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Genomic prediction using composite training sets is an effective method for exploiting germplasm conserved in rice gene banks

文献类型： 外文期刊

作者： He, Sang 1 ; Liu, Hongyan 4 ; Zhan, Junhui 1 ; Meng, Yun 1 ; Wang, Yamei 1 ; Wang, Feng 3 ; Ye, Guoyou 1 ;

作者机构： 1.Chinese Acad Agr Sci, Agr Genom Inst Shenzhen, CAAS IRRI Joint Lab Genom Assisted Germplasm Enhan, Shenzhen 518120, Guangdong, Peoples R China

2.Int Rice Res Inst, Rice Breeding Innovat Platform, DAPO Box 7777, Manila, Philippines

3.Guangdong Acad Agr Sci, Rice Res Inst, Guangdong Prov Key Lab New Technol Rice Breeding, Guangzhou 510640, Guangdong, Peoples R China

4.Hainan Univ, Coll Trop Crops, Hainan Key Lab Sustainable Utilizat Trop Bioresour, Haikou 570228, Hainan, Peoples R China

关键词： Genomic prediction; Composite training set; Rice germplasm; Gene bank; Reliability criterion

期刊名称：CROP JOURNAL （ 影响因子：4.647； 五年影响因子：5.781 ）

ISSN： 2095-5421

年卷期： 2022 年 10 卷 4 期

页码：

收录情况： SCI

摘要： Germplasm conserved in gene banks is underutilized, owing mainly to the cost of characterization. Genomic prediction can be applied to predict the genetic merit of germplasm. Germplasm utilization could be greatly accelerated if prediction accuracy were sufficiently high with a training population of practical size. Large-scale resequencing projects in rice have generated high quality genome-wide variation information for many diverse accessions, making it possible to investigate the potential of genomic prediction in rice germplasm management and exploitation. We phenotyped six traits in nearly 2000 indica (XI) and japonica (GJ) accessions from the Rice 3K project and investigated different scenarios for forming training populations. A composite core training set was considered in two levels which targets used for prediction of subpopulations within subspecies or prediction across subspecies. Composite training sets incorporating 400 or 200 accessions from either subpopulation of XI or GJ showed satisfactory prediction accuracy. A composite training set of 600 XI and GJ accessions showed sufficiently high prediction accuracy for both XI and GJ subspecies. Comparable or even higher prediction accuracy was observed for the composite training set than for the corresponding homogeneous training sets comprising accessions only of specific subpopulations of XI or GJ (within-subspecies level) or pure XI or GJ accessions (across-subspecies level) that were included in the composite training set. Validation using an independent population of 281 rice cultivars supported the predictive ability of the composite training set. Reliability, which reflects the robustness of a training set, was markedly higher for the composite training set than for the corresponding homogeneous training sets. A core training set formed from diverse accessions could accurately predict the genetic merit of rice germplasm.(c) 2022 Crop Science Society of China and Institute of Crop Science, CAAS. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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