Crops ›› 2024, Vol. 40 ›› Issue (1): 31-39.doi: 10.16035/j.issn.1001-7283.2024.01.005

;

Previous Articles     Next Articles

Multi-Locus Genome-Wide Association Study and Genomic Prediction for General Combining Ability of Maize Ear Diameter

Ma Juan(), Huang Lu, Yu Ting, Guo Guojun, Zhu Weihong, Liu Jingbao   

  1. Institute of Cereal Crops, Henan Academy of Agricultural Sciences, Zhengzhou 450002, Henan, China
  • Received:2022-06-07 Revised:2022-09-22 Online:2024-02-15 Published:2024-02-20

RichHTML

4

PDF (PC)

91

Abstract:

Ear diameter is an important trait for ear in maize, and general combining ability (GCA) is an important index to evaluate parental mating ability in hybrid breeding. A total of 537 F1 hybrid combinations derived from NCII genetic mating design were used as materials. Seven multi-locus genome-wide association study (MGWAS) methods were used to conduct mapping analysis of ear diameter GCA in Xinxiang, Zhoukou, and combined environment, and the effects of different significant loci from MGWAS methods as fixed effects on the prediction accuracy of ear diameter GCA were studied, which will provide a guidance for the breeding utilization of genomic selection (GS) for key loci of ear diameter GCA. The results showed that a total of 18 significant SNPs (P<8.52E-07) were detected for the GCA effect of ear diameter, of which six loci were simultaneously detected using two to five MGWAS methods and three of them were detected in two environments. Five GS random effect models were used in the three environments, and the prediction accuracy of ear diameter GCA ranged from 0.32 to 0.44. The genomic prediction ability of GCA of ear diameter could be effectively enhanced in all three environments by adding significant loci as fixed effects into the prediction model, increasing the prediction accuracy to 0.38-0.64.

Key words: Maize, Ear diameter, General combining ability, Multi-locus genome-wide association study, Genomic selection, Fixed effect model

Fig.1

Statistic analysis of effect values of general combining ability for ear diameter (a)“×”denotes means, the same below; (c)“***”indicates significant correlation at P < 0.001 level."

Table 1

Analysis of variance and heritability for GCA of ear diameter"

来源
Source		 自由度
Degree of freedom		 均方
Mean of square		 F值
F value		 P值
P value
环境Environment 1 39.42 341.33 0.00
重复Replicate 1 0.92 7.98 0.0048
GCAL 122 0.54 4.65 0.00
GCAT 7 13.64 118.16 0.00
特殊配合力Special combining ability 407 0.26 2.22 0.00
环境:GCAL Environment: GCAL 122 0.26 2.29 3.67E-12
环境:GCAT Environment: GCAT 7 0.75 6.47 1.80E-07
环境:特殊配合力Environment: special combining ability 358 0.22 1.88 1.28E-14
残差Residuals 1019 0.12
GCA遗传力Heritability of GCA 0.88

Table 2

Significant loci identified using multi-locus genome-wide association study and candidate genes for GCA of ear diameter"

