Accelerating Tomato Breeding by Exploiting Genomic Selection Approaches
Genomic selection (GS) is a predictive approach that was built up to increase the rate of genetic gain <i>per</i> 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 a...
Main Authors: | , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-09-01
|
Series: | Plants |
Subjects: | |
Online Access: | https://www.mdpi.com/2223-7747/9/9/1236 |
id |
doaj-68e92053eb56402d95124d8fa8168b8b |
---|---|
record_format |
Article |
spelling |
doaj-68e92053eb56402d95124d8fa8168b8b2020-11-25T03:27:37ZengMDPI AGPlants2223-77472020-09-0191236123610.3390/plants9091236Accelerating Tomato Breeding by Exploiting Genomic Selection ApproachesElisa Cappetta0Giuseppe Andolfo1Antonio Di Matteo2Amalia Barone3Luigi Frusciante4Maria Raffaella Ercolano5Department of Agricultural Sciences, University of Naples Federico II, Via Università 100, 80055 Naples, ItalyDepartment of Agricultural Sciences, University of Naples Federico II, Via Università 100, 80055 Naples, ItalyDepartment of Agricultural Sciences, University of Naples Federico II, Via Università 100, 80055 Naples, ItalyDepartment of Agricultural Sciences, University of Naples Federico II, Via Università 100, 80055 Naples, ItalyDepartment of Agricultural Sciences, University of Naples Federico II, Via Università 100, 80055 Naples, ItalyDepartment of Agricultural Sciences, University of Naples Federico II, Via Università 100, 80055 Naples, ItalyGenomic selection (GS) is a predictive approach that was built up to increase the rate of genetic gain <i>per</i> 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.https://www.mdpi.com/2223-7747/9/9/1236tomatogenetic breeding valuetraining populationgenotypingmarker effectphenotyping |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Elisa Cappetta Giuseppe Andolfo Antonio Di Matteo Amalia Barone Luigi Frusciante Maria Raffaella Ercolano |
spellingShingle |
Elisa Cappetta Giuseppe Andolfo Antonio Di Matteo Amalia Barone Luigi Frusciante Maria Raffaella Ercolano Accelerating Tomato Breeding by Exploiting Genomic Selection Approaches Plants tomato genetic breeding value training population genotyping marker effect phenotyping |
author_facet |
Elisa Cappetta Giuseppe Andolfo Antonio Di Matteo Amalia Barone Luigi Frusciante Maria Raffaella Ercolano |
author_sort |
Elisa Cappetta |
title |
Accelerating Tomato Breeding by Exploiting Genomic Selection Approaches |
title_short |
Accelerating Tomato Breeding by Exploiting Genomic Selection Approaches |
title_full |
Accelerating Tomato Breeding by Exploiting Genomic Selection Approaches |
title_fullStr |
Accelerating Tomato Breeding by Exploiting Genomic Selection Approaches |
title_full_unstemmed |
Accelerating Tomato Breeding by Exploiting Genomic Selection Approaches |
title_sort |
accelerating tomato breeding by exploiting genomic selection approaches |
publisher |
MDPI AG |
series |
Plants |
issn |
2223-7747 |
publishDate |
2020-09-01 |
description |
Genomic selection (GS) is a predictive approach that was built up to increase the rate of genetic gain <i>per</i> 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. |
topic |
tomato genetic breeding value training population genotyping marker effect phenotyping |
url |
https://www.mdpi.com/2223-7747/9/9/1236 |
work_keys_str_mv |
AT elisacappetta acceleratingtomatobreedingbyexploitinggenomicselectionapproaches AT giuseppeandolfo acceleratingtomatobreedingbyexploitinggenomicselectionapproaches AT antoniodimatteo acceleratingtomatobreedingbyexploitinggenomicselectionapproaches AT amaliabarone acceleratingtomatobreedingbyexploitinggenomicselectionapproaches AT luigifrusciante acceleratingtomatobreedingbyexploitinggenomicselectionapproaches AT mariaraffaellaercolano acceleratingtomatobreedingbyexploitinggenomicselectionapproaches |
_version_ |
1724588019193741312 |