow rust epidemics worldwide were caused by pathogen races from divergent genetic lineages. Front Plant Sci 8, 1057–1057 (2017). Fradgley, N. et al. A large-scale pedigree resource of wheat reveals evidence for adaptation and selection by breeders. Plos Biol 17, e3000071-e3000071 (2019). Qiaosheng, Z. Chinese wheat improvement and pedigree analysis , (China Agriculture Press, Beijing, 2003). Marchal, C. et al. BED-domain-containing immune receptors confer diverse resistance spectra to yellow rust. Nat Plants 4, 662–668 (2018). Gardiner, L.J. et al. Mapping-by‐sequencing in complex polyploid genomes using genic sequence capture: a case study to map yellow rust resistance in hexaploid wheat. Plant J 87, 403–419 (2016). Jia, J. et al. Genome resources for the elite bread wheat cultivar Aikang 58 and mining of elite homeologous haplotypes for accelerating wheat improvement. Mol Plant 16, 1893–1910 (2023). Xie, J. et al. A rare single nucleotide variant in Pm5e confers powdery mildew resistance in common wheat. New Phytol 228, 1011–1026 (2020). Pal, N. et al. Meta-QTLs for multiple disease resistance involving three rusts in common wheat ( Triticum aestivum L.). Theor Appl Genet 135, 2385–2405 (2022). Huang, S. et al. Utilization of the genomewide wheat 55K SNP array for genetic analysis of stripe rust resistance in common wheat line P9936. Phytopathology 109, 819–827 (2019). Mu, J. et al. Genetic architecture of wheat stripe rust resistance revealed by combining QTL mapping using SNP-based genetic maps and bulked segregant analysis. Theor Appl Genet 132, 443–455 (2019). Rosewarne, G.M. et al. Analysis of leaf and stripe rust severities reveals pathotype changes and multiple minor QTLs associated with resistance in an Avocet × Pastor wheat population. Theor Appl Genet 124, 1283–1294 (2012). Fu, D. et al. A kinase-START gene confers temperature-dependent resistance to wheat stripe rust. Science 323, 1357–60 (2009). Xiong, H. et al. A large-scale whole‐exome sequencing mutant resource for functional genomics in wheat. Plant Biotechnol J 21, 2047–2056 (2023). Marchal, C. et al. Comparative genomics and functional studies of wheat BED-NLR loci. Genes 11, 1406 (2020). Bourras, S. et al. The AvrPm3-Pm3 effector-NLR interactions control both race-specific resistance and host-specificity of cereal mildews on wheat. Nat Commun 10, 2292–2292 (2019). Zhang, J. et al. Single amino acid change alters specificity of the multi-allelic wheat stem rust resistance locus SR9 . Nat Commun 14, 7354–7354 (2023). Gill, B.K. et al. Function and evolution of allelic variations of Sr13 conferring resistance to stem rust in tetraploid wheat ( Triticum turgidum L.). Plant J 106, 1674–1691 (2021). Naz, A.A. et al. Lr21 diversity unveils footprints of wheat evolution and its new role in broad-spectrum leaf rust resistance. Plant Cell Environ 44, 3445–3458 (2021). Athiyannan, N. et al. Long-read genome sequencing of bread wheat facilitates disease resistance gene cloning. Nat Genet 54, 227–231 (2022). Bettgenhaeuser, J. et al. The barley immune receptor Mla recognizes multiple pathogens and contributes to host range dynamics. Nat Commun 12, 6915 (2021). O'Hara, T. et al. The wheat powdery mildew resistance gene Pm4 also confers resistance to wheat blast. Nat. Plants 10, 984–993 (2024). Asuke, S. et al. Evolution of wheat blast resistance gene Rmg8 accompanied by differentiation of variants recognizing the powdery mildew fungus. Nature Plants 10, 971–983 (2024). Arora, S. et al. A wheat kinase and immune receptor form host-specificity barriers against the blast fungus. Nat Plants 9, 385–392 (2023). Lu, P. et al. A rare gain of function mutation in a wheat tandem kinase confers resistance to powdery mildew. Nat Commun 11, 680–11 (2020). Bentham, A.R. et al. Allelic compatibility in plant immune receptors facilitates engineering of new effector recognition specificities. Plant Cell 35, 3809–3827 (2023). McDonald, B.A. & Stukenbrock, E.H. Rapid emergence of pathogens in agro-ecosystems: global threats to agricultural sustainability and food security. Philos Trans R Soc Lond B Biol Sci 371(2016). Brown, J.K.M. Yield penalties of disease resistance in crops. Curr Opin Plant Biol 5, 339–344 (2002). Zhang, H., Liu, Y., Zhang, X., Ji, W. & Kang, Z. A necessary considering factor for breeding: growth-defense tradeoff in plants. Stress biology 3, 6–6 (2023). Oliva, R. et al. Broad-spectrum resistance to bacterial blight in rice using genome editing. Nat Biotechnol 37, 1344–1350 (2019). Wang, Y. et al. Simultaneous editing of three homoeoalleles in hexaploid bread wheat confers heritable resistance to powdery mildew. Nat Biotechnol 32, 947–51 (2014). He, Z., Webster, S. & He, S.Y. Growth–defense trade-offs in plants. Curr Biol 32, R634-R639 (2022). Schrick, K. et al. Shared functions of plant and mammalian StAR-related lipid transfer (START) domains in modulating transcription factor activity. BMC Biol 12, 70 (2014). Cheng, H. et al. Frequent intra- and inter-species introgression shapes the landscape of genetic variation in bread wheat. Genome Biol 20, 136–136 (2019). Hao, C. et al. Resequencing of 145 landmark cultivars reveals asymmetric sub-genome selection and strong founder genotype effects on wheat breeding in China. Mol Plant 13, 1733–1751 (2020). Zhou, Y. et al. Triticum population sequencing provides insights into wheat adaptation. Nat Genet 52, 1412–1422 (2020). Guo, W. et al. Origin and adaptation to high altitude of Tibetan semi-wild wheat. Nat Commun 11, 5085 (2020). Niu, J. et al. Whole-genome sequencing of diverse wheat accessions uncovers genetic changes during modern breeding in China and the United States. Plant Cell 35, 4199–4216 (2023). Sharma-Poudyal, D. et al. Virulence characterization of international collections of the wheat stripe rust pathogen, Puccinia striiformis f. sp. tritici . Plant Dis 97, 379–386 (2013). Line, R. & Qayoum, A. Virulence, aggressiveness, evolution, and distribution of races of Puccinia striif ormis (the cause of stripe rust of wheat) in North America 1968–1987. US Department of Agriculture Technical Bulletin No. 1788 , p74 (1992). Li, H. et al. Cloning of the wheat leaf rust resistance gene Lr47 introgressed from Aegilops speltoides . Nat Commun 14, 6072–6072 (2023). Peterson, R., Campbell, A. & Hannah, A. A diagrammatic scale for estimating rust intensity on leaves and stems of cereals. Canadian J Res 26, 496–500 (1948). Bates, D., Maechler, M., Bolker, B. & Walker, S. lme4: linear mixed effects models using Eigen and S4., pp 1–23 (2014). Patro, R., Duggal, G., Love, M.I., Irizarry, R.A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods 14, 417–419 (2017). Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26, 589 – 95 (2010). Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009). McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 20, 1297–1303 (2010). Cingolani, P. et al. Using drosophila melanogaster as a model for genotoxic chemical mutational studies with a new program, SnpSift. Front Genet 3, 35 (2012). Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff. Fly 6, 80–92 (2014). Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–8 (2011). Browning, B.L., Tian, X., Zhou, Y. & Browning, S.R. Fast two-stage phasing of large-scale sequence data. Am J Hum Genet 108, 1880–1890 (2021). Purcell, S. et al. PLINK: A tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81, 559–575 (2007). Price, M.N., Dehal, P.S. & Arkin, A.P. FastTree 2–approximately maximum-likelihood trees for large alignments. PLoS One 5, e9490 (2010). Alexander, D.H., Novembre, J. & Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res 19, 1655–64 (2009). Cheng, S. et al. Harnessing landrace diversity empowers wheat breeding. Nature 632, 823–831 (2024). Pal, N. et al. Meta-QTLs for multiple disease resistance involving three rusts in common wheat ( Triticum aestivum L.). Theor Appl Genet 135, 2385–2405 (2022). Kumar, S. et al. Comprehensive meta-QTL analysis for dissecting the genetic architecture of stripe rust resistance in bread wheat. BMC Genomics 24 (2023). Yin, L. et al. rMVP: A memory-efficient, visualization-enhanced, and parallel-accelerated tool for genome-wide association study. Genom Proteom Bioinf 19, 619–628 (2021). Chen, H., Patterson, N. & Reich, D. Population differentiation as a test for selective sweeps. Genome Res 20, 393–402 (2010). Yano, K. et al. Genome-wide association study using whole-genome sequencing rapidly identifies new genes influencing agronomic traits in rice. Nat Genet 48, 927–34 (2016). Wang, J. & Zhang, Z. GAPIT version 3: Boosting power and accuracy for genomic association and prediction. Genom Proteom Bioinf 19, 629–640 (2021). Wu, J. et al. Comparative genome-wide mapping versus extreme pool-genotyping and development of diagnostic SNP markers linked to QTL for adult plant resistance to stripe rust in common wheat. Theor Appl Genet 131, 1777–1792 (2018). Meng, L., Li, H., Zhang, L. & Wang, J. QTL IciMapping: Integrated software for genetic linkage map construction and quantitative trait locus mapping in biparental populations. Crop J 3, 269–283 (2015). Van Ooijen, J. JoinMap4, software for the calculation of genetic linkage maps in experimental populations. Kyazma BV, Wageningen (2006). Kosambi, D.D. The estimation of map distances from recombination values. Ann Eugen 12, 172–175 (1943). Voorrips, R.E. MapChart: software for the graphical presentation of linkage maps and QTLs. J Hered 93, 77–8 (2002). Liu, S. et al. Enhanced stripe rust resistance obtained by combining Yr30 with a widely dispersed, consistent QTL on chromosome arm 4BL. Theor Appl Genet 135, 351–365 (2022). Wang, N. et al. Inactivation of a wheat protein kinase gene confers broad-spectrum resistance to rust fungi. Cell 185, 2961–2974.e19 (2022). Xiang, M. et al. Yr29 combined with QYr.nwafu-4BL.3 confers durable resistance to stripe rust in wheat cultivar Jing 411. Theor Appl Genet 137, 11 (2024). Ma, S. et al. WheatOmics: A platform combining multiple omics data to accelerate functional genomics studies in wheat. Mol Plant 14, 1965–1968 (2021). Ayliffe, M., et al. Nonhost resistance of rice to rust pathogens. Mol. Plant Microbe Interact. 24, 1143–1155 (2011). Kumar, S., Stecher, G. & Tamura, K. MEGA7: Molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol Biol Evol 33, 1870–1874 (2016). Jiao, C. et al. Pangenome bridges wheat structural variations with habitat and breeding. Nature https://doi.org/10.1038/s41586-024-08277-0 (2024). Li, G. et al. Genomic analysis of Zhou8425B, a key founder parent, reveals its genetic contributions to elite agronomic traits in wheat breeding. Plant Commun https://doi.org/10.1016/j.xplc.2024.101222 (2025). Liu, Z. et al. Chromosome-level assembly of the synthetic hexaploid wheat-derived cultivar Chuanmai 104. Sci Data 11, 670 (2024). Walkowiak, S. et al. Multiple wheat genomes reveal global variation in modern breeding. Nature 588, 277–283 (2020). Camacho, C. et al. BLAST+: architecture and applications. BMC Bioinformatics 10, 421 (2009). Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res 32, 1792–1797 (2004). CNCB-NGDC Members and Partners. Database resources of the National Genomics Data Center, China National Center for Bioinformation in 2022. Nucleic Acids Res 50, D27-D38 (2022). Chen, T. et al. The genome sequence archive family: toward explosive data growth and diverse data types. Genom Proteom Bioinforma 19, 578–583 (2021). Additional Declarations There is NO Competing Interest. 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Arid Areas, Northwest A\u0026F University","correspondingAuthor":false,"prefix":"","firstName":"zhensheng","middleName":"","lastName":"kang","suffix":""},{"id":411839114,"identity":"774ae86d-1cc4-46a1-9583-5ef6fca1bfda","order_by":20,"name":"Qingdong Zeng","email":"","orcid":"https://orcid.org/0000-0002-7856-2340","institution":"State Key Laboratory for Crop Stress Resistance and High-Efficiency Production, Northwest A\u0026F University","correspondingAuthor":false,"prefix":"","firstName":"Qingdong","middleName":"","lastName":"Zeng","suffix":""},{"id":411839115,"identity":"1a3c629a-8420-423e-aac5-6a7ca895d177","order_by":21,"name":"Hong-Qing Ling","email":"","orcid":"https://orcid.org/0000-0001-9988-2282","institution":"Hainan Yazhou Bay Seed Laboratory","correspondingAuthor":false,"prefix":"","firstName":"Hong-Qing","middleName":"","lastName":"Ling","suffix":""},{"id":411839116,"identity":"93f4cd3c-dc14-4020-8928-2b715f9e05e2","order_by":22,"name":"Yimin Wang","email":"","orcid":"","institution":"Northwest A\u0026F University","correspondingAuthor":false,"prefix":"","firstName":"Yimin","middleName":"","lastName":"Wang","suffix":""},{"id":411839117,"identity":"9ec1d489-a889-4d92-a9be-67906b2255c4","order_by":23,"name":"Jinyu Han","email":"","orcid":"","institution":"Northwest A\u0026F University","correspondingAuthor":false,"prefix":"","firstName":"Jinyu","middleName":"","lastName":"Han","suffix":""}],"badges":[],"createdAt":"2024-04-12 13:11:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4257976/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4257976/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41588-025-02259-2","type":"published","date":"2025-07-22T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":78218941,"identity":"646390e5-fd83-404b-83cd-ab857f894fc8","added_by":"auto","created_at":"2025-03-11 05:31:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2878020,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGlobal geography of wheat yellow rust and wheat accessions, population structure and diversity of yellow rust response.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e, Global geography and estimated proportions of cultivars with resistant (medium seagreen), intermediate resistant (skyblue) and susceptible (coral) responses. Regions in maroon have regular occurrence of YR; regions in sandybrown have intermittent occurrence. These are divided into several wheat yellow rust epidemic regions (ER). All data relating to occurrence and losses are based on, or inferred from published work\u003csup\u003e6-8,18,33,37,38\u003c/sup\u003e. \u003cstrong\u003eb\u003c/strong\u003e, Geographic distribution of the 1,629-accession diversity panel. The numbers and proportion of landrace and cultivar accessions from each region are indicated by size of pie charts; landraces indicated in goldenrod and cultivars in seagreen. \u003cstrong\u003ec\u003c/strong\u003e, PCA representation of 2,191 wheat accessions on to that of 4,506 INRAE GeneBank samples. \u003cstrong\u003ed\u003c/strong\u003e,\u003cstrong\u003ee\u003c/strong\u003e, PCA plot depicting genetic diversity among 2,191 wheat accessions. Accessions are colored by assignment to subpopulations defined by ADMIXTURE (\u003cem\u003eK \u003c/em\u003e= 3 and 9, respectively). \u003cstrong\u003ef\u003c/strong\u003e, Maximum-likelihood phylogeny tree and model-based clustering of 2,191 accessions with ADMIXTURE (\u003cem\u003eK \u003c/em\u003e= 2 to 9). \u003cstrong\u003eg\u003c/strong\u003e, Field trial sites for phenotyping yellow rust response in multiple environments. \u003cstrong\u003eh\u003c/strong\u003e, Susceptible response of cultivar Jinmai 47 at Yangling in 2020. \u003cstrong\u003ei\u003c/strong\u003e, Heatmap visualization of YR responses of 1,449 wheat accessions across 12 field environments based on infection type (IT) and disease severity (DS). In the bar at the top dark green indicates the highest level of resistance, and orange indicates highest level of susceptibility. Images below the heat map are examples of resistant, intermediate and susceptible responses. N is the number of accessions in each group. The thin colored bar between the dendrogram and heatmap represents each accession from the 9 subpopulations.\u003c/p\u003e","description":"","filename":"Figsall1.png","url":"https://assets-eu.researchsquare.com/files/rs-4257976/v1/98923603d98be8e6149d20bc.png"},{"id":78216883,"identity":"d7830693-eca2-43b6-ae39-98c06095d210","added_by":"auto","created_at":"2025-03-11 05:07:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":588806,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA landscape for tracking YR resistance genes and plentiful effective alleles\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e, Genome-wide reference map for \u003cem\u003eYr\u003c/em\u003e loci. Chromosome lengths are based on the physical IWGSC RefSeq v2.1 map. QTL in blue font are potentially novel; QTL in orange cover known loci; QTL in grey are known loci but not identified in this study; QTL in purple font and bar chart are known or putative introgressed chromosome segments; QTL in darkcyan bar chart are QTL hotspot regions; QTL in red represent cloned resistance genes or genes that are widely used in breeding. References and confidence intervals for all QTL are provided in Supplementary Table 8. \u003cstrong\u003eb\u003c/strong\u003e–\u003cstrong\u003ee\u003c/strong\u003e, Selection signatures. Resistance allele frequencies (RAFs) of lead SNPs associated with \u003cem\u003eYr\u003c/em\u003e genes from the 1950s to 2010s. Set of selected types are clustered based on frequency changes for different alleles. For ease of viewing, the corresponding trend curves were fitted according to the statistical results. N is the number of \u003cem\u003eYr\u003c/em\u003e loci in each group. \u003cstrong\u003ef\u003c/strong\u003e, RAFs of common QTL hotspot in different breeding groups. \u003cstrong\u003eg\u003c/strong\u003e,\u003cstrong\u003e \u003c/strong\u003eRAFsof selected QTL in different breeding groups.\u003cstrong\u003e h\u003c/strong\u003e, Gene Ontology(GO) enrichment analysis of 559 expressed candidate genes within the 357 QTL regions affecting YR response. The top 30 GO terms were classified into eight pathways, i.e., immune response (P1), kinase signaling (P2), plant hormone (P3), transportation (P4), stress response (P5), nutrition (P6), ubiquitination (P7), gene expression (P8).\u003cstrong\u003e i\u003c/strong\u003e, Representative types of candidate genes. \u003cem\u003eYr\u003c/em\u003e, cloned \u003cem\u003eYr\u003c/em\u003e genes; Other \u003cem\u003eR\u003c/em\u003e, known \u003cem\u003eR\u003c/em\u003egene conferring resistance to other pathogens/pests; BSR, broad-spectrum quantitative resistance.\u003c/p\u003e","description":"","filename":"Figsall2.png","url":"https://assets-eu.researchsquare.com/files/rs-4257976/v1/e0d62b31ec9691a60555edc3.png"},{"id":78218943,"identity":"4bb075f9-6611-4079-bab2-0ee2ed5c3002","added_by":"auto","created_at":"2025-03-11 05:31:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":21963469,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIsolation of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eYr5 \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003ealleles.