Literature DB >> 18944171

Characterization and mapping of oat crown rust resistance genes using three assessment methods.

E W Jackson, D E Obert, M Menz, G Hu, J B Avant, J Chong, J M Bonman.   

Abstract

ABSTRACT Resistance is the primary means of control for crown rust of oat (Avena sativa L.), caused by Puccinia coronata f. sp. avenae, and better knowledge of the genetics of resistance will enhance resistance breeding. Disease data were generated in the field and greenhouse for parents and recombinant inbred lines of the Ogle/TAM O-301 (OT) oat mapping population using (i) a new quantitative assay that employs quantitative real-time polymerase chain reaction (q-PCR) to estimate fungal growth in the host, (ii) digital image analysis, and (iii) visual ratings. The objectives of this study were to evaluate each assessment method's ability to map a major gene from cv. Ogle and potential quantitative trait loci (QTL) contributed by Ogle and TAM O-301. All three assessment methods identified the major gene in Ogle, which was mapped to linkage group OT6. The resolution produced by q-PCR, however, enabled more precise mapping of the major gene. Quantitative analysis indicated that 64% of the phenotypic variation was accounted for using q-PCR, whereas 41 and 52% were accounted for using visual and digital assessments, respectively. Data generated by q-PCR permitted identification of QTL on linkage groups OT32, accounting for 6% of the phenotypic variation, and OT2, accounting for 4% of the variation. QTL on both OT32 and OT2 were conferred by TAM O-301, one of which (OT2) was indiscernible using data from the visual and digital assessments. The new method of precisely phenotyping crown rust resistance provided a more accurate and thorough means of dissecting resistance in the OT mapping population. Similar methods could be developed and applied to other important cereal rust diseases.

Entities:  

Year:  2007        PMID: 18944171     DOI: 10.1094/PHYTO-97-9-1063

Source DB:  PubMed          Journal:  Phytopathology        ISSN: 0031-949X            Impact factor:   4.025


  7 in total

Review 1.  Puccinia coronata f. sp. avenae: a threat to global oat production.

Authors:  Eric S Nazareno; Feng Li; Madeleine Smith; Robert F Park; Shahryar F Kianian; Melania Figueroa
Journal:  Mol Plant Pathol       Date:  2017-12-10       Impact factor: 5.663

2.  New Diversity Arrays Technology (DArT) markers for tetraploid oat (Avena magna Murphy et Terrell) provide the first complete oat linkage map and markers linked to domestication genes from hexaploid A. sativa L.

Authors:  R E Oliver; E N Jellen; G Ladizinsky; A B Korol; A Kilian; J L Beard; Z Dumlupinar; N H Wisniewski-Morehead; E Svedin; M Coon; R R Redman; P J Maughan; D E Obert; E W Jackson
Journal:  Theor Appl Genet       Date:  2011-07-31       Impact factor: 5.699

Review 3.  Breeding oat for resistance to the crown rust pathogen Puccinia coronata f. sp. avenae: achievements and prospects.

Authors:  R F Park; W H P Boshoff; A L Cabral; J Chong; J A Martinelli; M S McMullen; J W Mitchell Fetch; E Paczos-Grzęda; E Prats; J Roake; S Sowa; L Ziems; D Singh
Journal:  Theor Appl Genet       Date:  2022-06-04       Impact factor: 5.699

4.  Qualitative and quantitative trait loci conditioning resistance to Puccinia coronata pathotypes NQMG and LGCG in the oat (Avena sativa L.) cultivars Ogle and TAM O-301.

Authors:  E W Jackson; D E Obert; M Menz; G Hu; J M Bonman
Journal:  Theor Appl Genet       Date:  2008-01-09       Impact factor: 5.699

5.  Identification of Genes in a Partially Resistant Genotype of Avena sativa Expressed in Response to Puccinia coronata Infection.

Authors:  Yolanda Loarce; Elisa Navas; Carlos Paniagua; Araceli Fominaya; José L Manjón; Esther Ferrer
Journal:  Front Plant Sci       Date:  2016-05-31       Impact factor: 5.753

6.  Identification of molecular markers for the Pc39 gene conferring resistance to crown rust in oat.

Authors:  Sylwia Sowa; Edyta Paczos-Grzęda
Journal:  Theor Appl Genet       Date:  2020-01-11       Impact factor: 5.699

7.  Rapid, automated detection of stem canker symptoms in woody perennials using artificial neural network analysis.

Authors:  Bo Li; Michelle T Hulin; Philip Brain; John W Mansfield; Robert W Jackson; Richard J Harrison
Journal:  Plant Methods       Date:  2015-12-24       Impact factor: 4.993

  7 in total

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