环境Environment 方法Method 标记Marker P值P value r2 (%) 预测基因Predicted gene
新乡Xinxiang FASTmrMLM 6_153041534 1.02E-07 15.94 Zm00001d038274
FASTmrMLM 10_3824944 1.09E-10 21.08 Zm00001d023343
pLARmEB 10_3824944 9.21E-09 11.90 Zm00001d023343
pLARmEB 10_142762498 2.40E-07 10.34 Zm00001d026284
ISIS EM-BLASSO 7_131856309 1.29E-07 8.32 Zm00001d020772
ISIS EM-BLASSO 10_3824944 1.51E-11 17.93 Zm00001d023343
周口Zhoukou mrMLM 1_186729735 8.18E-08 6.05 Zm00001d031338
pLARmEB 1_36247066 1.87E-10 12.85 Zm00001d028477
pLARmEB 2_196663320 1.87E-07 5.17 Zm00001d001001
pLARmEB 3_234977211 5.12E-08 16.21 Zm00001d044689
pLARmEB 10_2775283 2.66E-10 13.86 Zm00001d023314
ISIS EM-BLASSO 10_2775283 4.79E-08 22.04 Zm00001d023314
BLINK 10_2775283 1.91E-14 － Zm00001d023314
BLINK 1_36247066 9.40E-12 － Zm00001d028477
BLINK 3_234977211 1.31E-10 － Zm00001d044689
BLINK 2_196663320 9.31E-08 － Zm00001d001001
BLINK 7_127910914 8.28E-07 － Zm00001d020682, Zm00001d020683
FarmCPU 3_234977211 2.83E-12 － Zm00001d044689
FarmCPU 1_36247066 4.42E-07 － Zm00001d028477
综合环境Combined environment BLINK 10_3824944 4.06E-12 － Zm00001d023343
BLINK 10_139188724 6.17E-08 － －
BLINK 1_73368818 8.25E-08 － Zm00001d029499
FarmCPU 3_38863628 6.55E-09 － Zm00001d040341
mrMLM 2_25913905 1.18E-08 5.07 Zm00001d002889
mrMLM 10_3824944 1.80E-12 15.57 Zm00001d023343
FASTmrMLM 10_3824944 1.12E-10 18.12 Zm00001d023343
FASTmrEMMA 3_38952028 4.16E-07 0.00 Zm00001d040341, Zm00001d040342
pLARmEB 2_196663320 1.26E-08 7.02 Zm00001d001001
pLARmEB 6_153041644 1.79E-08 11.01 Zm00001d038274
pLARmEB 7_131856309 1.09E-08 7.08 Zm00001d020772
pLARmEB 10_3824944 4.79E-16 20.65 Zm00001d023343
pLARmEB 10_142058950 9.42E-08 7.62 Zm00001d026252
ISIS EM-BLASSO 3_38863604 1.14E-07 9.89 Zm00001d040341
ISIS EM-BLASSO 7_131856309 8.15E-09 8.60 Zm00001d020772
ISIS EM-BLASSO 10_3824944 4.33E-11 18.12 Zm00001d023343

Fig.2

Prediction accuracy for GCA of ear diameter using different random effect models"

Fig.3

The effect of significant loci as fixed effect on the prediction accuracy of GCA for ear diameter Except GBLUP and RKHS represent random effect models, the others are the combinations of MGWAS and predicted methods, which represent fixed effect models."