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e, Seedling responses of lines carrying different \u003cem\u003eYr5\u003c/em\u003e alleles and \u003cem\u003eYr7\u003c/em\u003e. \u003cem\u003eYr5a\u003c/em\u003e, \u003cem\u003eYr5b\u003c/em\u003e and \u003cem\u003eYr7\u003c/em\u003e were in near-isogenic lines (NILs) of Avocet S (AvS). \u003cem\u003eYr5x\u003c/em\u003e was in a recombinant inbred line (RIL) from cross AvS/XN3517. Scale bar, 1 cm. \u003cstrong\u003eb\u003c/strong\u003e, GWAS signals of \u003cem\u003eQYr.nwafu111\u003c/em\u003e on chromosome 2B for resistance to races CYR32 and TSA-V5. Each lead SNP variant (\u003cem\u003eP\u003c/em\u003e\u0026lt;10e-10) is highlighted in red. Progressive colors of the Manhattan plots indicate allele effects. \u003cstrong\u003ec\u003c/strong\u003e,\u003cstrong\u003ed\u003c/strong\u003e, Boxplot of IT scores to \u003cem\u003ePst \u003c/em\u003eraces CYR32 and TSA-V5 of two sets of genotypes at the leading SNP. N, number of accessions in each group. Statistical significance was determined by two-sided\u003cem\u003e t\u003c/em\u003e-tests. \u003cstrong\u003ee\u003c/strong\u003e, Fine mapping of \u003cem\u003eYr5b\u003c/em\u003eand \u003cem\u003eYr5x\u003c/em\u003e. N1, heterozygous inbred families (HIFs) from the ABM6 population; N2, HIFs from the AXN3517 population. Markers in: black, common to both N1 and N2; blue, only in N1; orange, only in N2; red, co-segregated with phenotype. \u003cstrong\u003ef\u003c/strong\u003e, Gene structures of \u003cem\u003eYr5a\u003c/em\u003e in Yr5NIL, \u003cem\u003eYr5b\u003c/em\u003e in AK58 and \u003cem\u003eYr5x\u003c/em\u003e in XN3517. White boxes, untranslated regions; lines, introns; other colored boxes, exons or specific domains; positions of the VIGS silencing probes are indicated in orange. \u003cstrong\u003eg\u003c/strong\u003e, BSMV-mediated silencing of \u003cem\u003eYr5x\u003c/em\u003e reduces resistance to yellow rust. Mild chlorotic mosaic symptoms were observed on leaves inoculated with BSMV:\u003cem\u003eYr5x\u003c/em\u003e-as1/2 at 9 dpi. Leaves inoculated with BSMV:\u003cem\u003eTaPDS\u003c/em\u003e and FES buffer were used as the positive control and BSMV:γ was used as the negative control. Responses of \u003cem\u003eYr5x\u003c/em\u003e-silenced and control plants 14 dpi with race TSA-V5. Scale bar, 1 cm. \u003cstrong\u003eh\u003c/strong\u003e, Seedling YR responses to \u003cem\u003ePst\u003c/em\u003e race TSA-V5 of wild type (WT) cultivar Fielder and transgenic \u003cem\u003eYr5x \u003c/em\u003eoverexpression (OE) lines. Scale bar, 1 cm. \u003cstrong\u003ei\u003c/strong\u003e, Development of \u003cem\u003ePst\u003c/em\u003e in seedling leaves of susceptible Fielder(WT) and Fielder(OE) at different days post-inoculation (dpi). Morphology of \u003cem\u003ePst\u003c/em\u003e race TSA-V5 mycelia observed using WGA staining. Histological analysis was performed at 1, 2, 5, and 16 dpi. Fielder(OE) seedlings had restricted mycelial development. \u003cem\u003eSV\u003c/em\u003e, substomatal vesicle; \u003cem\u003eIH\u003c/em\u003e, infected hypha; \u003cem\u003eHMC\u003c/em\u003e, haustorial mother cell. Scale bars, 25 μm.\u003c/p\u003e","description":"","filename":"Figsall3.png","url":"https://assets-eu.researchsquare.com/files/rs-4257976/v1/af6015c2c60f0d2d29ab519a.png"},{"id":78218942,"identity":"9dc7a13d-a65a-4f13-93bb-3a903cb3cf41","added_by":"auto","created_at":"2025-03-11 05:31:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1075631,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification and functional validation of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eYr6\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e/\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ePm5\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e, \u003cem\u003eQYr.nwafu405\u003c/em\u003e conferring resistance to \u003cem\u003ePst \u003c/em\u003erace V26/GS detected using FarmCPU models in GWAS. \u003cstrong\u003eb\u003c/strong\u003e, \u003cem\u003eYr6\u003c/em\u003e-based association mapping and pairwise LD analysis. The functional significant SNP variant (\u003cem\u003eP\u003c/em\u003e \u0026lt;10e-60) is highlighted in red. \u003cstrong\u003ec\u003c/strong\u003e, Boxplot of IT scores for the leading SNP in two sets of genotypes inoculated with race V26/GS. n denotes the number of accessions in each group. \u003cstrong\u003ed\u003c/strong\u003e, Map-based cloning of \u003cem\u003eYr6\u003c/em\u003e. N: HIFs, from the AAK58 population. Marker \u003cem\u003eM4417\u003c/em\u003e (red) developed from candidate gene \u003cem\u003eTaAK4851\u003c/em\u003e (red) co-segregating with YR response. The pentagons represent genes. \u003cstrong\u003ee\u003c/strong\u003e, Gene structure of \u003cem\u003eYr6\u003c/em\u003e in AK58 and mutations in the LRR domain generated by CRISPR/Cas9 editing. White boxes, untranslated regions; blue boxes, exons; lines, introns. The positions of the VIGS silencing probes are indicated in orange and sgRNA is indicated in red. The symbol ‘−’ indicates deleted nucleotides. \u003cstrong\u003ef\u003c/strong\u003e, Development of \u003cem\u003ePst\u003c/em\u003e in leaves of wild type (WT) cultivar Fielder containing \u003cem\u003eYr6 \u003c/em\u003eand CRISPR/Cas9-mediated \u003cem\u003eyr6\u003c/em\u003e-knockout Fielder lines (KO) at different days post-inoculation (dpi). Morphology of \u003cem\u003ePst\u003c/em\u003erace V26/GS hyphae in wheat leaves using WGA staining. Histological analysis was performed at 1, 2, 5 and 16 dpi. Restricted development of \u003cem\u003ePst\u003c/em\u003e in resistant Fielder containing \u003cem\u003eYr6\u003c/em\u003e. \u003cem\u003eYr6\u003c/em\u003e-KO Fielder lines showed extensive mycelial growth and colonization of mesophyll cells. \u003cem\u003eSV\u003c/em\u003e, substomatal vesicle; \u003cem\u003eIH\u003c/em\u003e, infection hypha; \u003cem\u003eHMC\u003c/em\u003e, haustorial mother cell. Scale bar, 25 μm. \u003cstrong\u003eg\u003c/strong\u003e,\u003cstrong\u003eh\u003c/strong\u003e, \u003cem\u003eYr6\u003c/em\u003e-KO Fielder lines with reduced yellow rust resistance. Responses (\u003cstrong\u003eg\u003c/strong\u003e) and IT scores (\u003cstrong\u003eh\u003c/strong\u003e) of Fielder(WT) and \u003cem\u003eyr6\u003c/em\u003e-knockout lines inoculated with \u003cem\u003ePst \u003c/em\u003erace V26/GS. CK, susceptible check, line 92R137.n denotes the number of plants in each group. Scale bar, 1 cm. \u003cstrong\u003ei\u003c/strong\u003e, Comparison of the Yr6 (left) and Pm5e (right) protein sequences. The two amino acids shown in red were found only in Yr6 and Pm5e, respectively, and are potentially associated with \u003cem\u003ePst\u003c/em\u003e and \u003cem\u003eBgt\u003c/em\u003eresistance highlighted by red arrows. \u003cstrong\u003ej\u003c/strong\u003e, Haplotype analysis of \u003cem\u003eYR6\u003c/em\u003ein 1,449 wheat accessions. Rows correspond to accessions and columns correspond to SNPs. Alleles of 0/0 and 1/1 are indicated by goldenrod and seagreen, respectively. \u003cem\u003eYr6\u003c/em\u003e-conferring\u003cem\u003e \u003c/em\u003eand \u003cem\u003ePm5\u003c/em\u003e-conferring\u003cem\u003e \u003c/em\u003ehaplotypes are framed. \u003cstrong\u003ek\u003c/strong\u003e, Disease responses of Fielder (\u003cem\u003eYr6\u003c/em\u003e)\u003cem\u003e \u003c/em\u003eand Fuzhuang 30 (\u003cem\u003ePm5e\u003c/em\u003e) inoculated with\u003cem\u003e Pst\u003c/em\u003e race V26/GS and \u003cem\u003eBgt\u003c/em\u003erace Bgt21-2, respectively. Scale bar, 1 cm. \u003cstrong\u003el\u003c/strong\u003e, IT scores of wheat accessions with or without \u003cem\u003ePm5e\u003c/em\u003e inoculated with \u003cem\u003eBgt\u003c/em\u003erace Bgt21-2. n denotes the number of accessions in each group. For Fig ‘c’, ‘h’, ‘l’, differences between groups were analyzed by two-sided\u003cem\u003e t\u003c/em\u003e-tests.\u003c/p\u003e","description":"","filename":"Figsall4.png","url":"https://assets-eu.researchsquare.com/files/rs-4257976/v1/bbbe5cf19032c1d836c452c9.png"},{"id":78216884,"identity":"fac00870-ded9-4f84-9fcd-23960e73c734","added_by":"auto","created_at":"2025-03-11 05:07:00","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1610603,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification and functional validation of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eTaEDR2-B\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e, Manhattan plots of GWAS for \u003cem\u003eQYr.nwafu406\u003c/em\u003e (\u003cem\u003eYrKB\u003c/em\u003e) using disease severity (DS) data from the BLUE. \u003cstrong\u003eb\u003c/strong\u003e, Fine mapping of \u003cem\u003eYrKB\u003c/em\u003e. HIFs from the AFLA population. Marker \u003cem\u003eM4996\u003c/em\u003e (red) developed from candidate gene \u003cem\u003eTa12368\u003c/em\u003e (red) co-segregated with YR response. The line and thick rectangle represent genes. \u003cstrong\u003ec\u003c/strong\u003e, Association mapping of genetic variation in \u003cem\u003eTaEDR2-B\u003c/em\u003e. Functionally significant variants (\u003cem\u003eP \u003c/em\u003e\u0026lt;10e-6) are highlighted in red. \u003cstrong\u003ed\u003c/strong\u003e, Boxplots of DS scores at the leading SNP for two set of genotypes. N, number of accessions in each group. \u003cstrong\u003ee\u003c/strong\u003e, Gene structure of \u003cem\u003eTaEDR2-B\u003c/em\u003e in Flanders. Mutations were generated in Jing 411 and subjected to pairwise LD analysis. White boxes, untranslated regions; blue boxes, exons; and lines, introns. Positions of five loss-of-function or alternative splicing mutations are indicated by dotted lines. \u003cstrong\u003ef\u003c/strong\u003e,\u003cstrong\u003eg\u003c/strong\u003e, Yellow rust responses (\u003cstrong\u003ef\u003c/strong\u003e) and corresponding DS scores (\u003cstrong\u003eg\u003c/strong\u003e, n=25) of wild type Jing 411 and mutants in the field. Scale bar, 2 cm. \u003cstrong\u003eh\u003c/strong\u003e–\u003cstrong\u003ek\u003c/strong\u003e, Genetic complementation suggests that \u003cem\u003eTaEDR2-B\u003c/em\u003e is necessary for \u003cem\u003eYrKB\u003c/em\u003e-mediated resistance. \u003cstrong\u003eh\u003c/strong\u003e, \u003cem\u003ePst\u003c/em\u003e development in seedling and adult leaves of susceptible Zhengmai 7698 (ZM7698, WT) and transgenic \u003cem\u003eTaEDR2-B\u003c/em\u003e overexpression lines (OE) at different days post-inoculation (dpi). Mycelial growth of mixed \u003cem\u003ePst\u003c/em\u003e races detected by WGA staining. Histological analysis at 1, 2, 5, and 16 dpi in seedling and adult leaves of ZM7698 (WT) and ZM7698(OE) lines. \u003cem\u003eSV\u003c/em\u003e, substomatal vesicle; \u003cem\u003eIH\u003c/em\u003e, infection hypha; \u003cem\u003eHMC\u003c/em\u003e, haustorial mother cell. Scale bar, 25 μm. \u003cstrong\u003ei\u003c/strong\u003e–\u003cstrong\u003ek\u003c/strong\u003e, Yellow rust responses and corresponding DS scores of ZM7698 and ZM7698 lines overexpressing \u003cem\u003eTaEDR2-B\u003c/em\u003e in the field. Scale bars: (i), 10 cm; (j), 2 cm. For Fig. ‘d’, ‘g’, ‘k’, statistical significance was determined by a two-sided\u003cem\u003e t\u003c/em\u003e-tests. Source data are provided as a Source Data file.\u003c/p\u003e","description":"","filename":"Figsall5.png","url":"https://assets-eu.researchsquare.com/files/rs-4257976/v1/a3ee5c1131c16962d94c4e35.png"},{"id":87371191,"identity":"a104023e-635d-4557-b106-b0e68b83bb19","added_by":"auto","created_at":"2025-07-23 07:12:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":32566014,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4257976/v1/eede9563-65e3-4ba4-96e4-82d6723590a3.pdf"},{"id":78216894,"identity":"526bbd84-6403-47b2-9002-0241f40f698e","added_by":"auto","created_at":"2025-03-11 05:07:00","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":29915017,"visible":true,"origin":"","legend":"","description":"","filename":"SuppleFigs.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4257976/v1/4d23141959b0ef7cb49476c9.pdf"},{"id":78216893,"identity":"f08fdf02-52a8-4402-86fa-574bbbac76c2","added_by":"auto","created_at":"2025-03-11 05:07:00","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":7736298,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4257976/v1/0901e0a6328d5ecc0fa45302.xlsx"},{"id":78216885,"identity":"30b928c3-0335-439c-9847-53c3fea951ca","added_by":"auto","created_at":"2025-03-11 05:07:00","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":27837,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-4257976/v1/e4d23abb6877fce0b997560c.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Genomics-guided landscape unlocks superior alleles and genes for yellow rust resistance in wheat","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBread wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.) is a leading cereal grain crop globally, serving as the main food source for 30% of the human population\u003csup\u003e \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e \u003c/sup\u003e. However, wheat production faces numerous constraints, with an average 21% of the global wheat harvest estimated to be lost due to diseases and pests\u003csup\u003e \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e \u003c/sup\u003e. Yellow rust (YR) or stripe rust, caused by the fungal pathogen \u003cem\u003ePuccinia striiformis\u003c/em\u003e Westend f. sp. \u003cem\u003etritici\u003c/em\u003e (\u003cem\u003ePst\u003c/em\u003e), remains a major problem in most wheat-producing regions\u003csup\u003e \u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e \u003c/sup\u003e. In the last 60 years, recurrent \u003cem\u003ePst\u003c/em\u003e epidemics have caused substantial yield losses\u003csup\u003e \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e \u003c/sup\u003e. It is estimated that an annual yield reduction of 5.47\u0026nbsp;million tons of wheat is attributable to this disease, equivalent to an annual loss of US\u003cspan\u003e$\u003c/span\u003e979 million\u003csup\u003e \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e \u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eChemical fungicides and resistant cultivars are the two most common strategies used to manage YR in wheat production. Fungicides are essential for controlling sudden YR outbreaks but with increased costs and a risk of environmental pollution. Resistant cultivars are more efficient, environmental-friendly and economic. With the use of high yielding clutivars and near mono-cropping in modern agriculture, the genetic diversity of a resistance (\u003cem\u003eR\u003c/em\u003e) gene deployed at any time is minimal and the crop is challenged by rare or evolving virulent pathogen races\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. This has necessitated a continuing search for new genetic resistance sources for deployment in future cultivars.\u003c/p\u003e \u003cp\u003eSo far, more than 200 new yellow rust genes (\u003cem\u003eYr\u003c/em\u003e) or quantitative trait loci (QTL) including 87 formally designated \u003cem\u003eYr\u003c/em\u003e genes have been reported\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Due to the complexity of the wheat genome, only 10 \u003cem\u003eYr\u003c/em\u003e genes have been cloned to date\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. The lack of regular and extensive worldwide surveys on the genetic basis of YR resistance limits the global deployment of \u003cem\u003eR\u003c/em\u003e genes based on pathogen-informed strategies\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. With the current availability of high-quality wheat reference genomes, advanced sequencing technologies, and rapid gene isolation methods, identification and cloning of disease-resistance genes become more efficient and cost-effective\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. These technologies provide opportunities for large-scale identification and cloning of novel disease-resistance genes and new alleles at known \u003cem\u003eR\u003c/em\u003e gene loci, and for identification of favorable haplotypes from diverse sequence variations.\u003c/p\u003e \u003cp\u003eIn this study, we generated variome data for 2,191 global common wheat accessions and more than 47,000 yellow rust response data-points across 12 \u003cem\u003ePst\u003c/em\u003e races and 12 field environments, and systematically analyzed YR resistance loci/QTL by a genome-wide association study (GWAS) (Supplementary Fig.\u0026nbsp;1). Based on the GWAS results, we constructed a genome-wide landscape of YR resistance genes, providing a valuable resource for their future deployment. Furthermore, we cloned three resistance genes and assessed their effectiveness against multiple \u003cem\u003ePst\u003c/em\u003e races and even different pathogen species. This comprehensive landscape of YR resistance genes and alleles enhances our understanding of YR resistance on a genome-wide level, assisting in geographic deployment of \u003cem\u003eR\u003c/em\u003e-gene diversity to match pathogen virulence profiles. The information and findings will help to develop wheat cultivars with durable YR resistance.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eOverview of a worldwide diverse panel of common wheat for YR resistance\u003c/h2\u003e \u003cp\u003e \u003cem\u003ePst\u003c/em\u003e urediniospores are windborne and can disperse at continental scales. Historically, based on the frequencies of widespread yellow rust epidemic occurrences and the worldwide population genetic structure of the pathogen, the global geography of wheat yellow rust can be divided into several main epidemic regions (ER)\u003csup\u003e6\u0026ndash;8,18\u0026minus;20\u003c/sup\u003e, i.e., South and East Asia (ER1-1, and ER1-2), Europe and Africa (ER2-1 and ER2-2), North and South of America, and Oceania (ER3-1, ER-3-2, and ER3-3) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Over past decades we collected 14,688 common wheat accessions covering most of the YR epidemic regions\u003csup\u003e\u003cspan additionalcitationids=\"CR22 CR23 CR24 CR25 CR26\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. To investigate the genetic resistance against YR in global wheat, 1,629 of 14,688 wheat accessions were carefully chosen to cover most of the diversity of the worldwide wheat collection according to growth habitat, status (i.e., landrace or cultivar), geographical origin, and genetic and phenotypic diversity (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb and Supplementary Table\u0026nbsp;1). Among them, 666 accessions had published genomic resequencing data (see Methods), 349 were newly genotyped by whole genome re-sequencing (WGS) (Supplementary Table\u0026nbsp;2) and the genotypes of the remaining 614 accessions were analyzed by the Wheat660K array. In addition, 562 of 768 common wheat accessions that had undergone WGS by the German national GeneBank (henceforth, IPK collection)\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e were integrated into the study after removing redundancy. Finally, a panel of 2,191 common wheat accessions assembled from worldwide sources was constructed for dissecting the genetic variations and resistances involved in global YR epidemics. For characterization of genotypic variation, we aligned more than 1.83 trillion reads from 1,577 accessions to the most recent reference genome of Chinese Spring (CS) RefSeq v2.1 assembly\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, and generated an extraordinarily abundant variation map comprising\u0026thinsp;~\u0026thinsp;84\u0026nbsp;million high-quality variants (80\u0026nbsp;million single nucleotide polymorphisms, SNPs, and 4\u0026nbsp;million insertion and deletions, InDels) (Supplementary Fig.