Enhanced Stripe Rust Resistance Obtained by Combining A Widely Dispersed, Stable QTL with Yr30 on Chromosome Arms 4BL And 3BS

doi:10.21203/rs.3.rs-645215/v1

Cultivars with durable resistance are the most popular means to control wheat stripe rust. Durable resistance can be achieved by stacking multiple adult-plant resistance (APR) genes that individually have relatively smaller effect. Chinese wheat cultivars Ruihua 520 (RH520) and Fengdecun 12 (FDC12) confer partial APR to stripe rust across environments. One hundred and seventy recombinant inbred lines from the cross RH520 × FDC12 were used to determine the genetic basis of resistance and identify genomic regions associated with stripe rust resistance. Genotyping was carried out using 55K SNP array and eight quantitative trait loci (QTL) were detected on chromosomes 2AL, 2DS, 3BS, 4BL, 5BL (2) and 7BL (2) by inclusive composite interval mapping. Only QYr.nwafu-3BS from RH520 and QYr.nwafu-4BL.2 (named YrFDC12 for convenience) from FDC12 were stable across the four testing environments. QYr.nwafu-3BS is likely the pleiotropic resistance gene Sr2/Yr30. YrFDC12 was mapped in a 2.1 cM interval corresponding to 12 Mb and flanked by SNP markers AX-111121224 and AX-89518393. Lines harboring both Yr30 and YrFDC12 displayed higher resistance than the parents and expressed pseudo-black chaff (PBC) controlled by two loci Pbc1 and PbcFDC12, which were co-located with Yr30 and YrFDC12, respectively. Both marker-based and pedigree-based kinship analyses revealed that YrFDC12 was inherited from founder parent Zhou 8425B. Fifty-four other wheat cultivars shared the same haplotype of YrFDC12 region. These results suggest an effective pyramiding strategy to acquire highly effective, durable stripe rust resistance in breeding.

Agronomy

Biotechnology and Bioengineering

Molecular Genetics

adult-plant resistance

gene pyramiding

haplotype tracing

quantitative trait locus

Triticum aestivum

Wheat, is an old and important cereal crop grown worldwide providing nutrition and 20% of the energy required by humans globally. Security of wheat production encompasses a series of components, namely production, access (physical and economic), stability, and nutritional value (Savary et al. 2017). Stripe rust, caused by the fungus Puccinia striiformis f. sp. tritici (Pst), is one of the most important diseases of wheat (Hovmøller et al. 2010; McIntosh et al. 1995), and can cause devastating losses in yield and quality (Murray et al. 1995). Strategies suggested for managing rusts include host resistance, fungicides, cultural practices, biological control, and integrated disease management. Among them, developing and growing resistant cultivars based on host resistance is the most economic, effective and environment-friendly method (Chen 2005; Wellings 2011).

Rust resistance is generally classified into two types; race-specific and race non-specific. Race-specific resistance that often fits the description of “seedling or all-stage resistance” is usually conferred by major genes that cause hypersensitive response following recognition of pathogen effector molecules by host receptor molecules. The precise interaction of pathogen- and host-generated gene products is the basis of the gene-for-gene relationship as proposed by Flor in the 1950s (Flor 1971). This resistance type is often short-lived due to mutation for virulence, leading to re-assortment of virulence alleles carried by heterozygous avirulent genotypes of the pathogen (Mcdonald and Linde 2002), or through selection of previously rare, undetected pathogen variants, or by introduction of virulent variants from other geographic areas. In contrast, race non-specific resistance conferred by minor effect genes that constrains the rate of disease development at post-seedling growth stages of plants and often referred to as “adult plant resistance (APR)” (Milus 1986; Chen 2013; Parlevliet 1985). Individual genes underlying this type of resistance have relatively small effects on disease response but often act additively in combination to reach near-immune levels effective across multiple environments (Singh et al. 2011). Some examples of well characterized stripe rust resistance genes of this type that have maintained effectiveness for long periods of time when in combinations include Yr18 (Krattinger et al. 2009; Lagudah et al. 2006), Yr29 (Singh et al. 2013), Yr30 (Singh et al. 2000), Yr36 (Fu et al. 2009) and Yr46 (Moore et al. 2015; Herrera-Foessel et al. 2011), and in addition confer pleotropic effects on other diseases. Therefore, these genes are now being preferentially targeted by breeders to enhance multiple disease resistance.

Although more than 200 APR genes/QTL for stripe rust resistance are reported in the literature (Chen and Kang 2017; Bulli et al. 2016; Rosewarne et al. 2013), not all combinations are effective in improving the level of response largely due to epistatic effects. The choice of genes for stacking by breeders is often difficult as progress can be achieved when such minor genes act additively in enhancing the levels of resistance. In addition, breeder friendly markers can greatly facilitate both selection and stacking of APR genes.

