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作物学报 ›› 2021, Vol. 47 ›› Issue (7): 1228-1238.doi: 10.3724/SP.J.1006.2021.03048

• 作物遗传育种·种质资源·分子遗传学 • 上一篇    下一篇

玉米穗轴粗全基因组关联分析

马娟, 曹言勇, 李会勇*()   

  1. 河南省农业科学院粮食作物研究所, 河南郑州 450002
  • 收稿日期:2020-08-14 接受日期:2020-12-01 出版日期:2021-07-12 网络出版日期:2021-01-04
  • 通讯作者: 李会勇
  • 作者简介:E-mail: majuanjuan85@126.com
  • 基金资助:
    本研究由河南省科技攻关项目(182102110368);河南省农业科学院优秀青年基金资助(2020YQ04)

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 Published:2021-07-12 Published online:2021-01-04
  • Contact: LI Hui-Yong
  • 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)

摘要:

玉米穗轴粗是一个影响玉米产量和穗轴产量的重要性状, 其遗传机制的解析可以对高产育种提供指导。本研究以309份玉米自交系为材料, 利用测序基因型分型技术对其进行基因型鉴定。采用FarmCPU (fixed and random model circulating probability unification)、MLMM (multiple loci mixed linear model)和CMLM (compressed mixed linear model)方法, 对2017年和2019年河南原阳、河南郸城、河南虞城、海南三亚以及最佳线性无偏估计值环境的穗轴粗, 进行全基因组关联分析, 共鉴定12个与穗轴粗显著关联的SNP (single nucleotide polymorphisms) (P<8.60E-07)。其中, S4_29277313利用FarmCPU和MLMM方法在2017年原阳均检测到。S1_29006330、S2_170889116、S2_2046026464和S4_83821463的表型变异解释率介于10.23%~14.17%, 为主效SNP。而且, S1_29006330位于已定位的穗轴粗QTL (quantitative trait loci)区间内。共挖掘17个候选基因, 其中WAKL14 (wall-associated receptor kinase-like 14)、转录因子ZIM35 (zinc-finger protein expressed in inflorescence meristem 35)、HMGA (HMG-Y-related protein A)、组蛋白-赖氨酸N甲基转移酶ATX4 (Arabidopsis trithorax 4)和XTH32 (xyloglucan endotransglucosylase/hydrolase protein 32), 可能是影响穗轴粗的重要基因。挖掘的4个穗轴粗主效SNP和5个候选基因可以为分子标记辅助育种、精细定位和基因克隆提供信息。

关键词: 玉米, 全基因组关联分析, FarmCPU, 穗轴粗

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

表1

多环境联合方差分析"

变异来源
Source
自由度
Degree of freedom
方差和
Sum of square
均方
Mean of square
F
F-value
P
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

图1

不同环境间穗轴粗相关性分析和柱状图 图中对角线方框为性状的柱状图, 上三角为相关系数和显著性程度。0.05、0.01、0.001 显著水平分别标*、**、***。下三角为不同环境间穗轴粗的散点图。SY、DC、YC和YY分别表示三亚、郸城、虞城和原阳。2017和2019代表年份。"

图2

穗轴粗显著SNP的曼哈顿图和QQ图 A和B: BLUE环境FarmCPU方法; C和D: 2017年郸城FarmCPU方法; E和F: 2017年三亚FarmCPU方法; G和H: 2017年虞城FarmCPU方法; I和J: 2017年原阳FarmCPU方法; K和L: 2017年原阳MLMM方法。"

表2

不同环境不同方法检测的穗轴粗显著SNP和候选基因"

方法Method 标记名称
Marker name
染色体
Chr.
位置
Position (Mb)
bin P
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)

附图1

CMLM方法不同环境穗轴粗(CD)全基因组关联分析的曼哈顿图和QQ图 DC、YC、SY、YY分别表示郸城、虞城、三亚和原阳。2017和2019代表年份。"

附图2

MLMM和FarmCPU方法没有检测到显著穗轴粗SNP的曼哈顿图和QQ图 DC、YC、SY、YY分别表示郸城、虞城、三亚和原阳。2017和2019代表年份。"

附图3

maizeGDB中17个候选基因在不同组织的表达情况"

[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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