\u0026nbsp;2 and Supplementary Table\u0026nbsp;3). The variation density was, on average, 5.6 variants per kilobase (kb), and more than 98% of high confidence genes of common wheat were covered by potential functional variants. Among the deleterious variants, 51,970 non-synonymous SNPs and 4,071 frameshift InDels were in genes annotated nucleotide-binding leucine rich repeat (NLR) immune receptors and kinase domains and covered 97% (5,422/5,562) of these two classes genes in common wheat (Supplementary Tables\u0026nbsp;3 and 4), suggesting their potential impact on gene function. Based on the results of principle component analysis (PCA) and genetic diversity (\u003cem\u003eπ\u003c/em\u003e), the panel exhibited comparable genetic diversity and geographic representativeness that of a phylogeographical and historical panel 4,506 wheat accessions reported previously (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec)\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePrincipal component analysis (PCA), phylogenetic tree, and ADMIXTURE were conducted using 200,000 randomly selected variants to investigate the population structure present in our wheat panel (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed\u0026ndash;f). The differentiation pattern of the population defined by ADMIXTURE with the number of ancestral populations (\u003cem\u003eK\u003c/em\u003e) ranging from 2 to 9 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef) was largely consistent with discrete clusters or branches in the PCA and phylogenetic tree (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed,e). At \u003cem\u003eK\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2, there was a dichotomous pattern of population divergence among landraces and cultivars (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef). At \u003cem\u003eK\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3, cultivars fell into two clusters corresponding to Asian and European origins (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef). Along with increased \u003cem\u003eK\u003c/em\u003e, the entire panel gradually separated into nine subpopulations (hereafter named Sp1\u0026ndash;Sp9). Landraces were separated into four subpopulations Sp1\u0026ndash;Sp4 mainly comprised of landraces from Western and Central Asia, Europe, Southern Asia, and Eastern Asia, hence appearing geographically and historically relevant to their origins\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Cultivars separated into five subpopulations Sp5\u0026ndash;Sp9, and genetic assignments were largely in accordance with geographical origins, breeding history, and YR epidemic region. For example, accessions in Sp5 were mainly from Eastern Asia, corresponding to the unique YR epidemic region ER1-1; Sp6 was largely composed of accessions from Southern Asia, Northern Africa, and Latin America, regions that widely used cultivars selected from materials distributed by the International Maize and Wheat Improvement Center (CIMMYT) and International Center for Agricultural Research in the Dry Areas (ICARDA); and Sp7 and Sp8 mainly comprised accessions collected from Canada, and United States of America and Oceania, respectively, with \u003cem\u003ePst\u003c/em\u003e populations ER3-1 and ER3-3 originating from the European epidemic region. Most accessions in cluster Sp9 were from Europe geographically coinciding with European YR epidemic region ER2-1. To gain a deeper insight into differences in yellow rust resistance between different wheat growing regions, we divided cultivars into four breeding groups (BG1 to BG4) based on genomic population architecture, breeding history and yellow rust epidemiology network. BG1, BG2, BG3 and BG4 comprised samples from East Asia (mainly Sp5); Latin America, Africa and South Asia (mainly Sp6); North America and Oceania (mainly Sp7 and Sp8), and Europe (mainly Sp9), respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef and Supplementary Table\u0026nbsp;1).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLandscape of YR resistance genes/loci and effective alleles\u003c/h3\u003e\n\u003cp\u003eTo uncover the genetic basis and selection characteristics of YR resistance, we assessed the seedling YR responses of 1,629 wheat accessions using 12 historically important and current \u003cem\u003ePst\u003c/em\u003e races (see Methods; Supplementary Fig.\u0026nbsp;3a,b), and performed adult plant tests at five field locations in China over four years (Yangling, YL; Guiyang, GY; Jiangyou, JY; Chongqing, CQ; Tianshui, TS in years 2019, 2020, 2021 and 2022) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eg,h and Supplementary Table\u0026nbsp;5). More than 47,000 YR response datapoints evaluated with infection type (IT) and disease severity (DS) were obtained. As shown in Supplementary Fig.\u0026nbsp;3b, the proportions of accessions with seedling resistance declined with emergence of current \u003cem\u003ePst\u003c/em\u003e races, coinciding with accumulation of larger numbers of virulence factors without apparent loss of aggressiveness. Cluster analysis of YR indices (IT and DS) from field tests showed a complete range of response that was divided into three groups (resistant, intermediate and susceptible) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ei). Accessions with different YR responses were evenly distributed in each epidemic region indicating that the selected wheat accessions were representative in terms of phenotype (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). The skewed distributions of YR indices were continuous in all environments with high correlation coefficients (Supplementary Fig.\u0026nbsp;3c), implying that resistance to yellow rust was conferred mainly by quantitative resistance. Then we performed analyses of variance for all environments except for GY and CQ as their single environment. Highly significant genotypic variation and high heritability (Supplementary Fig.\u0026nbsp;3d and Supplementary Table\u0026nbsp;6) suggested that the yellow rust responses were mainly controlled by genetic factors that conferred resistance across all environments.\u003c/p\u003e \u003cp\u003eA large-scale genome-wide association analysis (GWAS) identified more than 800,000 marker-trait associations (MTAs) exceeding the suggested threshold (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1.0e-06), and assigned them to 431 loci (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, Supplementary Fig.\u0026nbsp;4a\u0026ndash;f and Supplementary Table\u0026nbsp;7). For convenience, the lead SNP for each QTL was chosen based on its strongest association with the yellow rust response, coupled with the smallest associated \u003cem\u003eP\u003c/em\u003e-value and the highest \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e (phenotypic variance explained) value among the SNPs considered. To better characterize and compare the loci/genes identified in the present study, we compiled a reference dataset from 175 publications over the past three decades. This dataset comprises 1,125 QTL/genes for YR resistance, along with all cloned \u003cem\u003eR\u003c/em\u003e genes or homologs conferring resistance to various diseases in the \u003cem\u003eTriticeae\u003c/em\u003e tribe (Supplementary Table\u0026nbsp;8). These QTL/genes were positioned across the wheat 21 chromosomes using the IWGSC RefSeq v2.1 genome and were categorized into 217 independent QTL (iQTL) based on linkage disequilibrium (LD) blocks (see Methods). Next, we established a wheat genome landscape that provided a comprehensive summary of reported iQTL, cloned \u003cem\u003eR\u003c/em\u003e genes and the 431 YR resistance-associated loci identified in this study. Nearly 60.1% (259/431) of the loci co-located with 182 reported iQTL, more than 80% of the total (Supplementary Table\u0026nbsp;8). These loci included well-characterized \u003cem\u003eYr\u003c/em\u003e genes/QTL or cloned resistance genes such as \u003cem\u003eYr18\u003c/em\u003e, \u003cem\u003eYr29\u003c/em\u003e and \u003cem\u003eYr46\u003c/em\u003e, as well as well-known alien translocations such as 1BL.1RS (1RS introgression from \u003cem\u003eSecale cereale\u003c/em\u003e carrying \u003cem\u003eYr9\u003c/em\u003e), 2NS-2AS.2AL (2NS introgression from \u003cem\u003eAegilops ventricosa\u003c/em\u003e carrying \u003cem\u003eYr17\u003c/em\u003e), and 5AS.5AL-5A\u003csup\u003em\u003c/sup\u003eL (5A\u003csup\u003em\u003c/sup\u003eL introgression from \u003cem\u003eTriticum monococcum\u003c/em\u003e carrying \u003cem\u003eYr34\u003c/em\u003e) successfully used in wheat breeding historically, hence validating the effectiveness and robustness of our GWAS findings (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea and Supplementary Table\u0026nbsp;7). We further predicted 85 QTL-hotspot regions (QHRs) for YR resistance combined with meta-QTL (see Methods, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea and Supplementary Table\u0026nbsp;8). Notably, approximately 39.9% (172/431) of the identified loci were located in genomic regions distinct from those of reported YR resistance genes/QTL, suggesting that these loci may contain novel resistance genes with potential breeding value.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eSelection signatures for YR resistance and prioritization of candidate YR resistance genes\u003c/h3\u003e\n\u003cp\u003eTo better understand genomic selection signatures and epidemiological characteristics underlying YR response to historic \u003cem\u003ePst\u003c/em\u003e races, we assessed changes in resistance allele frequencies (RAFs) represented by lead SNPs in the 431 loci both chronologically (from 1920s to 2020s spanning more than 100 years) and geographically. RAFs at 55 loci increased at specific times and then declined (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb,c), consistent with \u0026lsquo;boom and bust\u0026rsquo; cycles associated with introduction of a single effective resistance gene into cultivars and then a decline following an increase in the frequency of virulent \u003cem\u003ePst\u003c/em\u003e races. Each resistance gene played a significant role in controlling YR during a specific period of effectiveness, and then failed due to the emergence of a new virulent race, for instance, \u003cem\u003eYr1\u003c/em\u003e in the 1950s, \u003cem\u003eYr2\u003c/em\u003e in the 1960s, \u003cem\u003eYrA\u003c/em\u003e (later designated \u003cem\u003eYr73\u003c/em\u003e and \u003cem\u003eYr74\u003c/em\u003e) in the 1970s, \u003cem\u003eYr7\u003c/em\u003e in the 1980s, \u003cem\u003eYr9\u003c/em\u003e and \u003cem\u003eYr17\u003c/em\u003e in the 1990s, \u003cem\u003eYr4\u003c/em\u003e and \u003cem\u003eYr27\u003c/em\u003e in 2000s, and \u003cem\u003eYr24/26\u003c/em\u003e and \u003cem\u003eYr34\u003c/em\u003e in 2010s (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb,c)\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan additionalcitationids=\"CR33 CR34 CR35\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Especially, the RAFs for 71 loci showed major changes after 2000 (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec,d), allegedly related to the emergence of new, more aggressive pathotypes\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, such as races within the molecular groupings PstS4, PstS7, PstS8 and PstS10 in Europe; PstS1 and PstS2 and derivatives in the United States and Australia; and the \u003cem\u003eYr26\u003c/em\u003e-virulent pathotype group (V26) including race CYR34 in China\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. In addition, alleles of 39 new loci identified in landraces have rarely been used in modern breeding (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee and Supplementary Table\u0026nbsp;7), indicating their potential for wheat improvement. On a geographical scale, we found that some resistance genes, such as \u003cem\u003eYr29\u003c/em\u003e, \u003cem\u003eYr30\u003c/em\u003e and \u003cem\u003eYr78\u003c/em\u003e, located in the YR resistance hotspot regions tend to have a common existence in each epidemic region (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef and Supplementary Table\u0026nbsp;7), suggesting that they were widely used worldwide. In contrast, we also observed that genes such as \u003cem\u003eYr26\u003c/em\u003e, \u003cem\u003eYr27\u003c/em\u003e, \u003cem\u003eYr75\u003c/em\u003e and \u003cem\u003eYrJ22\u003c/em\u003e tended to be region-specific or were preferentially selected by different breeding groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg and Supplementary Table\u0026nbsp;7). This is possibly attributable to widespread use of founder genotypes, for example, Frontana in the Americas, Cappelle Desprez in northwestern Europe, and Zhou 8425B and Jimai 22 in China\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. The above results provide the first comprehensive analysis of genomic selection signatures and epidemiological characteristics of wheat-\u003cem\u003ePst\u003c/em\u003e interactions over the past century, revealing the co-evolutionary dynamics between resistance genes and pathogen races and offering critical insights for developing durable rust-resistant wheat cultivars.\u003c/p\u003e \u003cp\u003eWe implemented a prioritized candidate gene pipeline for 357 of 431 YR-related QTL (the 74 other QTL with linkage disequilibrium\u0026thinsp;\u0026gt;\u0026thinsp;10 Mb were not considered). Briefly, this pipeline incorporated information of knowledge-based gene sets such as gene ontology (GO) category, homologous gene classification or related gene regulatory pathways, functional importance of variant effects in candidate gene regions, gene expression associated with the phenotype, association and causality degrees based on multi-model cross-validation (see Methods). After filtering with the pipeline, we identified 559 candidate genes in the top 30 GO terms that mainly belonged to eight pathways, i.e., immune response (P1), kinase signaling (P2), plant hormone (P3), transportation (P4), stress response (P5), nutrition (P6), ubiquitination (P7), and gene expression (P8) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eh and Supplementary Table\u0026nbsp;9). For each of these candidate genes, we performed haplotype-based association analysis and demonstrated that haplotypes carrying putative polymorphisms causing loss/gain-of-function mutations in the corresponding gene(s) were significantly associated with yellow rust resistance (Supplementary Table\u0026nbsp;9). Interestingly, these effective alleles of candidate genes could be divided into three major representative types (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ei): 1) cloned \u003cem\u003eYr\u003c/em\u003e genes with novel allelic variants possibly conferring different resistance spectra against \u003cem\u003ePst\u003c/em\u003e; 2) novel alleles of known \u003cem\u003eR\u003c/em\u003e genes conferring resistance to other pathogens/pests could be associated with YR resistance; 3) promising broad-spectrum resistance (BSR) genes conferring quantitative resistance. Since the gene, or new allele at a particular locus could thus be viewed as a high-confidence candidate gene we selected one locus of each type for further characterization (Supplementary Table\u0026nbsp;9).\u003c/p\u003e \u003cp\u003e \u003cb\u003eAllelic variants of\u003c/b\u003e \u003cb\u003eYr5\u003c/b\u003e \u003cb\u003eenable resistance to multiple\u003c/b\u003e \u003cb\u003ePst\u003c/b\u003e \u003cb\u003eraces\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eQYr.nwafu111\u003c/em\u003e was identified in the GWAS as a major QTL on chromosome 2B conferring resistance to five \u003cem\u003ePst\u003c/em\u003e races (CYR23, CYR31, CYR32, CYR33 and TSA-V5) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, Supplementary Fig.\u0026nbsp;4a and Supplementary Table\u0026nbsp;7). The candidate region of \u003cem\u003eQYr.nwafu111\u003c/em\u003e contained two previously cloned NLR genes \u003cem\u003eYr7\u003c/em\u003e and \u003cem\u003eYr5\u003c/em\u003e, and the \u003cem\u003eYR5\u003c/em\u003e locus has two designated resistance alleles \u003cem\u003eYr5a\u003c/em\u003e and \u003cem\u003eYr5b\u003c/em\u003e (earlier designated as \u003cem\u003eYrSp\u003c/em\u003e)\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Distinct resistance patterns for these genes were revealed in seedling tests using near-isogenic lines. \u003cem\u003eYr7\u003c/em\u003e was susceptible (S) to all tested Chinese \u003cem\u003ePst\u003c/em\u003e races and \u003cem\u003eYr5a\u003c/em\u003e displayed resistance (R) to most races except TSA-V5, while \u003cem\u003eYr5b\u003c/em\u003e showed specific effectiveness against CYR23 and CYR32 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Given that \u003cem\u003eYr5a\u003c/em\u003e originated from spelt wheat (\u003cem\u003eT. spelta\u003c/em\u003e) and has not been widely used in common wheat cultivars, it was unlikely to be the candidate gene. Therefore, based on the resistance patterns, \u003cem\u003eYr5b\u003c/em\u003e emerged as the most likely candidate gene for resistance to both CYR23 and CYR32. To validate this hypothesis, we conducted a detailed analysis of the GWAS results for CYR32. There was a peak for CYR32 harboring 79 significant variants in the 693.32\u0026ndash;694.09 Mb region based on IWGSC RefSeq v2.1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb, Supplementary Fig.\u0026nbsp;5a and Supplementary Table\u0026nbsp;7), and the lead SNP \u003cem\u003es2B_693786097\u003c/em\u003e significantly distinguished two sets of wheat accessions (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.2e-18, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). To verify the gene detected by GWAS, we performed bi-parental genetic mapping in a recombinant inbred line (RIL) population ABM6 (cross of susceptible Avocet S (AvS) \u0026times; resistant cultivar Baomai 6 (BM6)) and their heterozygous inbred families (HIFs). Linkage analysis showed that a major locus was present in the same region as detected by GWAS, explaining 87.2% of the phenotypic variance explained (PVE) (Supplementary Fig.\u0026nbsp;5b) was present in the same region as detected by GWAS. The underlying resistance gene was delimited to a 1,078.27 kb physical interval flanked by markers \u003cem\u003eM9374\u003c/em\u003e and \u003cem\u003eM8998\u003c/em\u003e in IWGSC RefSeq v2.1, encompassing nine high-confidence genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee,f and Supplementary Tables\u0026nbsp;10 and 11). According to functional annotations, RNA-seq data from \u003cem\u003ePst\u003c/em\u003e-infected seedling leaves of BM6 and AvS, and DNA variations between AvS and BM6 by genome resequencing, only NLR gene \u003cem\u003eTraesCS2B03G1231900\u003c/em\u003e (\u003cem\u003eTa12319\u003c/em\u003e in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee) was expressed (Supplementary Table\u0026nbsp;11) and showed nonsynonymous variations between the two parents (Supplementary Table\u0026nbsp;12). We cloned the gene by PCR and found that its sequence was identical to \u003cem\u003eYr5b\u003c/em\u003e, confirming that \u003cem\u003eYr5b\u003c/em\u003e in wheat cultivar BM6 should confer the resistance to CYR32.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eInterestingly, we detected another GWAS signal in this region associated with resistance to \u003cem\u003ePst\u003c/em\u003e race TSA-V5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb,d and Supplementary Fig.\u0026nbsp;5c). Given that TSA-V5 is virulent to all known alleles (\u003cem\u003eYr5a\u003c/em\u003e, \u003cem\u003eYr5b\u003c/em\u003e, and \u003cem\u003eYr7\u003c/em\u003e) in this region (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea), this signal suggested that the presence of either a novel resistance gene or a previously uncharacterized \u003cem\u003eYr5\u003c/em\u003e allele. To verify the hypothesis, we performed fine mapping of the locus using a RIL population AXN3517, derived from a cross between AvS (susceptible) and Xinong 3517 (XN3517, resistant) that showed contrasting responses to \u003cem\u003ePst\u003c/em\u003e race TSA-V5, along with their HIFs. A major locus with PVE 57.7% (Supplementary Fig.\u0026nbsp;5d) was mapped to a 604.22 kb region containing five high-confidence genes flanked by markers \u003cem\u003eM4345\u003c/em\u003e and \u003cem\u003eM8998\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee,f and Supplementary Tables\u0026nbsp;10 and 11). Only \u003cem\u003eYr5\u003c/em\u003e in the candidate region was expressed and was predicted as the underlying causative gene (Supplementary Table\u0026nbsp;12). To test whether \u003cem\u003eYr5\u003c/em\u003e was responsible for resistance to \u003cem\u003ePst\u003c/em\u003e race TSA-V5, we performed virus-induced gene silencing (VIGS). We isolated the open reading frame (ORF) of \u003cem\u003eYr5\u003c/em\u003e from XN3517 and designed specific RNA interference (RNAi) fragments based on sequence differences among published sequences of \u003cem\u003eYr5\u003c/em\u003e\u003csup\u003e41\u003c/sup\u003e. Barley stripe mosaic virus (BSMV)-induced gene silencing of \u003cem\u003eYr5\u003c/em\u003e in XN3517 suppressed its resistance to \u003cem\u003ePst\u003c/em\u003e race TSA-V5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eg and Supplementary Fig.