Several genotyping platforms in the last few decades have greatly enhanced gene discovery and characterization studies, develop markers that can be used to assay for the presence of the genes in breeding germplasm and for pyramiding strategies. Single nucleotide polymorphisms (SNPs), the most common type of genetic variation in genomes, are currently preferred for QTL analysis. Next generation and high-throughput SNP-based genotyping technologies enable rapid marker development and map construction, further facilitating basic genetic research and advances in marker-assistant breeding (Rasheed et al. 2017). Several high-density, SNP chips and platforms such as Affymetrix Gene Chip, Illumina Bead Array, and kompetitive allele-specific polymerase chain reaction (KASP) and allele-specific quantitative PCR-based genotyping assay (AQP) are available for wheat genetic studies (Rasheed et al. 2016; Semagn et al. 2014). A series of high-throughput genotyping technologies, including the Wheat 9K (Cavanagh et al. 2013), 35K (Mu et al. 2019), 55K (Huang et al. 2019), 90K (Wang et al. 2014), 660K (Cui et al. 2017; Sun et al. 2020) and 820K SNP (Winfield et al. 2016) arrays, are commercially available. The Wheat55K SNP array developed by the Chinese Academy of Agricultural Sciences and synthesized by Affymetrix is a simplified version of the Wheat660K array (Affymetrix® Axiom® Wheat660). While keeping the high density and high efficiency advantages of the Wheat660K, the Wheat55K SNP array is economic and is therefore widely used in genetic research in wheat (Huang et al. 2019, 2020).

Cultivars “Ruihua 520” (RH520) and “Fengdecun 12” (FDC12) are important lines used as parents in the breeding program of Jiangsu Ruihua Agricultural Science and Technology Co. Ltd in China. Both are susceptible at seedling stages to the prevalent races CYR32 and CYR34, but have expressed partial APR in the field since their release in 2014 and 2010, respectively. Recombinant inbred line (RIL) population combining these two sources (RH520 × FDC12) identified lines exhibiting transgressive segregation, some lines with near immunity and some showing high susceptibility. However, the genetic basis of the partial resistance in the both parent lines was unknown. The objectives of this study were to: 1) dissect the genetic components of resistance in RH520 and FDC12; 2) identify superior recombinants that provide more effective protection and extend the level of durable resistance; and 3) develop tightly linked AQP markers that can facilitate MAS for the QTL associated with YR resistance.

Plant materials

A total of 226 F_5:7 RILs derived from a cross between RH520 (Zhengzhou 891/Qianfeng 1) and FDC12 (Zhoumai 16/Shanyou 225//Aikang 58) was used in QTL mapping (170 lines) and validation (56 lines). Mingxian169 (MX169), a susceptible Chinese landrace was grown as a disease spreader and cultivar Xiaoyan 22 (XY22) was used as the susceptible check.

Stripe rust evaluations

Seedling tests indicated that RH520 and FDC12 were susceptible to all test Pst races (Yu et al. 2020). For assessments of stripe rust reactions in the field 170 RILs and parents were planted at Yangling (YL) in Shaanxi province in 2018-2019 and 2019-2020, and at Jiangyou (JY) in Sichuan province in 2019-2020 and 2020-2021. Trials at Yangling were inoculated with a urediniospore mixture of CYR32 and CYR34 suspended in a light oil sprayed onto MX169 and XY22 in early March to initiate disease. Each plot consisted of a 1 m row sown with approximately 20 seeds and 25 cm row spacing. Two rows of highly susceptible cv. XY22 were planted after every 20 rows to ensure uniform disease development. A randomized complete block design with two replications was used in all experiments. Adult plant stripe rust reactions were determined by infection type (IT) and disease severity (DS). IT was recorded using a 0 to 9 scale ranging from complete immunity to high susceptibility (Line and Qayoum 1992); and disease severity was based on the modified Cobb Scale (Peterson et al. 1948). The first scoring was made when MX169 reached approximately 80% severity or more during the period 5–15 April at JY and 3–17 May at YL. IT and DS of homozygous lines were recorded as single values; and for segregating lines IT and DS were recorded as two or more values, but later averaged for each line. Disease assessments were made at least twice. The IT and final DS was used in subsequent analysis.

Phenotyping for pseudo-black chaff

Although pseudo-black chaff (PBC) was present in all environments, phenotyping was carried out only in 2021 at Yangling. The phenotype data for PBC was recorded at grain filling stage based on the presence of the black pigmentation around the stem internodes and glumes. A visual score of 0 or 1 scale was used, where 0 indicated no pigmentation and 1 indicated the presence of pigmentation.

Phenotypic data analysis

The frequency distributions of IT and DS of F_5:7 RILs across four environments were calculated using Excel 2016. Analysis of variance (ANOVA) and Pearson’s correlation coefficients among environments were conducted using the “AOV” function in QTL IciMapping software 4.1 with the default parameters based on the IT and DS data (Meng et al. 2015). Broad-sense heritability (h2 b) was estimated as h2 b= σ2 g/(σ2 g+ σ2 ge/e + σ2 ε/re), where σ2 g, σ2 ge and σ2 r represented genotypic (line), genotype × environment and error variances, respectively, and e and r were the numbers of environments and replicates. Mean IT and DS data were used for subsequent QTL mapping.

