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Chinese Bulletin of Botany >

2024 , Vol. 59 >Issue 6: 1041 - 1053

DOI: https://doi.org/10.11983/CBB24087

RESEARCH PAPERS

Study on Multi-environment Genome-wide Prediction of Inbred Agronomic Traits in Maize Natural Populations

  • Yuan Li ,
  • Kaijian Fan ,
  • Tai An ,
  • Cong Li ,
  • Junxia Jiang ,
  • Hao Niu ,
  • Weiwei Zeng ,
  • Yanfang Heng ,
  • Hu Li ,
  • Junjie Fu ,
  • Huihui Li ,
  • Liang Li
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  • 1State Key Laboratory of Crop Gene Resources and Breeding, The National Engineering Center for Crop Molecular Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
    2College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China
    3College of Agronomy, Heilongjiang Bayi Agricultural University, Daqing 163000, China

Received date: 2024-06-06

  Accepted date: 2024-10-14

  Online published: 2024-10-16

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Abstract

Multi-environment field testing is an important way to select optimize maize yield and yield stability varieties. However, because of its high cost, it has gradually become a challenge in plant breeding. The combination of field sparse testing and genome-wide prediction method can be used to predict untested phenotypes, reduced the effort and cost on field testing. In this experiment, 244 inbred lines of natural populations were planted in Shunyi, Beijing and Mishan, Heilongjiang in 2022 and 2023. Six agronomic traits were studied, including days to anthesis, plant height, ear height, ear length, kernel number per row and ear row number. The effects of four different models (Single, Across, M×E and R-norm), two different cross-validation schemes (CV1 and CV2) and three different training sets sampling ratios (0.5, 0.7 and 0.9) on the prediction accuracy were compared. The results showed that the average prediction accuracy of the six agronomic traits was 0.67, 0.58, 0.50, 0.33, 0.33 and 0.48. The average prediction accuracy of the Single model, Across model, M×E model and R-norm model was 0.36, 0.52, 0.53 and 0.53 for each trait. In CV1, the average prediction accuracy of each model in six traits ranged from 0.19 to 0.65, and in CV2, the average prediction accuracy ranged from 0.47 to 0.89. The comparison of different training set sampling ratios shows that the improvement of the proportion of the training sets has limited improvement in the prediction accuracy of different traits in different models, and the maximum is only 0.05. The results show that the CV2 training set can be used to form a scheme and include phenotypic data from multiple environments in the prediction model to provide good prediction accuracy for multi-environment prediction.

Key words： maize; genome-wide prediction; multi-environment prediction; optimal training sets

Cite this article

Yuan Li , Kaijian Fan , Tai An , Cong Li , Junxia Jiang , Hao Niu , Weiwei Zeng , Yanfang Heng , Hu Li , Junjie Fu , Huihui Li , Liang Li . Study on Multi-environment Genome-wide Prediction of Inbred Agronomic Traits in Maize Natural Populations[J]. Chinese Bulletin of Botany, 2024 , 59(6) : 1041 -1053 . DOI: 10.11983/CBB24087