\u0026nbsp;6a). Further sequence analysis revealed that \u003cem\u003eYr5\u003c/em\u003e in XN3517 was a novel functional allele, hereafter named as \u003cem\u003eYr5x\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef). To further validate the function of \u003cem\u003eYr5x\u003c/em\u003e in resistance to yellow rust, we overexpressed (OE) it in cultivar Fielder (wild type, WT) which is susceptible to \u003cem\u003ePst\u003c/em\u003e race TSA-V5 and generated \u003cem\u003eYr5x\u003c/em\u003e-OE transgenic wheat plants (Supplementary Fig.\u0026nbsp;6b). Transgenic T\u003csub\u003e1\u003c/sub\u003e lines derived from nine independent T\u003csub\u003e0\u003c/sub\u003e individuals that were confirmed to have \u003cem\u003eYr5x\u003c/em\u003e by PCR and RT-qPCR conferred resistance to \u003cem\u003ePst\u003c/em\u003e race TSA-V5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eh and Supplementary Fig.\u0026nbsp;6b-d). We also investigated the disease phenotype in infected leaves of Fielder(WT) and Fielder(OE) through histological observations. No significant histological differences were observed at 1 day post inoculation (dpi) but colonization and development of secondary hyphae were strongly restricted in the Fielder(OE) compared with Fielder(WT) starting from 2 dpi (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ei), indicating that overexpression of \u003cem\u003eYr5x\u003c/em\u003e leads to restricted fungal development in the wheat\u003cem\u003e-Pst\u003c/em\u003e interaction. Furthermore, RT-qPCR estimates of \u003cem\u003eYr5x\u003c/em\u003e transcript levels relative to ACTIN in XN3517 showed notable differences between plants inoculated with \u003cem\u003ePst\u003c/em\u003e race TSA-V5 at 0, 2, 4, 5, 7, 10, and 14 dpi (Supplementary Fig.\u0026nbsp;6e), indicating that \u003cem\u003eYr5x\u003c/em\u003e was induced by the presence of the pathogen. The above results indicate that \u003cem\u003eYr5b\u003c/em\u003e and \u003cem\u003eYr5x\u003c/em\u003e are causal genes for resistance to CYR32 and that \u003cem\u003eYr5x\u003c/em\u003e additionally confers resistance to race TSA-V5.\u003c/p\u003e \u003cp\u003eTo investigate variations in \u003cem\u003eYr5x\u003c/em\u003e, we collected 60 publicly available wheat genomes, including chromosome-level and scaffold-level assemblies from diploid, tetraploid, and hexaploid wheat and related species (see Methods). Sequence alignment identified 23 homologs in different genomes, including \u003cem\u003eYr5a\u003c/em\u003e, \u003cem\u003eYr5b\u003c/em\u003e, and \u003cem\u003eYr5x\u003c/em\u003e (Source Data for \u003cem\u003eYr5\u003c/em\u003e). Phylogenetic analysis of these homologs resulted in their classification into nine distinct clusters based on sequence similarity (Supplementary Fig.\u0026nbsp;7). Among them, the homologous gene in Shi4185, which also conferred resistance to CYR32, matched \u003cem\u003eYr5b\u003c/em\u003e (Supplementary Table\u0026nbsp;1); the homologous genes in Cadenza, Paragon and Robigus were the same as \u003cem\u003eYr5x\u003c/em\u003e and Cadenza also conferred resistance to TSA-V5 (Supplementary Fig.\u0026nbsp;6c). No homolog was identical to \u003cem\u003eYr5a\u003c/em\u003e; the other homologs were classified into six types, but it remains uncertain whether they have functional roles. This points to complexity and diversity of the \u003cem\u003eYR5\u003c/em\u003e locus, and the functionality of other types that needs to be further verified.\u003c/p\u003e \u003cp\u003eIn summary, our results show that different alleles of a single NLR gene can confer different resistance spectra against \u003cem\u003ePst\u003c/em\u003e, and pave an alternative route to combat plant diseases by identifying or engineering novel alleles of NLR genes.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAllele-specific recognition of different pathogens in NLR locus\u003c/b\u003e \u003cb\u003eYR6\u003c/b\u003e\u003cb\u003e/\u003c/b\u003e\u003cb\u003ePM5\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAlthough numerous NLRs are characterized as conferring resistance to a single plant pathogen/pest, rare NLRs recognize effectors in taxonomically unrelated pathogens. Our GWAS results showed an extremely significant peak (\u003cem\u003eQYr.nwafu405\u003c/em\u003e) located in a QHR associated with resistance to \u003cem\u003ePst\u003c/em\u003e race V26/GS (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea\u0026ndash;c) and co-located with previously reported \u003cem\u003eYr6\u003c/em\u003e gene on chromosome 7B\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. To verify this GWAS signal, we performed map-based cloning of \u003cem\u003eYr6\u003c/em\u003e using the AAK58 RIL population and selected HIFs derived from a cross between AvS and resistant cultivar Aikang 58 (AK58). The causal gene was delimited to a 0.08 cM interval corresponding to a physical 205.3 kb region (716.10\u0026ndash;716.30 Mb) in the AK58 genome\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. The region contained six annotated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed and Supplementary Tables\u0026nbsp;13 and 14). Based on RNA-seq and genome resequencing data, only NLR gene \u003cem\u003eTraesAK58CH7B01G485100\u003c/em\u003e (hereafter abbreviated as \u003cem\u003eTaAK4851\u003c/em\u003e) both showed high expression in response to \u003cem\u003ePst\u003c/em\u003e infection (Supplementary Table\u0026nbsp;14) and contained nonsynonymous SNP variations between the two parents (Supplementary Table\u0026nbsp;15). Hence, \u003cem\u003eTaAK4851\u003c/em\u003e was predicted as the most promising candidate underlying \u003cem\u003eYr6\u003c/em\u003e. Interestingly, a previous study reported that \u003cem\u003ePm5-AK58\u003c/em\u003e (\u003cem\u003eTaAK4851\u003c/em\u003e) in AK58 is allelic to \u003cem\u003ePm5e\u003c/em\u003e, which was identified in cultivar Fuzhuang 30 and conferred resistance to powdery mildew caused by \u003cem\u003eBlumeria graminis\u003c/em\u003e f. sp. \u003cem\u003etritici\u003c/em\u003e (\u003cem\u003eBgt\u003c/em\u003e)\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. We hypothesized that the \u003cem\u003eYR6\u003c/em\u003e locus was responsible for resistance to both powdery mildew and yellow rust. To test this hypothesis, we transiently silenced \u003cem\u003eTaAK4851\u003c/em\u003e in AK58 by VIGS, and the silenced lines were susceptible to yellow rust (Supplementary Fig.\u0026nbsp;8a,b). We also generated knockout mutants by CRISPR\u0026ndash;Cas9 in the cultivar Fielder, which carries \u003cem\u003eYr6\u003c/em\u003e. Three independent \u003cem\u003eyr6\u003c/em\u003e knockout mutant lines (KO#20, KO#43 and KO#47) containing frameshift mutations (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee) were obtained and showed susceptibility to \u003cem\u003ePst\u003c/em\u003e race V26/GS in contrast to the Fielder control in both microscopic and visual assessments (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef\u0026ndash;h). Furthermore, RT-qPCR analysis revealed that \u003cem\u003eTaAK4851\u003c/em\u003e was strongly induced upon inoculation with \u003cem\u003ePst\u003c/em\u003e race V26/GS (Supplementary Fig.\u0026nbsp;8c), indicating that \u003cem\u003eTaAK4851\u003c/em\u003e was upregulated in response to YR infection. Subcellular localization analysis demonstrated that TaAK4851 is localized to the cytoplasm (Supplementary Fig.\u0026nbsp;8d). Ultimately, the comprehensive genetic, molecular and functional analyses indicated that \u003cem\u003eTaAK4851\u003c/em\u003e, the \u003cem\u003ePm5\u003c/em\u003e allele, corresponds to \u003cem\u003eYr6\u003c/em\u003e and confers resistance to \u003cem\u003ePst\u003c/em\u003e V26/GS.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWith \u003cem\u003eYr6\u003c/em\u003e cloned, we compared its sequence to that of \u003cem\u003ePm5\u003c/em\u003e. Amino acid sequence analysis showed that YR6 protein was 94.37% identical to PM5E protein, with differences in only 60 of the 1,061 amino acids (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ei and Supplementary Table\u0026nbsp;16). According to the degree of association between amino acid variation and phenotype, we identified two amino acid sites that possibly recognized the different pathogens (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ei). One, I366T located in the NBS domain, was essential for \u003cem\u003ePst\u003c/em\u003e resistance, and the other, M1011I, at the end of the carboxyl terminal (intrinsically disordered region) was required for \u003cem\u003eBgt\u003c/em\u003e resistance. To validate and expand upon these initial observations, we performed haplotype analysis in the present wheat accessions. Using 212 nonsynonymous variants of \u003cem\u003eYr6\u003c/em\u003e/\u003cem\u003ePm5\u003c/em\u003e, we removed samples with a missing rate more than 20% and classified 1,449 of 2,191 wheat accessions into 16 haplotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ej and Supplementary Fig.\u0026nbsp;9a and Supplementary Table\u0026nbsp;17). Plants with haplotype \u003cem\u003eYr6_h8\u003c/em\u003e in the representative cultivar \u0026lsquo;Fielder\u0026rsquo; conferred resistance to \u003cem\u003ePst\u003c/em\u003e, but were susceptible to \u003cem\u003eBgt\u003c/em\u003e; whereas those with \u003cem\u003ePm5_h4\u003c/em\u003e (corresponding to \u003cem\u003ePm5e\u003c/em\u003e) in the representative cultivar \u0026lsquo;Fuzhuang 30\u0026rsquo; had \u003cem\u003eBgt\u003c/em\u003e resistance, but were susceptible to \u003cem\u003ePst\u003c/em\u003e; the other haplotypes seemed to be susceptible to both pathogens (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ek,l and Supplementary Fig.\u0026nbsp;9b,c and Supplementary Table\u0026nbsp;17). Furthermore, we conducted a comprehensive search for homologs of \u003cem\u003eYr6/Pm5\u003c/em\u003e across 36 publicly available wheat genomes that include either B or S subgenomes (Source Data for \u003cem\u003eYr6\u003c/em\u003e). We classified these 36 homologs into twelve clusters based on sequence similarity (Supplementary Fig.\u0026nbsp;10). Notably, \u003cem\u003eYr6\u003c/em\u003e was specifically localized within cluster C1, carrying the key variant C1098T; while \u003cem\u003ePm5e\u003c/em\u003e was absent from all examined published wheat genome sequences, confirming previous reports of its limited distribution\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Numerous variations in the promoter region and the whole gene coding region indicate the wide diversity of the \u003cem\u003eYR6\u003c/em\u003e/\u003cem\u003ePM5\u003c/em\u003e locus and underscore the need for verification of the functionality of other haplotypes in the future.\u003c/p\u003e \u003cp\u003eOur findings indicate that different haplotypes of a single-copy NLR gene can confer resistance to taxonomically unrelated pathogens, and the two amino acid sites I366T and M1011I are potential targets for engineering \u003cem\u003eYr6\u003c/em\u003e/\u003cem\u003ePm5\u003c/em\u003e alleles with multiple recognition specificity in the future.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTaEDR2-B\u003c/b\u003e \u003cb\u003econfers broad-spectrum rust resistance without substantial yield penalty\u003c/b\u003e\u003c/p\u003e \u003cp\u003eBroad-spectrum rust resistance (BSR) is a desirable trait conferring resistance to multiple isolates or pathogens. In this study, we identified a QTL (\u003cem\u003eQYr.nwafu406\u003c/em\u003e) stably expressed in all field environments, and which exhibited BSR characteristics (Supplementary Fig.\u0026nbsp;4b). A meta-analysis\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e showed that \u003cem\u003eQYr.nwafu406\u003c/em\u003e co-located with the previously reported \u003cem\u003eYrKB\u003c/em\u003e (\u003cem\u003eQYr.nwafu-7BL.1\u003c/em\u003e) on chromosome arm 7BL\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, and considered to be a QHR\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. LD analysis on the peak region of \u003cem\u003eQYr.nwafu406\u003c/em\u003e (\u003cem\u003eYrKB\u003c/em\u003e) showed that the significant SNPs were located between 726.56 Mb and 727.35 Mb in IWGSC RefSeq v2.1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea and Supplementary Fig.\u0026nbsp;11a\u0026ndash;c).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further verify the GWAS signal and narrow down the candidate region, we fine-mapped \u003cem\u003eYrKB\u003c/em\u003e using the AFLA RIL population developed from a cross of AvS and resistant cultivar Flanders (FLA) and selected HIFs, and KJ RIL population from a cross of susceptible cultivar Kenong 9204 (KN9204) and resistant cultivar Jing 411 (J411) (see Methods). \u003cem\u003eQYr.nwafu-7BL.1\u003c/em\u003e (\u003cem\u003eYrKB\u003c/em\u003e) was identified with PVE ranging from 18.2 to 40.5% (Supplementary Fig.\u0026nbsp;11d), and \u003cem\u003eYrKB\u003c/em\u003e was mapped to a 0.09 cM genetic interval corresponding to 266.2 kb with 24 high-confidence genes in IWGSC RefSeq v2.1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb and Supplementary Table\u0026nbsp;18). RNA-seq and genome resequencing data revealed that these genes except for \u003cem\u003eTraesCS7B03G1236800\u003c/em\u003e (hereafter abbreviated as \u003cem\u003eTa12368\u003c/em\u003e) were neither expressed nor had nonsynonymous variations between the two pairs of parents (Supplementary Tables\u0026nbsp;19 and 20), indicating that \u003cem\u003eTa12368\u003c/em\u003e was the most likely candidate for \u003cem\u003eYrKB\u003c/em\u003e. Gene-based association analysis with 40 variants identified three significant variants in \u003cem\u003eTa12368\u003c/em\u003e between the parents. Two variants \u003cem\u003es7B_726855255\u003c/em\u003e and \u003cem\u003es7B_726862747\u003c/em\u003e caused G to T and C to G changes, resulting in a splice region variant and an arginine to glycine substitution, respectively. The third variation, \u003cem\u003es7B_726852804\u003c/em\u003e, in the promoter region caused an A to G change that possibly affected regulation of the gene expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec\u0026ndash;e and Supplementary Table\u0026nbsp;21). \u003cem\u003eTa12368\u003c/em\u003e encodes a protein containing a steroidogenic acute regulatory protein-related lipid transfer (START) domain, a putative pleckstrin homology (PH) domain and an \u003cem\u003eENHANCED DISEASE RESISTANCE 2\u003c/em\u003e domain (EDR2) and is a homolog of \u003cem\u003eArabidopsis thaliana AtEDR2\u003c/em\u003e (henceforth tentatively named as \u003cem\u003eTaEDR2-B\u003c/em\u003e in wheat). Phylogenetic analysis based on the START domain sequence revealed a close evolutionary relationship between \u003cem\u003eTaEDR2-B\u003c/em\u003e and the BSR gene \u003cem\u003eYr36\u003c/em\u003e\u003csup\u003e49\u003c/sup\u003e (Supplementary Fig.\u0026nbsp;12 and Supplementary Table\u0026nbsp;22). These findings imply that \u003cem\u003eTaEDR2-B\u003c/em\u003e could be associated with a broad-spectrum source of yellow rust resistance.\u003c/p\u003e \u003cp\u003eTo confirm the causal gene underlying \u003cem\u003eYrKB\u003c/em\u003e-mediated resistance, we performed a forward genetic screen using an exome-sequenced mutant population of J411\u003csup\u003e50\u003c/sup\u003e, which harbors the same \u003cem\u003eTaEDR2-B\u003c/em\u003e allele as in FLA (see Methods, Supplementary Table\u0026nbsp;21). Five mutant lines based on predicted functional impact and nucleotide base or amino acid change were obtained (231delATC_splice, 232delTCT_splice, G1882A_splice, G644D, and G665Q) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee and Supplementary Table\u0026nbsp;23). Following infection with a mixture \u003cem\u003ePst\u003c/em\u003e races in the field, all five mutant lines (M1\u0026ndash;M5) exhibited higher disease severities than Jing 411 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1.3e-05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ef,g). We confirmed their YR responses at both the seedling and adult plant stages in the greenhouse. All five mutant lines and wild type Jing 411 were susceptible as seedlings. However, these mutants displayed higher susceptibility than Jing 411 at the adult plant stage (Supplementary Fig.\u0026nbsp;13a,b). These results indicated that \u003cem\u003eTaEDR2-B\u003c/em\u003e was necessary for \u003cem\u003eYrKB\u003c/em\u003e-mediated broad-spectrum resistance.\u003c/p\u003e \u003cp\u003eWe further transformed \u003cem\u003eTaEDR2-B\u003c/em\u003e into the \u003cem\u003ePst\u003c/em\u003e-susceptible accession Zhengmai 7698 (ZM7698) that harbors a susceptible haplotype (Supplementary Table\u0026nbsp;21). Eleven positive T\u003csub\u003e3\u003c/sub\u003e transgenic overexpression (OE) lines were obtained and their responses to \u003cem\u003ePst\u003c/em\u003e were assessed. Histological observations on infected leaves of both seedlings and adult plants indicated that \u003cem\u003eTaEDR2\u003c/em\u003e-OE seedlings exhibited abundant hyphal growth like ZM7698 whereas the fungal growth was significantly restricted in adult plants (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eh). These observations were consistent with visible YR symptoms on the leaves in greenhouse (Supplementary Fig.\u0026nbsp;14). All 11 OE lines were much more resistant (mean disease severity, 28.8%) to yellow rust than ZM7698 (83.4%) when tested in the field (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ei\u0026ndash;k). Transcription of \u003cem\u003eTaEDR2-B\u003c/em\u003e in adult FLA plants was induced more than 7-fold at 5 dpi (Supplementary Fig.\u0026nbsp;15a) indicating that \u003cem\u003eTaEDR2\u003c/em\u003e-\u003cem\u003eB\u003c/em\u003e expression is induced by YR infection. Subcellular localization analysis demonstrated that TaEDR2-B was diffusely distributed in the cytoplasm (Supplementary Fig.\u0026nbsp;15b). These results showed that \u003cem\u003eTaEDR2-B\u003c/em\u003e was sufficient to confer \u003cem\u003eYrKB\u003c/em\u003e-mediated broad-spectrum rust resistance and contribute to our understanding the role of TaEDR2-B in plant defense.\u003c/p\u003e \u003cp\u003eAgronomic assessment of \u003cem\u003eTaEDR2-B\u003c/em\u003eOE lines along with ZM7698 in the field under rust-free and rust-affected conditions showed no significant difference in spike length, spikelet number per spike, kernel number per spike, heading date (Supplementary Fig.\u0026nbsp;16a,b). The mean grain yields of \u003cem\u003eTaEDR2-B\u003c/em\u003eOE lines was equal to ZM7698 under rust-free conditions (Supplementary Fig.\u0026nbsp;16c,d) and there was a significant increase (9.09%) in grain yield compared to ZM7698 in the presence of yellow rust (Supplementary Fig.\u0026nbsp;16d). These results indicate that \u003cem\u003eTaEDR2-B\u003c/em\u003e has no negative effect on agronomic traits under rust-free conditions, and it could potentially boost grain yield under rust-affected conditions by enhancing resistance to yellow rust.\u003c/p\u003e \u003cp\u003eFurther haplotype analysis with 40 variants based on WGS data covering the promoter and coding region of \u003cem\u003eTaEDR2-B\u003c/em\u003e identified eight haplotypes (\u003cem\u003eTaEDR2-B_h1\u003c/em\u003e~_\u003cem\u003eh8\u003c/em\u003e) among 2,183 wheat accessions (Supplementary Fig.