Construction and Application of a Rapid Method for Population Genetic of Plant Pathogens: A Case Study of Puccinia striiformis f. sp. tritici

Construction and Application of a Rapid Method for Population Genetic of Plant Pathogens: A Case Study of Puccinia striiformis f. sp. tritici | 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 Construction and Application of a Rapid Method for Population Genetic of Plant Pathogens: A Case Study of Puccinia striiformis f. sp. tritici Xiaoping Hu, Junjie Zhang, Sanhong Fan, Xiangming Xu, Yuxiang Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8699492/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Population genetic studies of plant pathogens by traditional molecular markers often rely on the purification and multiplication of pathogens, which is time-consuming, labor-intensive and costly. To address these limitations, we developed a rapid method for monitoring pathogen population diversity using the obligate biotrophic fungus Puccinia striiformis f. sp. tritici ( Pst ) as a case study. A set of specific and high-efficiency housekeeping gene SNP (HG-SNP) markers was developed using genome and transcriptome sequencing data (GRSD) to generate genotype data directly from field-collected Pst -infected wheat leaves for population genetic analysis. These markers showed high consistency in the results of population genetic diversity with GRSD. Furthermore, population genetic analysis of 2,101 field samples from China using HG-SNP markers revealed low exchange among Xinjiang, Xizang, and Inland regions and major dispersal routes from the Northwest Oversummering Region and Yunnan-Guizhou to Sichuan and eastern China. This framework supports rapid and accurate monitoring of population genetics and can be readily applied to other plant pathogens. The method facilitates the distribution of host resistance and the development of plant disease management strategies at a regional and national scale. Biological sciences/Microbiology/Fungi/Fungal genetics Biological sciences/Genetics/Sequencing/DNA sequencing Population genetic studies Puccinia striiformis f. sp. tritici HG-SNP markers specific high-efficiency accurate Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Plant pathogens constitute a major biological threat to global food security, leading not only to lower yields but also to loss of species diversity, mitigation costs due to control measures, and downstream impacts on human health 1 , 2 . Based on their lifestyle and relationship with the host, plant pathogens can be broadly classified into necrotrophic, biotrophic and hemibiotrophic pathogens. Necrotrophic pathogens obtain nutrients from dead host cells, whereas biotrophic pathogens obtain nutrients from living cells, and hemibiotrophic pathogens initiate an initial biotrophic interaction with their host, followed by a necrotrophic phase 3 . Population genetic studies of plant pathogens rely on purification and multiplication, which is labor-intensive, costly and time-consuming. This makes it difficult to obtain pathogen samples, which severely limit research into the genetic diversity of plant pathogens. Therefore, a new method for rapid monitoring of population genetics of plant pathogens has become an urgent scientific proposal. The Puccinia striiformis f. sp. tritici ( Pst ), the causal agent of wheat stripe rust, is one of the major epidemic pathogens affecting wheat production worldwide 4 , 5 . As an obligate biotrophic parasite, Pst makes population genetic studies several weeks or even months longer than those of necrotrophic and hemibiotrophic pathogens. Therefore, this study uses Pst as a case study to construct a new method for rapidly and accurately monitoring the genetic diversity of plant pathogens. Molecular markers, including restriction fragment length polymorphism (RFLP), random amplified polymorphic DNA (RAPD), amplified fragment length polymorphism (AFLP), simple sequence repeat (SSR) markers and single nucleotide polymorphism (SNP) markers, have been among the most effective methods for population genetic studies of Pst . Initially, dominant markers, such as RFLP 6 , 7 , RAPD 8 , 9 and AFLP 10 , were employed but are no longer widely used due to their complex procedures, high cost, radioactivity, low polymorphism, and poor reproducibility. Subsequently, co-dominance SSR markers gained prominence for studying the population genetic structure and migration of Pst due to their high reproducibility 11 . For instance, Hu et al. 12 demonstrated that the population of Pst in Xizang is distinct from those in other regions and revised classification of Chinese epidemic regions of Pst into Xinjiang, Xizang and inland region. Wang et al. 13 used SSR markers to determine the migration routes of Pst among the Central Shaanxi, Longnan and Longdong in China. Jiang et al. 14 explored genetic relationships and population dynamics of Pst in southwestern and northwestern China using SSR markers. Furthermore, SSR markers have been instrumental in elucidating global population genetic structure and migration routes of stripe rust. Ali et al. 15 reported infrequent intercontinental migration of Pst and suggested human activity as a key factor in its spread. Sharma-Poudyal et al. 16 and Bai et al. 17 used SSR markers to describe the distribution of different Pst genetic groups across countries and provided evidence of global migrations. In recent years, SNP markers have shown great potential for studying population genetic structure and migration of Pst due to their abundance, high polymorphism, and compatibility with high-throughput sequencing. Commonly used SNP markers include secreted protein SNP (SP-SNP) markers, few housekeeping genes SNP (FHG-SNP) markers and kompetitive allele specific polymerase chain reaction SNP (KASP-SNP) markers. SP-SNPs markers, the first SNP markers developed for Pst , were applied to study its population genetic structure and revealed that diversity was greater in western than in eastern US populations 18 . FHG-SNP markers, based on few housekeeping genes, emphasized role of Yunnan in the epidemic spread of Pst across China 19 , 20 . Similarly, KASP-SNP markers, developed from whole-genome sequences, were employed to investigate the population structure and dispersal routes between oversummering and its adjacent regions 21 . With the rapid development of sequencing technology, whole-genome sequencing (WGS) has also been widely applied to animals 22 , plants 23 , bacteria 24 and fungi 25 – 27 with great success. Li et al. 28 identified Longnan, the Himalayan region, and the Guizhou Plateau as the Pst sources in China by the first WGS. Although the conclusions of all current markers have been widely accepted by researchers, these markers still rely on the purification and multiplication of Pst , which limited the rapid monitoring of population genetics of Pst , especially during outbreak years. The multiplex polymerase chain reaction (PCR) technology, known for its high efficiency, simple operation, and low cost, has been widely applied in detecting, identificating and assessing the diversity of bacteria 29 and viruses 30 . For example, the population of Setosphaeria turcica ( St ) in the midwestern region of China showed low genetic differentiation and high gene flow was observed using the multiplex PCR technology 31 , 32 . However, this technique has not yet been applied to the population genetic studies of Pst . To enable rapid monitoring of Pst the genetic diversity, this study aimed to (i) develop specific housekeeping gene SNP (HG-SNP) markers for Pst based on high-throughput sequencing data to enable direct genotyping from field-collected Pst -infected wheat leaves, and establish a multiplex PCR system to further improve the efficiency of the HG-SNP markers, (ii) validate the accuracy of the HG-SNP markers to ensure reliability in population structure studies, and (iii) test the practical application of the HG-SNP markers using field-collected Pst -infected wheat leaves and further propose the dispersal routes of stripe rust in China. This study provides a new method for rapid and accurate monitoring of plant pathogens population genetics. Results Development of the HG-SNP markers A total of 1,052 Pst sequence data was collected from the National Center for Biotechnology Information (NCBI) (Fig. 1 A; Table S1 ), including 215 genome-seq and 837 RNA-seq datasets (GRSD). After SNP calling and quality filtering, 135,728 bi-allelic SNPs were identified, and 6,732 SNPs were retained in high density regions by linkage disequilibrium (Figure S1 ). Among them, approximately 700 SNPs associated with housekeeping genes were retained. Meanwhile, 560 identity-by-descent (IBD) fragments were identified (see Methods) for lineage tracing. A total of 53 key SNPs from both the housekeeping gene region and the IBD fragments were selected and distributed across 39 gene regions to design HG-SNP markers (Table S2; S3). Target fragments (150–280 bp) containing the key SNPs were selected for the development of HG-SNP markers. Only one such marker was designed for per target gene, and 39 HG-SNP markers were developed. A total of 165 SNPs, including key and additional SNPs, were included in 39 HG-SNP markers with each marker containing 1 to 13 SNPs. Based on the annealing temperature, the HG-SNP markers were divided into two groups: 58 ℃ (Fig. 1 B) and 65 ℃ (Fig. 1 C). These markers showed amplification exclusively in Pst and no amplification fragments observed in Triticum aestivum ( Ta ) or other common pathogens, including Blumeria graminis f. sp. tritici ( Bgt ), P. graminis f. sp. tritici ( Pgt ), P. triticina ( Pt ), Fusarium graminearum ( Fg ) and F. pseudograminearum ( Fpg ), demonstrating their specificity. Through iterative optimization, the 39 HG-SNP markers were divided into three multiplex PCR groups named as A, B and C based on their amplification characteristics (Table S2). Groups A and B were amplified at annealing temperatures of 58 ℃ and 65 ℃, respectively. While markers with low amplification efficiency from both groups were combined into group C and amplified at 58 ℃. This design enabled the effective amplification of all HG-SNP markers from a single sample using only three PCR reactions, significantly saving time and labor while improving the overall efficiency of marker application. Subsequently, a pooled library containing 9,984 target fragments from 256 barcodes along with 39 HG-SNP markers was constructed using only 768 PCR reactions (Table S4). To evaluate the impact of barcode type on amplification efficiency and to determine the sufficient sequencing depth of the target fragments, a mixed pool was constructed using DNA from the same Pst sample. With 1 Gb of sequencing data, fewer than 1% of target fragments showed coverage below 10× (log10(count + 1) ≤ 1) (Fig. 1 D), and low-coverage fragments were not associated with specific barcodes. These results confirmed the successful development of the multiplex PCR system for HG-SNP markers. Accuracy of the HG-SNP markers Population genetic analyses of 1,052 global samples using 135,728 SNPs from GRSD identified six molecular groups (MG1 to MG6) based on analyses of phylogenetic and genetic structure analyses (Fig. 2 A). Furthermore, fifteen geographic groups were identified based on geographic features and molecular group features: five in Europe (Europe_1 to Europe_5), three in Africa (Central_Africa_1, Central_Africa_2, South_Africa), two in Asia (Asia_1, Asia_2), two in South America (South_America_1, South_America_2), two in Oceania (Oceania_1, Oceania_2), and one in North America (North_America) (Table S1 ). European populations were distributed across five molecular groups (except MG5) (Fig. 2 A) and exhibited the highest Shannon index (Fig. 2 D), supporting Europe as a potential global source of Pst . Analysis of the fixation index ( F ST ) and gene flow ( Nm ) revealed that all Nm within molecular groups exceeded 1, whereas Nm between molecular groups was mostly below 1 (Fig. 2 C; Table S6). This result suggests that higher Nm was observed within than between molecular groups. MG5 and MG6 showed the same genetic structure in K = 2 to 4 (Fig. 2 A), suggesting Europe_5 as the source of South_ Africa, Central_Africa_1, Central_Africa_2, North_America, Asia_1 and Oceania_1. Similarly, Europe_1 and Europe_2 were identified as the source of Asia_2 and South_America_1, respectively, and Europe_4 as the source of South_America_2 and Oceania_2. However, the fifteen geographic groups were clustered separately in phylogenetic tree, genetic structure (Fig. 2 A) and the three-dimensional principal component analysis (3D-PCA) (Fig. 2 B). Furthermore, no airflow can spread between continents at 10 km above ground level over 120 h (Fig. 3 A). These results suggest that intercontinental migration of Pst is infrequent. To demonstrate the accuracy of the HG-SNP markers, we compared the population genetic differences for the 1,052 global samples between HG-SNP markers and GRSD. Phylogenetic and genetic structure analyses based on both GRSD (Fig. 2 ) and HG-SNP markers (Figure S2) revealed that the fifteen geographic groups were largely separated. One discrepancy was noted for the Asia_1, which clustered with MG5 using GRSD markers but with MG1 using HG-SNP markers. Although 3D-PCA based on GRSD (Fig. 2 B) showed clearer separation than that based on HG-SNP markers (Figure S2B), both methods effectively separated the geographic groups. Due to the fewer number of SNPs, the range of Nm and F ST values based on the HG-SNP marker (Figure S2C; Table S6) are narrower than that based on GRSD (Fig. 2 C; Table S5). However, both methods showed consistent trends in Nm and F ST analysis. Matrix correlation analysis of phylogenetic trees between GRSD and the HG-SNP marker showed a significant correlation with a high correlation coefficient ( r = 0.9973; p = 0.0001) (Fig. 3 C). Similarly, high correlations were observed for the genetic structure ( r = 0.8258; p = 0.0001) (Fig. 3 D), 3D-PCA ( r = 0.8074; p = 0.0001) (Fig. 3 E) and Nm and F ST ( r = 0.9099; p = 0.0001) (Fig. 3 F). These results further demonstrate the consistency between GRSD and HG-SNP marker methods. We also evaluated phylogenetic trees which were constructed by software, such as PLINK and RAxML. For GRSD methods, the phylogenetic matrix correlation coefficient was high between RAxML and PLINK software ( r = 0.998; p = 0.0001) (Figure S3A; S3B). Similarly, a high correlation coefficient was observed between the GRSD and HG-SNP marker methods when the phylogenetic tree was constructed using the RAxML software ( r = 0.996; p = 0.0001) (Figure S3C; S3D). We further assessed the consistency of the phylogenetic trees by assigning the outgroup as zero value and sequentially numbering the remaining samples from one to create the phylogenetic sequence. Correlation analysis of phylogenetic sequences from GRSD methods between RAxML and PLINK showed a high coefficient ( r = 0.637; p = 5.79 × 10⁻¹²¹) (Figure S3E). However, for the phylogenetic sequence correlation analysis between GRSD and HG-SNP marker methods, PLINK software ( r = 0.817; p = 3.89 × 10⁻²⁵³) (Figure S3F) demonstrated higher consistency than RAxML software ( r = -0.126; p = 4.4 × 10⁻⁵) (Figure S3G). These results suggest that PLINK is more suitable than RAxML for constructing phylogenetic trees using HG-SNP marker methods. Population genetic structure of Pst in China To test the practical application of the HG-SNP markers, we performed the population genetic analysis of Pst using the 2,101 samples of infected field leaves, which were collected from wheat stripe rust epidemic areas in 18 provinces and autonomous regions in China (Fig. 4 A; Table S7). Based on geographic and topographic characteristics, we identified 13 geographic groups (Fig. 4 A): Xinjiang (XJ); Qinghai (QH); Central Gansu (GSC); Longdong Gansu (GSLD); Longnan Gansu (GSLN); Central Shaanxi (SXC); Shandong (SD); Henan (HN); Anhui, Zhejiang and Jiangsu (AZJ); Sichuan Basin (SC); Hubei (HB); Yunnan-Guizhou (YG); and Xizang (XZ). Phylogenetic analysis clustered the 2,101 Pst samples into seven molecular groups (MG1 to MG7) (Fig. 4 B). In GSC, GSLN, and QH, the MG distribution was similar, dominated by MG1, MG2, and MG3, which together comprised over 80.00% of the population. GSLD and SXC also showed similar MG distributions, with MG2 and MG3 as predominant groups (each > 25%), while other MGs showed balanced distributions in these regions. MG4 and MG5 were the predominant groups in AZJ (39.13%) and XJ (82.29%), respectively, whereas MG7 was the predominant group in XZ (73.85%), HB (52.42%), SC (34.32%) and YG (28.88%) (Fig. 4 C; Table S8). In HN, all MGs had a relatively balanced distribution (7.41%-17.78%), resulting in the highest Shannon index (1.82). This is followed by the SC and SXC with Shannon index of 1.74 and 1.67, respectively. In XJ and XZ, the proportion of the predominant groups were more than 70.00%, leading to unbalanced distributions and the lowest Shannon index (XJ: 0.59; XZ: 0.70) (Fig. 4 D; Table S8). Admixture analysis further elucidated Pst population structure (Fig. 4 E). MG7 formed a distinct group compared to other MGs. There were similar genetic structures between MG1 and MG2, and between MG3, MG4, MG5, and MG6. Through 3D-PCA analysis (Fig. 4 F), most samples of MGs could be clustered separately, especially between MG7 and other MGs. However, there were overlaps between MG1 and MG2, and between MG3, MG4, MG5, and MG6. Field investigations and genetic exchanges of the Pst in China The wheat growing season, Pst epidemic seasons, and the average temperatures from July 16 to August 15 were monitored in China (Table S9). Except for XZ and QH, where spring wheat is mostly grown, all other areas mainly grow winter wheat. Stripe rust epidemics generally begin about two months before wheat harvest and continue until harvest ends. Average temperatures from July 16 to August 15 in XJ, XZ, YG, GSLD, GSLN, GSC and QH were all below 24 ℃, providing suitable conditions for the oversummering of Pst . Inland regions, including all regions except XJ and XZ, showed low gene flow ( F ST ≥ 0.047; Nm ≤ 5.029) with XJ and XZ ( F ST ≥ 0.063; Nm ≤ 3.726), suggesting less exchange among the three regions (Fig. 5 A; Table S10). Within inland regions, high gene flow ( F ST ≤ 0.020; Nm ≥ 12.429) was observed among the QH, GSC, GSLN and GSLD, and these regions can complete the oversummering of Pst . Therefore, these regions can be classified as a single group (the Northwest Oversummering Region, NOSR). High gene flow was detected between GSLD and SXC ( F ST = 0.003; Nm = 78.751), as well as between SXC and HN ( F ST = 0.020; Nm = 12.540), suggesting that inoculum disperses from GSLD to HN via SXC. In the YG region, the highest gene flow was detected with SC ( F ST = 0.016; Nm = 15.416), followed by HN ( F ST = 0.026; Nm = 9.440) and HB ( F ST = 0.026; Nm = 9.281), indicating that the inoculum from YG could spread to SC and HN via HB. Additionally, relatively high gene flow was observed between HN and SD ( F ST = 0.028; Nm = 8.804), as well as between HN and AZJ ( F ST = 0.020; Nm = 12.344), suggesting that HN may be an important bridging zone for the dispersal of Pst from YG and NOSR to these regions. Compared to YG, the NOSR showed high gene flow with SXC, but low gene flow with SC and HB, while its gene flow is similar to that of AZJ, SD and HN. These results suggest that SXC is primarily influenced by the NOSR inoculum, SC and HB are primarily influenced by the YG inoculum, and AZJ, SD and HN are influenced by both the NOSR and YG inoculum (Fig. 6 B). Through correlation analysis between population genetic distance and geographic distance, there is a low correlation coefficient in China ( r = 0.2106; p = 0.0001) (Fig. 5 B), especially in inland regions ( r = 0.2174; p = 0.0001) (Fig. 5 C), but a high correlation coefficient in XZ ( r = 0.4837; p = 0.0001) (Fig. 5 D), indicating the high frequency of Pst dispersal in China, especially in inland regions, but the low frequency in XZ. However, no significant correlation and the low correlation coefficient in XJ ( r = 0.0764; p = 0.0562) (Fig. 5 E) may be due to the concentration of sampling locations. Trajectory simulation and traceability analysis of Pst migration in China To delineate Pst dispersal routes in China, air trajectory tracking analyses were performed during Pst epidemic seasons. The average trajectory frequency (ATF) for Pst dispersal was calculated at 3,000 m above ground level over 120 h forward trajectories with two trajectory per day from 2005 to 2024 (Fig. 6 A). High ATF values (> 20%) were observed several dispersal pathways: from GSC to QH (20%), GSLD (22%) and GSLN (22%); from GSLN to GSC (40%); and from GSLD to GSLN (25%). These results supported high Nm among QH, GSC, GSLN and GSLD. The ATF from SXC to GSLD (37%), as well as to HN (28%), exceeded 25%, supporting high Nm between SXC and these regions. There is a higher ATF from YG to SC (47%) than from GSLN (21%), corroborating Nm analysis. A high ATF from YG to HB (32%) further suggests that HB was influenced by the YG inoculum. While direct ATF from YG to HN was negligible, high ATF from HB to HN (≥ 10%) suggests that HB may be an important bridging zone for Pst dispersion from YG to HN. However, the airflow from XJ and XZ hardly disperses to other regions, indicating less exchange among these regions and inland regions (Fig. 6 B). Discussion Traditional population genetic studies of plant pathogens relied on purification and multiplication work to obtain sufficient samples. The genetic diversities of the plant pathogens were then determined using traditional molecular markers, such as SSR and SNP markers. These processes are labor-intensive, costly and time-consuming, particularly for obligate biotrophic pathogens. These constraints have historically limited both sample sizes and the timeliness of population genetic studies of plant pathogens, especially during epidemic outbreaks. To enable rapid and accurate monitoring the population genetic of plant pathogens, this study developed specific, high-efficiency and accurate HG-SNP markers for Pst as a case study. Firstly, all HG-SNP markers showed amplification exclusively fragments in Pst and no amplification fragments in Ta or other common pathogens such as Bgt , Pgt, Pt , Fg and Fpg . Thus, compared to traditional markers, HG-SNP markers supported direct amplification of target fragments from Pst -infected wheat leaves, circumventing the necessary work for purification and multiplication, and allowing rapid population genetic monitoring. Additionally, genotyping costs of the HG-SNP markers are less than $ 1 per sample, which provides the opportunity for population genetic studies of Pst with large scale samples. Secondly, a total of 9,984 PCRs from 256 samples were required to amplify each mixed pool, which not only takes a considerable amount of time, but also increases the likelihood of human error. To enhance efficiency and reduce human error, we developed a multiplex PCR system for HG-SNP markers, using only 768 PCR reactions for each pool. This system further saves time and labor and improves the usage efficiency of the HG-SNP marker. Finally, population genetic analyses using the same stripe rust population showed consistent trends between the GRSD and HG-SNP marker, which ensured the accuracy of markers in the population genetic studies of Pst . In phylogenetic analysis, VCF files are typically converted into matrix or FASTQ format for subsequent processing. This study recommends converting VCF files to matrix format rather than FASTQ using HG-SNP markers. Stripe rust was first reported in Europe (1777) 33 earlier than in most other regions, such as Central Africa (1958) 34 , South Africa (1996) 35 , North America (early 1915) 36 , 37 , South America (1975) 38 , Australia (1979) 39 , and New Zealand (1980) 40 . In this study, we identified Europe as a potential global source of Pst . These results suggested a relationship between the source and the first reports of Pst . Although the first reports of stripe rust in China date back to AD 533–544 41 , the source and migrations of Pst may be also an associated with wheat domestication. Wheat originated in the Fertile Crescent around 10,000 years ago, reaching Europe (~ 7,000 year BP) before Asia (~ 5,400 year BP) 42 , 43 , which is consistent with the possibility that stripe rust colonized Europe earlier than Asia. This study also suggests that intercontinental migration of Pst is infrequent based on population genetic structure and air trajectory analyses, which is consistent with the migration of wheat pathogens such as Pst 15 , Bgt 26 , Zymoseptoria tritici 25 and Pt 27 . Evidence suggested that the Pst from Europe to Australia in 1979 was migrated by human activities 39 . Furthermore, strong air currents, such as Hurricane Guillermo 44 , moved westward across the Atlantic Ocean from the coast of Africa to North America over about ten days. Therefore, human activities and strong air currents may be the main media for intercontinental Pst migration. This study further tested the practical application of HG-SNP markers using field-collected Pst -infected wheat leaves. A total of 2,101 samples from 18 Chinese provinces and autonomous regions elucidated the population genetic structure and spread routes of Pst . The annual cycle of Pst consists of oversummering, seedling infection in autumn, overwintering, and spring epidemic 41 , with oversummering being critical stage. Stripe rust can oversummer where the average temperature between July 16 and August 15 is below 24 ℃ 45 . Our field surveys identified Xinjiang, Xizang, Yunnan-Guizhou, Longdong, Longnan, Central Gansu and Qinghai as regions with suitable oversummering conditions for Pst . Although wheat does not grow during this period in Xinjiang, Yunnan-Guizhou, Longdong, Longnan, and Central Gansu, stripe rust can persist on volunteer wheat. The Northwest Oversummering Region, especially Longnan and Eastern Qinghai 12 , 13 , 28 , 46 – 48 , and the Yungui Plateau 19 , 20 , 28 have long been regarded as major Pst sources in China due to their high diversity of Pst . In this study, we confirmed that the Northwest Oversummering Region, including Qinghai, Central Gansu, Longnan and Longdong regions, can be considered as a single group influencing other regions in China based on Nm , F ST , air trajectory and field survey analyses. Central Gansu, where mainly winter wheat is planted, and Qinghai, where mainly spring wheat is planted, may constitute the core annual cycle regions of Pst based on high gene flow and wheat growing season analysis. Frequent Pst dispersal from Longnan to the Sichuan Basin 28 , 49 and from Longdong to Central Shaanxi 13 , 28 , 50 has been documented based on gene flow analyses. Additionally, long-distance dispersal of Pst from the Yungui Plateau to the Sichuan Basin 28 , 51 and the Central Plain 52 – 54 has been identified through population genetic analyses. Based on trajectory tracking and gene flow analysis, our study indicates an overall west-to-east spread of stripe rust in China. The spread routes of stripe rust from Qinghai and Central Gansu are mainly divided into two dispersal directions: eastward through Longdong and Central Shaanxi, as important bridging zones, to the Huang-Huai-Hai wheat-growing region, and southward through Longnan to the Sichuan Basin. Similarly, the spread routes of stripe rust in the Yungui Plateau are mainly divided into two spread directions: northeastward through Hubei, as an important bridging zone, to the Huang-Huai-Hai wheat-growing region, and directly northward to the Sichuan Basin. Compared to the Yungui Plateau, the Northwest Oversummering Region showed high gene flow with Central Shaanxi but low gene flow with the Sichuan Basin and Hubei, while its gene flow is comparable with the Huang-Huai-Hai region, including Anhui, Zhejiang, Jiangsu, Shandong and Henan. This suggests that Central Shaanxi is mainly influenced by the Northwest Oversummering Region inoculum, the Sichuan Basin and Hubei are mainly influenced by the Yungui Plateau inoculum, and the Huang-Huai-Hai region is influenced by both sources. However, there was less exchange among Xinjiang, Xizang and inland regions based on trajectory tracking and gene flow analysis. In summary, this study successfully overcomes the limitations of purification and multiplication of plant pathogens in population genetic studies. It also provides a novel method for the rapid and accurate monitoring of the population genetics of plant pathogens. Compared to traditional population genetic studies, this method avoids altering the native population genetic structure of pathogens by host resistance selection during pathogen purification. This advancement will provide robust scientific and technological support for the development of plant disease management strategies at a regional and national scale. Method Sample collection and genome DNA extraction A total of 1,052 sequence data of Pst were collected from the National Center for Biotechnology Information (NCBI) (Table S1 ). A total of 2,101 Pst -infected wheat leaves were collected in 18 provinces and autonomous regions of China from 2020 to 2023 (Table S7). DNA of Pst was extracted directly from Pst -infected wheat leaves using the cetyltrimethyl ammonium bromide (CTAB) method 8 , 28 . Detection and filtering of variants Raw reads were cleaned by removing adapters and low-quality sequences using Trimmomatic ( https://github.com/usadellab/Trimmomatic ). Cleaned reads were mapped to the DK09_11 reference genome using BWA ( https://github.com/lh3/bwa ) with default parameters. SAM files were converted to BAM files and INDEX were built using SAMtools (v0.1.1) ( https://github.com/samtools/samtools ). The BAM files were cleaned and sorted, and PCR duplicates were removed and validated using Picard tools ( https://github.com/broadinstitute/picard ). Variants were called using the Genome Analysis Toolkit (GATK) version 4.1.9.0 ( https://github.com/broadinstitute/gatk ) with default parameters to generate genome Variant Call Format (GVCF) files, which were then merged into a single GVCF file using GATK CombineGVCFs. Joint genotyping was implemented on the combined file using GATK GenotypeGVCFs. The output file was filtered using VCFtools v0.1.13 ( https://github.com/vcftools/vcftools ), with the parameters “--min-alleles 2 --max-alleles 2 --maf 0.05 --mac 3 --minQ 1000 --max-missing 0.85 --min-meanDP 20”. Development of HG-SNP markers SNPs were filtered for linkage disequilibrium using PLINK ( https://github.com/chrchang/plink-ng ), with the parameters “--indep-pairwise 50 1 0.2”. Housekeeping gene SNPs were then saved based on the benchmarking universal single-copy orthologs (BUSCOs) 55 using Bedtools ( https://github.com/arq5x/bedtools ). All samples were phased using Beagle (v5.4) 56,57 . Pairwise shared haplotypes were extracted using the Beagle RefinedIBD function 58 . All samples were divided into six groups based on different continents, including Europe, Africa, Asia, North America, South America and Oceania. After removing duplicate identity-by-descent (IBD) fragments, those with a frequency ≥ 3 were retained. Housekeeping gene SNPs were again filtered based on IBD fragments and the DK09_11 Gff file using Bedtools. The fragments of 150–280 bp containing key SNPs were selected to develop HG-SNP markers for each gene. Amplification and specificity of HG-SNP markers PCR amplification was performed in a 10 µL mixture containing 0.5 µL each of the two primers, 1 µL template DNA, 5 µL CWBIO Taq-mix (Beijing ComWin Biotech Co., Ltd., China) and 3 µL H 2 O. PCR parameters were as follows: an initial denaturation for 3 min at 94 ℃; then 35 cycles consisting of denaturation at 94 ℃ for 20 s, annealing at 58/65 ℃ for 20 s and extension at 72 ℃ for 20 s; final extension at 72 ℃ for 5 min. Sterilized water was used as a negative control for each PCR amplification. PCR products were detected using a 1.3% (wt/vol) agarose gel containing 0.01% nucleic acid dye in 1× Tris-acetate-EDTA