文献类型： 外文期刊

作者： Liu, Ce ¹ ; Liu, Xiaoxiao ¹ ; Han, Yike ² ; Wang, Xi'ao ¹ ; Ding, Yuanyuan ¹ ; Meng, Huanwen ¹ ; Cheng, Zhihui ¹ ;

作者机构： 1.Northwest A&F Univ, Coll Hort, Yangling, Shaanxi, Peoples R China

2.Tianjin Acad Agr Sci, Cucumber Res Inst, State Key Lab Vegetable Germplasm Innovat, Tianjin Key Lab Vegetable Breeding Enterprise, Tianjin, Peoples R China

关键词： cucumber breeding; genomic prediction; GBLUP; bayesian ridge regression; model validation

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：5.754； 五年影响因子：6.612 ）

ISSN： 1664-462X

年卷期： 2021 年 12 卷

页码：

收录情况： SCI

摘要： Genomic prediction is an effective way for predicting complex traits, and it is becoming more essential in horticultural crop breeding. In this study, we applied genomic prediction in the breeding of cucumber plants. Eighty-one cucumber inbred lines were genotyped and 16,662 markers were identified to represent the genetic background of cucumber. Two populations, namely, diallel cross population and North Carolina II population, having 268 combinations in total were constructed from 81 inbred lines. Twelve cucumber commercial traits of these two populations in autumn 2018, spring 2019, and spring 2020 were collected for model training. General combining ability (GCA) models under five-fold cross-validation and cross-population validation were applied to model validation. Finally, the GCA performance of 81 inbred lines was estimated. Our results showed that the predictive ability for 12 traits ranged from 0.38 to 0.95 under the cross-validation strategy and ranged from -0.38 to 0.88 under the cross-population strategy. Besides, GCA models containing non-additive effects had significantly better performance than the pure additive GCA model for most of the investigated traits. Furthermore, there were a relatively higher proportion of additive-by-additive genetic variance components estimated by the full GCA model, especially for lower heritability traits, but the proportion of dominant genetic variance components was relatively small and stable. Our findings concluded that a genomic prediction protocol based on the GCA model theoretical framework can be applied to cucumber breeding, and it can also provide a reference for the single-cross breeding system of other crops.