\u0026nbsp;17a,b and Supplementary Table\u0026nbsp;21). Lines with \u003cem\u003eTaEDR2-B_h8\u003c/em\u003e (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;166, 7.4%), had the largest mean effect on resistance to yellow rust (1.1e-08\u0026thinsp;\u0026le;\u0026thinsp;\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;3.5e-04), and additionally showed resistance to leaf rust (Supplementary Fig.\u0026nbsp;17c,d and Supplementary Table\u0026nbsp;21). To fully resolve DNA sequence variations in \u003cem\u003eTaEDR2-B\u003c/em\u003e, a survey of sequences covering the promoter and gene coding regions of 60 genomes (Source Data for \u003cem\u003eTaEDR2-B\u003c/em\u003e) identified several new variations in the coding region and numerous structural variations in the promoter region (Supplementary Fig.\u0026nbsp;18), indicating that future studies should also focus on the promoter region. All the above findings reveal that \u003cem\u003eTaEDR2-B_h8\u003c/em\u003e is an elite haplotype conferring resistance to yellow rust and probably leaf rust without yield penalty and has significant potential for targeting in breeding.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe continuous emergence of new races of rust fungi enforces ongoing exploration for new resistance genes for use in breeding for resistance. The importance of incorporating diversity in breeding programs is widely acknowledged, but if diversity does not provide selectable allelic variation in key traits implementation may be questioned. In this study, high-throughput genome analysis, multirace/multi-environment phenotyping of yellow rust response, and GWAS analyses of large populations enabled us to decipher the global genetic basis of YR resistance. Using the prioritized candidate gene pipeline combining omics datasets and multiple bioinformatics methods, we identified candidate genes underlying resistance to yellow rust. The allele distributions of these genes in different populations constitute a valuable resource that can be used in crop improvement, such as trait identification and alteration, breeding programs and allele optimization. Moreover, it will be intriguing to investigate whether convergent gene networks or distinct genetic mechanisms underlie domestication and improvement of wheat and other grain crop species.\u003c/p\u003e \u003cp\u003eFunctional allele mining is a research field that investigates allelic variation for significant traits within genetic resource collections, especially regarding resistance genes of known function. We identified a substantial set of potential functional alleles of established \u003cem\u003eR\u003c/em\u003e genes, specifically confirming the presence of \u003cem\u003eYr5x\u003c/em\u003e and \u003cem\u003eYr6/Pm5.\u003c/em\u003e For locus \u003cem\u003eYR5\u003c/em\u003e, five haplotypes were previously identified in sequenced wheat cultivars and alleles \u003cem\u003eYr5a\u003c/em\u003e and \u003cem\u003eYr5b\u003c/em\u003e were functionally validated\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. In this study, we confirmed that the allele \u003cem\u003eYr5x\u003c/em\u003e in cultivar Xinong 3517 conferred resistance to Chinese \u003cem\u003ePst\u003c/em\u003e race TSA-V5. Due to an incomplete sequence of the \u003cem\u003eYR5\u003c/em\u003e locus in IWGSC RefSeq v2.1\u003csup\u003e51\u003c/sup\u003e, we could not explore \u003cem\u003eYR5\u003c/em\u003e diversity using the WGS data alone. We therefore carried out multiple alignment of \u003cem\u003eYR5\u003c/em\u003e homologs to find additional variations. The identified multiple \u003cem\u003eYr5\u003c/em\u003e alleles will serve as valuable tools for identifying their corresponding \u003cem\u003eAvr\u003c/em\u003e genes and resistance mechanisms. It is common to find allelic series of NLR receptors that serve as significant sources of genetic variation in wheat, for instance, 17 functional alleles of \u003cem\u003ePM3\u003c/em\u003e\u003csup\u003e52\u003c/sup\u003e; six verified stem rust resistance alleles of \u003cem\u003eSR9\u003c/em\u003e\u003csup\u003e53\u003c/sup\u003e; and four validated alleles of each of \u003cem\u003eSR13\u003c/em\u003e\u003csup\u003e54\u003c/sup\u003e and \u003cem\u003eLR21\u003c/em\u003e\u003csup\u003e55\u003c/sup\u003e, respectively. These multiallelic resistance loci are thought to have evolved under strong diversifying selection to recognize different specific avirulence (\u003cem\u003eAvr\u003c/em\u003e) effectors secreted by the respective pathogens. More importantly, \u003cem\u003eYr6\u003c/em\u003e and \u003cem\u003ePm5\u003c/em\u003e conferring immunity to two different fungal diseases were found to be different haplotypes at a single \u003cem\u003eR\u003c/em\u003e locus. To our knowledge, this is the first report of NLR variation in a wheat \u003cem\u003eR\u003c/em\u003e gene conferring resistance to yellow rust and powdery mildew. Other similar examples in the \u003cem\u003eTriticeae\u003c/em\u003e include \u003cem\u003eYr27\u003c/em\u003e/\u003cem\u003eLr13\u003c/em\u003e, with 97% amino acid homology and conferring resistance yellow rust and leaf rust respectively\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e; a single NLR, \u003cem\u003eLr85\u003c/em\u003e/\u003cem\u003eYr87\u003c/em\u003e, also confers resistance to both leaf rust and yellow rust\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e; \u003cem\u003eMla7\u003c/em\u003e and \u003cem\u003eMla8\u003c/em\u003e in \u003cem\u003eHordeum vulgare\u003c/em\u003e confer resistance to both barley powdery mildew and wheat stripe rust\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e; \u003cem\u003ePm4f\u003c/em\u003e functions against powdery mildew and wheat blast\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e,\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e; a pair of tandem kinase (WTK) genes \u003cem\u003ePm24\u003c/em\u003e/\u003cem\u003eRwt4\u003c/em\u003e have functional haplotypes for resistance to wheat blast and powdery mildew, respectively\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e,\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. As indicated above, these findings give a deeper comprehension of the evolutionary trajectory of allelic series of disease resistance genes. Encouragingly, in this study a considerable number of promising alleles of known \u003cem\u003eR\u003c/em\u003e genes having significant association with YR resistance were uncovered using large-scale genomic variation data coupled with multirace phenotype data. Obtaining the molecular identity of these genes by identification of key amino acids determining pathogen specificity will provide prospects for engineering novel variations conferring new specificities to single pathogens or possibly multiple pathogens\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. Consequently, by altering the adaptive landscape, breeders could impose evolutionary constraints on pathogen populations, leading to prolonged resistance durability\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAlthough utilization of highly resistant cultivars offers an effective approach for controlling crop disease, genetic immunity often carries an unintended reduction in growth and yield\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e,\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. Some resistance genes lead to substantial fitness penalties such as \u003cem\u003emlo\u003c/em\u003e in wheat and \u003cem\u003eSWEET\u003c/em\u003e genes in rice\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e,\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. There have been many recent advances in understanding the molecular mechanism underlying the trade-off between resistance and crop yield\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. Here, we isolated \u003cem\u003eTaEDR2-B\u003c/em\u003e, which conferred partial resistance to multiple \u003cem\u003ePst\u003c/em\u003e races with no evidence of yield penalty. Phylogenetic analysis of the gene indicated high similarity to \u003cem\u003eYr36\u003c/em\u003e. START domains, possessing a hydrophobic ligand binding pocket, have a role in lipid/sterol binding, transport, and signaling across animal and plant species\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e. Therefore, it will be of interest to investigate the lipid ligands associated with the START domain in \u003cem\u003eTaEDR2-B\u003c/em\u003e and further balancing of resistance and yield during \u003cem\u003ePst\u003c/em\u003e infection. The understanding will provide clues for engineering favorable crop cultivars carrying enhanced disease resistance without yield penalty. In addition, we developed a series of genetically diagnostic markers for detection of \u003cem\u003eYr5b\u003c/em\u003e/\u003cem\u003e5x\u003c/em\u003e, \u003cem\u003eYr6\u003c/em\u003e and \u003cem\u003eTaEDR2-B\u003c/em\u003e, and validated them in a panel of 576 wheat cultivars and breeding lines (Supplementary Tables\u0026nbsp;24 and 25).\u003c/p\u003e \u003cp\u003eIn summary, our investigation highlights the utility of whole-genome resequencing in large populations to provide a resource to improve understanding of the genetics of wheat disease resistance and to inform future studies on allelic variation of relevant traits within resource collections, thereby facilitating wheat improvement. The alarm bell is always rung to signify the message that \"rust never sleeps\", nevertheless, \u003cem\u003eR\u003c/em\u003e gene never dies and will be revived like the phoenix rising from the ashes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePlant and pathogen materials\u003c/h2\u003e \u003cp\u003eTo represent the widest genetic diversity in common wheat we collected 14,688 common wheat accessions from worldwide resources, including modern cultivars, advanced breeding lines, core germplasm collections, founder parents, and landraces\u003csup\u003e\u003cspan additionalcitationids=\"CR22 CR23 CR24 CR25 CR26\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. From these, 1,629 accessions were selected for GWAS covering most of the worldwide diversity, including 666 with accessible genome sequence information (29 from Cheng et al.\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e, 85 from Hao et al.\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e, 105 from Zhou et al.\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e, 92 from Guo et al.\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e, and 355 from Niu et al.\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e), 349 newly sequenced accessions using WGS, and 614 accessions genotyped using 660 K SNP array in the present study. Five hundred and sixty-two among 768 publicly published genome re-sequences from the Gatersleben Genebank of Leibniz Institute of Plant Genetics and Crop Plant Research\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e were also integrated to generate a high-density genome variation map and validate the frequencies of favorable haplotypes. Additionally, 280 K SNP genotyping data for 4,506 wheat accessions in the INRAE global collection were also used in evaluating genetic diversity\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIntegrated geographic and yellow rust epidemiological information identified 8 macrogeographic regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb)\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. The final panel included 2,191 accessions from the 8 macrogeographic regions: 174 from West and Central Asia, 97 from South Asia, 272 from Africa, 658 from East Asia, 106 from Latin America, 639 from Europe, 193 from North America, and 52 from Oceania. There were 1,382 (63.07%) winter, 739 (33.73%) spring, and 34 (1.55%) facultative wheat types; 684 (31.22%) were landraces, 135 (6.16%) were pre-Green Revolution before 1960, 608 (27.75%) registered 1960\u0026ndash;2000, and 764 (34.87%) registered after 2000. The plant materials from which information was extracted are available at the Chinese Crop Germplasm Resources Information System (CGRIS), the USA National Small Grains Collection (NSGC), Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) GeneBank, International Centre for Agricultural Research in Dry Areas (ICARDA) GeneBank, and the International Maize and Wheat Improvement Center (CIMMYT) Wheat Germplasm Bank. Passport data including year of registration or naming, geographic origin, growth habit, and pedigree where available are listed in Supplementary Table\u0026nbsp;1.\u003c/p\u003e \u003cp\u003eGenetic mapping populations recombinant inbred lines (RILs) and selected segregating RILs (heterozygous inbred families, HIFs) from five bi-parental crosses were used for validation of GWAS signals. They were Avocet S (AvS)/Baomai 6 (BM6) containing 142 ABM6-RILs and 3,645 HIFs, AvS/Xinong 3517 (XN3517) containing 162 AXN3517-RILs and 3,586 HIFs, AvS/Aikang 58 (AK58) containing 128 AAK58-RILs and 4,452 HIFs, Kenong 9204 (KN9204)/Jing 411 (J411) containing 188 KJ-RILs, and AvS/Flanders (FLA) containing 184 AFLA-RILs and 5,426 HIFs.\u003c/p\u003e \u003cp\u003eWe integrated seedling stage resistance spectrum signatures of the GWAS panel for a total of 12 historically widespread and emerging \u003cem\u003ePst\u003c/em\u003e races in China under controlled greenhouse. Although these \u003cem\u003ePst\u003c/em\u003e races were from China, they shared virulence factors with the races from other countries\u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e. The avirulence/virulence arrays and their distributions should be useful in breeding stripe-rust-resistant wheat cultivars. Among of them, CYR17, CYR23, CYR29, CYR31, Sull-4 and Sull-5 were predominant races pre-2000, whereas CYR32, CYR33, CYR34 predominated post-2000 and races V26/GS, V26/SC and TSA-V5 are currently widespread. The races were collected from Gansu, Shaanxi and Sichuan provinces and they are maintained by the Institute of Plant Pathology, Northwest A\u0026amp;F University. The virulence/avirulence patterns of the races were confirmed by testing near-isogenic lines of AvS, Chinese differentials, and specific \u003cem\u003eYr\u003c/em\u003e gene donor lines\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cem\u003ePuccinia triticina\u003c/em\u003e f. sp. \u003cem\u003etritici\u003c/em\u003e (\u003cem\u003ePt\u003c/em\u003e) race PHQS (provided by Prof. Shisheng Chen, Peking University Institute of Advanced Agricultural Sciences, Beijing) and \u003cem\u003eBlumeria graminis\u003c/em\u003e f. sp. \u003cem\u003etritici\u003c/em\u003e (\u003cem\u003eBgt\u003c/em\u003e) race Bgt21-2 (provided by Dr. Lijun Yang, Hubei Academy of Agricultural Sciences, Wuhan) were used in leaf rust and powdery mildew phenotyping.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePlanting and evaluation of disease response\u003c/h3\u003e\n\u003cp\u003e \u003cb\u003eGreenhouse trials\u003c/b\u003e Tests were conducted under controlled conditions to characterize seedling responses of the GWAS panel, genetic populations and transgenic lines. Generally, 10\u0026ndash;15 seedlings were grown in 9 \u0026times; 9 \u0026times; 9 cm pots, or three plants were grown in 20 \u0026times; 20 \u0026times; 15 cm pots for adult-plant tests. For \u003cem\u003ePst\u003c/em\u003e tests, seedlings at the two-leaf stage (14 days after planting) and adult-plants at booting were separately inoculated with \u003cem\u003ePst\u003c/em\u003e urediniospores of each race mixed with talc (approximately 1:20). Inoculated plants were incubated at 10\u0026deg;C in a dew chamber in darkness for 24 h, and then transferred to a greenhouse at 17\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;C with 14 h of light (22,000 lx) daily. Infection types (IT) were recorded 18\u0026ndash;21 days post inoculation (dpi) using a 0\u0026ndash;9 scale of increasing reaction\u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFor \u003cem\u003ePt\u003c/em\u003e tests, seedlings at the three-leaf stage were inoculated with fresh \u003cem\u003ePt\u003c/em\u003e urediniospores mixed with talcum powder at a ratio of 1:20. The inoculated plants were placed in a dark dew chamber set at 22\u0026deg;C for approximately 24 h and then maintained at 22\u0026ndash;24\u0026deg;C with a 16 h photoperiod. ITs of plants were scored at ~\u0026thinsp;12 dpi using a 0\u0026ndash;4 scale\u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFor \u003cem\u003eBgt\u003c/em\u003e tests, seedlings at the two-leaf stage were inoculated with \u003cem\u003eBgt\u003c/em\u003e isolates and ITs for each line were scored on a scale of 0\u0026ndash;4 at 7 dpi when susceptible control plants displayed severe symptoms\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eField trials\u003c/b\u003e The GWAS panel of 1,629 wheat accessions, genetic populations and transgenic plants were grown at 12 locations across four years (2019\u0026ndash;2022): Yangling (YL, 2019\u0026ndash;2022) in Shaanxi province; Jiangyou (JY, 2019\u0026ndash;2021) in Sichuan province; Tianshui (TS, 2019\u0026ndash;2021) in Gansu province; Chongqing (CQ, 2021); and Guiyang (GY, 2021) in Guizhou province. All field management activities were conducted according to local cultivation standards.\u003c/p\u003e \u003cp\u003eAll trials were arranged in single randomized complete blocks. Each line was grown in 100-cm, 2-row plots with 30 cm between rows. Xiaoyan 22 as a susceptible check was planted after every 20 rows. Inoculum spreader rows containing a mixture Mingxian 169 and Avocet S were planted around the plot areas. Tianshui, Jiangyou, Chongqing and Guiyang are hotspot regions for natural yellow rust development and nurseries regularly become infected without artificial inoculation. Trials at Yangling were inoculated with a mixture of \u003cem\u003ePst\u003c/em\u003e races CYR32, CYR33, CYR34 suspended in liquid parafin (1:300) sprayed onto the spreaders at flag leaf emergence. Test rows were visually rated for infection type (IT) and disease severity (DS) 18\u0026ndash;20 days post-flowering when severity levels on the susceptible checks reached maximum levels of 90\u0026ndash;100% (For \u003cem\u003ePt\u003c/em\u003e tests, only DS was recorded). Disease severity was assessed visually using percentage diseased leaf area based on the modified Cobb scale\u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eAnalyses of phenotypic data\u003c/h3\u003e\n\u003cp\u003eFor each environment, the maximum phenotypic score was used as a phenotypic measure. Genotypes (1,629 accessions) and environments were treated as random effects in a linear mixed model to estimate best linear unbiased estimators (BLUEs) using the lme4 package in R 3.5.3\u003csup\u003e79\u003c/sup\u003e. The yellow rust response data for each environment were subjected to analysis of variance (ANOVA) along with the mean data across environments (BLUE). Broad-sense heritability (\u003cem\u003eH\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e) estimates were calculated using the lme4 package with the formula \u003cem\u003eH\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;V\u003csub\u003eG\u003c/sub\u003e/(V\u003csub\u003eG\u003c/sub\u003e + V\u003csub\u003eE\u003c/sub\u003e + V\u003csub\u003eG\u0026times;E\u003c/sub\u003e), where V\u003csub\u003eG,\u003c/sub\u003e V\u003csub\u003eE\u003c/sub\u003e and V\u003csub\u003eG\u0026times;E\u003c/sub\u003e represent the genotypic, environmental and their interaction variances, respectively. Pearson\u0026rsquo;s correlation coefficients (\u003cem\u003er\u003c/em\u003e) of pairwise environments were computed using the Hmisc package to determine the consistency of yellow rust response across environments.