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References

[1] Alemu A, ?strand J, Montesinos-López OA, Isidro Y, Sánchez J, Fernández-Gónzalez J, Tadesse W, Vetukuri RR, Carlsson AS, Ceplitis A, Crossa J, Ortiz R, Chawade A (2024). Genomic selection in plant breeding: key factors shaping two decades of progress. Mol Plant 17, 552-578.
[2] Burgue?o J, De Los Campos G, Weigel K, Crossa J (2012). Genomic prediction of breeding values when modeling genotype × environment interaction using pedigree and dense molecular markers. Crop Sci 52, 707-719.
[3] Carena MJ, Hallauer AR, Filho JBM (2010). Quantitative Genetics in Maize Breeding. New York: Springer Science & Business Media. pp. 1-6.
[4] Crossa J, De Los Campos G, Pérez P, Gianola D, Burgue?o J, Araus JL, Makumbi D, Singh RP, Dreisigacker S, Yan JB, Arief V, Banziger M, Braun HJ (2010). Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers. Genetics 186, 713-724.
[5] Cuevas J, Crossa J, Soberanis V, Pérez-Elizalde S, Pérez- Rodríguez P, De Los Campos G, Montesinos-López OA, Burgue?o J (2016). Genomic prediction of genotype × environment interaction kernel regression models. Plant Genome 9, plantgenome2016. 03.0024.
[6] Ferr?o LFV, Ferr?o RG, Ferr?o MAG, Francisco A, Garcia AAF (2017). A mixed model to multiple harvest-location trials applied to genomic prediction in Coffea canephora. Tree Genet Genomes 13, 95.
[7] Fu JJ, Hao YF, Li HH, Reif JC, Chen SJ, Huang CL, Wang GY, Li XH, Xu YB, Li L (2022). Integration of genomic selection with doubled-haploid evaluation in hybrid breeding: from GS 1.0 to GS 4.0 and beyond. Mol Plant 15, 577-580.
[8] Hayes BJ, Bowman PJ, Chamberlain AJ, Goddard ME (2009). Invited review: genomic selection in dairy cattle: progress and challenges. J Dairy Sci 92, 433-443.
[9] Jannink JL, Lorenz AJ, Iwata H (2010). Genomic selection in plant breeding: from theory to practice. Brief Funct Genomics 9, 166-177.
[10] Jarquín D, Crossa J, Lacaze X, Du Cheyron P, Daucourt J, Lorgeou J, Piraux F, Guerreiro L, Pérez P, Calus M, Burgue?o J, De Los Campos G (2014). A reaction norm model for genomic selection using high-dimensional genomic and environmental data. Theor Appl Genet 127, 595-607.
[11] Jarquin D, Howard R, Crossa J, Beyene Y, Gowda M, Martini JWR, Pazaran GC, Burgue?o J, Pacheco A, Grondona M, Wimmer V, Prasanna BM (2020). Genomic prediction enhanced sparse testing for multi-environment trials. G3 (Bethesda) 10, 2725-2739.
[12] Lopez-Cruz M, Crossa J, Bonnett D, Dreisigacker S, Poland J, Jannink JL, Singh RP, Autrique E, De Los Campos G (2015). Increased prediction accuracy in wheat breeding trials using a marker × environment interaction genomic selection model. G3 (Bethesda) 5, 569-582.
[13] Lorenz AJ (2013). Resource allocation for maximizing prediction accuracy and genetic gain of genomic selection in plant breeding: a simulation experiment. G3(Bethesda) 3, 481-491.
[14] Luo P, Wang HW, Ni ZY, Yang RS, Wang F, Yong HY, Zhang L, Zhou ZQ, Song W, Li MS, Yang J, Weng JF, Meng ZD, Zhang DG, Han JN, Chen Y, Zhang RZ, Wang LW, Zhao M, Gao WW, Chen XY, Li WJ, Hao ZF, Fu JJ, Zhang XC, Li XH (2023). Genomic prediction of yield performance among single-cross maize hybrids using a partial diallel cross design. Crop J 11, 1884-1892.
[15] Meuwissen THE, Hayes BJ, Goddard ME (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819-1829.
[16] Pérez P, De Los Campos G (2014). Genome-wide regression and prediction with the BGLR statistical package. Genetics 198, 483-495.
[17] Roorkiwal M, Jarquin D, Singh MK, Gaur PM, Bharadwaj C, Rathore A, Howard R, Srinivasan S, Jain A, Garg V, Kale S, Chitikineni A, Tripathi S, Jones E, Robbins KR, Crossa J, Varshney RK (2018). Genomic-enabled prediction models using multi-environment trials to estimate the effect of genotype?×?environment interaction on predic-tion accuracy in chickpea. Sci Rep 8, 11701.
[18] Sousa MBE, Cuevas J, de Oliveira Couto EG, Pérez- Rodríguez P, Jarquín D, Fritsche-Neto R, Burgue?o J, Crossa J (2017). Genomic-enabled prediction in maize using kernel models with genotype × environment interaction. G3 (Bethesda) 7, 1995-2014.
[19] Terraillon J, Roeber FK, Flachenecker C, Frisch M (2023). Training set designs for prediction of yield and moisture of maize test cross hybrids with unreplicated trials. Front Plant Sci 14, 1080087.
[20] VanRaden PM (2008). Efficient methods to compute genomic predictions. J Dairy Sci 91, 4414-4423.
[21] Wang BB, Lin ZC, Li X, Zhao YP, Zhao BB, Wu GX, Ma XJ, Wang H, Xie YR, Li QQ, Song GS, Kong DX, Zheng ZG, Wei HB, Shen RX, Wu H, Chen CX, Meng ZD, Wang TY, Li Y, Li XH, Chen YH, Lai JS, Hufford MB, Ross-Ibarra J, He H, Wang HY (2020). Genome-wide selection and genetic improvement during modern maize breeding. Nat Genet 52, 565-571.
[22] Xu YB, Liu XG, Fu JJ, Wang HW, Wang JK, Huang CL, Prasanna BM, Olsen MS, Wang GY, Zhang AM (2019). Enhancing genetic gain through genomic selection: from livestock to plants. Plant Commun 16, 100005.
[23] Yang N, Lu YL, Yang XH, Huang J, Zhou Y, Ali F, Wen WW, Liu J, Li JS, Yan JB (2014). Genome wide association studies using a new nonparametric model reveal the genetic architecture of 17 agronomic traits in an enlarged maize association panel. PLoS Genet 10, e1004573.
[24] Zhu XT, Leiser WL, Hahn V, Würschum T (2021). Training set design in genomic prediction with multiple biparental families. Plant Genome 14, e20124.
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