SNP calling and clustering

Genomic DNA from single fresh leaf of each parent and RIL was extracted at the jointing stage using the CTAB protocol (Song et al. 1994) and the quality and quantity of DNA were assessed using a NanoDrop ND-1000 (Thermo Scientific, Wilmington, DE, USA). The wheat 55K SNP array improved by China Gold Marker (Beijing; http:// www.cgmb.com.cn ) was used to genotype the parents and 170 RILs. SNP genotype calling and allele clustering was processed with the polyploid version of the Affymetrix Genotyping Console™ (AGC) software. SNPs were classified into six groups: (i) Poly High Resolution (PHR) SNPs that were polymorphic and co-dominant with a minimum of two samples containing the minor allele; (ii) no minor homozygote (NMH); these polymorphic and dominant SNPs had only two clusters, one being the heterozygote; (iii) mono high resolution (MHR) or monomorphic SNPs having only one cluster/allele; (iv) off-target variants (OTV) showing four clusters including one for a null allele; (v) call rate below threshold (CRBT) having all cluster properties above the threshold except for the call rate cut-of; and (vi) other type SNPs with one or more cluster properties below quality thresholds.

Linkage map construction and QTL analysis

The filtering criteria of SNP markers for linkage map construction were as follows: PHR/polymorphic, <10% missing values, major allele frequencies (MAF) ≤95%, and 1:1 segregation ratios confirmed by chi-squared tests (P >0.001). A linkage map was constructed using QTL IciMapping V4.1 software and generated with Mapchart V2.3 (Meng et al. 2015; Voorrips 2002). Recombination fractions were converted to centiMorgans (cM) using the Kosambi function (Kosambi 1944). One marker was selected from each co-segregating marker group using the “BIN” function. Selected markers were used to construct the genetic map using the “MAP” function. To further narrow down the interval of target loci, 16 SNPs on chromosome 3BS and 76 SNPs on chromosome 4BL from 660K SNP array genotypes of RH520 and FDC12, were converted into AQP, respectively. A total of 13 AQP markers was used to genotype all 170 RILs to enrich the linkage map (Table S1). Inclusive composite interval mapping with the additive tool (ICIM-ADD) in IciMapping V4.1 was performed to detect QTL based on the phenotypic data including mean IT and DS scores, and PBC. Likelihood-of-odds (LOD) thresholds for declaring statistical significance were calculated by 1000 permutations at a p value ≤0.01. LOD significance thresholds estimated for each trait was 2.5. The phenotypic variances explained (PVE) by individual QTL and additive effects at the LOD peaks were also obtained. The physical positions in CS RefSeq v1.0 were obtained based on blast using flanking marker sequences for each QTL (http://wheatomics.sdau.edu.cn/). QTL were named according to the International Rules of Genetic Nomenclature (http://wheat.pw.usda. gov/ggpages/wgc/98/Intro.htm). Abbreviations ‘Yr’, ‘Pbc’ and ‘nwafu’ were adopted for ‘yellow (stripe) rust resistance’, ‘pseudo-black chaff’ and ‘Northwest A & F University’, respectively.

Comparisons with previously reported Yr genes and QTL

To determine the relationships between loci identified in this study and previously reported Yr genes/QTL, we compared the relative physical and genetic distances of loci based on the IWGSC RefSeq v.1.0 and integrated linkage map consisting of SNP, DArT, SSR, STS, EST, RAPD and RFLP markers provided by Dr. Fa Cui (Ludong University in Shandong province; pers. comm.). The closest flanking markers were used to generate confidence intervals for previously reported Pst resistance genes/QTL in genetic populations. Significant markers were assumed to identify loci detected in genome-wide association studies (GWAS).

Origins of resistant haplotypes and phylogenetic analysis

A pedigree tree of FDC12 was constructed based on the information of pedigree and neighbor-joining tree. The genotype data for SNPs located in confidence intervals of target QTL were extracted from a diversity panel of 1,400 wheat accessions and used to perform phylogenetic analysis using MAGE 7. A neighbor-joining (NJ) tree was drawn using iTOL (https://itol.embl.de/). The resistance haplotype of FDC12 was tracked based on both pedigree and kinship analysis. Accessions grouped in same branch as FDC12 were considered to harbor the resistance haplotype.

Plant materials

A total of 226 F_5:7 RILs derived from a cross between RH520 (Zhengzhou 891/Qianfeng 1) and FDC12 (Zhoumai 16/Shanyou 225//Aikang 58) was used in QTL mapping (170 lines) and validation (56 lines). Mingxian169 (MX169), a susceptible Chinese landrace was grown as a disease spreader and cultivar Xiaoyan 22 (XY22) was used as the susceptible check.

