Mapping QTL Associated with Stripe Rust, Leaf Rust, and Leaf Spotting in a Canadian Spring Wheat Population

Abstract

Stripe rust, leaf rust, and the leaf spot complex are economically important diseases of wheat (Triticum aestivum L.) in western Canada, and genetic host resistance is the most successful management strategy. This study was conducted to identify quantitative trait loci (QTL) associated with these diseases and to provide wheat breeders with sources of potential disease resistance genes. A total of 208 recombinant inbred lines derived from a cross between Canadian spring wheat cultivars ‘Peace’ and ‘Carberry’ were evaluated from 2014 to 2017 in stripe rust, leaf rust, and leaf spot nurseries in Alberta and British Columbia. All lines were genotyped with sequencing‐based Diversity Arrays Technology (DArTseq) markers. Using the least square means of the combined environments, two stripe rust resistance QTL, two leaf rust resistance QTL, and three leaf spot resistance QTL were identified. The stripe rust QTL were located on chromosomes 3A and 4B, the leaf rust QTL were located on chromosomes 4A and 3D, and the leaf spot QTL were located on 2A, 4B and 7D. The stripe rust resistance QTL on 4B, contributed by ‘Carberry’, was previously identified in other studies using a population derived from ‘Carberry’. Results from this study suggest that ‘Carberry’ may be an attractive parental source for breeders to enhance resistance against stripe rust and leaf spot with minor resistance alleles.

QTL mapping of adult plant and seedling resistance to leaf rust (Puccinia triticina Eriks.) in a multiparent advanced generation intercross (MAGIC) wheat population

“…In this study, phenotyping of 394 RILs from the BMW population resulted in a broad variability of resistance to Puccinia triticina . Despite an average correlation coefficient of 0.54 between the disease severities in five environments, a broad sense heritability of 0.83 was calculated which is in the range of previously published studies (Bemister et al 2019 ; Gao et al 2016 ; Zhang et al 2017 , 2019 ). This may hint to a quantitative inheritance due to QTL involved in slow rusting loci, which are characterized by relatively high heritabilities (Kolmer 1996 ).…”

Physical Mapping of QTL in Four Spring Wheat Populations under Conventional and Organic Management Systems. I. Earliness

“…The methodologies for DNA extraction and genotyping have been described in our previous studies in the CAB [ 23 ], ACG [ 24 ], PAC [ 26 ], and PCS [ 30 ] populations. DNA samples from the ACG, CAB, and PCS populations were genotyped at the University of Saskatchewan Wheat Genomics lab, Saskatoon, Canada, with the wheat 90K iSelect array that consisted of 81,587 SNPs [ 79 ], while the PAC population was genotyped with the DArTseq™ technology by DArT Pty Ltd., Canberra, Australia [ 80 , 81 ].…”

“…The wheat breeding group at the University of Alberta has been conducting extensive research in the Canada Western Red Spring (CWRS) wheat class, including (i) developing several improved cultivars [ 7 , 8 , 9 , 10 ], (ii) evaluating the phenotypic performance of diverse cultivars under conventional and/or organic managements [ 11 , 12 , 13 , 14 , 15 , 16 ], (iii) understanding the genetics of earliness that serves as baseline data for developing early maturing cultivars to avoid frost damage [ 17 , 18 , 19 , 20 ], and (iv) mapping genes and QTL associated with diverse traits using biparental populations [ 19 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ] and a genomewide association mapping panel [ 28 , 29 ]. The development of early-maturing CWRS cultivars is of paramount importance in the northern breeding programs to provide farmers not only an option of growing the crop with minimal loss due to frost but also help to escape from the late incidence of diseases, heat, and drought as compared with their late-maturing counterparts.…”

Mapping genomic regions controlling agronomic traits in spring wheat under conventional and organic managements

“…Least‐squares means of each phenotypic trait were estimated for each individual via a mixed‐effects model using PROC MIXED in SAS version 9.4 (SAS Institute, 2015), as described in Bemister et al. (2019). Frequency distributions of the phenotypic traits were visualized using R (version 3.2.3) and the ggplot2 (Wickham, 2016).…”

“…We constructed linkage maps using DArT and single nucleotide polymorphism (SNP) markers with known positions on “Wheat consensus map version 4.0” supplied by Diversity Arrays Technology. Marker data were curated as described previously (Bemister et al., 2019). The least‐squares means of management system (conventional and organic) and genotypic data were used to perform QTL analysis via IciMapping v4.1 (Meng, Li, Zhang, & Wang, 2015).…”