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Genomic prediction across years in a maize doubled haploid breeding program to accelerate early-stage testcross testing

文献类型： 外文期刊

作者： Wang, Nan 1 ; Wang, Hui 2 ; Zhang, Ao 5 ; Liu, Yubo 5 ; Yu, Diansi 2 ; Hao, Zhuanfang 1 ; Ilut, Dan 6 ; Glaubitz, Jeffrey 1 ;

作者机构： 1.Chinese Acad Agr Sci, Inst Crop Sci, Beijing, Peoples R China

2.Int Maize & Wheat Improvement Ctr CIMMYT, Texcoco, Mexico

3.Shanghai Acad Agr Sci, CIMMYT China Specialty Maize Res Ctr, Shanghai, Peoples R China

4.Shanghai Acad Agr Sci, Crop Breeding & Cultivat Res Inst, Shanghai, Peoples R China

5.Shenyang Agr Univ, Coll Biosci & Biotechnol, Shenyang, Liaoning, Peoples R China

6.Cornell Univ, Sch Integrat Plant Sci, Plant Breeding & Genet Sect, Ithaca, NY USA

7.Cornell Univ, Inst Biotechnol, Ithaca, NY USA

8.Int Maize & Wheat Improvement Ctr CIMMYT, POB 1041, Nairobi, Kenya

9.Colegio Postgrad, Texcoco, Estado De Mexic, Mexico

期刊名称：THEORETICAL AND APPLIED GENETICS （ 影响因子：5.699； 五年影响因子：5.565 ）

ISSN： 0040-5752

年卷期： 2020 年 133 卷 10 期

页码：

收录情况： SCI

摘要： Key message Genomic selection with a multiple-year training population dataset could accelerate early-stage testcross testing by skipping the first-stage yield testing, which significantly saves the time and cost of early-stage testcross testing. With the development of doubled haploid (DH) technology, the main task for a maize breeder is to estimate the breeding values of thousands of DH lines annually. In early-stage testcross testing, genomic selection (GS) offers the opportunity of replacing expensive multiple-environment phenotyping and phenotypic selection with lower-cost genotyping and genomic estimated breeding value (GEBV)-based selection. In the present study, a total of 1528 maize DH lines, phenotyped in multiple-environment trials in three consecutive years and genotyped with a low-cost per-sample genotyping platform of rAmpSeq, were used to explore how to implement GS to accelerate early-stage testcross testing. Results showed that the average prediction accuracy estimated from the cross-validation schemes was above 0.60 across all the scenarios. The average prediction accuracies estimated from the independent validation schemes ranged from 0.23 to 0.32 across all the scenarios, when the one-year datasets were used as training population (TRN) to predict the other year data as testing population (TST). The average prediction accuracies increased to a range from 0.31 to 0.42 across all the scenarios, when the two-years datasets were used as TRN. The prediction accuracies increased to a range from 0.50 to 0.56, when the TRN consisted of two-years of breeding data and 50% of third year's data converted from TST to TRN. This information showed that GS with a multiple-year TRN set offers the opportunity to accelerate early-stage testcross testing by skipping the first-stage yield testing, which significantly saves the time and cost of early-stage testcross testing.

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