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Development of the rubber tree 40K breeding chip with applications in genetic study and breeding prediction

文献类型： 外文期刊

作者： Wang, Xiaobo 1 ; Deng, Zhi 1 ; Hu, Yanshi 1 ; Rehman, Fazal 1 ; An, Zewei 1 ; Wu, Tingkai 1 ; Yuan, Yuan 1 ; Qiang, Runrun 1 ; Wu, Wenguan 1 ; Zeng, Xia 1 ; Cheng, Han 1 ;

作者机构： 1.Chinese Acad Trop Agr Sci, Rubber Res Inst, Natl Key Lab Trop Crop Breeding, Haikou 571101, Hainan, Peoples R China

2.Chinese Acad Trop Agr Sci, Sanya Res Inst, Sanya 572024, Hainan, Peoples R China

3.Chinese Acad Trop Agr Sci, Rubber Res Inst, Key Lab Biol & Genet Resources Rubber Tree, Minist Agr & Rural Affairs, Haikou 571101, Hainan, Peoples R China

4.Rubber Res Inst, State Key Lab Incubat Base Cultivat & Physiol Trop, Haikou 571101, Hainan, Peoples R China

关键词： Hevea brasiliensis; Breeding chip; Genomic selection; GWAS; Breeding prediction

期刊名称：INDUSTRIAL CROPS AND PRODUCTS （ 影响因子：6.2； 五年影响因子：6.2 ）

ISSN： 0926-6690

年卷期： 2025 年 226 卷

页码：

收录情况： SCI

摘要： As an important industrial crop, rubber tree (Hevea brasiliensis) provides the source of natural rubber globally. However, rubber tree breeding is confronted with the challenge of long breeding cycles and corresponding time, land, and labor costs. In this study, we developed a novel high-density breeding chip for rubber tree using genotyping by target sequencing (GBTS) technology, comprising approximately 40 K markers. The developed breeding chip was used to genotype 223 rubber tree clones. Results showed a prevalent low genetic diversity among the rubber tree clones, indicating a limited genetic foundation. Subsequently, markers associated with girth growth rate were identified through genome-wide association study (GWAS). Based on the GWAS results, seven genomic selection (GS) models were used to assess the prediction accuracy of different associated marker datasets for girth growth rate. The Bayesian least absolute shrinkage and selection operator (BayesLASSO), using the marker dataset with ap-value < 0.001, showed the highest prediction accuracy of 0.59, thus being selected as the optimal GS model and genotype dataset for breeding prediction. Fifty superior parental combinations were selected by usefulness criteria calculated based on genome estimated breeding values of simulated F-1 progenies. This study developed a reliable breeding chip applicable in genetic study and breeding prediction and provided a strategy for molecular breeding of rubber trees by determining suitable parental combinations without the need for extensive field trials.

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