Impact of Stripe Rust Resistance Alleles on Wheat Grain Yield Using Landraces and Improved Accessions

Special Article - Breeding Techniques

Ann Agric Crop Sci. 2021; 6(4): 1084.

Impact of Stripe Rust Resistance Alleles on Wheat Grain Yield Using Landraces and Improved Accessions

ElBasyoni IS1,2*

¹Department of Crop Science, Faculty of Agriculture,Damanhur University, Damanhur, Egypt

²Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA

*Corresponding author: Ibrahim S. ElBasyoni, Crop Science Department, Faculty of Agriculture, Damanhur University, Damanhur, Egypt

Received: May 11, 2021; Accepted: June 10, 2021; Published: June 17, 2021


Stripe rust is one of the most devastating biotic stresses to cause grain yield losses in wheat. In the current study, 227 imported accessions, and six widely grown modern cultivars (Sids14, Sids12, Misr1, Misr2, Giza171, and Gimmiza9), were used. All plant materials were planted in the field and evaluated for stripe rust resistance and grain yield. Five Simple Sequence Repeats (SSR) markers Xpsp3000, Xbarc8, Xgwm419, Xwmc44, and Xbarc32, respectively, are associated with five essential stripe rust resistance genes Yr10, Yr15, Yr26, Yr29, and Yr59, were also used. The results indicated a highly positive and significant correlation between grain yield and stripe rust resistance. Furthermore, as the number of stripe rust resistance alleles increased, both grain yield and stripe rust resistance increased. Out of the 233 accessions used, 11 accessions were found to contain the five resistance genes. The identified resistant accessions could be used as a gene source to enhance stripe rust resistance in wheat breeding programs. SSR markers used in the current study effectively capture a substantial part of the phenotypic variation caused by stripe rust. Thus, these five markers could be used effectively in marker-assisted selection for stripe rust resistance.

Keywords: SSR; Yellow Rust; Wild Wheat Types; Genetic Diversity


Wheat (Triticum aestivum L), the leading staple food crop worldwide, is often attacked by several fungal, bacterial, viral, and nematode pathogens. Wheat rusts (Puccinia spp.) are among the most devastating pathogens that attack wheat, causing significant yield losses [1]. The most common types of wheat rust are leaf rust caused by Puccinia triticina f.sp. tritici, stem rust caused by Puccinia graminis f.sp. tritici, and stripe rust caused by Puccinia striiformis f.sp. tritici [2]. Stripe rust is regarded as a deleterious wheat rust disease [3]. Therefore, stripe rust is considered the most critical hazard for global wheat production [4]. Stripe rust used to be a cool weatheradapted disease, but recently aggressive races have also spread to warm-weather parts of the world [5].

Several stripe rust pathogens originated from Europe, Australia, and North America, and some pathogen populations within these regions experience a high genetic diversity level [6]. Moreover, western China, Central Asia, and the Himalayas were considered the center of stripe rust pathogen evolution, where sexual recombination is common [7]. New stripe rust races that originated from the Himalayas region spread across Europe between 2011 and 2015 [6]. Egypt is one of the warm weather countries that recently suffered from stripe rust at high severity levels [8]. Grain yield losses between 14% and 26% due to stripe rust in the Nile Delta were recorded [8]. Shahin et al. [9] evaluate eight commercially grown Egyptian wheat cultivars for stripe rust under the field growth conditions. They concluded that most commercially grown wheat cultivars in Egypt possess low adult plant resistance levels to stripe rust.

Field evaluation for stripe rust has been a successful and effective way to identify stripe rust-resistant genotypes. However, for the field evaluation to be effective, a large number of accessions have to be grown and infected with several races or the predominant mixture of native races to identify resistant accessions. It also requires reliable rust screening nurseries [8]. To ensure the pathogenic races’ presence, it is desirable to inoculate the host plants with those races artificially [10]. But it is not acceptable to inoculate the host plants in the field with pathogenic races that are not naturally present in the evaluation environment [10]. Therefore, field evaluation for stripe rust is challenging because of the annual fluctuation of the climate conditions, which might also stimulate stripe rust evaluation and the presence of new races. Additionally, the traditional field evaluation methods are time-consuming. Therefore, several plant breeders have incorporated molecular markers in their breeding programs to identify and introgress rust resistance genes with minimal dragging effect into their elite lines [11].