buffer. To test the specificity of HG-SNP markers, seven isolates of Pst and one sample each of Pgt , Pt , Bgt , Fg , Fpg and Ta were used for PCR amplification of each HG-SNP marker and ITS (Table S2). The construction and sequencing of mixed pools of HG-SNP markers To construct a mixed pool including a lot of the samples for high-throughput sequencing, three nucleotide barcodes were added to the 5' ends of both the forward and reverse primers. To avoid repeating the barcodes of the forward and reverse primers, the last nucleotides of the forward primer were set as 'G' nucleotides, while the reverse primer ended with 'C' nucleotides. The remaining two nucleotides were combined using all four nucleotides (A, G, C, T) in pairs, resulting in 16 single-end barcode combinations and 256 paired-end barcode combinations (Table S4). The different barcodes were added to the forward and reverse primers of each HG-SNP marker. Each isolate was amplified by HG-SNP markers with the same barcode and different isolates were amplified by HG-SNP markers with different barcodes (Table S3). Each mixed pool was constructed from PCR products of 256 Pst isolates. DNA libraries were sequenced using the DNBSEQ-2000 platform by BGI Genomics Co., Ltd. (Shenzhen, China). Sequencing reads were demultiplexed by barcode using fastq-multx 59 . Phylogenetic and population structure analysis To construct phylogenetic trees, the VCF file needs first to be converted to matrix or FASTQ format. The genetic distance matrix of identity-by-state (IBS) was calculated using PLINK v1.90 (-distance 1-ibs) and then converted to Newick format using Phylip v2.0. The FASTQ file was converted from VCF file by vcf2phylip.py ( https://github.com/edgardomortiz/vcf2phylip ), and phylogenetic trees were then constructed using maximum likelihood methods with 1,000 replications by RAxML v8.2.13 60 . Phylogenetic trees were visualized and annotated using iTOL v6 61 . The population genetic structure was analyzed using Admixture 60 with K values from 2 to 10. The three-dimensional principal component analysis (3D-PCA) was performed with PLINK v1.90, and the first to third principal components (PCs) were plotted using the ggplot2 package in R v4.2.3. Determination of genetic diversity, introgression, and migration Fixation index ( F ST ) was estimated in 5 kb sliding windows using VCFtools, Gene flow ( N m ) was calculated as (1- F ST )/(4* F ST ). Diversity for geographic groups was estimated using Shannon’s diversity ( H ), where H = −∑ pi × ln( pi ), and pi is the frequency of the i th molecular group in the geographic group. The dist() command in the geo sphere v1.5-14 R package 62 was used to calculate geographic distance. Phylogenetic matrices were constructed based on geographic group and molecular group of samples. The correlation of matrices was analyzed using the mantel() command in the vegan v2.6-4 R package ( https://github.com/vegandevs/vegan ) with 9999 permutations. Phylogenetic sequence was constructed by assigning the outgroup as zero value and sequentially numbering the remaining samples from one. The correlation of the sequence was analyzed using the cor() command in R. Trajectory simulation and traceability analysis of Pst migration Weekly meteorological data from 2005 to 2024 were obtained from the National Oceanic and Atmospheric Administration (NOAA) website ( ftp://arlftp.arlhq.noaa.gov/pub/archives/gdas1 ). Regions were selected for air trajectory tracking (ATT) analyses based on sample distribution using Hybrid Single-Particle Lagrangian Integrated Trajectory version 4 (HYSPLIT-4). The average trajectory frequency (ATF) with forward trajectories for Pst dispersal was calculated and visualized using the ggplot2, tmap, and sp Gallery packages in R v4.1.2. Declarations Data Availability Raw sequenced reads of 42 samples have been deposited at the China National Genomics Data Center database (https://www.cncb.ac.cn) under BioProject PRJCA053835 (Table S7). Author Contribution X.H., Y.L., S.F. and M.X. designed the experiments. J.Z. performed the experiments and analyzed the data. X.H., Y.L. and J.Z. wrote the paper. Code Availability The code used in this study is described in the Methods section and is available on GitHub at https://github.com/junjie-zhang-92/population. Funding National Natural Science Foundation of China (32502448) to Y.L. References Fones, H. N. et al. Threats to global food security from emerging fungal and oomycete crop pathogens. Nature Food 1 , 332-342 (2020). Ristaino, J. B. et al. The persistent threat of emerging plant disease pandemics to global food security. Proc. Natl. Acad. Sci. USA 118 , e2022239118 (2021). Glazebrook, J. Contrasting mechanisms of defense against biotrophic and necrotrophic pathogens. Annu. Rev. Phytopathol. 43 , 205-227 (2005). Chen, X. Pathogens which threaten food security: Puccinia striiformis , the wheat stripe rust pathogen. Food Security 12 , 239-251 (2020). Wellings, C. R. Global status of stripe rust: a review of historical and current threats. Euphytica 179 , 129-141 (2011). Saiki, R. K. et al. Enzymatic amplification of beta-globin genomic sequences and restriction site analysis for diagnosis of sickle cell anemia. Science 230 , 1350-1354 (1985). Shan, W. et al. Genetic diversity in Puccinia striiformis Westend. f.sp. tritici revealed by pathogen genome-specific repetitive sequence. Canadian Journal of Botany 76 , 587-595 (1998). Chen, X. et al. Relationship between virulence variation and DNA polymorphism in Puccinia striiformis . Phytopathology 83 , 1489-1497 (1993). Williams, J. G. et al. DNA polymorphisms amplified by arbitrary primers are useful as genetic markers. Nucleic Acids Res. 18 , 6531-6535 (1990). Vos, P. et al. AFLP: a new technique for DNA fingerprinting. Nucleic Acids Res. 23 , 4407-4414 (1995). Enjalbert, J. et al. Isolation of twelve microsatellite loci, using an enrichment protocol, in the phytopathogenic fungus Puccinia striiformis f.sp. tritici . Molecular Ecology Notes 2 , 563–565 (2002). Hu, X. et al. Population Genetic Analysis of Puccinia striiformis f. sp. tritici Suggests Two Distinct Populations in Tibet and the Other Regions of China. Plant Dis. 101 , 288-296 (2017). Wang, C. et al. Genetic Analysis Reveals Relationships Among Populations of Puccinia striiformis f. sp. tritici from the Longnan, Longdong, and Central Shaanxi Regions of China. Phytopathology 112 , 278-289 (2022). Jiang, B. et al. Genetic Relationships of Puccinia striiformis f. sp. tritici in Southwestern and Northwestern China. Microbiol. Spectr. 10 , e0153022 (2022). Ali, S. et al. Origin, migration routes and worldwide population genetic structure of the wheat yellow rust pathogen Puccinia striiformis f. sp. tritici . PLoS Pathog. 10 , e1003903 (2014). Sharma-Poudyal, D. et al. Molecular Characterization of International Collections of the Wheat Stripe Rust Pathogen Puccinia striiformis f. sp. tritici Reveals High Diversity and Intercontinental Migration. Phytopathology 110 , 933-942 (2020). Bai, Q. et al. Molecular Characterization of Wheat Stripe Rust Pathogen ( Puccinia striiformis f. sp. tritici ) Collections from Nine Countries. Int. J. Mol. Sci. 22 , 9457 (2021). Xia, C. et al. Secreted protein gene derived-single nucleotide polymorphisms (SP-SNPs) reveal population diversity and differentiation of Puccinia striiformis f. sp. tritici in the United States. Fungal Biol. 120 , 729-744 (2016). Alam, M. A. et al. Genetic Diversity of Wheat Stripe Rust Fungus Puccinia striiformis f. sp. tritici in Yunnan, China. Plants (Basel) 10 , 100563 (2021). Li, M. et al. Evidence for Yunnan as the major origin center of the dominant wheat fungal pathogen Puccinia striiformis f. sp. tritici . Australasian Plant Pathology 50 , 241–252 (2021). Li, Y. et al. Migration of wheat stripe rust from the primary oversummering region to neighboring regions in China. Commun. Biol. 8 , 350 (2025). Chen, N. et al. Global genetic diversity, introgression, and evolutionary adaptation of indicine cattle revealed by whole genome sequencing. Nature Commun. 14 , 7803 (2023). He, F. et al. Exome sequencing highlights the role of wild-relative introgression in shaping the adaptive landscape of the wheat genome. Nature Genet. 51 , 896-904 (2019). Leeman-Neill, R. J. et al. Noncoding mutations cause super-enhancer retargeting resulting in protein synthesis dysregulation during B cell lymphoma progression. Nature Genet. 55 , 2160-2174 (2023). Feurtey, A. et al. A thousand-genome panel retraces the global spread and adaptation of a major fungal crop pathogen. Nature Commun. 14 , 1059 (2023). Sotiropoulos, A. G. et al. Global genomic analyses of wheat powdery mildew reveal association of pathogen spread with historical human migration and trade. Nature Commun. 13 , 4315 (2022). Sperschneider, J. et al. Nuclear exchange generates population diversity in the wheat leaf rust pathogen Puccinia triticina . Nature Microbiol. 8 , 2130-2141 (2023). Li, Y. et al. Genomic analysis, trajectory tracking, and field surveys reveal sources and long-distance dispersal routes of wheat stripe rust pathogen in China. Plant Commun. 4 , 100563 (2023). Xie, N. G. et al. Designing highly multiplex PCR primer sets with Simulated Annealing Design using Dimer Likelihood Estimation (SADDLE). Nature Commun. 13 , 1881 (2022). Huang, H. S. et al. Multiplex PCR system for the rapid diagnosis of respiratory virus infection: systematic review and meta-analysis. Clin. Microbiol. Infect. 24 , 1055-1063 (2018). Cui, L. et al. Genetic Diversity and Population Genetic Structure of Setosphaeria turcica From Sorghum in Three Provinces of China Using Single Nucleotide Polymorphism Markers. Front. Microbiol. 13 , 853202 (2022). Cui, L. et al. Genetic Diversity, Population Structure and Mating Type Distribution of Setosphaeria turcica on Corn in Midwestern China. J. Fungi (Basel) 8 , 1165 (2022). Stubbs, R. W. Cereal Rusts Diseases, Distribution, Epidemiology, and Control. Academic Press 2 , 61-101 (1985). Dawit, W. Virulence and diversity of Puccinia striiformis f. sp. tritici in Ethiopia. Can. F. Plant Pathol. Rev. Can. Phytopathol. 31 , 211-219 (2009). Boshoff, W. H. P. et al. Establishment, Distribution, and Pathogenicity of Puccinia striiformis f. sp. tritici in South Africa. Plant Dis. 86 , 485-492 (2002). Carleton, M. A. A SERIOUS NEW WHEAT RUST IN THIS COUNTRY. Science 42 , 58-59 (1915). Hovmøller, M. S. et al. Diversity of Puccinia striiformis on cereals and grasses. Annu. Rev. Phytopathol. 49 , 197-217 (2011). Brown, Jr. W. M. Barley yellow rust in North America. Annu. Rev. Phytopathol. 39 , 367-384 (2001). Wellings, C. R. Puccinia striiformis in Australia: a review of the incursion, evolution, and adaptation of stripe rust in the period 1979–2006. Australian Journal of Agricultural Research 58 , 567-575 (2007). Wellings, C. R. & McIntosh, R. A. Puccinia striiformis f. sp. tritici in Australasia: pathogenic changes during the first 10 years. Plant Pathology 39 , 316–325 (1990). Zhao, J. et al. Fighting wheat rusts in China: a look back and into the future. Phytopathol. Res. 5 , 6 (2023). Wang, Z. et al. Dispersed emergence and protracted domestication of polyploid wheat uncovered by mosaic ancestral haploblock inference. Nature Commun. 13 , 3891 (2022). Zhao, X. et al. Population genomics unravels the Holocene history of bread wheat and its relatives. Nature Plants 9 , 403-419 (2023). Lawrence, M. B. Eastern North Pacific Hurricane Season of 1997. Annual Weather Summaries in Monthly Weather Review 127 , 2440–2454 (1998). Zhang, J. et al. Re-delimiting of oversummering regions for Puccinia striiformis f. sp. tritici in China. Phytopathol Res. 7 (2025). Chen, W. et al. Race Dynamics, Diversity, and Virulence Evolution in Puccinia striiformis f. sp. tritici , the Causal Agent of Wheat Stripe Rust in China from 2003 to 2007. Plant Dis. 93 , 1093-1101 (2009). Liang, J. et al. Genetic Structure and Asymmetric Migration of Wheat Stripe Rust Pathogen in Western Epidemic Areas of China. Phytopathology 111 , 1252-1260 (2021). Wan, A. et al. Wheat Stripe Rust Epidemic and Virulence of Puccinia striiformis f. sp. tritici in China in 2002. Plant Dis. 88 , 896-904 (2004). Liang, J. et al. Population Genetic Structure and the Migration of Puccinia striiformis f. sp. tritici Between the Gansu and Sichuan Basin Populations of China. Phytopathology 106 , 192-201 (2016). Liang, J. et al. Population Genetic Structures of Puccinia striiformis in Ningxia and Gansu Provinces of China. Plant Dis. 97 , 501-509 (2013). Wang, H. et al. Long-Distance Spore Transport of Wheat Stripe Rust Pathogen from Sichuan, Yunnan, and Guizhou in Southwestern China. Plant Dis. 94 , 873-880 (2010). Awais, M. et al. Countrywide inter-epidemic region migration pattern suggests the role of southwestern population in wheat stripe rust epidemics in China. Environ. Microbiol. 24 , 4684-4701 (2022). Huang, L. et al. Long-Distance Transport of Puccinia striiformis f. sp. tritici by Upper Airflow on the Yunnan-Guizhou Plateau Disrupts the Balance of Agricultural Ecology in Central China. Plant Dis. 106 , 2940-2947 (2022). Zhan, G. et al. Populations of Puccinia striiformis f. sp. tritici in Winter Spore Production Regions Spread from Southwestern Oversummering Areas in China. Plant Dis. 106 , 2856-2865 (2022). Manni, M. et al. BUSCO: Assessing Genomic Data Quality and Beyond. Curr. Protoc. 1 , e323 (2021). Browning, B.L. et al. A One-Penny Imputed Genome from Next-Generation Reference Panels. Am. J. Hum. Genet. 103 , 338-348 (2018). Browning, B.L. et al. Fast two-stage phasing of large-scale sequence data. Am. J. Hum. Genet. 108 , 1880-1890 (2021). Browning, B. L, & Browning, S. R. Improving the accuracy and efficiency of identity-by-descent detection in population data. Genetics 194 , 459-471 (2013). Aronesty, E. Comparison of Sequencing Utility Programs. The Open Bioinformatics Journal 7 , 1-8 (2013). Alexander, D. H. et al. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 19 , 1655-1664 (2009). Letunic, I, & Bork, P. Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 49 , W293-w296 (2021). Karney, C. F. F. Algorithms for geodesics. Journal of Geodesy 87 , 43-55 (2013). Additional Declarations There is NO Competing Interest. Supplementary Files Supplementary.pdf Supplemental Information Figure S1. The pipeline involves the development, verification and application of the HG-SNP markers. Figure S2. Population genetic structure analysis of 1,052 Puccinia striiformis f. sp. tritici ( Pst ) isolates all over the world. (A) Phylogenetic and genetic structure analysis of Pst populations by the HG-SNP marker. Tracks from inner (a) to outer (h) rings indicate the following: a, Molecular group; b, Geographic group; c-h, Population genetic structure of Pst with K values from 2 to 6. (B) 3D-PCA analysis by the HG-SNP marker. (C) Fixation index (below diagonal) and gene flow (above diagonal) analysis by the HG-SNP marker. Figure S3. Accuracy detection of the HG-SNP markers. Phylogenetic tree of Puccinia striiformis f. sp. tritici ( Pst ) populations using RAxML based on the GRSD (A) and HG-SNP markers (C). a, Molecular group; b, Geographic group. (B) Correlation analysis of phylogenetic matrix between PLINK and RAxML software based on GRSD. (D) Correlation analysis of phylogenetic matrix between GRSD and HG-SNP markers using RAxML software. (E) Correlation analysis of phylogenetic sequence between PLINK and RAxML software based on GRSD. (E) Correlation analysis of phylogenetic sequence between GRSD and HG-SNP markers using PLINK (F) and RAxML (G) software. Table S1. Sample information statistical of Puccinia striiformis f. sp. tritici in the world. Table S2. Primer information statistics of HG-SNP markers. Table S3. Primer sequences of HG-SNP markers. Table S4. Barcode information statistics of HG-SNP markers. Table S5. Fixation index ( F ST ) (below diagonal) and gene flow ( Nm ) (above diagonal) analysis for all geographic groups in the world using GRSD. Table S6. Fixation index ( F ST ) (below diagonal) and gene flow ( Nm ) (above diagonal) analysis for all geographic groups in the world using HG-SNP markers. Table S7. Sample information statistical of Puccinia striiformis f. sp. tritici in China. Table S8. Genetic diversity statistics for geographic group based on HG-SNP markers in China. Table S9. Statistics of the average temperature from July 16 to August 15 from 2005 to 2024, and wheat growing and stripe rust epidemic season in the province from 2020 to 2023 in China. Table S10. Fixation index ( F ST ) (below diagonal) and gene flow ( Nm ) (above diagonal) analysis for all geographic groups in China. Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8699492","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":584687666,"identity":"9c6bee1a-818a-4d20-989d-2bc3757dd29f","order_by":0,"name":"Xiaoping Hu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYFACHgaGD2AG4wMGBjYitTDOADOYDYjXwsxDkhaD273HpG13HLY3OH4Y6MKywwz8sxvwa5Gccy5NOvfM4cQNZ5KBLjx3mEHizgH8Wvglcsykc9tuJxgcyD/AzNt2mMFAIgG/FjaQFsu22/YG5x8zMP8lRgvYFsa224wbbiQzMDMSo0Vyzhljy962/4kzbzxmONhzLp1H4gYBLQa3ewxv/GxLs+c7n8z44EeZtRz/DAJaGCSQ2AcYwImBEJAgrGQUjIJRMApGOgAA5itA0qN4ym0AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-8155-7040","institution":"Northwest A\u0026F University","correspondingAuthor":true,"prefix":"","firstName":"Xiaoping","middleName":"","lastName":"Hu","suffix":""},{"id":584687667,"identity":"52559156-e500-4832-a765-3b1c38fa4a80","order_by":1,"name":"Junjie Zhang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Junjie","middleName":"","lastName":"Zhang","suffix":""},{"id":584687668,"identity":"a524341e-383b-48c4-94f2-ebb464bc68fd","order_by":2,"name":"Sanhong Fan","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Sanhong","middleName":"","lastName":"Fan","suffix":""},{"id":584687669,"identity":"a241f116-f365-41d3-be7a-4f975625289f","order_by":3,"name":"Xiangming Xu","email":"","orcid":"","institution":"East Malling","correspondingAuthor":false,"prefix":"","firstName":"Xiangming","middleName":"","lastName":"Xu","suffix":""},{"id":584687670,"identity":"56ad001b-359f-4980-baef-b6ccf6451ceb","order_by":4,"name":"Yuxiang Li","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yuxiang","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2026-01-26 11:00:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8699492/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8699492/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101940370,"identity":"dc58d037-26f7-4a79-b478-bd42248cbd9e","added_by":"auto","created_at":"2026-02-05 09:13:56","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":300397,"visible":true,"origin":"","legend":"\u003cp\u003eGeographic distribution of sequence samples and detection of the HG-SNP markers. (A) Geographic distribution of 1,052 \u003cem\u003ePuccinia striiformis\u003c/em\u003e f. sp.\u003cem\u003e tritici\u003c/em\u003e (\u003cem\u003ePst\u003c/em\u003e) isolates all over the world. Specific testing of HG-SNP markers at annealing temperature 58 ℃ (B) and 65 ℃ (C), respectively. Tracks from left to right gel lane are H\u003csub\u003e2\u003c/sub\u003eO, \u003cem\u003eTriticum aestivum \u003c/em\u003e(\u003cem\u003eTa\u003c/em\u003e), \u003cem\u003eP. graminis \u003c/em\u003ef. sp. \u003cem\u003etritici\u003c/em\u003e (\u003cem\u003ePgt\u003c/em\u003e),\u003cem\u003e P. triticina\u003c/em\u003e (\u003cem\u003ePt\u003c/em\u003e),\u003cem\u003e Fusarium graminearum\u003c/em\u003e (\u003cem\u003eFg\u003c/em\u003e),\u003cem\u003e F. pseudograminearum\u003c/em\u003e (\u003cem\u003eFpg\u003c/em\u003e),\u003cem\u003e Blumeria graminis\u003c/em\u003e f. sp. \u003cem\u003etritici\u003c/em\u003e (\u003cem\u003eBgt\u003c/em\u003e), different isolates of \u003cem\u003ePst\u003c/em\u003e, H\u003csub\u003e2\u003c/sub\u003eO and DL-2000 marker, respectively. (D) Count of each barcode of HG-SNP marker by sequencing.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8699492/v1/a8e1ead80a4bc7da039d7c85.jpeg"},{"id":101940372,"identity":"79742718-76e8-4505-850b-aa9e62561264","added_by":"auto","created_at":"2026-02-05 09:13:56","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1467334,"visible":true,"origin":"","legend":"\u003cp\u003ePopulation genetic structure analysis of 1,052 \u003cem\u003ePuccinia striiformis\u003c/em\u003e f. sp.\u003cem\u003e tritici\u003c/em\u003e (\u003cem\u003ePst\u003c/em\u003e) isolates all over the world. (A) Phylogenetic and genetic structureanalysis of \u003cem\u003ePst\u003c/em\u003e populations by the GRSD. Tracks from inner (a) to outer (h) rings indicate the following: a, Molecular group; b, Geographic group; c-h, Population genetic structure of \u003cem\u003ePst\u003c/em\u003e with K values from 2 to 6. (B) 3D-PCA analysis by the GRSD. (C)Fixation index (below diagonal) and gene flow (above diagonal) analysis by the GRSD. (D)Shannon index analysis of continents.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8699492/v1/5b4edef53b62d2d0c30d520c.jpeg"},{"id":101940373,"identity":"2b0e7194-2b8d-4c2f-95de-1e75d970a46b","added_by":"auto","created_at":"2026-02-05 09:13:57","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1155738,"visible":true,"origin":"","legend":"\u003cp\u003eThe airflow trajectory analyses all over the worldand accuracydetection of the HG-SNP markers. (A) The airflow trajectory analyses all over the world with forward trajectories analysis in 10 km from 2005 to 2024 years. (B) Potential global source of \u003cem\u003ePst\u003c/em\u003e. Correlation analysis of matrix between GRSD and HG-SNP markers of Phylogenetic tree (C), genetic structure (K = 6) (D), 3D-PCA (E) and gene flow (F).\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8699492/v1/e2c5ac02fca7de764da815cc.jpeg"},{"id":101940369,"identity":"5625cd59-cfb5-4866-a76a-5fe82d93d64c","added_by":"auto","created_at":"2026-02-05 09:13:56","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1149605,"visible":true,"origin":"","legend":"\u003cp\u003eGeographic distribution and population genetic structure analysis of 2,101 \u003cem\u003ePuccinia striiformis\u003c/em\u003e f. sp.\u003cem\u003e tritici\u003c/em\u003e (\u003cem\u003ePst\u003c/em\u003e) isolates in China. (A) Samples distribution of 2,101 \u003cem\u003ePst\u003c/em\u003e isolates and 13 geographic regions, including Xinjiang (XJ); Qinghai (QH); Central Gansu (GSC); Longdong Gansu (GSLD); Longnan Gansu (GSLN); Central Shaanxi (SXC); Shandong (SD); Henan (HN); Anhui, Zhejiang and Jiangsu (AZJ); Hubei (HB); Yunnan-Guizhou (YG); Sichuan Basin (SC); and Xizang (XZ). (B) Phylogenetic tree based on HG-SNP markers using PLINK software. MG1 to MG7 with different background colors indicate molecular groups 1 to 7. (C) Stacked column chart showing the composition of \u003cem\u003ePst\u003c/em\u003e isolates from molecular groups in each geographic region. (D) Shannon index analysis for all geographic groups. (E) Genetic structure analysis with different numbers of ancestry kinship (K = 2-6). (F) 3D-PCA analysis.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8699492/v1/970195610d6d49e40edfdda0.jpeg"},{"id":101940390,"identity":"9ee339d1-9c13-4993-94c8-a2f18961ed2a","added_by":"auto","created_at":"2026-02-05 09:14:14","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":959320,"visible":true,"origin":"","legend":"\u003cp\u003eFixation index (below diagonal) and gene flow (above diagonal) analysis by the GRSD (A) andcorrelation analysis between geographic and genetic distances in China (B), inland (IL) (C), Xizang (XZ) (D) and Xinjiang (XJ) (E), respectively.\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8699492/v1/b776d0039858c8a767b3c4db.jpeg"},{"id":101940363,"identity":"68cbc514-c56a-48b7-be34-550e87d54a64","added_by":"auto","created_at":"2026-02-05 09:13:52","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1601741,"visible":true,"origin":"","legend":"\u003cp\u003eThe airflow trajectory analyses and major migration routes of \u003cem\u003ePuccinia striiformis\u003c/em\u003e f. sp. \u003cem\u003etritici\u003c/em\u003e (\u003cem\u003ePst\u003c/em\u003e) in China. (A) The airflow trajectory analyses of 13 geographic regions with forward trajectories analysis in 3 km from 2005 to 2024 years data. (B) Major migration routes from the \u003cem\u003ePst\u003c/em\u003esources to main wheat growing regions in China.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8699492/v1/d01531b55d07b0b5a5752751.jpeg"},{"id":101940393,"identity":"64902c8a-1e83-4a78-8d95-b81746b72222","added_by":"auto","created_at":"2026-02-05 09:14:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7613268,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8699492/v1/324edc7f-fbb8-464b-8f69-9f20c60ec795.pdf"},{"id":101940360,"identity":"1b311a7a-cc5b-42bd-a509-ea17c0b56ec4","added_by":"auto","created_at":"2026-02-05 09:13:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":13029306,"visible":true,"origin":"","legend":"\u003cp\u003eSupplemental Information\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S1.\u003c/strong\u003e The pipeline involves the development, verification and application of the HG-SNP markers. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S2.\u003c/strong\u003e Population genetic structure analysis of 1,052 \u003cem\u003ePuccinia striiformis\u003c/em\u003e f. sp.\u003cem\u003e tritici\u003c/em\u003e (\u003cem\u003ePst\u003c/em\u003e) isolates all over the world. (A) Phylogenetic and genetic structure analysis of \u003cem\u003ePst\u003c/em\u003e populations by the HG-SNP marker. Tracks from inner (a) to outer (h) rings indicate the following: a, Molecular group; b, Geographic group; c-h, Population genetic structure of \u003cem\u003ePst\u003c/em\u003e with K values from 2 to 6. (B) 3D-PCA analysis by the HG-SNP marker. (C) Fixation index (below diagonal) and gene flow (above diagonal) analysis by the HG-SNP marker.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S3.\u003c/strong\u003e Accuracy detection of the HG-SNP markers. Phylogenetic tree of \u003cem\u003ePuccinia striiformis\u003c/em\u003e f. sp.\u003cem\u003e tritici\u003c/em\u003e (\u003cem\u003ePst\u003c/em\u003e) populations using RAxML based on the GRSD (A) and HG-SNP markers (C). a, Molecular group; b, Geographic group. (B) Correlation analysis of phylogenetic matrix between PLINK and RAxML software based on GRSD. (D) Correlation analysis of phylogenetic matrix between GRSD and HG-SNP markers using RAxML software. (E) Correlation analysis of phylogenetic sequence between PLINK and RAxML software based on GRSD. (E) Correlation analysis of phylogenetic sequence between GRSD and HG-SNP markers using PLINK (F) and RAxML (G) software.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S1.\u003c/strong\u003e Sample information statistical of \u003cem\u003ePuccinia striiformis\u003c/em\u003e f. sp.\u003cem\u003e tritici\u003c/em\u003e in the world.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S2.\u003c/strong\u003e Primer information statistics of HG-SNP markers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S3.\u003c/strong\u003e Primer sequences of HG-SNP markers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S4.\u003c/strong\u003e Barcode information statistics of HG-SNP markers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S5.\u003c/strong\u003e Fixation index (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e) (below diagonal) and gene flow (\u003cem\u003eNm\u003c/em\u003e) (above diagonal) analysis for all geographic groups in the world using GRSD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S6.\u003c/strong\u003e Fixation index (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e) (below diagonal) and gene flow (\u003cem\u003eNm\u003c/em\u003e) (above diagonal) analysis for all geographic groups in the world using HG-SNP markers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S7.\u003c/strong\u003e Sample information statistical of \u003cem\u003ePuccinia striiformis\u003c/em\u003e f. sp.\u003cem\u003e tritici\u003c/em\u003e in China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S8.\u003c/strong\u003e Genetic diversity statistics for geographic group based on HG-SNP markers in China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S9. \u003c/strong\u003eStatistics of the average temperature from July 16 to August 15 from 2005 to 2024, and wheat growing and stripe rust epidemic season in the province from 2020 to 2023 in China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S10.\u003c/strong\u003e Fixation index (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e) (below diagonal) and gene flow (\u003cem\u003eNm\u003c/em\u003e) (above diagonal) analysis for all geographic groups in China.\u003c/p\u003e","description":"","filename":"Supplementary.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8699492/v1/d9db87f2f2b8b83bc2fca43f.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Construction and Application of a Rapid Method for Population Genetic of Plant Pathogens: A Case Study of Puccinia striiformis f. sp. tritici","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePlant pathogens constitute a major biological threat to global food security, leading not only to lower yields but also to loss of species diversity, mitigation costs due to control measures, and downstream impacts on human health\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Based on their lifestyle and relationship with the host, plant pathogens can be broadly classified into necrotrophic, biotrophic and hemibiotrophic pathogens. Necrotrophic pathogens obtain nutrients from dead host cells, whereas biotrophic pathogens obtain nutrients from living cells, and hemibiotrophic pathogens initiate an initial biotrophic interaction with their host, followed by a necrotrophic phase\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Population genetic studies of plant pathogens rely on purification and multiplication, which is labor-intensive, costly and time-consuming. This makes it difficult to obtain pathogen samples, which severely limit research into the genetic diversity of plant pathogens. Therefore, a new method for rapid monitoring of population genetics of plant pathogens has become an urgent scientific proposal.\u003c/p\u003e \u003cp\u003eThe \u003cem\u003ePuccinia striiformis\u003c/em\u003e f. sp. \u003cem\u003etritici\u003c/em\u003e (\u003cem\u003ePst\u003c/em\u003e), the causal agent of wheat stripe rust, is one of the major epidemic pathogens affecting wheat production worldwide\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. As an obligate biotrophic parasite, \u003cem\u003ePst\u003c/em\u003e makes population genetic studies several weeks or even months longer than those of necrotrophic and hemibiotrophic pathogens. Therefore, this study uses \u003cem\u003ePst\u003c/em\u003e as a case study to construct a new method for rapidly and accurately monitoring the genetic diversity of plant pathogens.\u003c/p\u003e \u003cp\u003eMolecular markers, including restriction fragment length polymorphism (RFLP), random amplified polymorphic DNA (RAPD), amplified fragment length polymorphism (AFLP), simple sequence repeat (SSR) markers and single nucleotide polymorphism (SNP) markers, have been among the most effective methods for population genetic studies of \u003cem\u003ePst\u003c/em\u003e. Initially, dominant markers, such as RFLP\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, RAPD\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e and AFLP\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, were employed but are no longer widely used due to their complex procedures, high cost, radioactivity, low polymorphism, and poor reproducibility. Subsequently, co-dominance SSR markers gained prominence for studying the population genetic structure and migration of \u003cem\u003ePst\u003c/em\u003e due to their high reproducibility\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. For instance, Hu et al.\u003csup\u003e12\u003c/sup\u003e demonstrated that the population of \u003cem\u003ePst\u003c/em\u003e in Xizang is distinct from those in other regions and revised classification of Chinese epidemic regions of \u003cem\u003ePst\u003c/em\u003e into Xinjiang, Xizang and inland region. Wang et al.