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Multi-Locus Mixed Model Analysis Of Stem Rust Resistance In Winter Wheat

Authors :
Paul D. Mihalyov
Virginia A. Nichols
Peter Bulli
Matthew N. Rouse
Michael O. Pumphrey
Source :
The Plant Genome, Vol 10, Iss 2 (2017)
Publication Year :
2017
Publisher :
Wiley, 2017.

Abstract

Genome-wide association mapping is a powerful tool for dissecting the relationship between phenotypes and genetic variants in diverse populations. With the improved cost efficiency of high-throughput genotyping platforms, association mapping is a desirable method of mining populations for favorable alleles that hold value for crop improvement. Stem rust, caused by the fungus f. sp. is a devastating disease that threatens wheat ( L.) production worldwide. Here, we explored the genetic basis of stem rust resistance in a global collection of 1411 hexaploid winter wheat accessions genotyped with 5390 single nucleotide polymorphism markers. To facilitate the development of resistant varieties, we characterized marker–trait associations underlying field resistance to North American races and seedling resistance to the races TTKSK (Ug99), TRTTF, TTTTF, and BCCBC. After evaluating several commonly used linear models, a multi-locus mixed model provided the maximum statistical power and improved the identification of loci with direct breeding application. Ten high-confidence resistance loci were identified, including SNP markers linked to and and at least three newly discovered resistance loci that are strong candidates for introgression into modern cultivars. In the present study, we assessed the power of multi-locus association mapping while providing an in-depth analysis for its practical ability to assist breeders with the introgression of rare alleles into elite varieties.

Details

Language :
English
ISSN :
19403372
Volume :
10
Issue :
2
Database :
Directory of Open Access Journals
Journal :
The Plant Genome
Publication Type :
Academic Journal
Accession number :
edsdoj.779f10cdc27a40e8a8dd92592608e7d6
Document Type :
article
Full Text :
https://doi.org/10.3835/plantgenome2017.01.0001