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Efficiency of genomic selection for tomato fruit quality

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Abstract

Fruit quality is polygenic; each component has variable heritability and is difficult to assess. Genomic selection, which allows the prediction of phenotypes based on the whole-genome genotype, could vastly help to improve fruit quality. The goal of this study is to evaluate the accuracy of genomic selection for several metabolomic and quality traits by cross-validation and to estimate the impact of different factors on its accuracy. We analyzed data from 45 phenotypic traits and genotypic data obtained from a previous study of genetic association on a collection of 163 tomato accessions. We tested the influence of (1) the size of training population, (2) the number and density of molecular markers and (3) individual relatedness on the accuracy of prediction. The prediction accuracy of phenotypic values was largely related to the heritability of the traits. The size of training population increased the accuracy of predictions. Using 122 accessions and 5995 single nucleotide polymorphisms (SNPs) was the optimal condition. The density of markers and their numbers also affected the accuracy of the prediction. Using 2313 SNP markers distributed 0.1 cM or more apart from each other reduced the accuracy of prediction, and no gain in prediction accuracy was found when more markers were used in the model. Additionally, the more accessions were related, the more accurate were the predictions. Finally, the structure of the population negatively affected the prediction accuracy. In conclusion, the results obtained by cross-validation illustrated the effect of several parameters on the accuracy of prediction and revealed the potential of genomic selection in tomato breeding programs.

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Acknowledgments

We gratefully acknowledge the support for JD from the Embassy of France in Thailand in Junior Research Fellowship Program 2014. The INRA SelGen Méta-programme also funded this work (http://www.selgen.inra.fr/en). The authors are indebted to Yolande Carretero and Renaud Duboscq from INRA UR1052 who provided the biological material and performed the molecular work required. Finally, we would like to thank David Cros (CIRAD, Montpellier, France) for his help and advice on our work, as well as Marie Christine Le Paslier and Dominque Brunel from INRA-EPGV for their help with the SNP genotyping.

Authors’ contributions

MC and CS designed the study. JD and CS analyzed the data, and JD, MC, and CS wrote the manuscript. All authors read and approved the final version.

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Authors and Affiliations

  1. Department of Horticulture, Faculty of Agriculture, Kasetsart University, Bangkok, 10900, Thailand

    Janejira Duangjit

  2. INRA, UR1052 GAFL, Génétique et Amélioration des Fruits et Légumes, 67 allée des chênes, CS60094, 84143, Montfavet Cedex, France

    Mathilde Causse & Christopher Sauvage

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  1. Janejira Duangjit
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  2. Mathilde Causse
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  3. Christopher Sauvage
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Correspondence to Christopher Sauvage.

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Duangjit, J., Causse, M. & Sauvage, C. Efficiency of genomic selection for tomato fruit quality. Mol Breeding 36, 29 (2016). https://doi.org/10.1007/s11032-016-0453-3

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