南川 舞

Abstract Vegetative and reproductive growth in fruit trees is interconnected, and analyzing this relationship can provide valuable insights into fruit quality. However, characterizing vegetative growth through growth models is challenging because of the difficulty in obtaining longitudinal data, given the slow growth rate. In breeding fields, in contrast, seedlings of different ages are planted, allowing for simultaneous measurements that yield a dataset resembling longitudinal data with missing values --termed “fragmented longitudinal data.” Because longitudinal data are obtained from a single measurement, they can potentially shorten the period required for growth curve estimation. Bayesian nonlinear models offer advantages in estimating curves from incomplete data. In this study, we generated fragmented longitudinal data using genome data with 45,929 markers from 624 citrus hybrid seedlings and applied a Bayesian nonlinear model to explore its potential. We also incorporated genomic information into the model to assess the impact of the estimation accuracy. Our simulations indicated that the Bayesian nonlinear model’s ability to interpolate missing values significantly improved the estimation performance. At best, the mean square error of the parameter characterizing the later growth stage was reduced by 84.3 mm². Although the improvement from incorporating genomic information was modest, it still surpassed models that lacked genomic data. We also predicted the curves of untested individuals using the estimated parameters. Although the prediction accuracy of each parameter measured by the correlation coefficient was lower than 0.5, one parameter consistently showed a better accuracy. Further research is required to reveal the advantages of integrating genomic data for better predictions.

Abstract With advances in next-generation sequencing technologies, various marker genotyping systems have been developed for genomics-based approaches such as genomic selection (GS) and genome-wide association study (GWAS). As new genotyping platforms are developed, data from different genotyping platforms must be combined. However, the potential use of combined data for GS and GWAS has not yet been clarified. In this study, the accuracy of genomic prediction (GP) and the detection power of GWAS increased for most fruit quality traits of apples when using combined data from different genotyping systems, Illumina Infinium single-nucleotide polymorphism array and genotyping by random amplicon sequencing-direct (GRAS-Di) systems. In addition, the GP model, which considered the inbreeding effect, further improved the accuracy of the seven fruit traits. Runs of homozygosity (ROH) islands overlapped with the significantly associated regions detected by the GWAS for several fruit traits. Breeders may have exploited these regions to select promising apples by breeders, increasing homozygosity. These results suggest that combining genotypic data from different genotyping platforms benefits the GS and GWAS of fruit quality traits in apples. Information on inbreeding could be beneficial for improving the accuracy of GS for fruit traits of apples; however, further analysis is required to elucidate the relationship between the fruit traits and inbreeding depression (e.g. decreased vigor).