\u003csup\u003e13\u003c/sup\u003e used SSR markers to determine the migration routes of \u003cem\u003ePst\u003c/em\u003e among the Central Shaanxi, Longnan and Longdong in China. Jiang et al.\u003csup\u003e14\u003c/sup\u003e explored genetic relationships and population dynamics of \u003cem\u003ePst\u003c/em\u003e in southwestern and northwestern China using SSR markers. Furthermore, SSR markers have been instrumental in elucidating global population genetic structure and migration routes of stripe rust. Ali et al.\u003csup\u003e15\u003c/sup\u003e reported infrequent intercontinental migration of \u003cem\u003ePst\u003c/em\u003e and suggested human activity as a key factor in its spread. Sharma-Poudyal et al.\u003csup\u003e16\u003c/sup\u003e and Bai et al.\u003csup\u003e17\u003c/sup\u003e used SSR markers to describe the distribution of different \u003cem\u003ePst\u003c/em\u003e genetic groups across countries and provided evidence of global migrations.\u003c/p\u003e \u003cp\u003eIn recent years, SNP markers have shown great potential for studying population genetic structure and migration of \u003cem\u003ePst\u003c/em\u003e due to their abundance, high polymorphism, and compatibility with high-throughput sequencing. Commonly used SNP markers include secreted protein SNP (SP-SNP) markers, few housekeeping genes SNP (FHG-SNP) markers and kompetitive allele specific polymerase chain reaction SNP (KASP-SNP) markers. SP-SNPs markers, the first SNP markers developed for \u003cem\u003ePst\u003c/em\u003e, were applied to study its population genetic structure and revealed that diversity was greater in western than in eastern US populations\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. FHG-SNP markers, based on few housekeeping genes, emphasized role of Yunnan in the epidemic spread of \u003cem\u003ePst\u003c/em\u003e across China\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Similarly, KASP-SNP markers, developed from whole-genome sequences, were employed to investigate the population structure and dispersal routes between oversummering and its adjacent regions\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWith the rapid development of sequencing technology, whole-genome sequencing (WGS) has also been widely applied to animals\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, plants\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, bacteria\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e and fungi\u003csup\u003e\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e with great success. Li et al.\u003csup\u003e28\u003c/sup\u003e identified Longnan, the Himalayan region, and the Guizhou Plateau as the \u003cem\u003ePst\u003c/em\u003e sources in China by the first WGS.\u003c/p\u003e \u003cp\u003eAlthough the conclusions of all current markers have been widely accepted by researchers, these markers still rely on the purification and multiplication of \u003cem\u003ePst\u003c/em\u003e, which limited the rapid monitoring of population genetics of \u003cem\u003ePst\u003c/em\u003e, especially during outbreak years.\u003c/p\u003e \u003cp\u003eThe multiplex polymerase chain reaction (PCR) technology, known for its high efficiency, simple operation, and low cost, has been widely applied in detecting, identificating and assessing the diversity of bacteria\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e and viruses\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. For example, the population of \u003cem\u003eSetosphaeria turcica\u003c/em\u003e (\u003cem\u003eSt\u003c/em\u003e) in the midwestern region of China showed low genetic differentiation and high gene flow was observed using the multiplex PCR technology\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. However, this technique has not yet been applied to the population genetic studies of \u003cem\u003ePst\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eTo enable rapid monitoring of \u003cem\u003ePst\u003c/em\u003e the genetic diversity, this study aimed to (i) develop specific housekeeping gene SNP (HG-SNP) markers for \u003cem\u003ePst\u003c/em\u003e based on high-throughput sequencing data to enable direct genotyping from field-collected \u003cem\u003ePst\u003c/em\u003e-infected wheat leaves, and establish a multiplex PCR system to further improve the efficiency of the HG-SNP markers, (ii) validate the accuracy of the HG-SNP markers to ensure reliability in population structure studies, and (iii) test the practical application of the HG-SNP markers using field-collected \u003cem\u003ePst\u003c/em\u003e-infected wheat leaves and further propose the dispersal routes of stripe rust in China. This study provides a new method for rapid and accurate monitoring of plant pathogens population genetics.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDevelopment of the HG-SNP markers\u003c/h2\u003e \u003cp\u003eA total of 1,052 \u003cem\u003ePst\u003c/em\u003e sequence data was collected from the National Center for Biotechnology Information (NCBI) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA; Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), including 215 genome-seq and 837 RNA-seq datasets (GRSD). After SNP calling and quality filtering, 135,728 bi-allelic SNPs were identified, and 6,732 SNPs were retained in high density regions by linkage disequilibrium (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Among them, approximately 700 SNPs associated with housekeeping genes were retained. Meanwhile, 560 identity-by-descent (IBD) fragments were identified (see Methods) for lineage tracing. A total of 53 key SNPs from both the housekeeping gene region and the IBD fragments were selected and distributed across 39 gene regions to design HG-SNP markers (Table S2; S3). Target fragments (150\u0026ndash;280 bp) containing the key SNPs were selected for the development of HG-SNP markers. Only one such marker was designed for per target gene, and 39 HG-SNP markers were developed. A total of 165 SNPs, including key and additional SNPs, were included in 39 HG-SNP markers with each marker containing 1 to 13 SNPs.\u003c/p\u003e \u003cp\u003eBased on the annealing temperature, the HG-SNP markers were divided into two groups: 58 ℃ (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB) and 65 ℃ (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). These markers showed amplification exclusively in \u003cem\u003ePst\u003c/em\u003e and no amplification fragments observed in \u003cem\u003eTriticum aestivum\u003c/em\u003e (\u003cem\u003eTa\u003c/em\u003e) or other common pathogens, including \u003cem\u003eBlumeria graminis\u003c/em\u003e f. sp. \u003cem\u003etritici\u003c/em\u003e (\u003cem\u003eBgt\u003c/em\u003e), \u003cem\u003eP. graminis\u003c/em\u003e f. sp. \u003cem\u003etritici\u003c/em\u003e (\u003cem\u003ePgt\u003c/em\u003e), \u003cem\u003eP. triticina\u003c/em\u003e (\u003cem\u003ePt\u003c/em\u003e), \u003cem\u003eFusarium graminearum\u003c/em\u003e (\u003cem\u003eFg\u003c/em\u003e) and \u003cem\u003eF. pseudograminearum\u003c/em\u003e (\u003cem\u003eFpg\u003c/em\u003e), demonstrating their specificity.\u003c/p\u003e \u003cp\u003eThrough iterative optimization, the 39 HG-SNP markers were divided into three multiplex PCR groups named as A, B and C based on their amplification characteristics (Table S2). Groups A and B were amplified at annealing temperatures of 58 ℃ and 65 ℃, respectively. While markers with low amplification efficiency from both groups were combined into group C and amplified at 58 ℃. This design enabled the effective amplification of all HG-SNP markers from a single sample using only three PCR reactions, significantly saving time and labor while improving the overall efficiency of marker application. Subsequently, a pooled library containing 9,984 target fragments from 256 barcodes along with 39 HG-SNP markers was constructed using only 768 PCR reactions (Table S4). To evaluate the impact of barcode type on amplification efficiency and to determine the sufficient sequencing depth of the target fragments, a mixed pool was constructed using DNA from the same \u003cem\u003ePst\u003c/em\u003e sample. With 1 Gb of sequencing data, fewer than 1% of target fragments showed coverage below 10\u0026times; (log10(count\u0026thinsp;+\u0026thinsp;1)\u0026thinsp;\u0026le;\u0026thinsp;1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD), and low-coverage fragments were not associated with specific barcodes. These results confirmed the successful development of the multiplex PCR system for HG-SNP markers.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAccuracy of the HG-SNP markers\u003c/h3\u003e\n\u003cp\u003ePopulation genetic analyses of 1,052 global samples using 135,728 SNPs from GRSD identified six molecular groups (MG1 to MG6) based on analyses of phylogenetic and genetic structure analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Furthermore, fifteen geographic groups were identified based on geographic features and molecular group features: five in Europe (Europe_1 to Europe_5), three in Africa (Central_Africa_1, Central_Africa_2, South_Africa), two in Asia (Asia_1, Asia_2), two in South America (South_America_1, South_America_2), two in Oceania (Oceania_1, Oceania_2), and one in North America (North_America) (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). European populations were distributed across five molecular groups (except MG5) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) and exhibited the highest Shannon index (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD), supporting Europe as a potential global source of \u003cem\u003ePst\u003c/em\u003e. Analysis of the fixation index (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e) and gene flow (\u003cem\u003eNm\u003c/em\u003e) revealed that all \u003cem\u003eNm\u003c/em\u003e within molecular groups exceeded 1, whereas \u003cem\u003eNm\u003c/em\u003e between molecular groups was mostly below 1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC; Table S6). This result suggests that higher \u003cem\u003eNm\u003c/em\u003e was observed within than between molecular groups. MG5 and MG6 showed the same genetic structure in K\u0026thinsp;=\u0026thinsp;2 to 4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), suggesting Europe_5 as the source of South_ Africa, Central_Africa_1, Central_Africa_2, North_America, Asia_1 and Oceania_1. Similarly, Europe_1 and Europe_2 were identified as the source of Asia_2 and South_America_1, respectively, and Europe_4 as the source of South_America_2 and Oceania_2. However, the fifteen geographic groups were clustered separately in phylogenetic tree, genetic structure (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) and the three-dimensional principal component analysis (3D-PCA) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Furthermore, no airflow can spread between continents at 10 km above ground level over 120 h (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). These results suggest that intercontinental migration of \u003cem\u003ePst\u003c/em\u003e is infrequent.\u003c/p\u003e \u003cp\u003eTo demonstrate the accuracy of the HG-SNP markers, we compared the population genetic differences for the 1,052 global samples between HG-SNP markers and GRSD. Phylogenetic and genetic structure analyses based on both GRSD (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and HG-SNP markers (Figure S2) revealed that the fifteen geographic groups were largely separated. One discrepancy was noted for the Asia_1, which clustered with MG5 using GRSD markers but with MG1 using HG-SNP markers. Although 3D-PCA based on GRSD (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) showed clearer separation than that based on HG-SNP markers (Figure S2B), both methods effectively separated the geographic groups. Due to the fewer number of SNPs, the range of \u003cem\u003eNm\u003c/em\u003e and \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e values based on the HG-SNP marker (Figure S2C; Table S6) are narrower than that based on GRSD (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC; Table S5). However, both methods showed consistent trends in \u003cem\u003eNm\u003c/em\u003e and \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e analysis. Matrix correlation analysis of phylogenetic trees between GRSD and the HG-SNP marker showed a significant correlation with a high correlation coefficient (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.9973; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Similarly, high correlations were observed for the genetic structure (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.8258; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD), 3D-PCA (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.8074; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE) and \u003cem\u003eNm\u003c/em\u003e and \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.9099; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF). These results further demonstrate the consistency between GRSD and HG-SNP marker methods.\u003c/p\u003e \u003cp\u003eWe also evaluated phylogenetic trees which were constructed by software, such as PLINK and RAxML. For GRSD methods, the phylogenetic matrix correlation coefficient was high between RAxML and PLINK software (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.998; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0001) (Figure S3A; S3B). Similarly, a high correlation coefficient was observed between the GRSD and HG-SNP marker methods when the phylogenetic tree was constructed using the RAxML software (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.996; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0001) (Figure S3C; S3D). We further assessed the consistency of the phylogenetic trees by assigning the outgroup as zero value and sequentially numbering the remaining samples from one to create the phylogenetic sequence. Correlation analysis of phylogenetic sequences from GRSD methods between RAxML and PLINK showed a high coefficient (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.637; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.79 \u0026times; 10⁻\u0026sup1;\u0026sup2;\u0026sup1;) (Figure S3E). However, for the phylogenetic sequence correlation analysis between GRSD and HG-SNP marker methods, PLINK software (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.817; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.89 \u0026times; 10⁻\u0026sup2;⁵\u0026sup3;) (Figure S3F) demonstrated higher consistency than RAxML software (\u003cem\u003er\u003c/em\u003e = -0.126; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.4 \u0026times; 10⁻⁵) (Figure S3G). These results suggest that PLINK is more suitable than RAxML for constructing phylogenetic trees using HG-SNP marker methods.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePopulation genetic structure of\u003c/b\u003e \u003cb\u003ePst\u003c/b\u003e \u003cb\u003ein China\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo test the practical application of the HG-SNP markers, we performed the population genetic analysis of \u003cem\u003ePst\u003c/em\u003e using the 2,101 samples of infected field leaves, which were collected from wheat stripe rust epidemic areas in 18 provinces and autonomous regions in China (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA; Table S7). Based on geographic and topographic characteristics, we identified 13 geographic groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA): Xinjiang (XJ); Qinghai (QH); Central Gansu (GSC); Longdong Gansu (GSLD); Longnan Gansu (GSLN); Central Shaanxi (SXC); Shandong (SD); Henan (HN); Anhui, Zhejiang and Jiangsu (AZJ); Sichuan Basin (SC); Hubei (HB); Yunnan-Guizhou (YG); and Xizang (XZ).