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSample preparation and sequencing\u003c/h2\u003e \u003cp\u003e Genomic DNA was extracted from seedlings of 349 wheat accessions using a Plant Genomic DNA Kit (Tiangen, Beijing) according to the manufacturer\u0026rsquo;s instructions. Each sample with approximately 10 mg of DNA was used to construction a 350-bp paired-end library with the Bioruptor Pico Sonication System (diagenode). Then, the libraries were sequenced with the DNBSEQ platform of BGI-Shenzhen, generating 150-bp paired-end reads by whole genome resequencing producing\u0026thinsp;~\u0026thinsp;4.9 \u0026times; 10\u003csup\u003e12\u003c/sup\u003e 100-bp paired-end reads and average sequencing coverage depth of ~\u0026thinsp;11\u0026times; for each accession.\u003c/p\u003e \u003cp\u003eTotal RNA with three biological replicates was isolated using a RNeasy Plant mini kit from seedling or flag leaves of 8 accessions (parents of genetic mapping populations, and susceptible controls Mingxian 169 and Xiaoyan 22) at 48 h post inoculation with \u003cem\u003ePst\u003c/em\u003e urediniospores or water, respectively. Prior to RNA isolation, the leaf tissues from the three replicates were pooled in equal proportions. As described above, RNA-seq libraries were constructed and sequenced using BGISEQ500 on the DNBSEQ platform to generate 150-bp paired-end reads. Transcripts Per Million reads (TPM) values were calculated for each gene with Salmon (v.1.9.0)\u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e, with TPM less than one considered as no expression.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSequencing data integration, read mapping and variant calling\u003c/h2\u003e \u003cp\u003eWGS reads from 1,577 accessions were mapped to the Chinese Spring (CS) Refseq v2.1 reference assembly using BWA mem (v.0.7.17)\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e. The mapped reads were subsequently sorted according to genomic position using the SAMtools command sort (v.1.11)\u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e. Duplicated reads were marked and read groups were flagged using the Picard tools (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://broadinstitute.github.io/picard/\u003c/span\u003e\u003cspan address=\"http://broadinstitute.github.io/picard/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). HaplotypeCaller from GATK (v.4.1.8.0)\u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e was utilized to detect variants and generate individual specific.gvcf files, which were then followed by a joint variant calling process conducted by GenotypeGVCF. SNPs were hard filtered using SnpSift Filter\u003csup\u003e\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e removing putative variants according to the following criteria: \u0026lsquo;(!(exists SNP) \u0026amp; (QD\u0026thinsp;\u0026gt;\u0026thinsp;2.0) \u0026amp; (FS\u0026thinsp;\u0026lt;\u0026thinsp;200.0) \u0026amp; (SOR\u0026thinsp;\u0026lt;\u0026thinsp;10.0)) | ((QD\u0026thinsp;\u0026gt;\u0026thinsp;2.0) \u0026amp; (MQ\u0026thinsp;\u0026gt;\u0026thinsp;40.0) \u0026amp; (FS\u0026thinsp;\u0026lt;\u0026thinsp;60.0) \u0026amp; (SOR\u0026thinsp;\u0026lt;\u0026thinsp;3.0) \u0026amp; (MQRankSum \u0026gt; -12.5) \u0026amp; (ReadPosRankSum \u0026gt; -8.0))\u0026rsquo;. These variants were annotated using snpEff (v.5.1d)\u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMapping statistics were calculated from the BAM files using SAMtools (v.1.11; option \u0026lsquo;flag-stat\u0026rsquo;) to obtain the number of mapped reads, and the mapping rate was then calculated as follows: (the number of mapped reads/the total number of reads) \u0026times; 100. The variant density was calculated using bin sizes of 1 Mb, and the nucleotide diversity was calculated in sliding windows of 10,000 bp per chromosome using VCFtools (v.0.1.17)\u003csup\u003e\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eGenotype imputation\u003c/h2\u003e \u003cp\u003eGenotype data from the 1,577 sequenced accessions was used as a reference panel for imputation. An integrated VCF file was created, including 614 accessions genotyped by the wheat660K SNP array and the reference panel. Beagle v.5.4 (beagle.22Jul22.46e.jar)\u003csup\u003e\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e\u003c/sup\u003e was then used to impute missing genotype calls with the following settings: \u0026lsquo;gp\u0026thinsp;=\u0026thinsp;true window\u0026thinsp;=\u0026thinsp;40 ne\u0026thinsp;=\u0026thinsp;2000\u0026rsquo;. Genotype calls with probability (GP)\u0026thinsp;\u0026lt;\u0026thinsp;0.8 were considered as missing. Sites with \u0026gt;\u0026thinsp;30% heterozygous genotype calls or \u0026gt;\u0026thinsp;30% missing data were removed, resulting in a set of about 84,855,324 variants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePopulation structure and linkage disequilibrium\u003c/h2\u003e \u003cp\u003eUsing 200,000 randomly selected variants, we performed PCA to identify genetic relationships between accessions with PLINK (v.1.90)\u003csup\u003e\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e; an unrooted neighbour-joining phylogenetic tree was generated using FastTree (v 2.1.11)\u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e with the following settings: \u0026lsquo;-nt -gtr\u0026rsquo;; the population structure was assessed using ADMIXTURE version 1.3.0\u003csup\u003e90\u003c/sup\u003e. Linkage disequilibrium (LD) decay was computed for WGS data as the squared correlation coefficient (\u003cem\u003er\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e) with plink (v.1.90) using the methods described by Wu et al.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. LD blocks were defined as groups of SNPs meeting a threshold of 0.8, with block size determined by the physical distance between the outermost flanking SNPs. Adjacent blocks that might still maintain strong linkage disequilibrium were merged into larger ones using the method described by Cheng et al.\u003csup\u003e\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u003c/sup\u003e. The average linkage disequilibrium (LD) decay distance for the whole genome was approximately 3.2 Mb (Supplementary Fig.\u0026nbsp;19a). The LD decay of all chromosomes ranged from 0.62 Mb to over 100 Mb, indicating that different genomic regions were subjected to artificial selection and the haplotype diversity is extensive in this diversity panel (Supplementary Table\u0026nbsp;26 and Supplementary Fig.\u0026nbsp;19b\u0026ndash;d). Therefore, establishment of confidence intervals for QTL-harboring regions was based on the independent LD block size at each side of the peak of significant associations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMeta-analysis and GWAS\u003c/h2\u003e \u003cp\u003eTo provide a more detailed characterization and comparison of the identified loci/genes in this study, we collected information on 1,125 QTL/genes for yellow rust resistance and all cloned \u003cem\u003eR\u003c/em\u003e genes or homologs for resistance to various diseases in the \u003cem\u003eTriticeae\u003c/em\u003e as well as QTL/gene mapping data from more recent GWAS, QTL mapping and meta-QTL studies\u003csup\u003e\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e,\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e\u003c/sup\u003e from 175 publications over the past three decades. With information for individual QTL/genes, such as: (i) type and size of the mapping population, (ii) flanking markers and their physical positions on the map, (iii) peak positions, (iv) phenotypic variance explained (PVE or \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e value), and (v) logarithm of the odds (LOD) score (QTL mapping) or \u003cem\u003ep\u003c/em\u003e-value (GWAS), we compiled an integrated reference physical map of YR resistance loci for ease of later comparison. A total of 1,125 QTL/genes and over 40 cloned resistance genes/alleles or homologous genes were positioned across the 21 wheat chromosomes based on reference genome IWGSC RefSeq v2.1 as follows: (1) the sequences of the closest linked markers or two flanking markers of the QTL confidence interval were retrieved from various databases, including WheatOmics (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://wheatomics.sdau.edu.cn\u003c/span\u003e\u003cspan address=\"http://wheatomics.sdau.edu.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), CerealsDB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cerealsdb.uk.net\u003c/span\u003e\u003cspan address=\"https://www.cerealsdb.uk.net\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), GrainGenes (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wheat.pw.usda.gov/GG3\u003c/span\u003e\u003cspan address=\"https://wheat.pw.usda.gov/GG3\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), DArT (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.diversityarrays.com\u003c/span\u003e\u003cspan address=\"https://www.diversityarrays.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and URGI (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wheat-urgi.versailles.inra.fr/\u003c/span\u003e\u003cspan address=\"https://wheat-urgi.versailles.inra.fr/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e); (2) physical positions of the above marker sequences obtained from the RefSeq v2.1 assembly using BLASTN; (3) physical locations of each QTL were determined by calculating the confidence intervals defined by flanking or closest linked marker positions. If the flanking interval was less than 10 Mb, the midpoint was used. Otherwise, the entire flanking interval was used to display the QTL. Presence of one independent QTL (iQTL) on the same chromosome was indicated if the QTL was in a different LD block, while QTL hotspot regions were classified if the distance was less than 10 Mb or within the Meta-QTL (MQTL) region. This distance was chosen as a conservative estimation that mirrored the scope of previous QTL mapping studies and meta-analysis studies\u003csup\u003e\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e,\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe performed multiple GWAS analyses at different levels using both the FarmCPU and MLM models\u003csup\u003e\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e\u003c/sup\u003e: that is, the entire panel of 1,629 accessions using all the genotypic data (614 accessions with imputation data); 614 accessions genotyped by the 660K SNP array; 1,015 accessions genotyped by WGS; landraces (n\u0026thinsp;=\u0026thinsp;384, WGS), and modern cultivars (n\u0026thinsp;=\u0026thinsp;637, WGS). In addition, to avoid detection of QTL for partial resistance masked by the presence of major resistance genes, accessions with high resistance (IT\u0026thinsp;\u0026le;\u0026thinsp;4, DS\u0026thinsp;\u0026le;\u0026thinsp;30) were removed and the remaining accessions (n\u0026thinsp;=\u0026thinsp;484, WGS) comprised a new panel for moderate resistance. Association tests were carried out for: (1) all single \u003cem\u003ePst\u003c/em\u003e race data sets, (2) all single environment data sets, (3) best linear unbiased estimation (BLUEs) across experiments (years) for each location, and (4) BLUEs across all 12 environments. High-confidence marker\u0026ndash;trait associations (MTAs) were filtered by the criteria: 1) significant DNA variations (SNPs and InDels) mapped within the interval of a meta-QTL associated with YR resistance; 2) significant DNA variations yielded by both the FarmCPU and MLM models; 3) significant DNA variations with large effect (-log\u003csub\u003e10\u003c/sub\u003e(\u003cem\u003eP\u003c/em\u003e)\u0026thinsp;\u0026gt;\u0026thinsp;6.00) in seedling tests; and 4) significant DNA variations simultaneously identified at least three field environments. QTL detected in at least six environments for IT and/or DS, or at least three environments when mapped within the interval of a meta-QTL were chosen to identify resistance genes with broad-spectrum resistance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eIdentification and prioritization of YR candidate genes\u003c/h2\u003e \u003cp\u003eIdentification of causal genes that underlie complicated agronomic traits directly from GWAS results remains difficult. Taking into consideration false-positive and false-negative issues associated with GWAS\u003csup\u003e\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e\u003c/sup\u003e, we employed a comprehensive analysis pipeline using multi-omics datasets and multiple bioinformatics methods to prioritize candidate genes in QTL regions identified by GWAS. The filtering procedure was: 1) search for high-confidence genes located within the LD block and evaluate the gene expression to predict whether genes were associated with phenotypes using transcriptome datasets; 2) perform GO analysis with agriGO (v.2.0) of candidate genes and homologous genes with known function that were involved in biological pathways of plant defense responses. Enrichment significance was analyzed by Fisher\u0026rsquo;s exact test. Enrichment results with more than 5 annotations and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were plotted with the R package clusterProfiler (v.3.10.0); 3) using a five-level grouping method (referred to as G1\u0026ndash;G5) to evaluate variant effects in gene regions based on the estimated functional importance of each nucleotide polymorphism as described by Yano et al.\u003csup\u003e\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e\u003c/sup\u003e. These levels included: G1, significant MTAs in the GWAS (-log\u003csub\u003e10\u003c/sub\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;the threshold value in a particular chromosome) that putatively caused amino acid conversion and alternative splicing; G2, significant MTAs in the 5\u0026prime; flanking sequences (\u0026le;\u0026thinsp;2 kb from the first ATG), which were considered to be promoter regions; G3, significant MTAs within the coding region but belonging to synonymous mutations, introns or 3\u0026prime; noncoding sequences; G4, significant MTAs outside coding regions; and G5, polymorphic but MTA not significant; 4) calculate the degree of haplotype-based association for each potential gene using GAPIT\u003csup\u003e\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eGenetic mapping and positional cloning\u003c/h2\u003e \u003cp\u003eThe four bi-parental RIL populations, i.e. ABM6, AAK58, AXN3517, and AFLA were genotyped using the wheat 16K SNP array. SNP marker filtering and evaluation was described in Wu et al.\u003csup\u003e\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e\u003c/sup\u003e. QTL detection was carried out using IciMapping 4.1 software\u003csup\u003e\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e\u003c/sup\u003e with default parameters and the PVE was used to evaluate the genetic effects of identified QTL. SNPs flanking target loci were converted to allele-specific quantitative PCR (AQP) markers to screen recombinants for fine mapping in the corresponding HIF populations. \u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e tests were used to determine agreement of observed segregation and theoretically expected ratios. Linkage analysis and high-density genetic map construction was established using JoinMap v.4.0\u003csup\u003e100\u003c/sup\u003e with default parameters. Linkage to target loci was estimated with the Kosambi mapping function\u003csup\u003e\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e\u003c/sup\u003e and a LOD score of 3.0 as a threshold. The genetic linkage map was drawn using Mapchart v.2.3\u003csup\u003e102\u003c/sup\u003e. All parents were re-sequenced, and their genomic sequences at target loci regions were compared to identify sequence polymorphisms. Key polymorphic SNPs in candidate genes were also converted to diagnostic AQP markers\u003csup\u003e\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e\u003c/sup\u003e. Primers used for position cloning are listed in Supplementary Table\u0026nbsp;24.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eVirus-induced gene silencing (VIGS)\u003c/h2\u003e \u003cp\u003eWe utilized siRNA-Finder (siFi21) software to search the predicted coding sequences of the \u003cem\u003eYr5x\u003c/em\u003e and \u003cem\u003eYr6\u003c/em\u003e/\u003cem\u003ePm5\u003c/em\u003e candidate genes to create candidate gene-specific probes for VIGS. Two fragments with a higher number of efficient and fewer off-targets to design silencing probes were selected and two probes designed for each candidate gene were flanked by \u003cem\u003ePacI\u003c/em\u003e and \u003cem\u003eNotI\u003c/em\u003e, then synthesized at Tsingke Biotech and subsequently cloned into the BSMV:γ vector. Each of the BSMV constructs (BSMV:\u003cem\u003eYr5x\u003c/em\u003e-1as and BSMV:\u003cem\u003eYr5x\u003c/em\u003e-2as for silencing \u003cem\u003eYr5x\u003c/em\u003e, BSMV:γ as control, and BSMV:\u003cem\u003eTaPDS\u003c/em\u003e (phytoene desaturase-antisense) as a virus-positive control) was inoculated into the second seedling leaves. Mock control plants were treated with 1xFes buffer as per the protocol described in our previous work\u003csup\u003e\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e\u003c/sup\u003e. The treated seedlings were exposed to 100% relative humidity in darkness for 24 h and then transferred to an incubator at 25℃ for 9 days before phenotypic analysis. When \u003cem\u003eTaPDS\u003c/em\u003e-silenced infection sites showed photobleaching, 4-leaf plants were infected with freshly harvested urediniospores of the \u003cem\u003ePst\u003c/em\u003e race TSA-V5. The inoculated leaves were sampled at 0 and 24 h for silencing efficiency assessment by Quantitative reverse transcription PCR (RT-qPCR). The images of yellow rust responses of the gene-silenced plants were recorded at 14 dpi using an Olympus BX-63 fluorescence microscope. BSMV infection and \u003cem\u003ePst\u003c/em\u003e race V26/GS inoculation for the \u003cem\u003eYr6/Pm5\u003c/em\u003e candidate gene were performed as described above. All inoculation experiments were repeated as three biological replicates.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMutagenesis analysis of\u003c/b\u003e \u003cb\u003eTaEDR2-B\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA forward genetic screen was conducted to validate \u003cem\u003eTaEDR2-B\u003c/em\u003e as the candidate gene responsible for \u003cem\u003eYrKB\u003c/em\u003e-mediated resistance. This was achieved using an exome-sequenced mutant population in the reference Chinese wheat cultivar Jing 411 (J411), which has the same \u003cem\u003eTaEDR2-B\u003c/em\u003e haplotype as Flanders (Supplementary Table\u0026nbsp;21). J411 has displayed durable adult plant YR resistance since its release in 1987 and likely carries the multipathogen resistance locus \u003cem\u003eYr29\u003c/em\u003e/\u003cem\u003eLr46\u003c/em\u003e and other resistance QTL, including \u003cem\u003eYrKB\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e\u003c/sup\u003e. We searched for information about mutations relevant to \u003cem\u003eTaEDR2-B\u003c/em\u003e using gene ID \u003cem\u003eTraesCS7B03G1236800\u003c/em\u003e on the public database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://jing411.molbreeding.com/#/\u003c/span\u003e\u003cspan address=\"http://jing411.molbreeding.com/#/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e50\u003c/sup\u003e and selected target mutant lines based on predicted functional impacts and nucleotide base or amino acid conversions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eWheat transformation\u003c/h2\u003e \u003cp\u003eTo produce transgenic wheat plants overexpressing \u003cem\u003eYr5x\u003c/em\u003e and \u003cem\u003eTaEDR2-B\u003c/em\u003e, the full-length coding sequences (CDS) from Xinong 3517 and Flanders were cloned behind the ubiquitin promoter using homologous recombination in a pCub vector (with the bar gene conferring basta resistance) to produce pUbi:Yr5x and pUbi:TaEDR2-B, respectively. Agrobacterium-mediated transformation generated independent transgenic lines of each construct in wheat cv. Fielder and cv. Zhengmai 7698, respectively. The presence of transgenes in T\u003csub\u003e0\u003c/sub\u003e to T\u003csub\u003e1\u003c/sub\u003e/T\u003csub\u003e2\u003c/sub\u003e generation plants was confirmed by PCR or RT-qPCR amplification using specific primers designed to detect the transgene from vectors.