Applications of Machine Learning Methods to Genomic Selection in Breeding Wheat for Rust Resistance
نویسندگان
چکیده
منابع مشابه
Genomic Selection for Quantitative Adult Plant Stem Rust Resistance in Wheat
Quantitative adult plant resistance (APR) to stem rust (Puccinia graminis f. sp. tritici) is an important breeding target in wheat (Triticum aestivum L.) and a potential target for genomic selection (GS). To evaluate the relative importance of known APR loci in applying GS, we characterized a set of CIMMYT germplasm at important APR loci and on a genome-wide profile using genotyping-by-sequenci...
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Hybrid populations (F3) derived from cross between four moderate salt-resistant spring wheat (Triticum aestivum L.) were assessed for transgressive segregation of salt-tolerant genotypes allowing germination and emergence at 350 mM NaCl for 30 days. The achieved Selection intensity was the order of 0.24-0.27 in these segregation populations (22-25 seedlings from 9000 seeds). Assessment of trans...
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Computer mating programs have helped breeders minimize pedigree inbreeding and avoid recessive defects by mating animals with parents that have fewer common ancestors. With genomic selection, breed associations, AI organizations, and on-farm software providers could use new programs to minimize genomic inbreeding by comparing genotypes of potential mates. Relationships could be computed between...
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Fungal diseases, especially leaf and stripe rusts are wheat yield reducing factors in Iran and the world. In this study, genetic diversity of 20 wheat varieties with different response to leaf and stripe rusts were studied using primers designed based on the conserved regions of plant disease resistance genes. The banding patterns of polymorphic markers were scored as dominant and number of amp...
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ژورنال
عنوان ژورنال: The Plant Genome
سال: 2018
ISSN: 1940-3372,1940-3372
DOI: 10.3835/plantgenome2017.11.0104