Stripe rust evaluations

Seedling tests indicated that RH520 and FDC12 were susceptible to all test Pst races (Yu et al. 2020). For assessments of stripe rust reactions in the field 170 RILs and parents were planted at Yangling (YL) in Shaanxi province in 2018-2019 and 2019-2020, and at Jiangyou (JY) in Sichuan province in 2019-2020 and 2020-2021. Trials at Yangling were inoculated with a urediniospore mixture of CYR32 and CYR34 suspended in a light oil sprayed onto MX169 and XY22 in early March to initiate disease. Each plot consisted of a 1 m row sown with approximately 20 seeds and 25 cm row spacing. Two rows of highly susceptible cv. XY22 were planted after every 20 rows to ensure uniform disease development. A randomized complete block design with two replications was used in all experiments. Adult plant stripe rust reactions were determined by infection type (IT) and disease severity (DS). IT was recorded using a 0 to 9 scale ranging from complete immunity to high susceptibility (Line and Qayoum 1992); and disease severity was based on the modified Cobb Scale (Peterson et al. 1948). The first scoring was made when MX169 reached approximately 80% severity or more during the period 5–15 April at JY and 3–17 May at YL. IT and DS of homozygous lines were recorded as single values; and for segregating lines IT and DS were recorded as two or more values, but later averaged for each line. Disease assessments were made at least twice. The IT and final DS was used in subsequent analysis.

Phenotyping for pseudo-black chaff

Although pseudo-black chaff (PBC) was present in all environments, phenotyping was carried out only in 2021 at Yangling. The phenotype data for PBC was recorded at grain filling stage based on the presence of the black pigmentation around the stem internodes and glumes. A visual score of 0 or 1 scale was used, where 0 indicated no pigmentation and 1 indicated the presence of pigmentation.

Phenotypic data analysis

The frequency distributions of IT and DS of F_5:7 RILs across four environments were calculated using Excel 2016. Analysis of variance (ANOVA) and Pearson’s correlation coefficients among environments were conducted using the “AOV” function in QTL IciMapping software 4.1 with the default parameters based on the IT and DS data (Meng et al. 2015). Broad-sense heritability (h2 b) was estimated as h2 b= σ2 g/(σ2 g+ σ2 ge/e + σ2 ε/re), where σ2 g, σ2 ge and σ2 r represented genotypic (line), genotype × environment and error variances, respectively, and e and r were the numbers of environments and replicates. Mean IT and DS data were used for subsequent QTL mapping.

SNP calling and clustering

Genomic DNA from single fresh leaf of each parent and RIL was extracted at the jointing stage using the CTAB protocol (Song et al. 1994) and the quality and quantity of DNA were assessed using a NanoDrop ND-1000 (Thermo Scientific, Wilmington, DE, USA). The wheat 55K SNP array improved by China Gold Marker (Beijing; http:// www.cgmb.com.cn ) was used to genotype the parents and 170 RILs. SNP genotype calling and allele clustering was processed with the polyploid version of the Affymetrix Genotyping Console™ (AGC) software. SNPs were classified into six groups: (i) Poly High Resolution (PHR) SNPs that were polymorphic and co-dominant with a minimum of two samples containing the minor allele; (ii) no minor homozygote (NMH); these polymorphic and dominant SNPs had only two clusters, one being the heterozygote; (iii) mono high resolution (MHR) or monomorphic SNPs having only one cluster/allele; (iv) off-target variants (OTV) showing four clusters including one for a null allele; (v) call rate below threshold (CRBT) having all cluster properties above the threshold except for the call rate cut-of; and (vi) other type SNPs with one or more cluster properties below quality thresholds.

Linkage map construction and QTL analysis

The filtering criteria of SNP markers for linkage map construction were as follows: PHR/polymorphic, <10% missing values, major allele frequencies (MAF) ≤95%, and 1:1 segregation ratios confirmed by chi-squared tests (P >0.001). A linkage map was constructed using QTL IciMapping V4.1 software and generated with Mapchart V2.3 (Meng et al. 2015; Voorrips 2002). Recombination fractions were converted to centiMorgans (cM) using the Kosambi function (Kosambi 1944). One marker was selected from each co-segregating marker group using the “BIN” function. Selected markers were used to construct the genetic map using the “MAP” function. To further narrow down the interval of target loci, 16 SNPs on chromosome 3BS and 76 SNPs on chromosome 4BL from 660K SNP array genotypes of RH520 and FDC12, were converted into AQP, respectively. A total of 13 AQP markers was used to genotype all 170 RILs to enrich the linkage map (Table S1). Inclusive composite interval mapping with the additive tool (ICIM-ADD) in IciMapping V4.1 was performed to detect QTL based on the phenotypic data including mean IT and DS scores, and PBC. Likelihood-of-odds (LOD) thresholds for declaring statistical significance were calculated by 1000 permutations at a p value ≤0.01. LOD significance thresholds estimated for each trait was 2.5. The phenotypic variances explained (PVE) by individual QTL and additive effects at the LOD peaks were also obtained. The physical positions in CS RefSeq v1.0 were obtained based on blast using flanking marker sequences for each QTL (http://wheatomics.sdau.edu.cn/). QTL were named according to the International Rules of Genetic Nomenclature (http://wheat.pw.usda. gov/ggpages/wgc/98/Intro.htm). Abbreviations ‘Yr’, ‘Pbc’ and ‘nwafu’ were adopted for ‘yellow (stripe) rust resistance’, ‘pseudo-black chaff’ and ‘Northwest A & F University’, respectively.

