The National Agriculture and Food Research Organization (NARO) is advancing “Citrus Breeding 2.0” to produce diverse, high-quality hybrid citrus cultivars more efficiently by integrating genomic prediction, genome-wide association studies (GWAS), and pedigree data. Reduced representation sequencing (RRS) methods, such as RAD-Seq, ddRAD-Seq, and GRAS-Di, facilitate large-scale, cost-effective genotyping; however, variable loci hinder cross-platform comparisons, limiting model reuse and GWAS follow-up. Therefore, we developed an Augmented Estimation of Unified Genotype (AEUG) workflow that converts RRS-derived genotypes into a unified set of predefined loci using a whole-genome resequencing reference panel that shares a common haplotype with target populations. Although Beagle-based whole-genome imputation achieved only 61.3–83.6% accuracy, genomic prediction for 17 fruit traits remained virtually unchanged after conversion, demonstrating the robustness of the workflow. The alignment of loci with ancestry informative markers for four pure citrus species also enabled the estimation of the ancestral origin of the trait-associated genomic regions. The AEUG workflow facilitates the integration and reuse of heterogeneous genotype datasets, enhances prediction accuracy, and enables ancestry-informed GWAS interpretation to accelerate citrus genomic breeding.
Kitajima et al. (Thu,) studied this question.