Genomic Prediction of Combining Ability for Agronomic Traits in Rice Based on NCII Design

doi:10.16819/j.1001-7216.2019.9025

Abstract

Abstract:

【Objective】It plays a key role in hybrid rice breeding to select hybrids with high specific combining ability based on high general combining ability of parental inbred lines. Genomic selection that is based on molecular markers across the whole genome and phenotypes of samples enable us to establish prediction models and achieve more reliable selection of varieties. 【Method】We investigated the genomic predictive ability of combining ability for agronomic traits in rice based on NCII design. And the effects of different training population construction methods on predictive ability of hybrid performance were compared. 【Result】The predictive abilities of general combining ability for eight agronomic traits, ranged from 0.3888 to 0.7367, were dominated by their heritability. The predictive ability of specific combining ability for hybrids was lower, but the ability of directly predicting phenotypes for hybrids was higher. 【Conclusion】The genomic prediction of combining ability for rice parental lines is effective and can help breeders to select parents effectively. With regard to hybrid selection, direct predicting phenotypes of hybrids is the most effective method. At this time, allowing more parents to participate in the crosses for hybrid training set in a balanced way is benefical to obtain higher predictive ability.

Key words: rice, incomplete diallel cross, phenotype, combining ability, genomic prediction

摘要：

【目的】在亲本一般配合力的基础上优选特殊配合力高的杂交种,是水稻杂种育种的关键。基因组选择基于覆盖全基因组的分子标记和样本的表型数据建立预测模型,实现对品种更加可靠的选择。【方法】本研究利用一组基于不完全双列杂交(NCII设计)的水稻数据集,考查其多个农艺性状配合力的基因组预测能力。并比较了不同训练群体构建方法对杂交种表型预测能力的影响。【结果】8个农艺性状一般配合力的预测能力由其遗传率主导,从0.3888到0.7367。杂交种特殊配合力的预测能力较低,但是直接预测杂交种的表型可以获得较高的预测能力。【结论】基因组预测水稻亲本一般配合力是有效的,能够帮助育种家实现对亲本的科学选择。如果要选配杂交种,直接预测杂交种表型是最有效的手段。此时让更多的亲本均衡地参与杂交种训练集的组配,有利于获得更高的预测能力。

关键词: 水稻, 不完全双列杂交, 表型, 配合力, 基因组预测

CLC Number:

S511.032

Xin WANG, Ying MA, Zhongli HU, Chenwu XU. Genomic Prediction of Combining Ability for Agronomic Traits in Rice Based on NCII Design[J]. Chinese Journal OF Rice Science, 2019, 33(4): 331-337.

王欣, 马莹, 胡中立, 徐辰武. 基于不完全双列杂交设计的水稻农艺性状配合力基因组预测[J]. 中国水稻科学, 2019, 33(4): 331-337.

