Single-step genomic prediction of fruit-quality traits using phenotypic records of non-genotyped relatives in citrus.

The potential of genomic selection (GS) is currently being evaluated for fruit breeding. GS models are usually constructed based on information from both the genotype and phenotype of population. However, information from phenotyped but non-genotyped relatives can also be used to construct GS models, and this additional information can improve their accuracy. In the present study, we evaluated the utility of single-step genomic best linear unbiased prediction (ssGBLUP) in citrus breeding, which is a genomic prediction method that combines the kinship information from genotyped and non-genotyped relatives into a single relationship matrix for a mixed model to apply GS. Fruit weight, sugar content, and acid content of 1,935 citrus individuals, of which 483 had genotype data of 2,354 genome-wide single nucleotide polymorphisms, were evaluated from 2009-2012. The prediction accuracy of ssGBLUP for genotyped individuals was similar to or higher than that of usual genomic best linear unbiased prediction method using only genotyped individuals, especially for sugar content. Therefore, ssGBLUP could yield higher accuracy in genotyped individuals by adding information from non-genotyped relatives. The prediction accuracy of ssGBLUP for non-genotyped individuals was also slightly higher than that of conventional best linear unbiased prediction method using pedigree information. This indicates that ssGBLUP can enhance prediction accuracy of breeding values for non-genotyped individuals using genomic information of genotyped relatives. These results demonstrate the potential of ssGBLUP for fruit breeding, including citrus.

Predicting segregation of multiple fruit-quality traits by using accumulated phenotypic records in citrus breeding.

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.

Quantification of carotenoids in citrus fruit by LC-MS and comparison of patterns of seasonal changes for carotenoids among citrus varieties.

To quantify the 18 carotenoids on the basic routes of the carotenoid biosynthesis in plants simultaneously, a method for liquid chromatography-mass spectrometry (LC-MS) using atmospheric pressure chemical ionization was developed. With this method, the seasonal changes of carotenoids in the flavedo and juice sacs of 39 citrus varieties were analyzed. On the basis of the patterns of seasonal changes of carotenoids in both flavedo and juice sacs, 39 citrus varieties were classified. In flavedo, 39 varieties were classified into 5 clusters, in which the carotenoid profiles were carotenoid-poor, phytoene-abundant, violaxanthin-abundant, violaxanthin- and beta-cryptoxanthin-abundant, and phytoene-, violaxanthin-, and beta-cryptoxanthin-abundant, respectively. In juice sacs, they were classified into 4 clusters, in which the carotenoid profiles were carotenoid-poor, violaxanthin-abundant, violaxanthin- and phytoene-abundant, and violaxanthin-, phytoene-, and beta-cryptoxanthin-abundant, respectively. In flavedo, many citrus varieties, except for the carotenoid-poor and phytoene-abundant varieties, massively accumulated beta,epsilon-carotenoids (e.g., lutein), beta,beta-carotenoids (e.g., beta-cryptoxanthin and violaxanthin), and phytoene, in that order. In juice sacs, the accumulation order among beta,beta-carotenoids was observed. Violaxanthin accumulation preceded beta-cryptoxanthin accumulation in violaxanthin-, phytoene-, and beta-cryptoxanthin-abundant varieties. In each variety, the carotenoid profiles of the flavedo and juice sacs on the basis of the concentration in violaxanthin and beta-cryptoxanthin were similar, with the exception of a few varieties.