\u003c/p\u003e \u003cp\u003ePhylogenetic analysis clustered the 2,101 \u003cem\u003ePst\u003c/em\u003e samples into seven molecular groups (MG1 to MG7) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). In GSC, GSLN, and QH, the MG distribution was similar, dominated by MG1, MG2, and MG3, which together comprised over 80.00% of the population. GSLD and SXC also showed similar MG distributions, with MG2 and MG3 as predominant groups (each \u0026gt;\u0026thinsp;25%), while other MGs showed balanced distributions in these regions. MG4 and MG5 were the predominant groups in AZJ (39.13%) and XJ (82.29%), respectively, whereas MG7 was the predominant group in XZ (73.85%), HB (52.42%), SC (34.32%) and YG (28.88%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC; Table S8). In HN, all MGs had a relatively balanced distribution (7.41%-17.78%), resulting in the highest Shannon index (1.82). This is followed by the SC and SXC with Shannon index of 1.74 and 1.67, respectively. In XJ and XZ, the proportion of the predominant groups were more than 70.00%, leading to unbalanced distributions and the lowest Shannon index (XJ: 0.59; XZ: 0.70) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD; Table S8).\u003c/p\u003e \u003cp\u003eAdmixture analysis further elucidated \u003cem\u003ePst\u003c/em\u003e population structure (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). MG7 formed a distinct group compared to other MGs. There were similar genetic structures between MG1 and MG2, and between MG3, MG4, MG5, and MG6. Through 3D-PCA analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF), most samples of MGs could be clustered separately, especially between MG7 and other MGs. However, there were overlaps between MG1 and MG2, and between MG3, MG4, MG5, and MG6.\u003c/p\u003e \u003cp\u003e \u003cb\u003eField investigations and genetic exchanges of the\u003c/b\u003e \u003cb\u003ePst\u003c/b\u003e \u003cb\u003ein China\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe wheat growing season, \u003cem\u003ePst\u003c/em\u003e epidemic seasons, and the average temperatures from July 16 to August 15 were monitored in China (Table S9). Except for XZ and QH, where spring wheat is mostly grown, all other areas mainly grow winter wheat. Stripe rust epidemics generally begin about two months before wheat harvest and continue until harvest ends. Average temperatures from July 16 to August 15 in XJ, XZ, YG, GSLD, GSLN, GSC and QH were all below 24 ℃, providing suitable conditions for the oversummering of \u003cem\u003ePst\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eInland regions, including all regions except XJ and XZ, showed low gene flow (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e \u0026ge; 0.047; \u003cem\u003eNm\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;5.029) with XJ and XZ (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e \u0026ge; 0.063; \u003cem\u003eNm\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;3.726), suggesting less exchange among the three regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA; Table S10). Within inland regions, high gene flow (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e \u0026le; 0.020; \u003cem\u003eNm\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;12.429) was observed among the QH, GSC, GSLN and GSLD, and these regions can complete the oversummering of \u003cem\u003ePst\u003c/em\u003e. Therefore, these regions can be classified as a single group (the Northwest Oversummering Region, NOSR). High gene flow was detected between GSLD and SXC (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e = 0.003; \u003cem\u003eNm\u003c/em\u003e\u0026thinsp;=\u0026thinsp;78.751), as well as between SXC and HN (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e = 0.020; \u003cem\u003eNm\u003c/em\u003e\u0026thinsp;=\u0026thinsp;12.540), suggesting that inoculum disperses from GSLD to HN via SXC. In the YG region, the highest gene flow was detected with SC (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e = 0.016; \u003cem\u003eNm\u003c/em\u003e\u0026thinsp;=\u0026thinsp;15.416), followed by HN (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e = 0.026; \u003cem\u003eNm\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9.440) and HB (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e = 0.026; \u003cem\u003eNm\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9.281), indicating that the inoculum from YG could spread to SC and HN via HB. Additionally, relatively high gene flow was observed between HN and SD (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e = 0.028; \u003cem\u003eNm\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8.804), as well as between HN and AZJ (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e = 0.020; \u003cem\u003eNm\u003c/em\u003e\u0026thinsp;=\u0026thinsp;12.344), suggesting that HN may be an important bridging zone for the dispersal of \u003cem\u003ePst\u003c/em\u003e from YG and NOSR to these regions. Compared to YG, the NOSR showed high gene flow with SXC, but low gene flow with SC and HB, while its gene flow is similar to that of AZJ, SD and HN. These results suggest that SXC is primarily influenced by the NOSR inoculum, SC and HB are primarily influenced by the YG inoculum, and AZJ, SD and HN are influenced by both the NOSR and YG inoculum (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eThrough correlation analysis between population genetic distance and geographic distance, there is a low correlation coefficient in China (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.2106; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), especially in inland regions (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.2174; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC), but a high correlation coefficient in XZ (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.4837; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD), indicating the high frequency of \u003cem\u003ePst\u003c/em\u003e dispersal in China, especially in inland regions, but the low frequency in XZ. However, no significant correlation and the low correlation coefficient in XJ (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0764; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0562) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE) may be due to the concentration of sampling locations.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTrajectory simulation and traceability analysis of\u003c/b\u003e \u003cb\u003ePst\u003c/b\u003e \u003cb\u003emigration in China\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo delineate \u003cem\u003ePst\u003c/em\u003e dispersal routes in China, air trajectory tracking analyses were performed during \u003cem\u003ePst\u003c/em\u003e epidemic seasons. The average trajectory frequency (ATF) for \u003cem\u003ePst\u003c/em\u003e dispersal was calculated at 3,000 m above ground level over 120 h forward trajectories with two trajectory per day from 2005 to 2024 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). High ATF values (\u0026gt;\u0026thinsp;20%) were observed several dispersal pathways: from GSC to QH (20%), GSLD (22%) and GSLN (22%); from GSLN to GSC (40%); and from GSLD to GSLN (25%). These results supported high \u003cem\u003eNm\u003c/em\u003e among QH, GSC, GSLN and GSLD. The ATF from SXC to GSLD (37%), as well as to HN (28%), exceeded 25%, supporting high \u003cem\u003eNm\u003c/em\u003e between SXC and these regions. There is a higher ATF from YG to SC (47%) than from GSLN (21%), corroborating \u003cem\u003eNm\u003c/em\u003e analysis. A high ATF from YG to HB (32%) further suggests that HB was influenced by the YG inoculum. While direct ATF from YG to HN was negligible, high ATF from HB to HN (\u0026ge;\u0026thinsp;10%) suggests that HB may be an important bridging zone for \u003cem\u003ePst\u003c/em\u003e dispersion from YG to HN. However, the airflow from XJ and XZ hardly disperses to other regions, indicating less exchange among these regions and inland regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTraditional population genetic studies of plant pathogens relied on purification and multiplication work to obtain sufficient samples. The genetic diversities of the plant pathogens were then determined using traditional molecular markers, such as SSR and SNP markers. These processes are labor-intensive, costly and time-consuming, particularly for obligate biotrophic pathogens. These constraints have historically limited both sample sizes and the timeliness of population genetic studies of plant pathogens, especially during epidemic outbreaks. To enable rapid and accurate monitoring the population genetic of plant pathogens, this study developed specific, high-efficiency and accurate HG-SNP markers for \u003cem\u003ePst\u003c/em\u003e as a case study.\u003c/p\u003e \u003cp\u003eFirstly, all HG-SNP markers showed amplification exclusively fragments in \u003cem\u003ePst\u003c/em\u003e and no amplification fragments in Ta or other common pathogens such as \u003cem\u003eBgt\u003c/em\u003e, \u003cem\u003ePgt, Pt\u003c/em\u003e, \u003cem\u003eFg\u003c/em\u003e and \u003cem\u003eFpg\u003c/em\u003e. Thus, compared to traditional markers, HG-SNP markers supported direct amplification of target fragments from \u003cem\u003ePst\u003c/em\u003e-infected wheat leaves, circumventing the necessary work for purification and multiplication, and allowing rapid population genetic monitoring. Additionally, genotyping costs of the HG-SNP markers are less than \u003cspan\u003e$\u003c/span\u003e1 per sample, which provides the opportunity for population genetic studies of \u003cem\u003ePst\u003c/em\u003e with large scale samples. Secondly, a total of 9,984 PCRs from 256 samples were required to amplify each mixed pool, which not only takes a considerable amount of time, but also increases the likelihood of human error. To enhance efficiency and reduce human error, we developed a multiplex PCR system for HG-SNP markers, using only 768 PCR reactions for each pool. This system further saves time and labor and improves the usage efficiency of the HG-SNP marker. Finally, population genetic analyses using the same stripe rust population showed consistent trends between the GRSD and HG-SNP marker, which ensured the accuracy of markers in the population genetic studies of \u003cem\u003ePst\u003c/em\u003e. In phylogenetic analysis, VCF files are typically converted into matrix or FASTQ format for subsequent processing. This study recommends converting VCF files to matrix format rather than FASTQ using HG-SNP markers.\u003c/p\u003e \u003cp\u003eStripe rust was first reported in Europe (1777)\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e earlier than in most other regions, such as Central Africa (1958)\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, South Africa (1996)\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, North America (early 1915)\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, South America (1975)\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, Australia (1979)\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, and New Zealand (1980)\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. In this study, we identified Europe as a potential global source of \u003cem\u003ePst\u003c/em\u003e. These results suggested a relationship between the source and the first reports of \u003cem\u003ePst\u003c/em\u003e. Although the first reports of stripe rust in China date back to AD 533\u0026ndash;544\u003csup\u003e41\u003c/sup\u003e, the source and migrations of \u003cem\u003ePst\u003c/em\u003e may be also an associated with wheat domestication. Wheat originated in the Fertile Crescent around 10,000 years ago, reaching Europe (~\u0026thinsp;7,000\u0026nbsp;year BP) before Asia (~\u0026thinsp;5,400\u0026nbsp;year BP)\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e, which is consistent with the possibility that stripe rust colonized Europe earlier than Asia. This study also suggests that intercontinental migration of \u003cem\u003ePst\u003c/em\u003e is infrequent based on population genetic structure and air trajectory analyses, which is consistent with the migration of wheat pathogens such as \u003cem\u003ePst\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, \u003cem\u003eBgt\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, \u003cem\u003eZymoseptoria tritici\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e and \u003cem\u003ePt\u003c/em\u003e\u003csup\u003e27\u003c/sup\u003e. Evidence suggested that the \u003cem\u003ePst\u003c/em\u003e from Europe to Australia in 1979 was migrated by human activities\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Furthermore, strong air currents, such as Hurricane Guillermo\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, moved westward across the Atlantic Ocean from the coast of Africa to North America over about ten days. Therefore, human activities and strong air currents may be the main media for intercontinental \u003cem\u003ePst\u003c/em\u003e migration.\u003c/p\u003e \u003cp\u003eThis study further tested the practical application of HG-SNP markers using field-collected \u003cem\u003ePst\u003c/em\u003e-infected wheat leaves. A total of 2,101 samples from 18 Chinese provinces and autonomous regions elucidated the population genetic structure and spread routes of \u003cem\u003ePst\u003c/em\u003e. The annual cycle of \u003cem\u003ePst\u003c/em\u003e consists of oversummering, seedling infection in autumn, overwintering, and spring epidemic\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e, with oversummering being critical stage. Stripe rust can oversummer where the average temperature between July 16 and August 15 is below 24 ℃\u003csup\u003e45\u003c/sup\u003e. Our field surveys identified Xinjiang, Xizang, Yunnan-Guizhou, Longdong, Longnan, Central Gansu and Qinghai as regions with suitable oversummering conditions for \u003cem\u003ePst\u003c/em\u003e. Although wheat does not grow during this period in Xinjiang, Yunnan-Guizhou, Longdong, Longnan, and Central Gansu, stripe rust can persist on volunteer wheat. The Northwest Oversummering Region, especially Longnan and Eastern Qinghai\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e, and the Yungui Plateau\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e have long been regarded as major \u003cem\u003ePst\u003c/em\u003e sources in China due to their high diversity of \u003cem\u003ePst\u003c/em\u003e. In this study, we confirmed that the Northwest Oversummering Region, including Qinghai, Central Gansu, Longnan and Longdong regions, can be considered as a single group influencing other regions in China based on \u003cem\u003eNm\u003c/em\u003e, \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e, air trajectory and field survey analyses. Central Gansu, where mainly winter wheat is planted, and Qinghai, where mainly spring wheat is planted, may constitute the core annual cycle regions of \u003cem\u003ePst\u003c/em\u003e based on high gene flow and wheat growing season analysis.\u003c/p\u003e \u003cp\u003eFrequent \u003cem\u003ePst\u003c/em\u003e dispersal from Longnan to the Sichuan Basin\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e and from Longdong to Central Shaanxi\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e has been documented based on gene flow analyses. Additionally, long-distance dispersal of \u003cem\u003ePst\u003c/em\u003e from the Yungui Plateau to the Sichuan Basin\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e and the Central Plain\u003csup\u003e\u003cspan additionalcitationids=\"CR53\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e has been identified through population genetic analyses. Based on trajectory tracking and gene flow analysis, our study indicates an overall west-to-east spread of stripe rust in China. The spread routes of stripe rust from Qinghai and Central Gansu are mainly divided into two dispersal directions: eastward through Longdong and Central Shaanxi, as important bridging zones, to the Huang-Huai-Hai wheat-growing region, and southward through Longnan to the Sichuan Basin. Similarly, the spread routes of stripe rust in the Yungui Plateau are mainly divided into two spread directions: northeastward through Hubei, as an important bridging zone, to the Huang-Huai-Hai wheat-growing region, and directly northward to the Sichuan Basin. Compared to the Yungui Plateau, the Northwest Oversummering Region showed high gene flow with Central Shaanxi but low gene flow with the Sichuan Basin and Hubei, while its gene flow is comparable with the Huang-Huai-Hai region, including Anhui, Zhejiang, Jiangsu, Shandong and Henan. This suggests that Central Shaanxi is mainly influenced by the Northwest Oversummering Region inoculum, the Sichuan Basin and Hubei are mainly influenced by the Yungui Plateau inoculum, and the Huang-Huai-Hai region is influenced by both sources. However, there was less exchange among Xinjiang, Xizang and inland regions based on trajectory tracking and gene flow analysis.