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Acta Agronomica Sinica ›› 2021, Vol. 47 ›› Issue (7): 1228-1238.doi: 10.3724/SP.J.1006.2021.03048

• CROP GENETICS & BREEDING · GERMPLASM RESOURCES · MOLECULAR GENETICS • Previous Articles     Next Articles

Genome-wide association study of ear cob diameter in maize

MA Juan, CAO Yan-Yong, LI Hui-Yong*()   

  1. Institute of Cereal Crops, Henan Academy of Agricultural Sciences, Zhengzhou 450002, Henan, China
  • Received:2020-08-14 Accepted:2020-12-01 Online:2021-07-12 Published:2021-01-04
  • Contact: LI Hui-Yong E-mail:lihuiyong1977@126.com
  • Supported by:
    This study was supported by the Science and Technology Project of Henan Province(182102110368);the Science-Technology Foundation for Outstanding Young Scientists of Henan Academy of Agricultural Sciences(2020YQ04)

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Abstract:

Maize ear cob diameter is an important trait impacting the yield of grain and cob, and the analysis of its genetic mechanism will provide a guidance for high-yield breeding. In this study, the genotypes of 309 inbred lines were identified by genotyping-by-sequencing technology. FarmCPU (fixed and random model circulating probability unification), MLMM (multiple loci mixed linear model), and CMLM (compressed mixed linear model) were used to identify significant single nucleotide polymorphisms (SNP) for ear cob diameter of Yuanyang of Henan province, Dancheng of Henan province, Yucheng of Henan province, Sanya of Hainan province in 2017 and 2019, and best linear unbiased estimate environment. A total of 12 significant SNP for ear cob diameter were detected at P < 8.60E-07. S4_29277313 was detected from Yuanyang in 2017 using FarmCPU and MLMM. The phenotypic variance explained of S1_29006330, S2_170889116, S2_2046026464, and S4_83821463 ranged from 10.23% to 14.17%, and were considered major-effect SNP. In addition, S1_29006330 was mapped in the interval of known QTL for ear cob diameter. A total of 17 candidate genes were identified. Among them, WAKL14 (wall-associated receptor kinase-like 14), transcription factor ZIM35 (zinc-finger protein expressed in inflorescence meristem 35), HMGA (HMG-Y-related protein A), histone-lysine N-methyltransferase ATX4 (Arabidopsis trithorax 4), and XTH32 (xyloglucan endotransglucosylase/hydrolase protein 32) might be important genes for ear cob diameter. The identification of four major-effect SNP and five candidate genes can provide an information for molecular marker-assisted breeding, fine mapping, and gene cloning.

Key words: maize, genome-wide association study (GWAS), FarmCPU, ear cob diameter

Table 1

Multi-environment analysis of variance"

变异来源
Source		 自由度
Degree of freedom		 方差和
Sum of square		 均方
Mean of square		 F值
F-value		 P值
P-value
区组/环境Block/Environment 9 0.77 0.086 1.65 0.0970
基因型Genotype 308 184.95 0.60 11.55 <0.0001
环境Environment 4 4.84 1.21 23.28 <0.0001
基因型与环境互作
Genotype and environment interaction		 1036 170.71 0.16 3.17 <0.0001
误差Error 1694 88.04 0.052

Fig. 1

Correlation analysis and histograms for ear cob diameter in multi-environments Histograms are showed in diagonal. Correlation values and significant levels represented by asterisk are showed in upper triangular matrix. Significant levels 0.05, 0.01, and 0.001 are denoted by *, **, and ***, respectively. Scatter plots of ear cob diameter are showed in lower triangular matrix. SY, DC, YC, and YY represent Sanya, Dancheng, Yucheng, and Yuanyang, respectively. 2017 and 2019 denote years."

Fig. 2

Manhattan plots and quantile-quantile plots for significant SNP for ear cob diameter A and B: FarmCPU in BLUE environment; C and D: FarmCPU in Dancheng in 2017; E and F: FarmCPU in Sanya in 2017; G and H: FarmCPU in Yucheng in 2017; I and J: FarmCPU in Yuanyang in 2017; K and L: MLMM in Yuanyang in 2017."