[1] Su C F, Wang W, Gong S L, et al. High density linkage map construction and mapping of yield trait QTLs in maize (Zea mays) using the genotyping-by-sequencing (GBS) technology. Frontiers in Plant Science, 2017, 8:706-719.
doi: 10.3389/fpls.2017.00706
[2] 监立强. 玉米产量相关性状及其一般配合力的关联分析. 保定:河北农业大学, 2017.
[3] 刘文童, 监立强, 郭晋杰, 等. 玉米穗部性状及其一般配合力的关联分析. 植物遗传资源学报, 2020, 21(3):706-715.
[4] Wang J B, Zhang Z W. GAPIT Version 3: Boosting power and accuracy for genomic association and prediction. Genomics Proteomics Bioinformatics, 2021, 19(4):629-640.
doi: 10.1016/j.gpb.2021.08.005
[5] Zhang Y W, Tamba C L, Wen Y J, et al. mrMLM v4.0.2: An R platform for multi-locus genome-wide association studies. Genomics Proteomics Bioinformatics, 2020, 18(4):481-487.
doi: 10.1016/j.gpb.2020.06.006
[6] He J B, Meng S, Zhao T J, et al. An innovative procedure of genome-wide association analysis fits studies on germplasm population and plant breeding. Theoretical and Applied Genetics, 2017, 130(11):2327-2343.
doi: 10.1007/s00122-017-2962-9 pmid: 28828506
[7] Ma J, Cao Y Y. Genetic dissection of grain yield of maize and yield-related traits through association mapping and genomic prediction. Frontiers in Plant Science, 2021, 12:690059-690069.
doi: 10.3389/fpls.2021.690059
[8] Ma J, Wang L F, Cao Y Y, et al. Association mapping and transcriptome analysis reveal the genetic architecture of maize kernel size. Frontiers in Plant Science, 2021, 12:632788-632799.
doi: 10.3389/fpls.2021.632788
[9] Malik P, Kumar J, Sharma S, et al. Multi-locus genome-wide association mapping for spike-related traits in bread wheat (Triticum aestivum L.). BMC Genomics, 2021, 22(1):597-617.
doi: 10.1186/s12864-021-07834-5
[10] Su J J, Wang C X, Hao F S, et al. Genetic detection of lint percentage applying single-locus and multi-locus genome-wide association studies in Chinese early-maturity upland cotton. Frontiers in Plant Science, 2019, 10:964-974.
doi: 10.3389/fpls.2019.00964 pmid: 31428110
[11] 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.
doi: 10.1093/genetics/157.4.1819 pmid: 11290733
[12] Park T, Casella G. The Bayesian Lasso. Journal of the American Statistical Association, 2008, 103(482):681-686.
doi: 10.1198/016214508000000337
[13] Gianola D, van Kaam J B C H M. Reproducing kernel Hilbert spaces regression methods for genomic assisted prediction of quantitative traits. Genetics, 2008, 178(4):2289-2303.
doi: 10.1534/genetics.107.084285 pmid: 18430950
[14] Guo Z, Tucker D M, Lu J, et al. Evaluation of genome-wide selection efficiency in maize nested association mapping populations. Theoretical and Applied Genetics, 2012, 124(2):261-275.
doi: 10.1007/s00122-011-1702-9 pmid: 21938474
[15] Lian L, Jacobson A, Zhong S. Genome wide prediction accuracy within 969 maize biparental populations. Crop Science, 2014, 54(4):1514-1522.
doi: 10.2135/cropsci2013.12.0856
[16] Liu X G, Wang H W, 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
[17] Liu X G, Wang H W, Hu X J, et al. Improving genomic selection with quantitative trait loci and nonadditive effects revealed by empirical evidence in maize. Frontiers in Plant Science, 2019, 10:1129-1141.
doi: 10.3389/fpls.2019.01129 pmid: 31620155
[18] Sitonik C, Suresh L M, Beyene Y, et al. Genetic architecture of maize chlorotic mottle virus and maize lethal necrosis through GWAS, linkage analysis and genomic prediction in tropical maize germplasm. Theoretical and Applied Genetics, 2019, 132 (8):2381-2399.
doi: 10.1007/s00122-019-03360-x pmid: 31098757
[19] Yuan Y, Cairns J E, Babu R, et al. Genome-wide association mapping and genomic prediction analyses reveal the genetic architecture of grain yield and flowering time under drought and heat stress conditions in maize. Frontiers in Plant Science, 2019, 9:1919-1933.
doi: 10.3389/fpls.2018.01919
[20] Bian Y, Holland J B. Enhancing genomic prediction with genome-wide association studies in multiparental maize populations. Heredity, 2017, 118(6):585-593.
doi: 10.1038/hdy.2017.4 pmid: 28198815
