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作物杂志,2024, 第1期: 31–39 doi: 10.16035/j.issn.1001-7283.2024.01.005

所属专题： 玉米专题

• 遗传育种·种质资源·生物技术 • 上一篇    下一篇

玉米穗粗一般配合力多位点全基因组关联分析和基因组预测

马娟(), 黄璐, 宇婷, 郭国俊, 朱卫红, 刘京宝   

  1. 河南省农业科学院粮食作物研究所，450002，河南郑州
  • 收稿日期:2022-06-07 修回日期:2022-09-22 出版日期:2024-02-15 发布日期:2024-02-20
  • 作者简介:马娟，主要从事生物信息学分析，E-mail：majuanjuan85@126.com
  • 基金资助:
    河南省科技攻关项目(222102110043);河南省农业科学院优秀青年(2020YQ04)

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

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摘要：

穗粗是一个重要的穗部性状，一般配合力（GCA）是杂交育种中衡量亲本组配能力的一个重要指标。以NCII遗传交配设计组配的537个F1杂交组合为材料，利用7种多位点全基因组关联分析方法（MGWAS）对河南新乡、周口和综合环境穗粗GCA开展定位分析，并研究MGWAS方法挖掘的显著关联位点作为固定效应对穗粗GCA预测准确性的影响，为穗粗GCA关键位点的基因组选择（GS）育种利用提供指导。结果表明，共检测到18个穗粗GCA显著关联SNP（P<8.52E-07），其中6个位点利用2~5种MGWAS方法同时检测到，3个位点在2个环境中均检测到。5种GS随机效应模型对3个环境穗粗GCA预测准确性均较低，为0.32~0.44。3个环境中，显著位点作为固定效应加入预测模型中均能有效地提高穗粗GCA基因组预测能力，可将预测精度提高到0.38~0.64。

关键词: 玉米, 穗粗, 一般配合力, 多位点全基因组关联分析, 基因组选择, 固定效应模型

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

图1

穗粗一般配合力效应值统计分析 (a)“×”表示均值，下同；(c)“***”表示P < 0.001水平显著相关。

表1

方差分析和穗粗GCA的遗传力

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

表2

多位点全基因组关联分析鉴定的穗粗GCA显著位点以及候选基因

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

图2

不同随机效应模型对穗粗GCA的预测准确性

图3

显著位点作为固定效应对穗粗GCA准确性预测的影响除了GBLUP和RKHS表示随机效应模型，其他为MGWAS方法和预测方法的组合代表固定效应模型。

[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
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