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Article|22 Aug 2025|OPEN
Decoding hybrid origins and genetic architecture of leaf traits variation in camellia via high-density 21K SNP array for genomic prediction
Jiayu Li1,2 , Yixuan Luo1 , Rui Zhang1,2 and Xinchun Li3 , Hongwei Pan1 , , Hengfu Yin,1 ,
1State Key Laboratory of Tree Genetics and Breeding, Zhejiang Key Laboratory of Forest Genetics and Breeding, Research Institute of Subtropical Forestry, Chinese Academy of Forestry, Hancgzhou, Zhejiang Province 311400, China
2College of Landscape and Tourism, Hebei Agricultural University, Baoding, Hebei 071000, China
3Tianjin GenoAgri Company Co., Ltd, Tianjin 300385, China
*Corresponding author. E-mail: cafpanhw@126.com,hfyin@caf.ac.cn

Horticulture Research 12,
Article number: uhaf221 (2025)
doi: https://doi.org/10.1093/hr/uhaf221
Views: 196

Received: 18 May 2025
Accepted: 10 Aug 2025
Published online: 22 Aug 2025

Abstract

The domestication of ornamental plants is primarily driven by aesthetic values and usually involves frequent hybridization events. Camellia spp., a globally famous woody flower, exemplifies the complex origins and extensive phenotypic variation. Here, based on the whole genome resequencing 220 germplasms, we developed Camellia21K, a high-density SNP array enabling cost-effective genome-wide genotyping. We demonstrated that Camellia21K accurately resolves 69 cultivars with complex hybridization histories. For molecular identification of closely related varieties, we developed a set of fingerprinting SNPs to support variety discrimination. To dissect the genomic basis of ornamental traits, we performed a genome-wide association study (GWAS) analysis of five leaf shape traits using the Camellia21K array and screened 31 SNP loci significantly associated with the traits. Further, by analyzing the genotypes of the SNP loci and the haplotypes of the surrounding segments, we identified potential genes regulating leaf tip length, thus demonstrating the versatility of the array. To enhance breeding efficiency, we evaluated and optimized four genomic selection (GS) models for leaf trait prediction. We found that the number of SNPs and model selection significantly affected prediction performance, with optimal predictive accuracy (PC) from 0.362 to 0.542, which was positively correlated with heritability. Finally, we integrated fixed-effects SNPs from GWAS and found significant enhancement of PC (24.7%–64.7%), indicating that the combination of GWAS and GS is indispensable for precision breeding applications. We demonstrated that Camellia21K is effective in discriminating the origin of varieties, in genetic analysis of traits and in genomic prediction, and thus informative for crop breeding.