Figures/Tables 6

References 31

[1]	Meuwissen T H, Hayes B J, Goddard M E.Prediction of total genetic value using genome-wide dense marker maps.Genetics, 2001, 157(4): 1819-1829.
[2]	Lande R, Thompson R.Efficiency of marker-assisted selection in the improvement of quantitative traits.Genetics, 1990, 124(3): 743-756.
[3]	王欣, 孙辉, 胡中立, 徐辰武. 基因组选择方法研究进展. 扬州大学学报: 农业与生命科学版, 2018, 39(1): 64-70.
	Wang X, Sun H, Hu Z L, Xu C W.The research progress of genomic selection methods.J Yangzhou Univ: Agric& Life Sci Ed, 2018, 39(1): 64-70.
[4]	Heslot N, Yang H-P, Sorrells M E, Jannink J-L.Genomic selection in plant breeding: A comparison of models.Crop Sci, 2012, 52(1): 146-160.
[5]	Lee Y-S, Kim H-J, Cho S, Kim H.The usage of an SNP-SNP relationship matrix for best linear unbiased prediction (BLUP) analysis using a community-based cohort study.Genom & Inform, 2014, 12(4): 254-260.
[6]	Wang X, Xu Y, Hu Z, Xu C.Genomic selection methods for crop improvement: Current status and prospects.Crop J, 2018, 6(4): 330-340.
[7]	Riedelsheimer C, Czedik-Eysenberg A, Grieder C, Lisec J, Technow F, Sulpice R, Altmann T, Stitt M, Willmitzer L, Melchinger A E.Genomic and metabolic prediction of complex heterotic traits in hybrid maize.Nat Genet, 2012, 44(2): 217-220.
[8]	Crossa J, Perez-Rodriguez P, Cuevas J, Montesinos- Lopez O, Jarquin D, de los Campos G, Burgueno J, Gonzalez-Camacho J M, Perez-Elizalde S, Beyene Y, Dreisigacker S, Singh R, Zhang X C, Gowda M, Roorkiwal M, Rutkoski J, Varshney R K. Genomic selection in plant breeding: Methods, models, and perspectives.Trends Plant Sci, 2017, 22(11): 961-975.
[9]	Guo T, Yang N, Tong H, Pan Q, Yang X, Tang J, Wang J, Li J, Yan J.Genetic basis of grain yield heterosis in an "immortalized F-2" maize population. Theor Appl Genet, 2014, 127(10): 2149-2158.
[10]	Da Y, Wang C, Wang S, Hu G.Mixed model methods for genomic prediction and variance component estimation of additive and dominance effects using SNP markers.PLoS One, 2014, 9(1): e87666.
[11]	Finley AO, Banerjee S, Waldmann P, Ericsson T.Hierarchical spatial modeling of additive and dominance genetic variance for large spatial trial datasets. Biometrics, 2009, 65(2): 441-451.
[12]	Gianola D, de los Campos G, Hill W G, Manfredi E, Fernando R. Additive genetic variability and the Bayesian alphabet.Genetics, 2009, 183(1): 347-363.
[13]	Ibanez-Escriche N, Fernando RL, Toosi A, Dekkers J C M. Genomic selection of purebreds for crossbred performance.Genet Select Evol, 2009, 41(1): 1-10.
[14]	Wang X, Li L, Yang Z, Zheng X, Yu S, Xu C, Hu Z.Predicting rice hybrid performance using univariate and multivariate GBLUP models based on North Carolina mating design II.Heredity, 2017, 118(3): 302-310.
[15]	Wang X, Yang Z, Xu C.A comparison of genomic selection methods for breeding value prediction.Sci Bull, 2015, 60(10): 925-935.
[16]	Zhang Z, Erbe M, He J, Ober U, Gao N, Zhang H, Simianer H, Li J.Accuracy of whole-genome prediction using a genetic architecture-enhanced variance- covariance matrix.Genes Genomes Genetics, 2015, 5(4): 615-627.
[17]	van Raden P M. Efficient methods to compute genomic predictions.J Dairy Sci, 2008, 91(11): 4414-4423.
[18]	Nishio M, Satoh M.Including dominance effects in the genomic BLUP method for genomic evaluation. PLoS One, 2014, 9(1): e85792.
[19]	Forni S, Aguilar I, Misztal I.Different genomic relationship matrices for single-step analysis using phenotypic, pedigree and genomic information. Genet Select Evol, 2011, 43(1): 43-53.
[20]	Gilmour A R, Thompson R, Cullis B R.Average information REML: An efficient algorithm for variance parameter estimation in linear mixed models.Biometrics, 1995, 51(4): 1440-1450.
[21]	Ashida I, Iwaisaki H.An expression for average information matrix for a mixed linear multi-component of variance model and REML iteration equations.Animal Sci J, 1999, 70(5): 282-289.
[22]	Xu S, Zhu D, Zhang Q.Predicting hybrid performance in rice using genomic best linear unbiased prediction.Proc Natl Acad Sci, 2014, 111(34): 12456-12461.
[23]	Dan Z, Chen Y, Xu Y, Huang J, Huang J, Hu J, Yao G, Zhu Y, Huang W. A metabolome-based core hybridization strategy for the prediction of rice grain weight across environments. Plant Biotechnol J, 2018, .
[24]	Goddard M.Genomic selection: prediction of accuracy and maximisation of long term response.Genetica, 2009, 136(2): 245-257.
[25]	Goddard M E, Hayes B J.Mapping genes for complex traits in domestic animals and their use in breeding programmes.Nat Rev Genet, 2009, 10(6): 381-391.
[26]	van Raden P, van Tassell C, Wiggans G, Sonstegard T, Schnabel R, Taylor J, Schenkel F. Invited review: Reliability of genomic predictions for North American Holstein bulls.J Dairy Sci, 2009, 92(1): 16-24.
[27]	Habier D, Tetens J, Seefried F-R, Lichtner P, Thaller G.The impact of genetic relationship information on genomic breeding values in German Holstein cattle.Genet Select Evol, 2010, 42(1): 5.
[28]	Riedelsheimer C, Endelman J B, Stange M, Sorrells M E, Jannink J L, Melchinger A E.Genomic predictability of interconnected biparental maize populations.Genetics, 2013, 194(2): 493-503.
[29]	Velez-Torres M, Garcia-Zavala J J, Hernandez-Rodriguez M, Lobato-Ortiz R, Lopez-Reynoso J J, Benitez- Riquelme I, Mejia-Contreras J A, Esquivel-Esquivel G, Molina-Galan J D, Perez-Rodriguez P, Zhang X C. Genomic prediction of the general combining ability of maize lines (Zea mays L.) and the performance of their single crosses. Plant Breeding, 2018, 137(3): 379-387.
[30]	Mao D, Liu T, Xu C, Li X, Xing Y.Epistasis and complementary gene action adequately account for the genetic bases of transgressive segregation of kilo-grain weight in rice.Euphytica, 2011, 180(2): 261-271.
[31]	钏兴宽. 配合力理论及其在水稻育种中的应用. 种子, 2014, 33(6): 39-41.
	Chuan X K, Combining ability theory and its application in rice breeding. Seed, 2014, 33(6): 39-41.