Table 2

Significant SNP for ear cob diameter and candidate genes using different methods in different environments"

方法Method 标记名称
Marker name		 染色体
Chr.		 位置
Position (Mb)		 bin P值
P-value		 MAF 表型变异解释率
Phenotypic variance explained (%)		 环境
Environment		 类型
Type		 候选基因
Candidate genes
FarmCPU S1_143964620 1 143.96 1.05 1.82E-08 0.47 0.34 YY2017 intergenic Zm00001d030556 (organic cation/carnitine transporter 7 OCT7); Zm00001d030557 (alanine aminotransferase 2 ALT2)
FarmCPU S1_29006330 1 29.01 1.02 1.36E-07 0.34 13.10 SY2017 intergenic Zm00001d028279; Zm00001d028280 (wall-associated receptor kinase-like 14WAKL14)
FarmCPU S1_5537879 1 5.54 1.01 2.15E-07 0.37 3.27 BLUE intronic Zm00001d027451 (S-adenosyl-L-methionine-dependent methyltransferases superfamily protein)
FarmCPU S1_88127156 1 88.13 1.05 3.53E-07 0.27 0.0010 YY2017 intergenic Zm00001d029812 (threonine synthase 1 TS1); Zm00001d029814 (xyloglucan endotransglucosylase/hydrolase protein XTH32)
FarmCPU S2_170090146 2 170.09 2.06 6.12E-07 0.14 3.02 DC2017 intergenic Zm00001d005339; Zm00001d005342
FarmCPU S2_170889116 2 170.89 2.06 1.83E-08 0.11 14.17 DC2017 intronic Zm00001d005358 (nuclear pore complex protein GP210)
FarmCPU S2_204602646 2 204.60 2.07 8.45E-08 0.14 10.23 BLUE exonic Zm00001d006323 (histone-lysine N-methyltransferase ATX4)
FarmCPU S3_170875493 3 170.88 3.06 1.12E-09 0.11 8.55 BLUE intronic Zm00001d042528 (chromatin assembly factor 1 subunit FAS1)
FarmCPU S4_29277313 4 29.28 4.04 7.28E-08 0.09 0.01 YY2017 exonic Zm00001d049399 (putative nucleolin-like family protein)
FarmCPU S4_83821463 4 83.82 4.05 2.84E-07 0.23 12.83 YC2017 intergenic Zm00001d050365 (ZIM-transcription factor 35 ZIM35); Zm00001d050368 (HMG-Y-related protein A HMGA)
FarmCPU S5_17720377 5 17.72 5.03 5.46E-08 0.37 0.81 BLUE intronic Zm00001d013694 (single myb histone 6)
FarmCPU S7_127839936 7 127.84 7.02 5.41E-09 0.29 8.10 SY2017 exonic Zm00001d020679
MLMM S4_29277313 4 29.28 4.04 7.96E-07 0.09 0.01 YY2017 exonic Zm00001d049399 (putative nucleolin-like family protein)

Fig. S1

Manhattan plots and quantile-quantile plots for genome-wide association analysis for ear cob diameter using CMLM method in different environments DC, YC, SY, and YY represent Dancheng, Yucheng, Sanya, and Yuanyang, respectively. 2017 and 2019 denote in 2017 and in 2019."

Fig. S2

Manhattan plots and quantile-quantile plots for no significant SNP for ear cob diameter using MLMM and FarmCPU DC, YC, SY, and YY represent Dancheng, Yucheng, Sanya, and Yuanyang, respectively. 2017 and 2019 denote in 2017 and in 2019."

