Crops ›› 2023, Vol. 39 ›› Issue (5): 43-48.doi: 10.16035/j.issn.1001-7283.2023.05.007

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Whole-Genome Predictive Analysis of Fresh Weight per Plant Using the Maize F1 Population

Yang Zongying1(), Xiao Gui2(), Zhang Hongwei3()   

  1. 1College of Agronomy, Jilin Agricultural University, Changchun 130118, Jilin, China
    2Dingxi Academy of Agricultural Sciences, Dingxi 743000, Gansu, China
    3Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100193, China
  • Received:2022-04-06 Revised:2022-07-27 Online:2023-10-15 Published:2023-10-16

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Abstract:

Genomic prediction is a molecular technique that can improve the efficiency of molecular breeding. In this study, 120 maize inbred lines were crossed to the parents of Zhongbei 410 (SN915, YH-1) and Beinong 368 (60271, 2193) to construct four testcross populations. The 10k SNP chip was used to genotype the parental lines, and the whole-plant fresh weight of the four populations were evaluated in three environments. The results showed that the fresh weight was highest in Gansu environment, and population 2 had the highest whole plant fresh weight. The coefficients of variances of the four populations ranged from 0.27 to 0.33, indicating the phenotypic variations of the four populations were great. By implementing the one-to-one (one population was used as the training population to predict other populations) and multiple-to-one (three populations and a half of the forth population were used as the training population to predict the other half of the forth population) prediction schemes, we found that the prediction accuracy of one-to-one genome prediction was lower than that of the multiple-to-one prediction, and closely related groups predicted better. The accuracy of genomic prediction could be improved by adding the relatives of the prediction population in the training population and optimizing the relationship between the training population and the predicted population.

Key words: Maize, Genomic prediction, Training population, Biomass

Fig.1

Summary of fresh weight data of different environmental materials and different groups"

Table 1

Summary of the basic statistics of fresh weight of four groups"

群体Population 群体大小Population size 平均值Mean (kg) 标准差SD 最小值Minimum (kg) 最大值Maximum (kg) 变异系数CV
P1 324 1.18 0.38 0.35 2.38 0.32
P2 346 1.36 0.37 0.49 2.64 0.27
P3 342 1.13 0.37 0.41 2.05 0.33
P4 343 1.26 0.36 0.55 2.76 0.29

Fig.2

Correlation analysis of fresh weight per plant in three environments “**”and“***”indicate the extremely significant differences at 0.01 and 0.001 levels, respectively"

Fig.3

Sequencing markers and genetic similarity analysis (a) Distribution of sequencing markers; (b) Evolutionary tree analysis of all parental materials, 1-120 is the parent material, 121, 122, 123, and 124 are the parent of Zhongbei 410 (SN915, YH-1), and the parents of Beinong 368 (60271, 2193); (c) Principal component analysis of all labeled four populations, blue, red, green and yellow are P1, P2, P3, and P4, respectively; (d) Using the heatmap to analyze the kinship of the four groups, from left to right are P1, P2, P3, and P4, respectively"

Fig.4

Genome-wide prediction of fresh weight per plant in different populations using GBLUP and BayesB models (a) use one group as a training group to predict other groups, (b) predict the remaining half of the population with the random half of the material of a random population plus the remaining three populations as the training population"

