Exploiting the effector repertoire of Monilinia fructicola as a breeding strategy for disease resistance

2021 ◽  
pp. 77-84
Author(s):  
L. Vilanova ◽  
C.A. Valero Jiménez ◽  
D. Schreurs ◽  
J.A.L. van Kan
2003 ◽  
Vol 43 (6) ◽  
pp. 617 ◽  
Author(s):  
R. C. Magarey ◽  
J. I. Bull

Plant breeders conduct a range of yield trials each year to estimate the yielding potential of sugarcane clones progressing through the breeding program. Only the highest-yielding clones are selected for further testing with very small numbers being released as commercial cultivars. Disease susceptibility varies greatly amongst the tested clones and a number of diseases influence the yield of clones in various stages of the selection process. Disease resistance testing is an important routine aspect of the breeding program. All clones for northern Queensland are screened for disease resistance, while selected clones from other areas are tested for resistance to Pachymetra root rot. Two new terms are introduced: resistance index (RI) and the yield loss resistance index (YLRI). Analyses were conducted relating yielding ability of clones in stage 3 trials to Pachymetra resistance. Pachymetra root rot on average reduced tonnes cane per hectare by 15.8% and tonnes sugar per hectare by 10.2%. There was a slight positive effect on commercial cane sugar. YLRI5 for tonnes cane was 3.5 and for tonnes sugar 5.7. With a RI of 3.7, the current breeding strategy for northern Queensland appears appropriate. The data reported here will be valuable for refining selection strategies to improve breeding efficiencies. These analyses could be undertaken each year using data from all breeding trials throughout Queensland, not only with Pachymetra root rot, but also with other diseases normally endemic in cane fields. The advantage of this technique is that with a minimum of further expenditure, ongoing estimates of disease-induced yield losses can be obtained with the information guiding the selection program.


2003 ◽  
pp. 665-670 ◽  
Author(s):  
E. Peterlunger ◽  
G. Di Gaspero ◽  
G. Cipriani ◽  
P. Sivilotti ◽  
L. Zulini ◽  
...  

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260997
Author(s):  
Emilly Ruas Alkimim ◽  
Eveline Teixeira Caixeta ◽  
Tiago Vieira Sousa ◽  
Itamara Bomfim Gois ◽  
Felipe Lopes da Silva ◽  
...  

Breeding programs of the species Coffea canephora rely heavily on the significant genetic variability between and within its two varietal groups (conilon and robusta). The use of hybrid families and individuals has been less common. The objectives of this study were to evaluate parents and families from the populations of conilon, robusta, and its hybrids and to define the best breeding and selection strategies for productivity and disease resistance traits. As such, 71 conilon clones, 56 robusta clones, and 20 hybrid families were evaluated over several years for the following traits: vegetative vigor, incidence of rust and cercosporiosis, fruit ripening time, fruit size, plant height, canopy diameter, and yield per plant. Components of variance and genetic parameters were estimated via residual maximum likelihood (REML) and genotypic values were predicted via best linear unbiased prediction (BLUP). Genetic variability among parents (clones) and hybrid families was detected for most of the evaluated traits. The Mulamba-Rank index suggests potential gains up to 17% for the genotypic aggregate of traits in the hybrid population. An intrapopulation recurrent selection within the hybrid population would be the best breeding strategy because the genetic variability, narrow and broad senses heritabilities and selective accuracies for important traits were maximized in the crossed population. Besides, such strategy is simple, low cost and quicker than the concurrent reciprocal recurrent selection in the two parental populations, and this maximizes the genetic gain for unit of time.


2015 ◽  
Vol 6 ◽  
Author(s):  
Sadegh Ashkani ◽  
Mohd Y. Rafii ◽  
Mahmoodreza Shabanimofrad ◽  
Gous Miah ◽  
Mahbod Sahebi ◽  
...  

2012 ◽  
Vol 65 ◽  
pp. 61-68 ◽  
Author(s):  
Jia Liu ◽  
Yuan Sui ◽  
Michael Wisniewski ◽  
Samir Droby ◽  
Shiping Tian ◽  
...  

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