Author
Listed:
- Atsushi Imai
- Takeshi Kuniga
- Terutaka Yoshioka
- Keisuke Nonaka
- Nobuhito Mitani
- Hiroshi Fukamachi
- Naofumi Hiehata
- Masashi Yamamoto
- Takeshi Hayashi
Abstract
In the breeding of citrus (Citrus spp.), suitable fruit quality is essential for consumer acceptance of new cultivars. To identify parental combinations producing F1 progeny with fruit-quality traits exceeding certain selection criteria, we developed a simple and practical method for predicting multiple-trait segregation in an F1 progeny population. This method uses breeding values of parental genotypes and an additive genetic (co)variance matrix calculated by the best linear unbiased prediction method to construct a model for trait segregation in F1 progeny. To confirm the validity of our proposed method, we calculated the breeding values and additive genetic (co)variances based on phenotypic records on nine fruit-quality traits in 2122 genotypes, and constructed a trait segregation model. Subsequently, we applied the trait segregation model to all pairs of the 2122 genotypes (i.e., 2,252,503 combinations), and predicted the most promising combinations and evaluated their probabilities of producing superior genotypes exceeding the nine fruit-quality traits of satsuma mandarin (Citrus unshiu Marcow.) or ‘Shiranuhi’ (‘Kiyomi’ × ‘Nakano No. 3’ ponkan), two popular citrus cultivars in Japan. We consider these results to be useful not only for selecting good parental combinations for fruit quality or other important traits but also for determining the scale of breeding programs required to achieve specific breeding goals.
Suggested Citation
Atsushi Imai & Takeshi Kuniga & Terutaka Yoshioka & Keisuke Nonaka & Nobuhito Mitani & Hiroshi Fukamachi & Naofumi Hiehata & Masashi Yamamoto & Takeshi Hayashi, 2018.
"Predicting segregation of multiple fruit-quality traits by using accumulated phenotypic records in citrus breeding,"
PLOS ONE, Public Library of Science, vol. 13(8), pages 1-13, August.
Handle:
RePEc:plo:pone00:0202341
DOI: 10.1371/journal.pone.0202341
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