\u003c/p\u003e \u003cp\u003eIn summary, this study successfully overcomes the limitations of purification and multiplication of plant pathogens in population genetic studies. It also provides a novel method for the rapid and accurate monitoring of the population genetics of plant pathogens. Compared to traditional population genetic studies, this method avoids altering the native population genetic structure of pathogens by host resistance selection during pathogen purification. This advancement will provide robust scientific and technological support for the development of plant disease management strategies at a regional and national scale.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eSample collection and genome DNA extraction\u003c/h2\u003e\n \u003cp\u003eA total of 1,052 sequence data of \u003cem\u003ePst\u003c/em\u003e were collected from the National Center for Biotechnology Information (NCBI) (Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). A total of 2,101 \u003cem\u003ePst\u003c/em\u003e-infected wheat leaves were collected in 18 provinces and autonomous regions of China from 2020 to 2023 (Table S7). DNA of \u003cem\u003ePst\u003c/em\u003e was extracted directly from \u003cem\u003ePst\u003c/em\u003e-infected wheat leaves using the cetyltrimethyl ammonium bromide (CTAB) method\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eDetection and filtering of variants\u003c/h2\u003e\n \u003cp\u003eRaw reads were cleaned by removing adapters and low-quality sequences using Trimmomatic (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/usadellab/Trimmomatic\u003c/span\u003e\u003c/span\u003e). Cleaned reads were mapped to the DK09_11 reference genome using BWA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/lh3/bwa\u003c/span\u003e\u003c/span\u003e) with default parameters. SAM files were converted to BAM files and INDEX were built using SAMtools (v0.1.1) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/samtools/samtools\u003c/span\u003e\u003c/span\u003e). The BAM files were cleaned and sorted, and PCR duplicates were removed and validated using Picard tools (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/broadinstitute/picard\u003c/span\u003e\u003c/span\u003e). Variants were called using the Genome Analysis Toolkit (GATK) version 4.1.9.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/broadinstitute/gatk\u003c/span\u003e\u003c/span\u003e) with default parameters to generate genome Variant Call Format (GVCF) files, which were then merged into a single GVCF file using GATK CombineGVCFs. Joint genotyping was implemented on the combined file using GATK GenotypeGVCFs. The output file was filtered using VCFtools v0.1.13 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/vcftools/vcftools\u003c/span\u003e\u003c/span\u003e), with the parameters \u0026ldquo;--min-alleles 2 --max-alleles 2 --maf 0.05 --mac 3 --minQ 1000 --max-missing 0.85 --min-meanDP 20\u0026rdquo;.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eDevelopment of HG-SNP markers\u003c/h3\u003e\n\u003cp\u003eSNPs were filtered for linkage disequilibrium using PLINK (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/chrchang/plink-ng\u003c/span\u003e\u003c/span\u003e), with the parameters \u0026ldquo;--indep-pairwise 50 1 0.2\u0026rdquo;. Housekeeping gene SNPs were then saved based on the benchmarking universal single-copy orthologs (BUSCOs)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e using Bedtools (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/arq5x/bedtools\u003c/span\u003e\u003c/span\u003e). All samples were phased using Beagle (v5.4)\u003csup\u003e56,57\u003c/sup\u003e. Pairwise shared haplotypes were extracted using the Beagle RefinedIBD function\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. All samples were divided into six groups based on different continents, including Europe, Africa, Asia, North America, South America and Oceania. After removing duplicate identity-by-descent (IBD) fragments, those with a frequency\u0026thinsp;\u0026ge;\u0026thinsp;3 were retained. Housekeeping gene SNPs were again filtered based on IBD fragments and the DK09_11 Gff file using Bedtools. The fragments of 150\u0026ndash;280 bp containing key SNPs were selected to develop HG-SNP markers for each gene.\u003c/p\u003e\n\u003ch3\u003eAmplification and specificity of HG-SNP markers\u003c/h3\u003e\n\u003cp\u003ePCR amplification was performed in a 10 \u0026micro;L mixture containing 0.5 \u0026micro;L each of the two primers, 1 \u0026micro;L template DNA, 5 \u0026micro;L CWBIO Taq-mix (Beijing ComWin Biotech Co., Ltd., China) and 3 \u0026micro;L H\u003csub\u003e2\u003c/sub\u003eO. PCR parameters were as follows: an initial denaturation for 3 min at 94 ℃; then 35 cycles consisting of denaturation at 94 ℃ for 20 s, annealing at 58/65 ℃ for 20 s and extension at 72 ℃ for 20 s; final extension at 72 ℃ for 5 min. Sterilized water was used as a negative control for each PCR amplification. PCR products were detected using a 1.3% (wt/vol) agarose gel containing 0.01% nucleic acid dye in 1\u0026times; Tris-acetate-EDTA buffer. To test the specificity of HG-SNP markers, seven isolates of \u003cem\u003ePst\u003c/em\u003e and one sample each of \u003cem\u003ePgt\u003c/em\u003e, \u003cem\u003ePt\u003c/em\u003e, \u003cem\u003eBgt\u003c/em\u003e, \u003cem\u003eFg\u003c/em\u003e, \u003cem\u003eFpg\u003c/em\u003e and Ta were used for PCR amplification of each HG-SNP marker and ITS (Table S2).\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eThe construction and sequencing of mixed pools of HG-SNP markers\u003c/h2\u003e\n \u003cp\u003eTo construct a mixed pool including a lot of the samples for high-throughput sequencing, three nucleotide barcodes were added to the 5\u0026apos; ends of both the forward and reverse primers. To avoid repeating the barcodes of the forward and reverse primers, the last nucleotides of the forward primer were set as \u0026apos;G\u0026apos; nucleotides, while the reverse primer ended with \u0026apos;C\u0026apos; nucleotides. The remaining two nucleotides were combined using all four nucleotides (A, G, C, T) in pairs, resulting in 16 single-end barcode combinations and 256 paired-end barcode combinations (Table S4).\u003c/p\u003e\n \u003cp\u003eThe different barcodes were added to the forward and reverse primers of each HG-SNP marker. Each isolate was amplified by HG-SNP markers with the same barcode and different isolates were amplified by HG-SNP markers with different barcodes (Table S3). Each mixed pool was constructed from PCR products of 256 \u003cem\u003ePst\u003c/em\u003e isolates. DNA libraries were sequenced using the DNBSEQ-2000 platform by BGI Genomics Co., Ltd. (Shenzhen, China). Sequencing reads were demultiplexed by barcode using fastq-multx\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003ePhylogenetic and population structure analysis\u003c/h2\u003e\n \u003cp\u003eTo construct phylogenetic trees, the VCF file needs first to be converted to matrix or FASTQ format. The genetic distance matrix of identity-by-state (IBS) was calculated using PLINK v1.90 (-distance 1-ibs) and then converted to Newick format using Phylip v2.0. The FASTQ file was converted from VCF file by vcf2phylip.py (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/edgardomortiz/vcf2phylip\u003c/span\u003e\u003c/span\u003e), and phylogenetic trees were then constructed using maximum likelihood methods with 1,000 replications by RAxML v8.2.13\u003csup\u003e60\u003c/sup\u003e. Phylogenetic trees were visualized and annotated using iTOL v6\u003csup\u003e61\u003c/sup\u003e. The population genetic structure was analyzed using Admixture\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e with K values from 2 to 10. The three-dimensional principal component analysis (3D-PCA) was performed with PLINK v1.90, and the first to third principal components (PCs) were plotted using the ggplot2 package in R v4.2.3.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eDetermination of genetic diversity, introgression, and migration\u003c/h2\u003e\n \u003cp\u003eFixation index (\u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e) was estimated in 5 kb sliding windows using VCFtools, Gene flow (\u003cem\u003eN\u003c/em\u003e\u003csub\u003e\u003cem\u003em\u003c/em\u003e\u003c/sub\u003e) was calculated as (1- \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e)/(4* \u003cem\u003eF\u003c/em\u003e\u003csub\u003e\u003cem\u003eST\u003c/em\u003e\u003c/sub\u003e). Diversity for geographic groups was estimated using Shannon\u0026rsquo;s diversity (\u003cem\u003eH\u003c/em\u003e), where \u003cem\u003eH\u003c/em\u003e = \u0026minus;\u0026sum;\u003cem\u003epi\u003c/em\u003e \u0026times; ln(\u003cem\u003epi\u003c/em\u003e), and \u003cem\u003epi\u003c/em\u003e is the frequency of the \u003cem\u003ei\u003c/em\u003eth molecular group in the geographic group. The dist() command in the geo sphere v1.5-14 R package\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e was used to calculate geographic distance. Phylogenetic matrices were constructed based on geographic group and molecular group of samples. The correlation of matrices was analyzed using the mantel() command in the vegan v2.6-4 R package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/vegandevs/vegan\u003c/span\u003e\u003c/span\u003e) with 9999 permutations. Phylogenetic sequence was constructed by assigning the outgroup as zero value and sequentially numbering the remaining samples from one. The correlation of the sequence was analyzed using the cor() command in R.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTrajectory simulation and traceability analysis of\u003c/strong\u003e \u003cstrong\u003ePst\u003c/strong\u003e \u003cstrong\u003emigration\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eWeekly meteorological data from 2005 to 2024 were obtained from the National Oceanic and Atmospheric Administration (NOAA) website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eftp://arlftp.arlhq.noaa.gov/pub/archives/gdas1\u003c/span\u003e\u003c/span\u003e). Regions were selected for air trajectory tracking (ATT) analyses based on sample distribution using Hybrid Single-Particle Lagrangian Integrated Trajectory version 4 (HYSPLIT-4). The average trajectory frequency (ATF) with forward trajectories for \u003cem\u003ePst\u003c/em\u003e dispersal was calculated and visualized using the ggplot2, tmap, and sp Gallery packages in R v4.1.2.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eData Availability\u003c/p\u003e\n\u003cp\u003eRaw sequenced reads of 42 samples have been deposited at the China National Genomics Data Center database (https://www.cncb.ac.cn) under BioProject PRJCA053835 (Table S7).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAuthor Contribution\u003c/p\u003e\n\u003cp\u003eX.H., Y.L., S.F. and M.X. designed the experiments. J.Z. performed the experiments and analyzed the data. X.H., Y.L. and J.Z. wrote the paper.\u003c/p\u003e\n\u003cp\u003eCode Availability\u003c/p\u003e\n\u003cp\u003eThe code used in this study is described in the Methods section and is available on GitHub at https://github.com/junjie-zhang-92/population.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eNational Natural Science Foundation of China (32502448) to Y.L.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFones, H. N. et al. Threats to global food security from emerging fungal and oomycete crop pathogens. \u003cem\u003eNature Food\u003c/em\u003e \u003cstrong\u003e1\u003c/strong\u003e, 332-342 (2020).\u003c/li\u003e\n\u003cli\u003eRistaino, J. B. et al. The persistent threat of emerging plant disease pandemics to global food security. \u003cem\u003eProc. Natl. Acad. Sci. USA\u003c/em\u003e \u003cstrong\u003e118\u003c/strong\u003e, e2022239118 (2021).\u003c/li\u003e\n\u003cli\u003eGlazebrook, J. Contrasting mechanisms of defense against biotrophic and necrotrophic pathogens. \u003cem\u003eAnnu. Rev. Phytopathol.\u003c/em\u003e \u003cstrong\u003e43\u003c/strong\u003e, 205-227 (2005).\u003c/li\u003e\n\u003cli\u003eChen, X. Pathogens which threaten food security: \u003cem\u003ePuccinia striiformis\u003c/em\u003e, the wheat stripe rust pathogen. \u003cem\u003eFood Security\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 239-251 (2020).\u003c/li\u003e\n\u003cli\u003eWellings, C. R. Global status of stripe rust: a review of historical and current threats. \u003cem\u003eEuphytica\u003c/em\u003e \u003cstrong\u003e179\u003c/strong\u003e, 129-141 (2011).\u003c/li\u003e\n\u003cli\u003eSaiki, R. K. et al. Enzymatic amplification of beta-globin genomic sequences and restriction site analysis for diagnosis of sickle cell anemia. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e230\u003c/strong\u003e, 1350-1354 (1985).\u003c/li\u003e\n\u003cli\u003eShan, W. et al. Genetic diversity in \u003cem\u003ePuccinia striiformis\u003c/em\u003e Westend. f.sp. \u003cem\u003etritici\u003c/em\u003e revealed by pathogen genome-specific repetitive sequence. \u003cem\u003eCanadian Journal of Botany\u003c/em\u003e \u003cstrong\u003e76\u003c/strong\u003e, 587-595 (1998).\u003c/li\u003e\n\u003cli\u003eChen, X. et al. Relationship between virulence variation and DNA polymorphism in \u003cem\u003ePuccinia striiformis\u003c/em\u003e. \u003cem\u003ePhytopathology\u003c/em\u003e \u003cstrong\u003e83\u003c/strong\u003e, 1489-1497 (1993).\u003c/li\u003e\n\u003cli\u003eWilliams, J. G. et al. DNA polymorphisms amplified by arbitrary primers are useful as genetic markers. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 6531-6535 (1990).\u003c/li\u003e\n\u003cli\u003eVos, P. et al. AFLP: a new technique for DNA fingerprinting. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cstrong\u003e23\u003c/strong\u003e, 4407-4414 (1995).\u003c/li\u003e\n\u003cli\u003eEnjalbert, J. et al. Isolation of twelve microsatellite loci, using an enrichment protocol, in the phytopathogenic fungus \u003cem\u003ePuccinia striiformis\u003c/em\u003e f.sp. \u003cem\u003etritici\u003c/em\u003e. \u003cem\u003eMolecular Ecology Notes\u003c/em\u003e \u003cstrong\u003e2\u003c/strong\u003e, 563\u0026ndash;565 (2002).\u003c/li\u003e\n\u003cli\u003eHu, X. et al. Population Genetic Analysis of \u003cem\u003ePuccinia striiformis\u003c/em\u003e f. sp. \u003cem\u003etritici\u003c/em\u003e Suggests Two Distinct Populations in Tibet and the Other Regions of China. \u003cem\u003ePlant Dis.\u003c/em\u003e \u003cstrong\u003e101\u003c/strong\u003e, 288-296 (2017).\u003c/li\u003e\n\u003cli\u003eWang, C. et al. Genetic Analysis Reveals Relationships Among Populations of \u003cem\u003ePuccinia striiformis\u003c/em\u003e f. sp. \u003cem\u003etritici\u003c/em\u003e from the Longnan, Longdong, and Central Shaanxi Regions of China. \u003cem\u003ePhytopathology\u003c/em\u003e \u003cstrong\u003e112\u003c/strong\u003e, 278-289 (2022).\u003c/li\u003e\n\u003cli\u003eJiang, B. et al. Genetic Relationships of \u003cem\u003ePuccinia striiformis\u003c/em\u003e f. sp. \u003cem\u003etritici\u003c/em\u003e in Southwestern and Northwestern China. \u003cem\u003eMicrobiol. Spectr.\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, e0153022 (2022).