玉米行粒数的全基因组关联分析

doi:10.3724/SP.J.1006.2017.01569

摘要/Abstract

摘要：

行粒数是玉米重要的产量构成性状之一, 对其遗传机理进行深入研究具有重要的理论和现实意义。本研究以吉林省80份核心玉米自交系作为关联群体, 于2014年和2015年分别在吉林省长春和梅河口进行行粒数测定。同时利用第2代测序技术对关联群体进行全基因组重测序, 获得的SNP标记用于后续分析。结果显示, 不同环境下玉米行粒数表型性状变异范围在12.0~41.6之间, 遗传力为36.4%。关联分析结果共得到19个与玉米行粒数显著关联的SNP标记, 其中位于染色体框2.04和3.08的两个标记在2015年长春和梅河口均被检测到, 14个SNP标记位于前人已定位到的QTL置信区间内。在显著性SNP标记的连锁不平衡区域内挖掘出4个候选基因, 分别预测编码泛素化目标受体蛋白、金属依赖性磷酸水解酶、重金属转运/解毒蛋白及一个无特征功能的假定蛋白, 可能与玉米行粒数的发育形成密切相关。

关键词: 玉米, 行粒数, 单核苷酸多态性, 关联分析

Abstract:

Kernel number per row in maize is a significant trait in determining yield components and it has great significance to study its genetic mechanism. This report studied 80 Jilin maize inbred lines in field experiments at Jilin Changchun and Jilin Meihekou, and measured kernel number per row in 2014 and 2015. At the same time, whole-genome resequencing was performed for the association population using second generation sequencing technology, and the obtained single nucleotide polymorphisms (SNPs) markers were used for subsequent analysis. The results revealed that the range of phenotypic traits of kernel number per row was from 12.0 to 41.6 and the broad-sensed heritability was 70.5% in four environments. A total of 19 SNP markers significantly associated with kernel number per row were detected by a genome-wide association study. Of these, two markers located at bins 2.04 and 3.08 of chromosome frame were detected in the experiments at Changchun and Meihekou in 2015, respectively, and 14 SNP markers located within the quantitative trait loci had been previously mapped. Four candidate genes, such as the genes encoding the receptor for ubiquitination targets protein, metal dependent phosphohydrolase, heavy metal transport/detoxification protein and putative protein with no characteristic function, were identified from the range of linkage disequilibrium of the significant SNP makers and predicted that they were closely associated to the development of the kernel number per row.

Key words: Maize, Kernel number per row, Single nucleotide polymorphism, Association analysis

吴律,代力强,董青松,施婷婷,王丕武*. 玉米行粒数的全基因组关联分析[J]. 作物学报, 2017, 43(10): 1559-1564.

WU Lyu, DAI Li-Qiang, DONG Qing-Song, SHI Ting-Ting,WANG Pi-Wu*. Genome-wide Association Analysis of Kernel Number per Row in Maize[J]. Acta Agron Sin, 2017, 43(10): 1559-1564.

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链接本文: https://zwxb.chinacrops.org/CN/10.3724/SP.J.1006.2017.01569