Fig. S3

Expression profiles of 17 candidate genes in different tissues retrieved from maize GDB"

[1] Choe E, Torbert R R. Genetic and QTL analysis of pericarp thickness and ear architecture traits of Korean waxy corn germplasm. Euphytica, 2012,183:243-260.
[2] Guo J, Chen Z, Liu Z, Wang B, Song W, Li W, Chen J, Dai J, Lai J. Identification of genetic factors affecting plant density response through QTL mapping of yield component traits in maize (Zea mays L.). Euphytica, 2011,182:409-422.
[3] Su C, Wang W, Gong S, Zuo J, Li S, Xu S. High density linkage map construction and mapping of yield trait QTLs in maize ( Zea mays) using the genotyping-by-sequencing (GBS) technology. Front Plant Sci, 2017,8:706-719.
pmid: 28533786
[4] Zhu X M, Shao X Y, Pei Y H, Guo X M, Li J, Song X Y, Zhao M A. Genetic diversity and genome-wide association study of major ear quantitative traits using high-density SNPs in maize. Front Plant Sci, 2018,9:966-981.
doi: 10.3389/fpls.2018.00966 pmid: 30038634
[5] Upadyayula N, da Silva H S, Bohn M O, Rocheford T R. Genetic and QTL analysis of maize tassel and ear inflorescence architecture. Theor Appl Genet, 2006,112:592-606.
doi: 10.1007/s00122-005-0133-x pmid: 16395569
[6] Zhao Y, Su C. Mapping quantitative trait loci for yield-related traits and predicting candidate genes for grain weight in maize. Sci Rep, 2019,9:16112-16121.
doi: 10.1038/s41598-019-52222-5 pmid: 31695075
[7] Jansen C, Lübberstedt T. Turning maize cobs into a valuable feedstock. Bioener Res, 2012,5:20-31.
[8] Yi Q, Liu Y, Hou X, Zhang X, Li H, Zhang J, Liu H, Hu Y, Yu G, Li Y, Wang Y, Huang Y. Genetic dissection of yield-related traits and mid-parent heterosis for those traits in maize ( Zea mays L.). BMC Plant Biol, 2019 19:392-411.
[9] 王帮太, 吴建宇, 丁俊强, 席章营. 玉米产量及产量相关性状QTL的图谱整合. 作物学报, 2009,35:1836-1843.
Wang B T, Wu J Y, Ding J Q, Xi Z Y. Map integration of QTLs for grain yield and its related traits in maize. Acta Agron Sin, 2009,35:1836-1843.
[10] Zhang X, Guan Z, Li Z, Liu P, Ma L, Zhang Y, Pan L, He S, Zhang Y, Li P, Ge F, Zou C, He Y, Gao S, Pan G, Shen Y. A combination of linkage mapping and GWAS brings new elements on the genetic basis of yield-related traits in maize across multiple environments. Theor Appl Genet, 2020,33:2881-2895.
[11] Meng L, Li H H, Zhang L Y, Wang J K. QTL IciMapping: integrated software for genetic linkage map construction and quantitative trait locus mapping in biparental populations. Crop J, 2015,3:269-283.
[12] Liu X, Huang M, Fan B, Buckler E S, Zhang Z. Iterative usage of fixed and random effect models for powerful and efficient genome-wide association studies. PLoS Genet, 2016,12:e1005767.
doi: 10.1371/journal.pgen.1005767 pmid: 26828793
[13] Segura V, Vilhjálmsson B J, Platt A, Korte A, Seren Ü, Long Q, Nordborg M. An efficient multi-locus mixed-model approach for genome-wide association studies in structured populations. Nat Genet, 2012,44:825-830.
doi: 10.1038/ng.2314 pmid: 22706313
[14] Zhang Z, Ersoz E, Lai C Q, Todhunter R J, Tiwari H K, Gore M A, Bradbury P J, Yu J, Arnett D K, Ordovas J M, Buckler E S. Mixed linear model approach adapted for genome-wide association studies. Nat Genet, 2010,42:355-360.
doi: 10.1038/ng.546 pmid: 20208535
[15] Pandis N. Linear regression. Am J Orthod Dentofacial Orthop, 2016,149:431-434.
pmid: 26926032
[16] Lipka A E, Tian F, Wang Q, Peiffer J, Li M, Bradbury P J, Gore M A, Buckler E S, Zhang Z. GAPIT: genome association and prediction integrated tool. Bioinform, 2012,28:2397-2399.