Comparisons with previously reported Yr genes and QTL

To determine the relationships between loci identified in this study and previously reported Yr genes/QTL, we compared the relative physical and genetic distances of loci based on the IWGSC RefSeq v.1.0 and integrated linkage map consisting of SNP, DArT, SSR, STS, EST, RAPD and RFLP markers provided by Dr. Fa Cui (Ludong University in Shandong province; pers. comm.). The closest flanking markers were used to generate confidence intervals for previously reported Pst resistance genes/QTL in genetic populations. Significant markers were assumed to identify loci detected in genome-wide association studies (GWAS).

Origins of resistant haplotypes and phylogenetic analysis

A pedigree tree of FDC12 was constructed based on the information of pedigree and neighbor-joining tree. The genotype data for SNPs located in confidence intervals of target QTL were extracted from a diversity panel of 1,400 wheat accessions and used to perform phylogenetic analysis using MAGE 7. A neighbor-joining (NJ) tree was drawn using iTOL (https://itol.embl.de/). The resistance haplotype of FDC12 was tracked based on both pedigree and kinship analysis. Accessions grouped in same branch as FDC12 were considered to harbor the resistance haplotype.

Plant materials

A total of 226 F_5:7 RILs derived from a cross between RH520 (Zhengzhou 891/Qianfeng 1) and FDC12 (Zhoumai 16/Shanyou 225//Aikang 58) was used in QTL mapping (170 lines) and validation (56 lines). Mingxian169 (MX169), a susceptible Chinese landrace was grown as a disease spreader and cultivar Xiaoyan 22 (XY22) was used as the susceptible check.

Stripe rust evaluations

Seedling tests indicated that RH520 and FDC12 were susceptible to all test Pst races (Yu et al. 2020). For assessments of stripe rust reactions in the field 170 RILs and parents were planted at Yangling (YL) in Shaanxi province in 2018-2019 and 2019-2020, and at Jiangyou (JY) in Sichuan province in 2019-2020 and 2020-2021. Trials at Yangling were inoculated with a urediniospore mixture of CYR32 and CYR34 suspended in a light oil sprayed onto MX169 and XY22 in early March to initiate disease. Each plot consisted of a 1 m row sown with approximately 20 seeds and 25 cm row spacing. Two rows of highly susceptible cv. XY22 were planted after every 20 rows to ensure uniform disease development. A randomized complete block design with two replications was used in all experiments. Adult plant stripe rust reactions were determined by infection type (IT) and disease severity (DS). IT was recorded using a 0 to 9 scale ranging from complete immunity to high susceptibility (Line and Qayoum 1992); and disease severity was based on the modified Cobb Scale (Peterson et al. 1948). The first scoring was made when MX169 reached approximately 80% severity or more during the period 5–15 April at JY and 3–17 May at YL. IT and DS of homozygous lines were recorded as single values; and for segregating lines IT and DS were recorded as two or more values, but later averaged for each line. Disease assessments were made at least twice. The IT and final DS was used in subsequent analysis.

Phenotyping for pseudo-black chaff

Although pseudo-black chaff (PBC) was present in all environments, phenotyping was carried out only in 2021 at Yangling. The phenotype data for PBC was recorded at grain filling stage based on the presence of the black pigmentation around the stem internodes and glumes. A visual score of 0 or 1 scale was used, where 0 indicated no pigmentation and 1 indicated the presence of pigmentation.

Phenotypic data analysis

The frequency distributions of IT and DS of F_5:7 RILs across four environments were calculated using Excel 2016. Analysis of variance (ANOVA) and Pearson’s correlation coefficients among environments were conducted using the “AOV” function in QTL IciMapping software 4.1 with the default parameters based on the IT and DS data (Meng et al. 2015). Broad-sense heritability (h2 b) was estimated as h2 b= σ2 g/(σ2 g+ σ2 ge/e + σ2 ε/re), where σ2 g, σ2 ge and σ2 r represented genotypic (line), genotype × environment and error variances, respectively, and e and r were the numbers of environments and replicates. Mean IT and DS data were used for subsequent QTL mapping.