[21] Spindel J E, Begum H, Akdemir D, et al. Genome-wide prediction models that incorporate de novo GWAS are a powerful new tool for tropical rice improvement. Heredity, 2016, 116(4):395-408.
doi: 10.1038/hdy.2015.113 pmid: 26860200
[22] Arruda M, Lipka A, Brown P, et al. Comparing genomic selection and marker-assisted selection for Fusarium head blight resistance in wheat (Triticum aestivum). Molecular Breeding, 2016, 36(7):1-11.
doi: 10.1007/s11032-015-0425-z
[23] Herter C P, Ebmeyer E, Kollers S, et al. Accuracy of within- and among-family genomic prediction for Fusarium head blight and Septoria tritici blotch in winter wheat. Theoretical and Applied Genetics, 2019, 132(4):1121-1135.
doi: 10.1007/s00122-018-3264-6
[24] Michel S, Kummer C, Gallee M, et al. Improving the baking quality of bread wheat by genomic selection in early generations. Theoretical and Applied Genetics, 2018, 131(2):477-493.
doi: 10.1007/s00122-017-2998-x pmid: 29063161
[25] Moore J K, Manmathan H K, Anderson V A, et al. Improving genomic prediction for pre-harvest sprouting tolerance in wheat by weighting large-effect quantitative trait loci. Crop Science, 2017, 57(3):1315-1324.
doi: 10.2135/cropsci2016.06.0453
[26] Zhao Y, Mette M F, Gowda M, et al. Bridging the gap between marker-assisted and genomic selection of heading time and plant height in hybrid wheat. Heredity, 2014, 112(6):638-645.
doi: 10.1038/hdy.2014.1 pmid: 24518889
[27] Pérez P, de los Campos G,. Genome-wide regression and prediction with the BGLR statistical package. Genetics, 2014, 198(2):483-495.
doi: 10.1534/genetics.114.164442 pmid: 25009151
[28] Wen Y J, Zhang H, Ni Y L, et al. Methodological implementation of mixed linear models in multi-locus genome-wide association studies. Briefings in Bioinformatics, 2018, 19(4):700-712.
doi: 10.1093/bib/bbw145
[29] Zhang J, Feng J Y, Ni Y L, et al. pLARmEB:integration of least angle regression with empirical Bayes for multilocus genome- wide association studies. Heredity, 2017, 118(6):517-524.
doi: 10.1038/hdy.2017.8 pmid: 28295030
[30] Zhou G F, Zhu Q L, Mao Y X, et al. Multi-locus genome-wide association study and genomic selection of kernel moisture content at the harvest stage in maize. Frontiers in Plant Science, 2021, 12:697688-697700.
doi: 10.3389/fpls.2021.697688
[31] Cui Y R, Zhang F, Zhou Y L. The application of multi-locus GWAS for the detection of salt-tolerance loci in rice. Frontiers in Plant Science, 2018, 9:1464-1472.
doi: 10.3389/fpls.2018.01464 pmid: 30337936
[32] Zhou Z P, Li G L, Tan S Y, et al. A QTL atlas for grain yield and its component traits in maize (Zea mays). Plant Breeding, 2020, 139(3):562-574.
doi: 10.1111/pbr.v139.3
[33] Lu X, Zhou Z Q, Yuan Z H, et al. Genetic dissection of the general combining ability of yield-related traits in maize. Frontiers in Plant Science, 2020, 11:788-802.
doi: 10.3389/fpls.2020.00788 pmid: 32793248
[34] Zhang X X, Guan Z R, Li Z L, et al. A combination of linkage mapping and GWAS brings new elements on the genetic basis of yield-related traits in maize across multiple environments. Theoretical and Applied Genetics, 2020, 133(10):2881-2895.
doi: 10.1007/s00122-020-03639-4 pmid: 32594266
[35] Chen L, An Y, Li Y X, et al. Candidate loci for yield-related traits in maize revealed by a combination of MetaQTL analysis and regional association mapping. Frontiers in Plant Science, 2017, 8:2190-2203.
doi: 10.3389/fpls.2017.02190 pmid: 29312420
[36] Brooks L, Strable J, Zhang X, et al. Microdissection of shoot meristem functional domains. PLoS Genetics, 2009, 5(5):e1000476.
doi: 10.1371/journal.pgen.1000476
[37] Zou C S, Jiang W B, Yu D Q. Male gametophyte-specific WRKY34 transcription factor mediates cold sensitivity of mature pollen in Arabidopsis. Journal of Experimental Botany, 2010, 61(14):3901-3914.
doi: 10.1093/jxb/erq204 pmid: 20643804
[38] Wang Y, Li Y, He S P, et al. A cotton (Gossypium hirsutum) WRKY transcription factor (GhWRKY22) participates in regulating anther/pollen development. Plant Physiology and Biochemistry, 2019, 141:231-239.
doi: S0981-9428(19)30240-2 pmid: 31195253
