Genetic diversity of loquat (Eriobotrya japonica) revealed using RAD-Seq SNP markers.

Loquat (Eriobotrya japonica) have originated in southeastern China and spread as a cultivated plant worldwide. Many of the loquat genetic resources collected internationally are of unknown origin, and their genetic background requires clarification. This study analyzed the genetic diversity of 95 accessions by using Rad-Seq SNP markers. Data analysis broadly classified loquat into three groups: (1) Japanese and Chinese cultivars and some Japanese strains (wild plants that are not used for commercial cultivation), (2) Vietnamese, Israeli, Greek, USA, and Mexican cultivars and strains, and (3) other Japanese strains. Group 2 is cultivated mostly outside of East Asia and was clearly distinct from the other groups, indicating that varieties of unknown origin with genetic backgrounds different from those of Japanese and Chinese cultivars may have been introduced to Mediterranean countries and North America. Because Japanese and Chinese cultivars belong to group 1, the current Japanese cultivars are derived from genetic resources brought from China. Some of group 1 may have been introduced to Japan before excellent varieties were developed in China, while group 3 may have been indigenous to Japan that have not been introduced by human activities, or may have been brought to Japan by human activities from China.

Draft Genome Sequences of Three Strains of Pseudomonas syringae pv. eriobotryae, a Pathogen Causing Canker Disease in Loquat, Isolated in Japan.

The phytopathogenic bacterium Pseudomonas syringae pv. eriobotryae causes canker disease in loquat. Isolates from Japan are classified into three groups based on pathogenicity and pigment production. In this study, we report the draft genome sequences of three strains, one belonging to each of the three groups.

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.