[17] Lippert C, Listgarten J, Liu Y, Kadie C M, Davidson R I, Heckerman D. FaST linear mixed models for genome-wide association studies. Nat Methods, 2011,8:833-835.
doi: 10.1038/nmeth.1681 pmid: 21892150
[18] Liu M, Tan X, Yang Y, Liu P, Zhang X, Zhang Y, Wang L, Hu Y, Ma L, Li Z, Zhang Y, Zou C, Lin H, Gao S, Lee M, Lubberstedt T, Pan G, Shen Y. Analysis of the genetic architecture of maize kernel size traits by combined linkage and association mapping. Plant Biotechnol J, 2020,18:207-221.
doi: 10.1111/pbi.13188 pmid: 31199064
[19] Chen L, An Y, Li Y X, Li C, Shi Y, Song Y, Zhang D, Wang T, Li Y. Candidate loci for yield-related traits in maize revealed by a combination of metaQTL analysis and regional association mapping. Front Plant Sci, 2017,22:2190-2202.
[20] Kanneganti V, Gupta A K. Wall associated kinase from plants—an overview. Physiol Mol Biol Plants, 2008,14:109-118.
[21] Kanneganti V, Gupta A K. RNAi mediated silencing of a wall associated kinase, OsiWAK1 in Oryza sativa results in impaired root development and sterility due to anther indehiscence: wall associated kinases from Oryza sativa. Physiol Mol Biol Plants, 2011,17:65-77.
doi: 10.1007/s12298-011-0050-1 pmid: 23572996
[22] Shikata M, Takemura M, Yokota A, Kohchi T. Arabidopsis ZIM, a plant-specific GATA factor, can function as a transcriptional activator. Biosci Biotechnol Biochem, 2003,67:2495-2497.
doi: 10.1271/bbb.67.2495 pmid: 14646219
[23] Wang Z, Zhu T, Ma W, Fan E, Lu N, Ou-Yang F, Wang N, Yang G, Kong L, Qu G, Zhang S, Wang J. Potential function of CbuSPL and gene encoding its interacting protein during flowering in Catalpa bungei. BMC Plant Biol, 2020,20:105.
doi: 10.1186/s12870-020-2303-z pmid: 32143577
[24] Chuck G S, Brown P J, Meeley R, Hake S. Maize SBP-box transcription factors unbranched2 and unbranched3 affect yield traits by regulating the rate of lateral primordia initiation. Proc Natl Acad Sci USA, 2014,111:18775-18780.
doi: 10.1073/pnas.1407401112 pmid: 25512525
[25] Du Y, Liu L, Peng Y, Li M, Li Y, Liu D, Li X, Zhang Z. UNBRANCHED3 expression and inflorescence development is mediated by UNBRANCHED2 and the distal enhancer, KRN4, in maize. PLoS Genet, 2020,16:e1008764.
doi: 10.1371/journal.pgen.1008764 pmid: 32330129
[26] Doucet C M, Hetzer M W. Nuclear pore biogenesis into an intact nuclear envelope. Chromosoma, 2010,119:469-477.
doi: 10.1007/s00412-010-0289-2 pmid: 20721671
[27] Chen L Q, Luo J H, Cui Z H, Xue M, Wang L, Zhang X Y, Pawlowski W P, He Y. ATX3, ATX4, and ATX5 encode putative H3K4 methyltransferases and are critical for plant development. Plant Physiol, 2017,174:1795-1806.
pmid: 28550207
[28] Wang Y, Wang Y, Wang X, Deng D. Integrated meta-QTL and genome-wide association study analyses reveal candidate genes for maize yield. J Plant Growth Regul, 2020,39:229-238.
[29] Miedes E, Suslov D, Vandenbussche F, Kenobi K, Ivakov A, Van Der Straeten D, Lorences E P, Mellerowicz E J, Verbelen J P, Vissenberg K. Xyloglucan endotransglucosylase/hydrolase (XTH) overexpression affects growth and cell wall mechanics in etiolated Arabidopsis hypocotyls. J Exp Bot, 2013,64:2481-2497.
doi: 10.1093/jxb/ert107 pmid: 23585673
[30] Shikata M, Matsuda Y, Ando K, Nishii A, Takemura M, Yokota A, Kohchi T. Characterization of Arabidopsis ZIM, a member of a novel plant-specific GATA factor gene family. J Exp Bot, 2004,55:631-639.
doi: 10.1093/jxb/erh078 pmid: 14966217
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