SNP calling and clustering

Genomic DNA from single fresh leaf of each parent and RIL was extracted at the jointing stage using the CTAB protocol (Song et al. 1994) and the quality and quantity of DNA were assessed using a NanoDrop ND-1000 (Thermo Scientific, Wilmington, DE, USA). The wheat 55K SNP array improved by China Gold Marker (Beijing; http:// www.cgmb.com.cn ) was used to genotype the parents and 170 RILs. SNP genotype calling and allele clustering was processed with the polyploid version of the Affymetrix Genotyping Console™ (AGC) software. SNPs were classified into six groups: (i) Poly High Resolution (PHR) SNPs that were polymorphic and co-dominant with a minimum of two samples containing the minor allele; (ii) no minor homozygote (NMH); these polymorphic and dominant SNPs had only two clusters, one being the heterozygote; (iii) mono high resolution (MHR) or monomorphic SNPs having only one cluster/allele; (iv) off-target variants (OTV) showing four clusters including one for a null allele; (v) call rate below threshold (CRBT) having all cluster properties above the threshold except for the call rate cut-of; and (vi) other type SNPs with one or more cluster properties below quality thresholds.

Linkage map construction and QTL analysis

The filtering criteria of SNP markers for linkage map construction were as follows: PHR/polymorphic, <10% missing values, major allele frequencies (MAF) ≤95%, and 1:1 segregation ratios confirmed by chi-squared tests (P >0.001). A linkage map was constructed using QTL IciMapping V4.1 software and generated with Mapchart V2.3 (Meng et al. 2015; Voorrips 2002). Recombination fractions were converted to centiMorgans (cM) using the Kosambi function (Kosambi 1944). One marker was selected from each co-segregating marker group using the “BIN” function. Selected markers were used to construct the genetic map using the “MAP” function. To further narrow down the interval of target loci, 16 SNPs on chromosome 3BS and 76 SNPs on chromosome 4BL from 660K SNP array genotypes of RH520 and FDC12, were converted into AQP, respectively. A total of 13 AQP markers was used to genotype all 170 RILs to enrich the linkage map (Table S1). Inclusive composite interval mapping with the additive tool (ICIM-ADD) in IciMapping V4.1 was performed to detect QTL based on the phenotypic data including mean IT and DS scores, and PBC. Likelihood-of-odds (LOD) thresholds for declaring statistical significance were calculated by 1000 permutations at a p value ≤0.01. LOD significance thresholds estimated for each trait was 2.5. The phenotypic variances explained (PVE) by individual QTL and additive effects at the LOD peaks were also obtained. The physical positions in CS RefSeq v1.0 were obtained based on blast using flanking marker sequences for each QTL (http://wheatomics.sdau.edu.cn/). QTL were named according to the International Rules of Genetic Nomenclature (http://wheat.pw.usda. gov/ggpages/wgc/98/Intro.htm). Abbreviations ‘Yr’, ‘Pbc’ and ‘nwafu’ were adopted for ‘yellow (stripe) rust resistance’, ‘pseudo-black chaff’ and ‘Northwest A & F University’, respectively.

Comparisons with previously reported Yr genes and QTL

To determine the relationships between loci identified in this study and previously reported Yr genes/QTL, we compared the relative physical and genetic distances of loci based on the IWGSC RefSeq v.1.0 and integrated linkage map consisting of SNP, DArT, SSR, STS, EST, RAPD and RFLP markers provided by Dr. Fa Cui (Ludong University in Shandong province; pers. comm.). The closest flanking markers were used to generate confidence intervals for previously reported Pst resistance genes/QTL in genetic populations. Significant markers were assumed to identify loci detected in genome-wide association studies (GWAS).

Origins of resistant haplotypes and phylogenetic analysis

A pedigree tree of FDC12 was constructed based on the information of pedigree and neighbor-joining tree. The genotype data for SNPs located in confidence intervals of target QTL were extracted from a diversity panel of 1,400 wheat accessions and used to perform phylogenetic analysis using MAGE 7. A neighbor-joining (NJ) tree was drawn using iTOL (https://itol.embl.de/). The resistance haplotype of FDC12 was tracked based on both pedigree and kinship analysis. Accessions grouped in same branch as FDC12 were considered to harbor the resistance haplotype.

ADD, additive

ANOVA, analysis of variance

APR, adult plant resistance

AQP, allele-specific quantitative PCR

CIMMYT, International Maize and Wheat Improvement Center

cM, centiMorgans

CS, Chinese spring

DS, disease severity

EPI, epistasis

GWAS, genome-wide association analysis

h2 b, broad-sense heritability

ICIM, inclusive composite interval mapping

IT, infection type

IWGSC, International Wheat Genome Sequencing Consortium

KASP, kompetitive allele-specific polymerase chain reaction

LG, linkage group

LOD, likelihood-of-odds

MAF, major allele frequency

MAS, marker-assisted selection

NJ, neighbor-joining

PBC, pseudo-black chaff

PCR, polymerase chain reaction

PBC, pseudo-black chaff

Pst, Puccinia striiformis f. sp. tritici

QTL, quantitative trait loci

RIL, recombinant inbred lines

SNP, single nucleotide polymorphism

SSR, simple sequence repeat

YR, yellow rust

Conﬂicts of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.