\u003c/p\u003e \u003cp\u003eThe CRISPR-Cas9 genome editing system was used to knock out \u003cem\u003eYr6\u003c/em\u003e/\u003cem\u003ePm5\u003c/em\u003e and generate \u003cem\u003eyr6\u003c/em\u003e mutant lines. The target sequences for the single guide RNA (sgRNA) were designed based on the exon sequences of \u003cem\u003eYr6\u003c/em\u003e using online website WheatOmics (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://wheatomics.sdau.edu.cn\u003c/span\u003e\u003cspan address=\"http://wheatomics.sdau.edu.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e106\u003c/sup\u003e. The sgRNA was constructed in a pENTR:gRNA4 vector, and converted to the final vector Cas9-PCL4 by Gateway technology and transformed into cv. Fielder using Agrobacterium. Positive transgenic plants were screened with hygromycin (100 mg/L) and genomic DNA was extracted to detect the Cas9 gene fragment and the hygromycin B phosphotransferase gene by PCR. The specific primers are listed in Supplementary Table\u0026nbsp;24.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMicroscopy of\u003c/b\u003e \u003cb\u003ePst\u003c/b\u003e\u003cb\u003e\u0026ndash;wheat interactions within infected leaves\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo observe the development of \u003cem\u003ePst\u003c/em\u003e on different genotypes, we collected \u003cem\u003ePst\u003c/em\u003e-infected leaf samples at 1, 2, 5, and 16 dpi. \u003cem\u003ePst\u003c/em\u003e hyphae were stained with wheat germ agglutinin conjugated to Alexa-488 (Invitrogen, USA) as described previously\u003csup\u003e\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e\u003c/sup\u003e with slight modifications. Briefly, the decolorized leaves were cut into 2 cm pieces, placed in a 10 mL centrifuge tube, washed twice with 5 mL of 50% ethanol, washed twice with distilled water for 10 min, boiled in water for 10 min, washed twice with distilled water, and then soaked in 5 mL of 50 mM Tris (pH 7.5) for 30 min before staining with 20 \u0026micro;g/mL WGA in darkness. The tissues were stained for 15 min and then washed with distilled water. WGA-stained tissues were examined under blue light excitation using a Zeiss LSM 880 confocal microscope.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eRNA extraction and RT-qPCR analysis\u003c/h2\u003e \u003cp\u003eFresh leaves from plant materials were frozen in liquid nitrogen, and total RNA from inoculated leaves was extracted using TRIzol reagent (Takara, Dalian). We synthesized 1 \u0026micro;g of complementary DNA (cDNA) using TransScript\u0026reg; Uni One-Step gDNA Removal and cDNA Synthesis SuperMix (TransGen Biotech, Beijing). The resulting reverse transcript was diluted 1-fold and used for quantitative real-time PCR (RT-qPCR). A 20 \u0026micro;L RT-qPCR mixture was used, including 10 \u0026micro;L 2\u0026times; ChamQ Blue Universal SYBR qPCR Master Mix (Vazyme, Nanjing), 0.4 \u0026micro;L 10 mM forward and reverse primers, and 3 \u0026micro;L diluted cDNA. PCR amplification started at 95\u0026deg;C for 30 sec, followed by 40 cycles of 95\u0026deg;C for 10 sec and 60\u0026deg;C for 30 sec. The default melting curve acquisition program of the CFX Connect Real-Time Instrument (Bio-Rad, Hercules, USA) was then used. Wheat genome \u003cem\u003eTaActin\u003c/em\u003e was used as an internal control. Primers used for VIGS and RT-qPCR are listed in Supplementary Table\u0026nbsp;24.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSubcellular location in\u003c/b\u003e \u003cb\u003eN. benthamiana\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo determine the subcellular localization of YR6, and TaEDR2-B, the full-length CDS of the three genes were constructed into pCAMBIA1305-GFP vector to generate the corresponding GFP fusion vectors. All constructs were individually transformed into \u003cem\u003eA. tumefaciens\u003c/em\u003e cells GV3101(pSoup-p19) (Weidibio, Shanghai). \u003cem\u003eAgrobacterium\u003c/em\u003e cultures held overnight were collected by centrifugation, resuspended in MMA induction buffer, and incubated at room temperature for 2 h. For co-expression assays, two \u003cem\u003eA. tumefaciens\u003c/em\u003e strains (e.g., strains carrying eGFP-tagged and mCherry-tagged constructs) were mixed in a 1:1 ratio. The mixture was infiltrated into \u003cem\u003eN. benthamiana\u003c/em\u003e leaves and held in darkness for about 24 h. Leaf samples were collected after 72 h, and GFP and mCherry fluorescence was observed with a Zeiss LSM880 confocal laser microscope.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003ePhylogenetic analysis\u003c/h2\u003e \u003cp\u003eThe protein sequences of START and its orthologues from different plant species were extracted from the EnsemblPlants database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://plants.ensembl.org/index.html\u003c/span\u003e\u003cspan address=\"http://plants.ensembl.org/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). A phylogenetic tree was constructed using the maximum-likelihood method in the MEGA7.0 program\u003csup\u003e\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e\u003c/sup\u003e with bootstrap (1,000 replicates) and complete deletion.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAlignments of\u003c/b\u003e \u003cb\u003eYr5x, Yr6\u003c/b\u003e \u003cb\u003eand\u003c/b\u003e \u003cb\u003eTaEDR2-B\u003c/b\u003e \u003cb\u003ehomologs from different genomes\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe collected 60 complete and scaffolded genomes from diploid, tetraploid and hexaploid wheat as well as its related species (WheatOmics, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://wheatomics.sdau.edu.cn\u003c/span\u003e\u003cspan address=\"http://wheatomics.sdau.edu.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; Supplementary Table\u0026nbsp;27)\u003csup\u003e\u003cspan additionalcitationids=\"CR110 CR111\" citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e\u003c/sup\u003e. First, we employed the full-length target genes or additional promoter regions as reference sequence to align with each genome using BLASTN\u003csup\u003e\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e\u003c/sup\u003e and then performed multiple sequence alignments using Muscle\u003csup\u003e\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e\u003c/sup\u003e before presenting the results visually to deliver a detailed analysis of the evolutionary conservation and variability of homologous genes across different genomes. Throughout this process, we checked for the presence of the start \u0026ldquo;ATG\u0026rdquo; codon and stop codons to ensure completeness of the gene sequences. Finally, we determined the classification of the homologous gene sequences based on similarity clustering in the multiple sequence alignments.\u003c/p\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw sequence data for WGS and RNA-seq reported in this paper have been deposited in the Genome Sequence Archive\u003csup\u003e115\u003c/sup\u003e in National Genomics Data Center\u003csup\u003e116\u003c/sup\u003e, China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences (GSA: CRA004322, CRA014998, CRA005878 and CRA021484) that are publicly accessible at\u0026nbsp;https://ngdc.cncb.ac.cn/gsa. The genotype data of the variant call format (VCF) was available in NGDC under accession number GVM000830 (https://ngdc.cncb.ac.cn/gvm/getProjectDetail?Project=GVM000830). To facilitate easier access and accelerate stripe rust resistance breeding, we have developed a dedicated database (https://wheat.dftianyi.com). All cloned genes PQ112656 (\u003cem\u003eYr5x\u003c/em\u003e, original name\u003cem\u003e\u0026nbsp;Yr5c\u003c/em\u003e), PQ112657 (\u003cem\u003eYr6\u003c/em\u003e) and PQ112658 (\u003cem\u003eTaEDR2-B\u003c/em\u003e) were available in NCBI GeneBank.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Key R\u0026amp;D Program of China (2021YFD1401000, 2021YFD1200600 and 2023YFD1200400), National Natural Science Foundation of China (Grant No. 31961143019, 32272088, 32201745, 32372138, 32372562, 32302377), Key R\u0026amp;D Program of Qinghai Province (2022-NK-125), Natural Science Basic Research Plan in Shaanxi Province of China (2019JCW-18, 2020JCW-16).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.W. designed the experiments, managed the project, performed most of the experiments, analyzed the data and wrote the manuscript. D.H., Q.Z., Z.K., and H.L. conceived and supervised the project and revised the manuscript. S.M. performed most of the genotype data analysis and GWAS results and wrote the manuscript. J.N. analyzed the data and wrote the manuscript. W.S., W.Z., Y.L., L.C., Y.W., and J.H. performed most of the experiments related to gene cloning and functional validation. H.D., J.Z., C.Z., T.C., and B.D. participated in data analysis and revised the manuscript. S.Z., R.Y., S.L., W.X., W.Z., and C.L. contributed to the field trails and conducted some phenotypic assays. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe take this opportunity to pay deep memory of Prof. Changfa Wang for his contributions in our group\u0026rsquo;s resistance breeding. The authors thank all members of the plant immunity research team in the State Key Laboratory of Crop Stress Resistance and High-Efficiency Production at Northwest A\u0026amp;F University for helpful comments; Drs. Meng Wang, Gang Li, Weilong Guo, Guangwei Li and Cong Jiang for their helpful suggestions and discussions; Prof. R.A. McIntosh for language editing and proofreading of the draft manuscript; Prof. Jun Guo, Drs. Jia Guo, Xingxuan Bai, Xueling Huang for assistance with genetic transformation; Drs. Xueling Huang, Qiong Zhang and Ms. Xiaona Zhou for RT-qPCR assays and AQP genotyping; Drs. Hua Zhao and Fengping Yuan for confocal experimental assistance; Ms. Haiying Wang for construction of a genomic DNA library; Dr. Guohao Han for assistance with provision of the pCAMBIA1305-GFP vector; Dr. Jingyang Tong for sharing most of the YR resistance QTL information; Drs. Jiuyuan Du, Yunliang Peng, Liyi Zhang, Wentao Zhang, Zhi Xu, Yuheng Yang, Bin Cheng and Qiang Yao for help in phenotyping.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrespondence and requests for materials\u003c/strong\u003e should be addressed to Jianhui Wu, Dejun Han, Qingdong Zeng, Zhensheng Kang or Hong-Qing Ling.\u003c/p\u003e"},{"header":"References ","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFAO. Food and Agriculture Organization of the United Nations (FAO) (2022). FAOSTAT. Crops. Latest update: December 27, 2023. Available online at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fao.org/faostat/en/#data/QC\u003c/span\u003e\u003cspan address=\"https://www.fao.org/faostat/en/#data/QC\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed January 12, 2024) (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSavary, S. et al. The global burden of pathogens and pests on major food crops. Nat Ecol Evol 3, 430\u0026ndash;439 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHafeez, A.N. et al. Creation and judicious application of a wheat resistance gene atlas. Mol Plant 14, 1053\u0026ndash;1070 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHovm\u0026oslash;ller, M.S., Walter, S. \u0026amp; Justesen, A.F. Escalating threat of wheat rusts. Science 329, 369\u0026ndash;369 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHovm\u0026oslash;ller, M.S., Thach, T. \u0026amp; Justesen, A.F. Global dispersal and diversity of rust fungi in the context of plant health. Curr Opin Microbiol 71, 102243\u0026ndash;102243 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWellings, C.R. Global status of stripe rust: a review of historical and current threats. Euphytica 179, 129\u0026ndash;141 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, X. Pathogens which threaten food security: \u003cem\u003ePuccinia striiformis\u003c/em\u003e, the wheat stripe rust pathogen. Food Secur 12, 239\u0026ndash;251 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeddow, J.M. et al. Research investment implications of shifts in the global geography of wheat stripe rust. Nat Plants 1, 15132 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBouvet, L. et al. The evolving battle between yellow rust and wheat: implications for global food security. Theor Appl Genet 135, 741\u0026ndash;753 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcIntosh, R., Mu, J., Han, D. \u0026amp; Kang, Z. Wheat stripe rust resistance gene \u003cem\u003eYr24/Yr26\u003c/em\u003e: A retrospective review. Crop J 6, 321\u0026ndash;329 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcIntosh, R.A. Catalogue of gene symbols for wheat: 2024 supplement. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wheat.pw.usda.gov/GG3/WGC\u003c/span\u003e\u003cspan address=\"https://wheat.pw.usda.gov/GG3/WGC\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma, D. et al. A single NLR gene confers resistance to leaf and stripe rust in wheat. Nat Commun 15, 9925 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, R., Gajendiran, K. \u0026amp; Wulff, B.B.H. R we there yet? Advances in cloning resistance genes for engineering immunity in crop plants. Curr Opin Plant Biol 77, 102489 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDracatos, P.M., Lu, J., S\u0026aacute;nchez Mart\u0026iacute;n, J. \u0026amp; Wulff, B.B.H. Resistance that stacks up: engineering rust and mildew disease control in the cereal crops wheat and barley. Plant Biotechnol J 21, 1938\u0026ndash;1951 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKeller, B., Wicker, T. \u0026amp; Krattinger, S.G. Advances in wheat and pathogen genomics: Implications for disease control. Annu Rev Phytopathol 56, 67\u0026ndash;87 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJaganathan, D., Bohra, A., Thudi, M. \u0026amp; Varshney, R.K. Fine mapping and gene cloning in the post-NGS era: advances and prospects. Theor Appl Genet 133, 1791\u0026ndash;1810 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcGuire, A.L. et al. The road ahead in genetics and genomics. Nat Rev Genet 21, 581\u0026ndash;596 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAli, S. et al. Origin, migration routes and worldwide population genetic structure of the wheat yellow rust pathogen \u003cem\u003ePuccinia striif\u003c/em\u003eormis f. sp. \u003cem\u003etritici\u003c/em\u003e. PLoS Pathog 10, e1003903 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, Z. \u0026amp; Zeng, S. \u003cem\u003eWheat rusts in China\u003c/em\u003e, (Chinese Agricultural Press (In Chinese), Beijing, 2002).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStubbs, R. Stripe rust. In: Roelfs AP, Bushnell WR, editors. The cereal rusts., Vol. Vol. II61-101 (New York (NY): Academic Press, 1985).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan, D. et al. Identification and evaluation of resistance to stripe rust in 1980 wheat landraces and abroad germplasm. Scientia Agricultura Sinica [In Chinese with English summary] 45, 5013\u0026ndash;5023 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMu, J. et al. Identification of sources of resistance in geographically diverse wheat accessions to stripe rust pathogen in China. Crop Prot 122, 1\u0026ndash;8 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, M. et al. Variation in \u003cem\u003eTaSPL6-D\u003c/em\u003e confers salinity tolerance in bread wheat by activating \u003cem\u003eTaHKT1;5-D\u003c/em\u003e while preserving yield-related traits. Nat Genet, 56, 1257\u0026ndash;1269 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu, J. et al. Association analysis identifies new loci for resistance to Chinese \u003cem\u003eYr26\u003c/em\u003e-virulent races of the stripe rust pathogen in a diverse panel of wheat germplasm. Plant Dis 104, 1751\u0026ndash;1762 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu, J. et al. A large-scale genomic association analysis identifies the candidate causal genes conferring stripe rust resistance under multiple field environments. Plant Biotechnol J 19, 177\u0026ndash;191 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeng, Q. et al. Stripe rust resistance and genes in Chinese wheat cultivars and breeding lines. Euphytica 196, 271\u0026ndash;284 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, G. et al. Evaluation of the potential risk of the emerging \u003cem\u003eYr5\u003c/em\u003e-virulent races of \u003cem\u003ePuccinia striiformis\u003c/em\u003e f. sp. \u003cem\u003etritici\u003c/em\u003e to 165 Chinese wheat cultivars. Plant Disease 106, 1867\u0026ndash;1874 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchulthess, A.W. et al. Genomics-informed prebreeding unlocks the diversity in genebanks for wheat improvement. Nat Genet 54, 1544\u0026ndash;1552 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu, T. et al. Optical maps refine the bread wheat \u003cem\u003eTriticum aestivum\u003c/em\u003e cv. Chinese Spring genome assembly. Plant J 107, 303\u0026ndash;314 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBalfourier, F. et al. Worldwide phylogeography and history of wheat genetic diversity. Sci Adv 5, eaav0536-eaav0536 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao, X. et al. Population genomics unravels the Holocene history of bread wheat and its relatives. Nat Plants 9, 403\u0026ndash;419 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrar, G.S., Fetch, T., McCallum, B.D., Hucl, P.J. \u0026amp; Kutcher, H.R. Virulence dynamics and breeding for resistance to stripe, stem, and leaf rust in Canada since 2000. Plant Dis 103, 2981\u0026ndash;2995 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHovm\u0026oslash;ller, M.S. et al. Replacement of the European wheat yellow rust population by new races from the centre of diversity in the near-Himalayan region. Plant Pathol 65, 402\u0026ndash;411 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, T., Wan, A., Liu, D. \u0026amp; Chen, X. Changes of races and virulence genes in \u003cem\u003ePuccinia striiformis\u003c/em\u003e f. sp. \u003cem\u003etritici\u003c/em\u003e, the wheat stripe rust pathogen, in the United States from 1968 to 2009. Plant Dis 101, 1522\u0026ndash;1532 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMilus, E.A., Kristensen, K. \u0026amp; Hovm\u0026oslash;ller, M.S. Evidence for increased aggressiveness in a recent widespread strain of \u003cem\u003ePuccinia striiformis\u003c/em\u003e f. sp. \u003cem\u003etritici\u003c/em\u003e causing stripe rust of wheat. Phytopathology 99, 89\u0026ndash;94 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWellings, C.R. \u0026amp; McIntosh, R.A. \u003cem\u003ePuccinia striiformis\u003c/em\u003e f. sp. \u003cem\u003etritici\u003c/em\u003e in Australasia: Pathogenic changes during the first 10 years. Plant pathol 39, 316\u0026ndash;325 (1990).