[39] Yao J, Zhao D H, Chen X M, et al. Use of genomic selection and breeding simulation in cross prediction for improvement of yield and quality in wheat (Triticum aestivum L.). The Crop Journal, 2018, 6(4):353-365.
doi: 10.1016/j.cj.2018.05.003
[40] Ali M, Zhang Y, Rasheed A, et al. Genomic prediction for grain yield and yield-related traits in Chinese winter wheat. International Journal of Molecular Sciences, 2020, 21(4):1342-1359.
doi: 10.3390/ijms21041342
[41] 马娟, 曹言勇, 朱卫红. 玉米穗轴粗和出籽率全基因组预测分析. 植物遗传资源学报, 2021, 22(6):1708-1715.
doi: 10.13430/j.cnki.jpgr. 20210405002
[42] 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
[1] Feng Yong, Hou Xuguang, Xue Chunlei, Zhang Laihou, Song Guodong, Su Minli, Fu Xiaohua, Sun Yuyan. Division of Suitable Ecological Regions of Maize Varieties in Inner Mongolia [J]. Crops, 2024, 40(1): 23-30.
[2] Wang Haitao, Ren Chunmei, Dong Yan, Li Shuo, Cheng Zhaobang, Ji Yinghua. Molecular Detection and Identification of Maize Yellow Mosaic Virus on Sorghum in Huai’an, Jiangsu [J]. Crops, 2024, 40(1): 233-238.
[3] Wu Ying, Hu Die, Li Ting, Duan Qianyuan, Wei Ningning, Zhang Xinghua, Xu Shutu, Xue Jiquan. Analysis of WRKY Transcription Factor IIc Subfamily in Maize and Its Expression Profile under Drought [J]. Crops, 2024, 40(1): 80-89.
[4] Jin Yu, Guo Xinyu, Zhang Ying, Li Dazhuang, Wang Jinglu. Stomatal Phenotypic Identification and Research Progress in Maize Leaves [J]. Crops, 2023, 39(6): 1-10.
[5] Wu Qi, Ming Bo, Gao Shang, Yang Hongye, Zhang Chuan, Chu Zhendong, Li Shaokun. Research on the Construction Strategy of Maize Grain Dehydration Model in Cold Northeast China [J]. Crops, 2023, 39(6): 108-113.
[6] Liang Zhongyu, Xue Jun, Zhang Guoqiang, Ming Bo, Shen Dongping, Fang Liang, Zhou Linli, Zhang Yuqin, Yang Hengshan, Wang Keru, Li Shaokun. Effects of Phosphorus Application Rate on Lodging Resistance of Maize under Integrated Water and Fertilizer [J]. Crops, 2023, 39(6): 190-194.
[7] Cao Qingjun, Li Gang, Yang Hao, Lou Yuyong, Yang Fentuan, Kong Fanli, Li Xinbei, Zhao Xinkai, Jiang Xiaoli. The Effects of Different Tillage Practices on Seedbed Quality and Its Relationships with Seedling Population Construction and Grain Yield of Spring Maize [J]. Crops, 2023, 39(5): 249-254.
[8] Yu Le, Li Lin, Huang Hongjuan, Huang Zhaofeng, Zhu Wenda, Wei Shouhui. Weed Species Composition and Community Characterization in Maize Fields in Hubei Province [J]. Crops, 2023, 39(5): 272-279.
[9] Yang Zongying, Xiao Gui, Zhang Hongwei. Whole-Genome Predictive Analysis of Fresh Weight per Plant Using the Maize F1 Population [J]. Crops, 2023, 39(5): 43-48.
[10] Qu Haitao, Li Zhongnan, Wang Yueren, Ma Yiwen, Xiang Yang, Wu Shenghui, Tan Zhuo, Wang Chun, Wei Qiang, Luo Yao, Li Guangfa. Study on Genetic and Breeding Effects of 100-Grain Weight in Maize [J]. Crops, 2023, 39(5): 66-70.
[11] Yang Mi, Wang Meijuan, Xu Haitao. Study on the Dynamic Development Difference of Husk of Maize Inbred Lines in Different Ecological Regions [J]. Crops, 2023, 39(5): 81-90.
[12] Yuan Liuzheng, Wang Huiqiang, WangQiuling , Zhu Shidie, ZhaoYueqiang , Yuan Manman, Wang Huitao, Zhang Yundong, Liu Jiayou, Yuan Yongqiang. Analysis of Combining Ability and Genetic Effect of Maize Inbred Lines under Shading Condition [J]. Crops, 2023, 39(4): 104-109.
[13] Zheng Fei, Chen Jing, Cui Yakun, Kong Lingjie, Meng Qingchang, Li Jie, Liu Ruixiang, Zhang Meijing, Zhao Wenming, Chen Yanping. Screening of High and Stable Yield Maize Varieties Suitable for Grain Mechanical Harvesting in Different Ecological Areas of the Huaibei Region [J]. Crops, 2023, 39(4): 110-117.
[14] Wang Liping, Bai Lanfang, Wang Tianhao, Wang Xiaoxuan, Bai Yunhe, Wang Yufen. Effects of Different Nitrogen Levels on Nitrogen Accumulation and Transport in Silage Maize [J]. Crops, 2023, 39(4): 165-173.
[15] Li Yuxin, Lu Min, Zhao Jiuran, Wang Ronghuan, Xu Tianjun, Lü Tianfang, Cai Wantao, Zhang Yong, Xue Honghe, Liu Yueʼe. The Production Status Investigation and Analysis of Summer Maize in Beijing-Tianjin-Tangshan Region [J]. Crops, 2023, 39(4): 174-181.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!