Authors’ contribution statement

SJ Liu and JH Wu designed and conducted the experiments, analyzed the data, and wrote the manuscript. YG Jin, ZH Xia, QD Zeng, and QL Wang participated in creation of the genetic populations and assisted in analysis of the SNP array data. XT Wang, YY Zhang, R Yu, S Huang, WJ Zheng, LY Qiao and MJ Xiang participated in greenhouse and field experiments and contributed to genotyping. RP Singh, S Bhavani and ZS Kang participated in revision of the manuscript. CF Wang, JH Wu and DJ Han conceived and directed the project and revised the manuscript.

Acknowledgments

The authors are grateful to Prof. R.A. McIntosh, Plant Breeding Institute, University of Sydney, for language editing and proofreading of the draft manuscript. This study was supported financially by National Science Foundation for Young Scientists in China (Grant no. 31901869 and 31901494), National Natural Science Foundation of China (Grant no. 31971890 and 31871611), Open Project Fund of Shanxi Key Laboratory of Crop Genetics and Molecular Improvement (KFJJ201904), International Cooperation and Exchange of the National Natural Science Foundation of China (Grant no. 31961143019), the Integrated Extension Project of Agricultural Science and Technology Innovation in Shaanxi Province (NYKJ-2021-YL(XN)15).

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Table 1 Variance components for infection type (IT) and disease severity (DS) scores in a RIL population derived from the cross RH520 × FDC12 and tested across four environments

Sources of variation	IT				DS
	Df	Mean square	F value	P-value	Df	Mean square	F value	P-value
RILs	169	36.9	20.3	<0.0001	169	7567.0	55.0	<0.0001
Environments	3	67.4	37.1	<0.0001	3	6077.3	45.0	<0.0001
Lines × Environments	506	4.1	2.3	<0.0001	506	557.9	4.1	<0.0001
Replicates	1	26.2	21.8	0.017	1	1504.8	10.24	0.134
Error	679	1.8			680	135.2
h2 b	0.90				0.93

Table 2 Correlation coefficients (r) of mean disease severity (DS) and infection type (IT) for the RH520 × FDC12RIL population across four environments

Environment^a (location, year)		r values based on MDS (IT)^b
	YL2019		YL2020	JY2020
YL2020	0.81 (0.75)
JY2020	0.83 (0.72)		0.85 (0.79)
JY2021	0.66 (0.51)		0.71 (0.66)	0.71 (0.61)

^aYL, Yangling and JY, Jiangyou. 2019, 2020 and 2021 represent growing seasons 2018-2019, 2019–2020 and 2020-2021

^br values calculated for IT are shown in parenthesis. All r values were significant (P < 0.001).

Table 3 Quantitative trait loci (QTL) for pseudo-black chaff (PBC) and stripe rust resistance detected in the RH520 ×FDC12 RIL population using infection type (IT) and disease severity (DS) data across four environments