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, X. \u0026amp; Kang, Z. \u003cem\u003eStripe Rust\u003c/em\u003e, (Springer, Netherlands, 2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAli, S. et al. Yellow rust epidemics worldwide were caused by pathogen races from divergent genetic lineages. Front Plant Sci 8, 1057\u0026ndash;1057 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFradgley, N. et al. A large-scale pedigree resource of wheat reveals evidence for adaptation and selection by breeders. Plos Biol 17, e3000071-e3000071 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQiaosheng, Z. \u003cem\u003eChinese wheat improvement and pedigree analysis\u003c/em\u003e, (China Agriculture Press, Beijing, 2003).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarchal, C. et al. BED-domain-containing immune receptors confer diverse resistance spectra to yellow rust. Nat Plants 4, 662\u0026ndash;668 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGardiner, L.J. et al. Mapping-by‐sequencing in complex polyploid genomes using genic sequence capture: a case study to map yellow rust resistance in hexaploid wheat. Plant J 87, 403\u0026ndash;419 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJia, J. et al. Genome resources for the elite bread wheat cultivar Aikang 58 and mining of elite homeologous haplotypes for accelerating wheat improvement. Mol Plant 16, 1893\u0026ndash;1910 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie, J. et al. A rare single nucleotide variant in \u003cem\u003ePm5e\u003c/em\u003e confers powdery mildew resistance in common wheat. New Phytol 228, 1011\u0026ndash;1026 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePal, N. et al. Meta-QTLs for multiple disease resistance involving three rusts in common wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.). Theor Appl Genet 135, 2385\u0026ndash;2405 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang, S. et al. Utilization of the genomewide wheat 55K SNP array for genetic analysis of stripe rust resistance in common wheat line P9936. \u003cem\u003ePhytopathology\u003c/em\u003e 109, 819\u0026ndash;827 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMu, J. et al. Genetic architecture of wheat stripe rust resistance revealed by combining QTL mapping using SNP-based genetic maps and bulked segregant analysis. Theor Appl Genet 132, 443\u0026ndash;455 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRosewarne, G.M. et al. Analysis of leaf and stripe rust severities reveals pathotype changes and multiple minor QTLs associated with resistance in an Avocet \u0026times; Pastor wheat population. Theor Appl Genet 124, 1283\u0026ndash;1294 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFu, D. et al. A kinase-START gene confers temperature-dependent resistance to wheat stripe rust. Science 323, 1357\u0026ndash;60 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiong, H. et al. A large-scale whole‐exome sequencing mutant resource for functional genomics in wheat. Plant Biotechnol J 21, 2047\u0026ndash;2056 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarchal, C. et al. Comparative genomics and functional studies of wheat BED-NLR loci. Genes 11, 1406 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBourras, S. et al. The \u003cem\u003eAvrPm3-Pm3\u003c/em\u003e effector-NLR interactions control both race-specific resistance and host-specificity of cereal mildews on wheat. Nat Commun 10, 2292\u0026ndash;2292 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, J. et al. Single amino acid change alters specificity of the multi-allelic wheat stem rust resistance locus \u003cem\u003eSR9\u003c/em\u003e. Nat Commun 14, 7354\u0026ndash;7354 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGill, B.K. et al. Function and evolution of allelic variations of \u003cem\u003eSr13\u003c/em\u003e conferring resistance to stem rust in tetraploid wheat (\u003cem\u003eTriticum turgidum\u003c/em\u003e L.). Plant J 106, 1674\u0026ndash;1691 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNaz, A.A. et al. \u003cem\u003eLr21\u003c/em\u003e diversity unveils footprints of wheat evolution and its new role in broad-spectrum leaf rust resistance. Plant Cell Environ 44, 3445\u0026ndash;3458 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAthiyannan, N. et al. Long-read genome sequencing of bread wheat facilitates disease resistance gene cloning. Nat Genet 54, 227\u0026ndash;231 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBettgenhaeuser, J. et al. The barley immune receptor \u003cem\u003eMla\u003c/em\u003e recognizes multiple pathogens and contributes to host range dynamics. Nat Commun 12, 6915 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO'Hara, T. et al. The wheat powdery mildew resistance gene \u003cem\u003ePm4\u003c/em\u003e also confers resistance to wheat blast. Nat. Plants 10, 984\u0026ndash;993 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsuke, S. et al. Evolution of wheat blast resistance gene \u003cem\u003eRmg8\u003c/em\u003e accompanied by differentiation of variants recognizing the powdery mildew fungus. Nature Plants 10, 971\u0026ndash;983 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArora, S. et al. A wheat kinase and immune receptor form host-specificity barriers against the blast fungus. Nat Plants 9, 385\u0026ndash;392 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu, P. et al. A rare gain of function mutation in a wheat tandem kinase confers resistance to powdery mildew. Nat Commun 11, 680\u0026ndash;11 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBentham, A.R. et al. Allelic compatibility in plant immune receptors facilitates engineering of new effector recognition specificities. Plant Cell 35, 3809\u0026ndash;3827 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcDonald, B.A. \u0026amp; Stukenbrock, E.H. Rapid emergence of pathogens in agro-ecosystems: global threats to agricultural sustainability and food security. Philos Trans R Soc Lond B Biol Sci 371(2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrown, J.K.M. Yield penalties of disease resistance in crops. Curr Opin Plant Biol 5, 339\u0026ndash;344 (2002).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, H., Liu, Y., Zhang, X., Ji, W. \u0026amp; Kang, Z. A necessary considering factor for breeding: growth-defense tradeoff in plants. Stress biology 3, 6\u0026ndash;6 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOliva, R. et al. Broad-spectrum resistance to bacterial blight in rice using genome editing. Nat Biotechnol 37, 1344\u0026ndash;1350 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, Y. et al. Simultaneous editing of three homoeoalleles in hexaploid bread wheat confers heritable resistance to powdery mildew. Nat Biotechnol 32, 947\u0026ndash;51 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe, Z., Webster, S. \u0026amp; He, S.Y. Growth\u0026ndash;defense trade-offs in plants. Curr Biol 32, R634-R639 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchrick, K. et al. Shared functions of plant and mammalian StAR-related lipid transfer (START) domains in modulating transcription factor activity. BMC Biol 12, 70 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng, H. et al. Frequent intra- and inter-species introgression shapes the landscape of genetic variation in bread wheat. Genome Biol 20, 136\u0026ndash;136 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHao, C. et al. Resequencing of 145 landmark cultivars reveals asymmetric sub-genome selection and strong founder genotype effects on wheat breeding in China. Mol Plant 13, 1733\u0026ndash;1751 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou, Y. et al. \u003cem\u003eTriticum\u003c/em\u003e population sequencing provides insights into wheat adaptation. Nat Genet 52, 1412\u0026ndash;1422 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo, W. et al. Origin and adaptation to high altitude of Tibetan semi-wild wheat. Nat Commun 11, 5085 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNiu, J. et al. Whole-genome sequencing of diverse wheat accessions uncovers genetic changes during modern breeding in China and the United States. Plant Cell 35, 4199\u0026ndash;4216 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma-Poudyal, D. et al. Virulence characterization of international collections of the wheat stripe rust pathogen, \u003cem\u003ePuccinia striiformis\u003c/em\u003e f. sp. \u003cem\u003etritici\u003c/em\u003e. Plant Dis 97, 379\u0026ndash;386 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLine, R. \u0026amp; Qayoum, A. Virulence, aggressiveness, evolution, and distribution of races of \u003cem\u003ePuccinia striif\u003c/em\u003eormis (the cause of stripe rust of wheat) in North America 1968\u0026ndash;1987. US Department of Agriculture Technical Bulletin No. \u003cem\u003e1788\u003c/em\u003e, p74 (1992).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, H. et al. Cloning of the wheat leaf rust resistance gene \u003cem\u003eLr47\u003c/em\u003e introgressed from \u003cem\u003eAegilops speltoides\u003c/em\u003e. Nat Commun 14, 6072\u0026ndash;6072 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeterson, R., Campbell, A. \u0026amp; Hannah, A. A diagrammatic scale for estimating rust intensity on leaves and stems of cereals. Canadian J Res 26, 496\u0026ndash;500 (1948).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBates, D., Maechler, M., Bolker, B. \u0026amp; Walker, S. lme4: linear mixed effects models using Eigen and S4., pp 1\u0026ndash;23 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatro, R., Duggal, G., Love, M.I., Irizarry, R.A. \u0026amp; Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods 14, 417\u0026ndash;419 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, H. \u0026amp; Durbin, R. Fast and accurate long-read alignment with Burrows-Wheeler transform. \u003cem\u003eBioinformatics\u003c/em\u003e 26, 589\u0026thinsp;\u0026ndash;\u0026thinsp;95 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078\u0026ndash;2079 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 20, 1297\u0026ndash;1303 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCingolani, P. et al. Using drosophila melanogaster as a model for genotoxic chemical mutational studies with a new program, SnpSift. Front Genet 3, 35 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff. Fly 6, 80\u0026ndash;92 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDanecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156\u0026ndash;8 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrowning, B.L., Tian, X., Zhou, Y. \u0026amp; Browning, S.R. Fast two-stage phasing of large-scale sequence data. Am J Hum Genet 108, 1880\u0026ndash;1890 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePurcell, S. et al. PLINK: A tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81, 559\u0026ndash;575 (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrice, M.N., Dehal, P.S. \u0026amp; Arkin, A.P. FastTree 2\u0026ndash;approximately maximum-likelihood trees for large alignments. PLoS One 5, e9490 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlexander, D.H., Novembre, J. \u0026amp; Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res 19, 1655\u0026ndash;64 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng, S. et al. Harnessing landrace diversity empowers wheat breeding. Nature 632, 823\u0026ndash;831 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePal, N. et al. Meta-QTLs for multiple disease resistance involving three rusts in common wheat (\u003cem\u003eTriticum aestivum\u003c/em\u003e L.). Theor Appl Genet 135, 2385\u0026ndash;2405 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar, S. et al. Comprehensive meta-QTL analysis for dissecting the genetic architecture of stripe rust resistance in bread wheat. BMC Genomics 24 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYin, L. et al. rMVP: A memory-efficient, visualization-enhanced, and parallel-accelerated tool for genome-wide association study. Genom Proteom Bioinf 19, 619\u0026ndash;628 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, H., Patterson, N. \u0026amp; Reich, D. Population differentiation as a test for selective sweeps. Genome Res 20, 393\u0026ndash;402 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYano, K. et al. Genome-wide association study using whole-genome sequencing rapidly identifies new genes influencing agronomic traits in rice. Nat Genet 48, 927\u0026ndash;34 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, J. \u0026amp; Zhang, Z. GAPIT version 3: Boosting power and accuracy for genomic association and prediction. Genom Proteom Bioinf 19, 629\u0026ndash;640 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu, J. et al. Comparative genome-wide mapping versus extreme pool-genotyping and development of diagnostic SNP markers linked to QTL for adult plant resistance to stripe rust in common wheat. Theor Appl Genet 131, 1777\u0026ndash;1792 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeng, L., Li, H., Zhang, L. \u0026amp; Wang, J. QTL IciMapping: Integrated software for genetic linkage map construction and quantitative trait locus mapping in biparental populations. Crop J 3, 269\u0026ndash;283 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Ooijen, J. JoinMap4, software for the calculation of genetic linkage maps in experimental populations. Kyazma BV, Wageningen (2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKosambi, D.D. The estimation of map distances from recombination values. Ann Eugen 12, 172\u0026ndash;175 (1943).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVoorrips, R.E. MapChart: software for the graphical presentation of linkage maps and QTLs. J Hered 93, 77\u0026ndash;8 (2002).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, S. et al. Enhanced stripe rust resistance obtained by combining \u003cem\u003eYr30\u003c/em\u003e with a widely dispersed, consistent QTL on chromosome arm 4BL. Theor Appl Genet 135, 351\u0026ndash;365 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, N. et al. Inactivation of a wheat protein kinase gene confers broad-spectrum resistance to rust fungi. Cell 185, 2961\u0026ndash;2974.e19 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiang, M. et al. \u003cem\u003eYr29\u003c/em\u003e combined with \u003cem\u003eQYr.nwafu-4BL.3\u003c/em\u003e confers durable resistance to stripe rust in wheat cultivar Jing 411. Theor Appl Genet 137, 11 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa, S. et al. WheatOmics: A platform combining multiple omics data to accelerate functional genomics studies in wheat. Mol Plant 14, 1965\u0026ndash;1968 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAyliffe, M., et al. Nonhost resistance of rice to rust pathogens. Mol. Plant Microbe Interact. 24, 1143\u0026ndash;1155 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar, S., Stecher, G. \u0026amp; Tamura, K. MEGA7: Molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol Biol Evol 33, 1870\u0026ndash;1874 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiao, C. et al. Pangenome bridges wheat structural variations with habitat and breeding. Nature \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41586-024-08277-0\u003c/span\u003e\u003cspan address=\"10.1038/s41586-024-08277-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, G. et al. Genomic analysis of Zhou8425B, a key founder parent, reveals its genetic contributions to elite agronomic traits in wheat breeding. Plant Commun \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.xplc.2024.101222\u003c/span\u003e\u003cspan address=\"10.1016/j.xplc.2024.101222\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, Z. et al. Chromosome-level assembly of the synthetic hexaploid wheat-derived cultivar Chuanmai 104. Sci Data 11, 670 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWalkowiak, S. et al. Multiple wheat genomes reveal global variation in modern breeding. Nature 588, 277\u0026ndash;283 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCamacho, C. et al. BLAST+: architecture and applications. BMC Bioinformatics 10, 421 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEdgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res 32, 1792\u0026ndash;1797 (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCNCB-NGDC Members and Partners. Database resources of the National Genomics Data Center, China National Center for Bioinformation in 2022. Nucleic Acids Res 50, D27-D38 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, T. et al. The genome sequence archive family: toward explosive data growth and diverse data types. Genom Proteom Bioinforma 19, 578\u0026ndash;583 (2021).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Common wheat, gene cloning, genome-wide association study, landscape of R genes, yellow/stripe rust resistance","lastPublishedDoi":"10.21203/rs.3.rs-4257976/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4257976/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eYellow rust (YR), caused by \u003cem\u003ePuccinia striiformis\u003c/em\u003e f. sp. \u003cem\u003etritici\u003c/em\u003e (\u003cem\u003ePst\u003c/em\u003e), poses a significant threat to wheat production worldwide. Breeding resistant cultivar is crucial for managing this disease. However, understanding of the genetic mechanisms underlying YR resistance remains fragmented. To address this, we conducted a comprehensive analysis with variome data from 2,191 wheat accessions worldwide and over 47,000 YR response records across multiple environments and pathogen races. Through genome-wide association studies, we established a landscape for 431 YR resistance loci, providing a rich resource for resistance (\u003cem\u003eR\u003c/em\u003e) gene deployment. Furthermore, we cloned genes corresponding to three resistance loci, namely \u003cem\u003eYr5x\u003c/em\u003e effective against multiple \u003cem\u003ePst\u003c/em\u003e races, \u003cem\u003eYr6/Pm5\u003c/em\u003e that conferred resistance to two pathogen species, and \u003cem\u003eYrKB\u003c/em\u003e (\u003cem\u003eTaEDR2-B\u003c/em\u003e) conferring broad-spectrum rust resistance without yield penalty. These findings offer valuable insights into the genetic basis of YR resistance in wheat and lay the foundation for engineering wheat with durable disease resistance.\u003c/p\u003e","manuscriptTitle":"Genomics-guided landscape unlocks superior alleles and genes for yellow rust resistance in wheat","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-11 05:06:55","doi":"10.21203/rs.3.rs-4257976/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-genetics","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"ng","sideBox":"Learn more about [Nature Genetics](http://www.nature.com/ng/)","snPcode":"","submissionUrl":"","title":"Nature Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"65240e88-f7a7-4ca8-aee2-ea87fe75322d","owner":[],"postedDate":"March 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":43914870,"name":"Biological sciences/Genetics/Sequencing/DNA sequencing"},{"id":43914871,"name":"Biological sciences/Genetics/Sequencing/DNA sequencing"},{"id":43914872,"name":"Biological sciences/Plant sciences/Plant genetics"},{"id":43914873,"name":"Biological sciences/Plant sciences/Plant genetics"},{"id":43914874,"name":"Biological sciences/Plant sciences/Plant breeding"}],"tags":[],"updatedAt":"2025-07-23T07:11:42+00:00","versionOfRecord":{"articleIdentity":"rs-4257976","link":"https://doi.org/10.1038/s41588-025-02259-2","journal":{"identity":"nature-genetics","isVorOnly":false,"title":"Nature Genetics"},"publishedOn":"2025-07-22 04:00:00","publishedOnDateReadable":"July 22nd, 2025"},"versionCreatedAt":"2025-03-11 05:06:55","video":"","vorDoi":"10.1038/s41588-025-02259-2","vorDoiUrl":"https://doi.org/10.1038/s41588-025-02259-2","workflowStages":[]},"version":"v1","identity":"rs-4257976","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4257976","identity":"rs-4257976","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}