In total, 81 stripe rust resistant genes were identified on various wheat chromosomes [1], designated Yr1 to Yr67, Yr73, and Yr74 [12,13]. Out of the 81 stripe rust-resistant genes, 18 are adult-plant resistance genes; Yr11-Yr14, Yr16, Yr18, Yr29, Yr30, Yr34, Yr36, Yr39, Yr46, Yr48, Yr49, Yr52, Yr54, Yr59, and Yr62, whereas 54 are seedling resistance genes [14]. Pyramiding adult and seedling resistant genes in a genotype could confer enhanced durable resistance to stripe rust of wheat [14]. The first step to pyramid several effective resistance genes is to identify genuinely resistant genotypes. Resistance genes can be tagged and identified rapidly and accurately using molecular markers in Marker-Assisted Selection (MAS). MAS have been used successfully to facilitate gene identification for crucial traits such as stripe, stem, and leaf rust resistance. Several DNA molecular marker platforms were commonly used successfully in MAS, such as RFLP [15], RAPD [16], SSR [17], SNP [18], and KASP [19]. SSR is the common molecular marker platform in MAS studies due to its reproducibility, multi-allelic nature, co-dominant inheritance, and robust amplification [20]. Simple Sequence Repeats (SSR) flanking genes that control a trait can be used to screen large populations for that trait in a short time. In addition, markers with diagnostic alleles, i.e., the size difference in the parental accessions, are ideal markers for MAS because they will be completely correlated with the trait [21]. Furthermore, MAS can be used in pyramiding adult and seedling resistant genes in a genotype [14].

Therefore, in the current study, we characterized a panel of 233 spring wheat accessions for stripe rust using five diagnostic SSR markers. The panel was also evaluated under the field conditions for two growing seasons in which stripe rust resistance and grain yield were measured. The current study’s objectives were to (1) screen the wheat collection for resistance to stripe rust and (2) estimate the impact of stripe rust favorable alleles accumulation on stripe rust resistance and grain yield production.

Materials and Methods


A panel comprised of 227 imported accessions and five widely grown modern cultivars (Sids14, Misr1, Misr2, Giza171, and Gimmiza9) were used in the current study. The imported wheat accessions contain 96 elite breeding lines, 74 cultivars, and 57 landraces. Thereafter, we will refer to the elite lines and cultivars as the improved accessions. The seeds of the imported accessions were collected by the USDA-ARS from several geographic regions around the world. In comparison, the commercial cultivars were obtained from the Agricultural Research Center (ARC), Egypt. For further information about the accessions details such as pedigrees and origin regions, the reader is referred to the supporting information Table S1.


The spring wheat panel was phenotyped at Elbasotan region in an experimental farm for Damanhour University (30°46'46'' N, 30°82'32'' E) during two consecutive growing seasons 2018 and 2019. Grain yield (ton/hectare) and stripe rust score were measured on plots that were four 2.5-meter rows, 30cm apart. Plots were laid out in a randomized block design with three replicates per year. Planting dates were November 21st and November 14th for the first and second growing seasons, respectively. Stripe rust (incited by Puccinia striiformis f.sp) susceptible cultivar “Morocco” was planted around the experiment as a one-meter wide border. During the booting growth stage, Morocco was dusted with 200mg urediniospores of five prevalent and aggressive pathotypes of stripe rust, i.e., 0E0, 6E4, 70E20, 128E28, and 134E244, mixed with talcum powder (1:20, spores: talcum). Stripe rust was scored according to modified Cobb’s scale [22]. The infection type was expressed in the following classes, i.e., Immune = I, R = Resistant, small uredinia surrounded by necrosis; MR = Moderately Resistant, medium to large uredinia surrounded by necrosis; MS = Moderately Susceptible, medium to large uredinia surrounded by chlorosis; S = Susceptible, large uredinia without necrosis or chlorosis [23]. The statistical analysis was conducted on the infection types after converting it into 0, 2, 4, 6, and 8 for immune, resistant, moderately resistant, moderately susceptible, and susceptible, respectively. After physiological maturity, all plants in each plot were manually cut at 5cm above soil service and left to dry in the middle of the plots. Three days later, plants from each plot were threshed separately using a locally made single plot thresher, in which seeds were collected, weighed, and converted to tons/ha. Standard agronomic practices, including weed control, recommended nitrogen, phosphorus, and potassium applications, were followed.


The total genomic DNA was extracted from 200 mg of fresh leaves during the seedling stage. DNA extraction kit (Promega, USA) was used, and the manufacturer’s instructions were followed. The DNA concentration of each sample was measured using a spectrophotometer at a wavelength of 260 and 280 nm using a CARY 50 probe UV-visible spectrophotometer (Varian, CA, USA). The DNA quality was confirmed by running 5μl diluted DNA on a 0.8% agarose gel. Table 1 shows the specific band (bp) for stripe rust resistance for the SSR markers used in the current study. The primer sequences, linkage map location, and the amplification requirements for the SSR markers were obtained from the GrainGenes website ( PCR products were scored as present (1) or absent (0) across the 233 accessions for each primer.