QTL, environments^a	Flanking marker	Genetic position^b（cM）	Physical interval^c（Mb）	LOD^d	PVE^e	ADD^f
QYr.nwafu-2AL
20YL-IT	AX-94417710- AX-111141805	106	713.9-718.3	8.1	7.4	-0.8
20YL-DS	AX-94417710- AX-111141805	107	713.9-718.3	6.3	6.6	-8.0
21JY-DS	AX-94417710- AX-111141805	106	713.9-718.3	6.4	9.1	-9.7
QYr.nwafu-2DS
20YL-IT	AX-109525831-AX-111726271	119	73.4-75.0	7.9	7.1	0.7
20JY-IT	AX-109525831-AX-111726271	119	73.4-75.0	3.8	5.9	0.5
21JY-IT	AX-89563243- AX-111128536	118	57.9-73.3	3.5	4.6	0.5
20JY-DS	AX-110999397- AX-110483509	120	75.7-79.7	3.3	4.7	6.7
21JY-DS	AX-89563243- AX-111128536	117	57.9-73.3	7.3	10.9	10.6
QYr.nwafu-3BS
19YL-IT	AX-110108039-AX-111197043	1	4.3-9.1	13.4	30.7	-1.5
20YL-IT	AX-110108039-AX-111197043	1	4.3-9.1	16.5	36.5	-1.7
20JY-IT	AX-110108039-AX-111197043	1	4.3-9.1	9.7	23.0	-1.0
21JY-IT	AX-110108039-AX-111197043	0	4.3-9.1	9.7	23.2	-1.1
20YL-DS	AX-110108039-AX-111197043	1	4.3-9.1	17.5	38.0	-20.8
19YL-DS	AX-110108039-AX-111197043	0	4.3-9.1	13.5	30.9	-20.2
20YL-DS	AX-110108039-AX-111197043	1	4.3-9.1	17.5	38.0	-20.8
21JY-DS	AX-110108039-AX-111197043	0	4.3-9.1	12.8	29.4	-18.4
QYr.nwafu-4BL.2
19YL-IT	AX-111121224- AX-89518393	75	622.0-634.3	4.1	11.2	0.9
20YL-IT	AX-111121224- AX-89518393	75	622.0-634.3	5.5	13.7	1.1
20JY-IT	AX-111121224- AX-89518393	75	622.0-634.3	6.0	15.6	0.9
21JY-IT	AX-111121224- AX-89518393	75	622.0-634.3	4.7	12.4	0.9
19YL-DS	AX-111121224- AX-89518393	75	622.0-634.3	8.6	13.4	17.8
20YL-DS	AX-111121224- AX-89518393	75	622.0-634.3	8.4	14.0	16.2
20JY-DS	AX-111121224- AX-89518393	75	622.0-634.3	6.9	17.5	14.3
21JY-DS	AX-111121224- AX-89518393	75	622.0-634.3	4.2	11.5	11.8
QYr.nwafu-5BL.1
19YL-IT	AX-109900321- AX-109951695	108	584.9-586.9	3.9	5.4	0.6
20YL-IT	AX-111152749- AX-110508006	109	585.8-595.6	7.1	6.3	0.7
20JY-DS	AX-109900321- AX-109951695	108	584.9-586.9	2.7	4.3	6.5
QYr.nwafu-5BL.2
20JY-IT	AX-109827342- AX-94987192	138	661.2	5.7	9.0	0.6
21JY-IT	AX-110399021- AX-109827342	136	644.9-661.2	3.7	5.4	0.6
QYr.nwafu-7BL.1
20YL-IT	AX-110514055- AX-110498108	64	363.2-478.1	4.6	4.1	-0.6
20JY-IT	AX-110498108- AX-111236639	65	478.1-486.9	4.2	7.0	-0.5
19YL-DS	AX-110484893- AX-109930265	68	504.7-605.1	3.3	4.6	-7.2
QYr.nwafu-7BL.2
21JY-IT	AX-111551132 -AX-109421983	50	85.4-85.8	4.5	8.9	0.7
20JY-DS	AX-111551132 -AX-109421983	50	85.4-85.8	3.9	7.5	8.5
QPbc.nwafu-3BS	AX-110108039-AX-111197043	1	4.3-9.1	15.6	26.9	-0.24
QPbc.nwafu-7AL	AX-94513729- AX-110505644	81	797.4	3.1	3.6	0.28
QPbc.nwafu-5DS	AX-109898339- AX-110415968	21	295.5-303.4	4.1	4.8	-0.1

^aYL, Yangling and JY, Jiangyou. 2019, 2020 and 2021 represent growing seasons 2018-2019, 2019–2020 and 2020-2021

^bPeak position in centimorgans (cM) from the first linked marker of the relevant linkage group

^cPhysical location in mega base (Mb) from linked markers in wheat genome

^dLogarithm of odds score

^ePercentages of the phenotypic variation explained by individual QTL

^fAdditive effect of the allele. Negative signs indicate that the favorable allele is from RH520.

Table 4 The phenotype range and effect on IT and DS of 170 RH520 × FDC12 RILs in different groups based on their QTL status derived from flanking molecular markers

Group	Number	Mean-IT	Mean-DS	Effect		PBC
Group				IT	DS(%)
None	3	7.3-7.5	85.0-96.7	/	/	No
3BS	3	7.0-8.5	70.0-93.3	/	/	No
4BL	10	7.7-8.6	82.5-94.5	/	/	No
X	25	6.5-6.9	63.6-70.1	0.4-1.0	14.9-33.1	No
3BS+X	20	5.5-6.6	43.3-63.4	0.7-2.0	21.6-53.4	No
4BL+X	39	5.6-6.4	51.0-61.7	0.9-1.9	23.3-45.7	No
3BS+4BL	4	3.5-4.3	13.8-25.0	3.0-4.0	60.0-82.9	Yes
3BS+4BL+X	45	1.5-3.5	6.6-13.5	3.8-6.0	71.5-90.1	Yes

Fig.S1.pptx
Fig. S1 The epistasis mapping for PBC using function of ICIM-EPI in QTL IciMapping4.1. Different color indicated different linkage group. Linkage group1 and group2 in figure represent chr3B and Chr4B, respectively. The number in the small ellipses represents the genetic position of Pbc1 and PbcFDC on the corresponding linkage group, respectively. The red dotted line indicated epistasis interaction between Pbc1 and PbcFDC. The LOD was 9.5.
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Enhanced Stripe Rust Resistance Obtained by Combining A Widely Dispersed, Stable QTL with Yr30 on Chromosome Arms 4BL And 3BS

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