性状 Trait	遗传率 Heritability	5倍交叉验证 5-fold cross-validation	留一法 Leave-one method
单株产量Grain yield per plant (GY)	0.3876	0.3888	0.3583
千粒重Thousand-grain weight (TGW)	0.8321	0.7367	0.7549
有效穗数Productive panicle number per plant (PN)	0.4182	0.2310	0.1732
株高Plant height (PH)	0.8930	0.6112	0.6522
一次枝梗数Primary rachis branch number (PB)	0.7435	0.5120	0.4945
二次枝梗数Secondary rachis branch number (SB)	0.7602	0.5655	0.5840
主穗实粒数Grain number per panicle (GN)	0.6926	0.5183	0.5115
穗长Panicle length (PL)	0.6975	0.4697	0.4636

性状 Trait	遗传率 Heritability	5倍交叉验证 5-fold cross-validation	留一法 Leave-one method
单株产量Grain yield per plant (GY)	0.3876	0.3888	0.3583
千粒重Thousand-grain weight (TGW)	0.8321	0.7367	0.7549
有效穗数Productive panicle number per plant (PN)	0.4182	0.2310	0.1732
株高Plant height (PH)	0.8930	0.6112	0.6522
一次枝梗数Primary rachis branch number (PB)	0.7435	0.5120	0.4945
二次枝梗数Secondary rachis branch number (SB)	0.7602	0.5655	0.5840
主穗实粒数Grain number per panicle (GN)	0.6926	0.5183	0.5115
穗长Panicle length (PL)	0.6975	0.4697	0.4636

性状 Trait	SCA方法1 Method 1 for SCA	SCA方法2 Method 2 for SCA
单株产量Grain yield per plant (GY)	0.0655	0.2875
千粒重Thousand-grain weight (TGW)	0.1541	0.2198
有效穗数Productive panicle number per plant (PN)	0.0000	0.2528
株高Plant height (PH)	0.1505	0.1983
一次枝梗数Primary rachis branch number (PB)	0.1566	0.2495
二次枝梗数Secondary rachis branch number (SB)	0.1791	0.2747
主穗实粒数Grain number per panicle (GN)	0.1244	0.2520
穗长Panicle length (PL)	0.1042	0.2383

性状 Trait	SCA方法1 Method 1 for SCA	SCA方法2 Method 2 for SCA
单株产量Grain yield per plant (GY)	0.0655	0.2875
千粒重Thousand-grain weight (TGW)	0.1541	0.2198
有效穗数Productive panicle number per plant (PN)	0.0000	0.2528
株高Plant height (PH)	0.1505	0.1983
一次枝梗数Primary rachis branch number (PB)	0.1566	0.2495
二次枝梗数Secondary rachis branch number (SB)	0.1791	0.2747
主穗实粒数Grain number per panicle (GN)	0.1244	0.2520
穗长Panicle length (PL)	0.1042	0.2383

性状 Trait	完全随机分组 Random cross-validation	均匀随机分组 Uniform random cross-validation	横向随机分组 Horizontal random cross-validation	纵向随机分组 Vertical random cross-validation
单株产量Grain yield per plant (GY)	0.3947	0.4205	0.2615	0.4022
千粒重Thousand-grain weight (TGW)	0.8800	0.8863	0.7303	0.8704
有效穗数Productive panicle number per plant (PN)	0.4087	0.4299	0.2280	0.4184
株高Plant height (PH)	0.8637	0.8745	0.5738	0.8662
一次枝梗数Primary rachis branch number (PB)	0.6827	0.7015	0.4810	0.6398
二次枝梗数Secondary rachis branch number (SB)	0.7197	0.7349	0.4986	0.7070
主穗实粒数Grain number per panicle (GN)	0.6444	0.6636	0.4170	0.6529
穗长Panicle length (PL)	0.7939	0.7967	0.6976	0.6110