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Phenotype–genotype integration Using j<i>edae-unshiu</i> reference SNPs for citrus line characterization
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Citrus breeding is challenged by seedlessness, polyembryony, and high heterozygosity, making it difficult to evaluate genetic variation and phenotypic diversity across lines. Jedae-unshiu is a γ-irradiation–derived mutant known for its unique peel morphology and fruit-quality traits. Although previous genomic studies have characterized its SNP and InDel variation, an integrated phenotype–genotype comparison across multiple citrus lines has not yet been performed.
In this study, we analyzed phenotypic traits and whole-genome SNP variation from seven citrus lines, including Jedae-unshiu, to investigate line-level relationships. Phenotypic measurements collected from 2022 to 2024—covering size, color, peel thickness, sugar content, acidity, and hardness—were normalized to remove annual effects. Principal component analysis and clustering (hierarchical and k-means) revealed three major phenotype-driven clusters. PCA loadings indicated that color traits (L, a, b), acidity, and hardness were the primary factors driving the cluster containing Jedae-unshiu, Araunshiu, and 6b4-16, with sugar content contributing more weakly.
Genotype analysis was performed using a pre-filtered SNP set (~660k variants) and IUPAC-encoded genotypes. IBS distances calculated with Jedae-unshiu as the reference showed that Araunshiu, 6b4-16, and Satsuma were relatively close genetically, reflecting a pattern broadly consistent with phenotype-based clustering.
These findings demonstrate that phenotypic similarity and genomic proximity converge to reveal underlying structure among citrus lines. The agreement between trait-driven clusters and IBS-based distances suggests that some of the 160 Jedae-unshiu–specific SNP markers may be associated with key fruit-quality traits such as acidity, sugar content, or color characteristics. This integrated framework provides a foundation for identifying candidate markers and supporting data-driven citrus breeding strategies.
Title: Phenotype–genotype integration Using j<i>edae-unshiu</i> reference SNPs for citrus line characterization
Description:
Citrus breeding is challenged by seedlessness, polyembryony, and high heterozygosity, making it difficult to evaluate genetic variation and phenotypic diversity across lines.
Jedae-unshiu is a γ-irradiation–derived mutant known for its unique peel morphology and fruit-quality traits.
Although previous genomic studies have characterized its SNP and InDel variation, an integrated phenotype–genotype comparison across multiple citrus lines has not yet been performed.
In this study, we analyzed phenotypic traits and whole-genome SNP variation from seven citrus lines, including Jedae-unshiu, to investigate line-level relationships.
Phenotypic measurements collected from 2022 to 2024—covering size, color, peel thickness, sugar content, acidity, and hardness—were normalized to remove annual effects.
Principal component analysis and clustering (hierarchical and k-means) revealed three major phenotype-driven clusters.
PCA loadings indicated that color traits (L, a, b), acidity, and hardness were the primary factors driving the cluster containing Jedae-unshiu, Araunshiu, and 6b4-16, with sugar content contributing more weakly.
Genotype analysis was performed using a pre-filtered SNP set (~660k variants) and IUPAC-encoded genotypes.
IBS distances calculated with Jedae-unshiu as the reference showed that Araunshiu, 6b4-16, and Satsuma were relatively close genetically, reflecting a pattern broadly consistent with phenotype-based clustering.
These findings demonstrate that phenotypic similarity and genomic proximity converge to reveal underlying structure among citrus lines.
The agreement between trait-driven clusters and IBS-based distances suggests that some of the 160 Jedae-unshiu–specific SNP markers may be associated with key fruit-quality traits such as acidity, sugar content, or color characteristics.
This integrated framework provides a foundation for identifying candidate markers and supporting data-driven citrus breeding strategies.
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