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Accelerating Tomato Breeding by Exploiting Genomic Selection Approaches.

Elisa Cappetta1, Giuseppe Andolfo1, Antonio Di Matteo1

  • 1Department of Agricultural Sciences, University of Naples Federico II, Via Università 100, 80055 Naples, Italy.

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Summary

Genomic selection (GS) accelerates genetic gain in plant breeding by using genome-wide markers. This study evaluates key factors for effective GS implementation in tomato breeding programs.

Keywords:
genetic breeding valuegenotypingmarker effectphenotypingselection schemestomatotraining population

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Area of Science:

  • Plant breeding
  • Genetics
  • Bioinformatics

Background:

  • Genomic selection (GS) is a predictive method utilizing genome-wide markers to enhance genetic gain and shorten generation intervals.
  • It is particularly valuable for complex traits controlled by numerous genes with small effects.
  • GS predicts the breeding value of genotypes, aiding selection in breeding programs.

Purpose of the Study:

  • To address critical issues and parameters affecting genomic selection accuracy in plant breeding.
  • To emphasize the implementation and optimization of GS in tomato breeding.
  • To compare different GS approaches for selecting superior tomato genotypes.

Main Methods:

  • Evaluation of genomic constraints and critical parameters influencing prediction accuracy (marker number, statistical models, phenotyping, trait complexity, training population size/composition).
  • Comparison of various GS approaches for facilitating selection in tomato breeding.
  • Illustration of GS's impact on elite line selection and breeding schemes (descendent, backcross).

Main Results:

  • Genomic selection has proven feasible and effective in tomato breeding.
  • GS can significantly improve the rate of genetic gain in elite line selection and other breeding schemes.
  • The study highlights the importance of carefully evaluating parameters for optimal GS performance.

Conclusions:

  • Genomic selection offers a promising framework for optimizing tomato improvement procedures.
  • Computer science can support future selection strategies within GS.
  • Careful consideration of genomic constraints and parameters is crucial for maximizing GS accuracy and effectiveness.