\u003c/li\u003e\n\u003cli\u003eAli, S. et al. Origin, migration routes and worldwide population genetic structure of the wheat yellow rust pathogen \u003cem\u003ePuccinia striiformis\u003c/em\u003e f. sp. \u003cem\u003etritici\u003c/em\u003e. \u003cem\u003ePLoS Pathog.\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, e1003903 (2014).\u003c/li\u003e\n\u003cli\u003eSharma-Poudyal, D. et al. Molecular Characterization of International Collections of the Wheat Stripe Rust Pathogen \u003cem\u003ePuccinia striiformis\u003c/em\u003e f. sp. \u003cem\u003etritici\u003c/em\u003e Reveals High Diversity and Intercontinental Migration. \u003cem\u003ePhytopathology\u003c/em\u003e \u003cstrong\u003e110\u003c/strong\u003e, 933-942 (2020).\u003c/li\u003e\n\u003cli\u003eBai, Q. et al. Molecular Characterization of Wheat Stripe Rust Pathogen (\u003cem\u003ePuccinia striiformis\u003c/em\u003e f. sp. \u003cem\u003etritici\u003c/em\u003e) Collections from Nine Countries. \u003cem\u003eInt. J. Mol. Sci.\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e, 9457 (2021).\u003c/li\u003e\n\u003cli\u003eXia, C. et al. Secreted protein gene derived-single nucleotide polymorphisms (SP-SNPs) reveal population diversity and differentiation of \u003cem\u003ePuccinia striiformis\u003c/em\u003e f. sp. \u003cem\u003etritici\u003c/em\u003e in the United States. \u003cem\u003eFungal Biol.\u003c/em\u003e \u003cstrong\u003e120\u003c/strong\u003e, 729-744 (2016).\u003c/li\u003e\n\u003cli\u003eAlam, M. A. et al. Genetic Diversity of Wheat Stripe Rust Fungus \u003cem\u003ePuccinia striiformis\u003c/em\u003e f. sp. \u003cem\u003etritici\u003c/em\u003e in Yunnan, China. \u003cem\u003ePlants (Basel)\u003c/em\u003e \u003cstrong\u003e10\u003c/strong\u003e, 100563 (2021).\u003c/li\u003e\n\u003cli\u003eLi, M. et al. Evidence for Yunnan as the major origin center of the dominant wheat fungal pathogen \u003cem\u003ePuccinia striiformis\u003c/em\u003e f. sp. \u003cem\u003etritici\u003c/em\u003e. \u003cem\u003eAustralasian Plant Pathology\u003c/em\u003e \u003cstrong\u003e50\u003c/strong\u003e, 241\u0026ndash;252 (2021).\u003c/li\u003e\n\u003cli\u003eLi, Y. et al. Migration of wheat stripe rust from the primary oversummering region to neighboring regions in China. \u003cem\u003eCommun. Biol.\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 350 (2025).\u003c/li\u003e\n\u003cli\u003eChen, N. et al. Global genetic diversity, introgression, and evolutionary adaptation of indicine cattle revealed by whole genome sequencing. \u003cem\u003eNature Commun.\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 7803 (2023).\u003c/li\u003e\n\u003cli\u003eHe, F. et al. Exome sequencing highlights the role of wild-relative introgression in shaping the adaptive landscape of the wheat genome. \u003cem\u003eNature Genet.\u003c/em\u003e \u003cstrong\u003e51\u003c/strong\u003e, 896-904 (2019).\u003c/li\u003e\n\u003cli\u003eLeeman-Neill, R. J. et al. Noncoding mutations cause super-enhancer retargeting resulting in protein synthesis dysregulation during B cell lymphoma progression. \u003cem\u003eNature Genet.\u003c/em\u003e \u003cstrong\u003e55\u003c/strong\u003e, 2160-2174 (2023).\u003c/li\u003e\n\u003cli\u003eFeurtey, A. et al. A thousand-genome panel retraces the global spread and adaptation of a major fungal crop pathogen. \u003cem\u003eNature Commun.\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 1059 (2023).\u003c/li\u003e\n\u003cli\u003eSotiropoulos, A. G. et al. Global genomic analyses of wheat powdery mildew reveal association of pathogen spread with historical human migration and trade. \u003cem\u003eNature Commun.\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 4315 (2022).\u003c/li\u003e\n\u003cli\u003eSperschneider, J. et al. Nuclear exchange generates population diversity in the wheat leaf rust pathogen \u003cem\u003ePuccinia triticina\u003c/em\u003e. \u003cem\u003eNature Microbiol.\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 2130-2141 (2023).\u003c/li\u003e\n\u003cli\u003eLi, Y. et al. Genomic analysis, trajectory tracking, and field surveys reveal sources and long-distance dispersal routes of wheat stripe rust pathogen in China. \u003cem\u003ePlant Commun.\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, 100563 (2023).\u003c/li\u003e\n\u003cli\u003eXie, N. G. et al. Designing highly multiplex PCR primer sets with Simulated Annealing Design using Dimer Likelihood Estimation (SADDLE). \u003cem\u003eNature Commun.\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 1881 (2022).\u003c/li\u003e\n\u003cli\u003eHuang, H. S. et al. Multiplex PCR system for the rapid diagnosis of respiratory virus infection: systematic review and meta-analysis. \u003cem\u003eClin. Microbiol. Infect.\u003c/em\u003e \u003cstrong\u003e24\u003c/strong\u003e, 1055-1063 (2018).\u003c/li\u003e\n\u003cli\u003eCui, L. et al. Genetic Diversity and Population Genetic Structure of \u003cem\u003eSetosphaeria turcica\u003c/em\u003e From Sorghum in Three Provinces of China Using Single Nucleotide Polymorphism Markers. \u003cem\u003eFront. Microbiol.\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 853202 (2022).\u003c/li\u003e\n\u003cli\u003eCui, L. et al. Genetic Diversity, Population Structure and Mating Type Distribution of \u003cem\u003eSetosphaeria turcica\u003c/em\u003e on Corn in Midwestern China. \u003cem\u003eJ. Fungi (Basel)\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 1165 (2022).\u003c/li\u003e\n\u003cli\u003eStubbs, R. W. Cereal Rusts Diseases, Distribution, Epidemiology, and Control. \u003cem\u003eAcademic Press\u003c/em\u003e \u003cstrong\u003e2\u003c/strong\u003e, 61-101 (1985).\u003c/li\u003e\n\u003cli\u003eDawit, W. Virulence and diversity of \u003cem\u003ePuccinia striiformis\u003c/em\u003e f. sp. \u003cem\u003etritici\u003c/em\u003e in Ethiopia. \u003cem\u003eCan. F. Plant Pathol. Rev. Can. Phytopathol. \u003c/em\u003e\u003cstrong\u003e31\u003c/strong\u003e, 211-219 (2009).\u003c/li\u003e\n\u003cli\u003eBoshoff, W. H. P. et al. Establishment, Distribution, and Pathogenicity of \u003cem\u003ePuccinia striiformis\u003c/em\u003e f. sp. \u003cem\u003etritici\u003c/em\u003e in South Africa. \u003cem\u003ePlant Dis.\u003c/em\u003e \u003cstrong\u003e86\u003c/strong\u003e, 485-492 (2002).\u003c/li\u003e\n\u003cli\u003eCarleton, M. A. A SERIOUS NEW WHEAT RUST IN THIS COUNTRY. \u003cem\u003eScience\u003c/em\u003e \u003cstrong\u003e42\u003c/strong\u003e, 58-59 (1915).\u003c/li\u003e\n\u003cli\u003eHovm\u0026oslash;ller, M. S. et al. Diversity of \u003cem\u003ePuccinia striiformis\u003c/em\u003e on cereals and grasses. \u003cem\u003eAnnu. Rev. Phytopathol.\u003c/em\u003e \u003cstrong\u003e49\u003c/strong\u003e, 197-217 (2011).\u003c/li\u003e\n\u003cli\u003eBrown, Jr. W. M. Barley yellow rust in North America. \u003cem\u003eAnnu. Rev. Phytopathol.\u003c/em\u003e \u003cstrong\u003e39\u003c/strong\u003e, 367-384 (2001).\u003c/li\u003e\n\u003cli\u003eWellings, C. R. \u003cem\u003ePuccinia striiformis \u003c/em\u003ein Australia: a review of the incursion, evolution, and adaptation of stripe rust in the period 1979\u0026ndash;2006. \u003cem\u003eAustralian Journal of Agricultural Research\u003c/em\u003e \u003cstrong\u003e58\u003c/strong\u003e, 567-575 (2007).\u003c/li\u003e\n\u003cli\u003eWellings, C. R. \u0026amp; McIntosh, R. A. \u003cem\u003ePuccinia striiformis\u003c/em\u003e f. sp. \u003cem\u003etritici \u003c/em\u003ein Australasia: pathogenic changes during the first 10 years. \u003cem\u003ePlant Pathology\u003c/em\u003e \u003cstrong\u003e39\u003c/strong\u003e, 316\u0026ndash;325 (1990).\u003c/li\u003e\n\u003cli\u003eZhao, J. et al. Fighting wheat rusts in China: a look back and into the future. \u003cem\u003ePhytopathol. Res.\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e, 6 (2023).\u003c/li\u003e\n\u003cli\u003eWang, Z. et al. Dispersed emergence and protracted domestication of polyploid wheat uncovered by mosaic ancestral haploblock inference. \u003cem\u003eNature Commun.\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 3891 (2022).\u003c/li\u003e\n\u003cli\u003eZhao, X. et al. Population genomics unravels the Holocene history of bread wheat and its relatives. \u003cem\u003eNature Plants\u003c/em\u003e \u003cstrong\u003e9\u003c/strong\u003e, 403-419 (2023).\u003c/li\u003e\n\u003cli\u003eLawrence, M. B. Eastern North Pacific Hurricane Season of 1997. \u003cem\u003eAnnual Weather Summaries in Monthly Weather Review\u003c/em\u003e \u003cstrong\u003e127\u003c/strong\u003e, 2440\u0026ndash;2454 (1998).\u003c/li\u003e\n\u003cli\u003eZhang, J. et al. Re-delimiting of oversummering regions for \u003cem\u003ePuccinia striiformis\u003c/em\u003e f. sp. \u003cem\u003etritici\u003c/em\u003e in China. \u003cem\u003ePhytopathol Res.\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e (2025).\u003c/li\u003e\n\u003cli\u003eChen, W. et al. Race Dynamics, Diversity, and Virulence Evolution in \u003cem\u003ePuccinia striiformis\u003c/em\u003e f. sp. \u003cem\u003etritici\u003c/em\u003e, the Causal Agent of Wheat Stripe Rust in China from 2003 to 2007. \u003cem\u003ePlant Dis.\u003c/em\u003e \u003cstrong\u003e93\u003c/strong\u003e, 1093-1101 (2009).\u003c/li\u003e\n\u003cli\u003eLiang, J. et al. Genetic Structure and Asymmetric Migration of Wheat Stripe Rust Pathogen in Western Epidemic Areas of China. \u003cem\u003ePhytopathology\u003c/em\u003e \u003cstrong\u003e111\u003c/strong\u003e, 1252-1260 (2021).\u003c/li\u003e\n\u003cli\u003eWan, A. et al. Wheat Stripe Rust Epidemic and Virulence of \u003cem\u003ePuccinia striiformis\u003c/em\u003e f. sp. \u003cem\u003etritici\u003c/em\u003e in China in 2002. \u003cem\u003ePlant Dis.\u003c/em\u003e \u003cstrong\u003e88\u003c/strong\u003e, 896-904 (2004).\u003c/li\u003e\n\u003cli\u003eLiang, J. et al. Population Genetic Structure and the Migration of \u003cem\u003ePuccinia striiformis\u003c/em\u003e f. sp. \u003cem\u003etritici\u003c/em\u003e Between the Gansu and Sichuan Basin Populations of China. \u003cem\u003ePhytopathology\u003c/em\u003e \u003cstrong\u003e106\u003c/strong\u003e, 192-201 (2016).\u003c/li\u003e\n\u003cli\u003eLiang, J. et al. Population Genetic Structures of \u003cem\u003ePuccinia striiformis\u003c/em\u003e in Ningxia and Gansu Provinces of China. \u003cem\u003ePlant Dis.\u003c/em\u003e \u003cstrong\u003e97\u003c/strong\u003e, 501-509 (2013).\u003c/li\u003e\n\u003cli\u003eWang, H. et al. Long-Distance Spore Transport of Wheat Stripe Rust Pathogen from Sichuan, Yunnan, and Guizhou in Southwestern China. \u003cem\u003ePlant Dis.\u003c/em\u003e \u003cstrong\u003e94\u003c/strong\u003e, 873-880 (2010).\u003c/li\u003e\n\u003cli\u003eAwais, M. et al. Countrywide inter-epidemic region migration pattern suggests the role of southwestern population in wheat stripe rust epidemics in China. \u003cem\u003eEnviron. Microbiol.\u003c/em\u003e \u003cstrong\u003e24\u003c/strong\u003e, 4684-4701 (2022).\u003c/li\u003e\n\u003cli\u003eHuang, L. et al. Long-Distance Transport of \u003cem\u003ePuccinia striiformis\u003c/em\u003e f. sp. \u003cem\u003etritici\u003c/em\u003e by Upper Airflow on the Yunnan-Guizhou Plateau Disrupts the Balance of Agricultural Ecology in Central China. \u003cem\u003ePlant Dis.\u003c/em\u003e \u003cstrong\u003e106\u003c/strong\u003e, 2940-2947 (2022).\u003c/li\u003e\n\u003cli\u003eZhan, G. et al. Populations of \u003cem\u003ePuccinia striiformis\u003c/em\u003e f. sp. \u003cem\u003etritici\u003c/em\u003e in Winter Spore Production Regions Spread from Southwestern Oversummering Areas in China. \u003cem\u003ePlant Dis.\u003c/em\u003e \u003cstrong\u003e106\u003c/strong\u003e, 2856-2865 (2022).\u003c/li\u003e\n\u003cli\u003eManni, M. et al. BUSCO: Assessing Genomic Data Quality and Beyond. \u003cem\u003eCurr. Protoc.\u003c/em\u003e \u003cstrong\u003e1\u003c/strong\u003e, e323 (2021).\u003c/li\u003e\n\u003cli\u003eBrowning, B.L. et al. A One-Penny Imputed Genome from Next-Generation Reference Panels. \u003cem\u003eAm. J. Hum. Genet.\u003c/em\u003e \u003cstrong\u003e103\u003c/strong\u003e, 338-348 (2018).\u003c/li\u003e\n\u003cli\u003eBrowning, B.L. et al. Fast two-stage phasing of large-scale sequence data. \u003cem\u003eAm. J. Hum. Genet.\u003c/em\u003e \u003cstrong\u003e108\u003c/strong\u003e, 1880-1890 (2021).\u003c/li\u003e\n\u003cli\u003eBrowning, B. L, \u0026amp; Browning, S. R. Improving the accuracy and efficiency of identity-by-descent detection in population data. \u003cem\u003eGenetics\u003c/em\u003e \u003cstrong\u003e194\u003c/strong\u003e, 459-471 (2013).\u003c/li\u003e\n\u003cli\u003eAronesty, E. Comparison of Sequencing Utility Programs. \u003cem\u003eThe Open Bioinformatics Journal\u003c/em\u003e \u003cstrong\u003e7\u003c/strong\u003e, 1-8 (2013).\u003c/li\u003e\n\u003cli\u003eAlexander, D. H. et al. Fast model-based estimation of ancestry in unrelated individuals. \u003cem\u003eGenome Res.\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 1655-1664 (2009).\u003c/li\u003e\n\u003cli\u003eLetunic, I, \u0026amp; Bork, P. Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cstrong\u003e49\u003c/strong\u003e, W293-w296 (2021).\u003c/li\u003e\n\u003cli\u003eKarney, C. F. F. Algorithms for geodesics. \u003cem\u003eJournal of Geodesy\u003c/em\u003e \u003cstrong\u003e87\u003c/strong\u003e, 43-55 (2013).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"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":"Population genetic studies, Puccinia striiformis f. sp. tritici, HG-SNP markers, specific, high-efficiency, accurate","lastPublishedDoi":"10.21203/rs.3.rs-8699492/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8699492/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePopulation genetic studies of plant pathogens by traditional molecular markers often rely on the purification and multiplication of pathogens, which is time-consuming, labor-intensive and costly. To address these limitations, we developed a rapid method for monitoring pathogen population diversity using the obligate biotrophic fungus \u003cem\u003ePuccinia striiformis\u003c/em\u003e f. sp. \u003cem\u003etritici\u003c/em\u003e (\u003cem\u003ePst\u003c/em\u003e) as a case study. A set of specific and high-efficiency housekeeping gene SNP (HG-SNP) markers was developed using genome and transcriptome sequencing data (GRSD) to generate genotype data directly from field-collected \u003cem\u003ePst\u003c/em\u003e-infected wheat leaves for population genetic analysis. These markers showed high consistency in the results of population genetic diversity with GRSD. Furthermore, population genetic analysis of 2,101 field samples from China using HG-SNP markers revealed low exchange among Xinjiang, Xizang, and Inland regions and major dispersal routes from the Northwest Oversummering Region and Yunnan-Guizhou to Sichuan and eastern China. This framework supports rapid and accurate monitoring of population genetics and can be readily applied to other plant pathogens. The method facilitates the distribution of host resistance and the development of plant disease management strategies at a regional and national scale.\u003c/p\u003e","manuscriptTitle":"Construction and Application of a Rapid Method for Population Genetic of Plant Pathogens: A Case Study of Puccinia striiformis f. sp. tritici","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-05 09:13:05","doi":"10.21203/rs.3.rs-8699492/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-biology","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsbio","sideBox":"Learn more about [Communications Biology](http://www.nature.com/commsbio/)","snPcode":"","submissionUrl":"","title":"Communications Biology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"9ed22e0e-44fd-4386-8058-deffedade75d","owner":[],"postedDate":"February 5th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":62198627,"name":"Biological sciences/Microbiology/Fungi/Fungal genetics"},{"id":62198628,"name":"Biological sciences/Genetics/Sequencing/DNA sequencing"}],"tags":[],"updatedAt":"2026-03-24T13:03:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-05 09:13:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8699492","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8699492","identity":"rs-8699492","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}