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Accelerating Tomato Breeding by Exploiting Genomic Selection Approaches
by
Andolfo, Giuseppe
, Ercolano, Maria Raffaella
, Cappetta, Elisa
, Frusciante, Luigi
, Barone, Amalia
, Di Matteo, Antonio
in
backcrossing
/ breeding programs
/ breeding value
/ computer science
/ genes
/ genetic breeding value
/ genetic improvement
/ genomics
/ genotype
/ genotyping
/ marker effect
/ marker-assisted selection
/ phenotype
/ phenotyping
/ plant breeding
/ population size
/ prediction
/ Review
/ Solanum lycopersicum
/ statistical models
/ tomato
/ tomatoes
/ training population
2020
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Accelerating Tomato Breeding by Exploiting Genomic Selection Approaches
by
Andolfo, Giuseppe
, Ercolano, Maria Raffaella
, Cappetta, Elisa
, Frusciante, Luigi
, Barone, Amalia
, Di Matteo, Antonio
in
backcrossing
/ breeding programs
/ breeding value
/ computer science
/ genes
/ genetic breeding value
/ genetic improvement
/ genomics
/ genotype
/ genotyping
/ marker effect
/ marker-assisted selection
/ phenotype
/ phenotyping
/ plant breeding
/ population size
/ prediction
/ Review
/ Solanum lycopersicum
/ statistical models
/ tomato
/ tomatoes
/ training population
2020
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Do you wish to request the book?
Accelerating Tomato Breeding by Exploiting Genomic Selection Approaches
by
Andolfo, Giuseppe
, Ercolano, Maria Raffaella
, Cappetta, Elisa
, Frusciante, Luigi
, Barone, Amalia
, Di Matteo, Antonio
in
backcrossing
/ breeding programs
/ breeding value
/ computer science
/ genes
/ genetic breeding value
/ genetic improvement
/ genomics
/ genotype
/ genotyping
/ marker effect
/ marker-assisted selection
/ phenotype
/ phenotyping
/ plant breeding
/ population size
/ prediction
/ Review
/ Solanum lycopersicum
/ statistical models
/ tomato
/ tomatoes
/ training population
2020
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Accelerating Tomato Breeding by Exploiting Genomic Selection Approaches
Journal Article
Accelerating Tomato Breeding by Exploiting Genomic Selection Approaches
2020
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Overview
Genomic selection (GS) is a predictive approach that was built up to increase the rate of genetic gain per unit of time and reduce the generation interval by utilizing genome-wide markers in breeding programs. It has emerged as a valuable method for improving complex traits that are controlled by many genes with small effects. GS enables the prediction of the breeding value of candidate genotypes for selection. In this work, we address important issues related to GS and its implementation in the plant context with special emphasis on tomato breeding. Genomic constraints and critical parameters affecting the accuracy of prediction such as the number of markers, statistical model, phenotyping and complexity of trait, training population size and composition should be carefully evaluated. The comparison of GS approaches for facilitating the selection of tomato superior genotypes during breeding programs is also discussed. GS applied to tomato breeding has already been shown to be feasible. We illustrated how GS can improve the rate of gain in elite line selection, and descendent and backcross schemes. The GS schemes have begun to be delineated and computer science can provide support for future selection strategies. A new promising breeding framework is beginning to emerge for optimizing tomato improvement procedures.
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