南川 舞

With the increasing ability to integrate pedigree and genomic data, it is essential to evaluate their potential to uncover valuable genetic insights that can drive the advancement of crop breeding and conservation of genetic diversity. Pedigree analysis remains a fundamental approach for investigating the inheritance of phenotypic traits, exploring evolutionary history, and understanding hybridization processes in crop plants. Among these approaches, gene drop simulations using pedigree and allele origin data enable the construction of genetic maps and provide insights into complex genetic backgrounds. In this study, we developed a new method to identify useful genetic regions associated with single-nucleotide polymorphism (SNP) markers based on gene drop simulations, focusing on 185 Japanese domestic apple cultivars. By performing 10 million gene drop simulations, we generated null distributions for each founder haplotype, which revealed SNP markers with significant frequency biases, which is a potential signal for selection. Frequency biases were identified in eight founder haplotypes that were particularly consistent with genome-wide association studies peaks associated with key fruit traits such as malic acid and fructose content. Gene Ontology enrichment analysis suggested that these SNPs are not only associated with fruit traits but may also play a role in critical biological functions, including stress tolerance and reproductive processes, highlighting their broader relevance to crop resilience. Our integrative approach, which combines founder haplotype analysis with extensive gene drop simulations, effectively detects selection pressure, provides new insights into the genetic basis of apple breeding, and identifies SNP markers with strong potential to improve breeding programs.

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.