[1] 李少昆, 赵久然, 董树亭, 等. 中国玉米栽培研究进展与展望. 中国农业科学, 2017, 50(11):1941-1959.
doi: 10.3864/j.issn.0578-1752.2017.11.001
[2] Tilman D, Balzer C, Hill J, et al. Global food demand and the sustainable intensification of agriculture. Proceedings of the National Academy of Sciences of the United States of America, 2011, 108 (50):20260-20264.
[3] 闫亚飞. 河套灌区不同饲草生产性能与品质研究. 北京: 中国农业科学院, 2016.
[4] Iwata H. Genomic selection in plant breeding: from theory to practice. Briefings in Functional Genomics, 2010, 9(2):166-177.
doi: 10.1093/bfgp/elq001
[5] Heffner E L, Lorenz A J, Jannink J L, et al. Plant breeding with genomic selection: gain per unit time and cost. Crop Science, 2010, 50(5):1681-1690.
doi: 10.2135/cropsci2009.11.0662
[6] Crossa J, Pérez P, Hickey J M, et al. Genomic prediction in CIMMYT maize and wheat breeding program. Heredity, 2013, 112 (1):48.
doi: 10.1038/hdy.2013.16
[7] Li D, Wang P, Gu R, et al. Genetic relatedness and the ratio of subpopulation-common alleles are related in genomic prediction across structured subpopulations in maize. Plant Breeding, 2019, 138(6):802-809.
doi: 10.1111/pbr.v138.6
[8] Xu S, Zhu D, Zhang Q. Predicting hybrid performance in rice using genomic best linear unbiased prediction. Proceedings of the National Academy of Sciences of the United States of America, 2014, 111(34):12456-12461.
[9] Cui Y, Li R, Li G, et al. Hybrid breeding of rice via genomic selection. Plant Biotechnology Journal, 2020, 18(1):57-67.
doi: 10.1111/pbi.13170 pmid: 31124256
[10] Huang X, Yang S, Gong J, et al. Genomic analysis of hybrid rice varieties reveals numerous superior alleles that contribute to heterosis. Nature Communications, 2015, 6(7052):6258.
doi: 10.1038/ncomms7258
[11] Nickolai A, Tai S, Wang W, et al. SNP-Seek database of SNPs derived from 3000 rice genomes. Nucleic Acids Research, 2015 (D1):1023-1027.
[12] Hadasch S, Simko I, Hayes R J, et al. Comparing the predictive abilities of phenotypic and marker-assisted selection methods in a biparental lettuce population. Plant Genome, 2016, 9(1):1-11.
[13] Yang J, Mezmouk S, Baumgarten A, et al. Incomplete dominance of deleterious alleles contributes substantially to trait variation and heterosis in maize. PLoS Genetics, 2016, 13(9):e1007019.
doi: 10.1371/journal.pgen.1007019
[14] Bates D, Mächler M, Bolker B, et al. Fitting linear mixed-effects models using lme4. Statistics & Computing, 2014, 1406(1):133-199.
[15] Hallauer A R, Carena M J, Miranda Filho J B. Quantitative genetics in maize breeding. New York: Springer Science & Business Media, 2010.
[16] Murray M G, Thompson C L, Wendel J F. Rapid isolation of high molecular weight plant DNA. Nucleic Acids Research, 1980, 8 (19):4321-4325.
doi: 10.1093/nar/8.19.4321 pmid: 7433111
[17] Pérez P, Campos G D L. Genome-wide regression and prediction with the BGLR statistical package. Genetics, 2014, 198(2):483-495.
doi: 10.1534/genetics.114.164442 pmid: 25009151
[18] Liu X, Wang H, Wang H, et al. Factors affecting genomic selection revealed by empirical evidence in maize. The Crop Journal, 2018, 6(4):341-352.
doi: 10.1016/j.cj.2018.03.005
[19] Dias K O D G, Gezan S A, Guimarães C T, et al. Improving accuracies of genomic predictions for drought tolerance in maize by joint modeling of additive and dominance effects in multi- environment trials. Heredity, 2018, 121(1):24-37.
doi: 10.1038/s41437-018-0053-6
[20] Massman J M, Gordillo A, Lorenzana R E, et al. Genomewide predictions from maize single-cross data. Theoretical and Applied Genetics, 2013, 126(1):13-22.
doi: 10.1007/s00122-012-1955-y pmid: 22886355
[21] Hayes B J, Bowman P J, Chamberlain A C, et al. Accuracy of genomic breeding values in multi-breed dairy cattle populations. Genetics Selection Evolution, 2009, 41(1):1-9.
doi: 10.1186/1297-9686-41-1
[22] 刘策, 孟焕文, 程智慧. 植物全基因组选择育种技术原理与研究进展. 分子植物育种, 2020, 18(16):5335-5342.
[23] 陈雨, 姜淑琴, 孙炳蕊, 等. 基因组选择及其在作物育种中的应用. 广东农业科学, 2017, 44(9):1-7.
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