https://zwxb.chinacrops.org/CN/Y2017/V43/I10/1559

参考文献

[1]张怀胜, 陈士林, 王铁固. 玉米行粒数主基因+多基因混合遗传模型分析. 河南农业科学, 2013, 42(2): 30–33 Zhang H S, Chen S L, Wang T G. Genetic analysis on kernel number per row by mixed inheritance model of major gene and polygene in maize. J Henan Agric Sci, 2013, 42(2): 30–33 (in Chinese with English abstract) [2]孙峰成, 冯勇, 于卓, 赵瑞霞, 张来厚, 苏二虎, 刘志雄, 石海波. 12个玉米群体的主要农艺性状与产量品质的灰色关联度分析. 华北农学报, 2012, 27(1): 102–105 Sun F C, Feng Y, Yu Z, Zhao Y X, Zhang L H, Su E H, Liu Z X, Shi H B. Grey relativity analysis on main agronomic characters of 12 maize populations with their yields and traits. Acta Agric Boreali-Sin, 2012, 27(1): 102–105 (in Chinese with English abstract) [3]兰进好, 李新海, 高树仁, 张宝石, 张世煌. 不同生态环境下玉米产量性状QTL分析. 作物学报, 2005, 31: 1253–1259 Lan J H, Li X H, Gao S R, Zhang B S, Zhang S H. QTL analysis of yield components in maize under different environments. Acta Agron Sin, 2005, 31: 1253–1259 (in Chinese with English abstract) [4]Li M, Guo X H, Zhang M, Wang X P, Zhang G D, Tian Y C, Wang Z L. Mapping QTLs for grain yield and yield compo-nents under high and low phosphorus treatments in maize (Zea mays L.). Plant Sci, 2010, 178: 454–462 [5]Huo D, Ning Q, Shen X, Liu L, Zhang Z. QTL mapping of kernel number-related traits and validation of one major QTL for ear length in maize. PLoS One, 2016, 11: e0155506 [6]Chen J, Zhang L, Liu S, Li Z, Huang R, Li Y, Cheng H, Li X, Zhou B, Wu S, Chen W, Wu J, Ding J. The genetic basis of natural variation in kernel size and related traits using a four-way cross population in maize. PLoS One, 2016, 11: e0153428 [7]Knapp S J, Stroup W W, Ross W M. Exact confidence intervals for heritability on a progeny mean basis. Crop Sci, 1985, 25: 192–194 [8]Li H, Durbin R. Fast and accurate short read alignment with burrows-wheeler transform. Bioinformatics, 2009, 25: 1754–1760 [9]Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R. The sequence alignment/map format and SAMtools. Bioinformatics, 2009, 25: 2078–2079 [10]Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira M A, Bender D, Maller J, Sklar P, de Bakker P I, Daly M J, Sham P C. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet, 2007, 81: 559–575 [11]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. Bioinformatics, 2012, 28: 2397–2399 [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 [13]Tuberosa R, Salvi S, Sanguineti M C, Landi P, Maccaferri M, Conti S. Mapping QTL regulating morpho-physiological traits and yield: case studies, shortcomings and perspectives in drought-stressed maize. Ann Bot, 2002, 89: 941–963 [14]Tenaillon M I, Sawkins M C, Long A D, Gaut R L, Doebley J F, Gaut B S. Patterns of DNA sequence polymorphism along chromosome 1 of maize (Zea mays ssp. mays L.). Proc Natl Acad Sci USA, 2001, 98: 9161–9166 [15]Lu G H, Tang J H, Yan J B, Ma X Q, Li J S, Chen S J, Ma J C, Liu Z X, Zhu L, Zhang Y R, Dai J R. Quantitative trait loci mapping of maize yield and its components under different water treatments at flowering time. J Integr Plant Biol, 2006, 48: 1233–1243 [16]刘宗华, 汤继华, 卫晓轶, 王春丽, 田国伟, 胡彦民, 陈伟程. 氮胁迫和正常条件下玉米穗部性状的QTL分析. 中国农业科学, 2007, 40: 2409–2417 Liu Z H, Tang J H, Wei X Y, Wang C L, Tian G W, Hu Y M, Chen W C. QTL mapping of ear traits under low and high nitrogen conditions in maize. Sci Agric Sin, 2007, 40: 2409–2417 (in Chinese with English abstract) [17]代国丽, 蔡一林, 徐德林, 吕学高, 王国强, 王久光, 孙海艳. 玉米穗部性状的QTL定位. 西南师范大学学报(自然科学版), 2009, 34(5): 133–138 Dai G L, Cai Y L, Xu D L, Lyu X G, Wang G Q, Wang J G, Sun H Y. QTL mapping for ear traits in maize (Zea mays L.). J Southwest China Norm Univ (Nat Sci Edn), 2009, 34(5): 133–138 (in Chinese with English abstract) [18]杨俊品, 荣廷昭, 向道权, 唐海涛, 黄烈健, 戴景瑞. 玉米数量性状基因定位. 作物学报, 2005, 31: 188–196 Yang J P, Rong Y S, Xiang D Q, Tang H T, Huang L J, Dai J R. QTL mapping of quantitative traits in maize. Acta Agron Sin, 2005, 31: 188–196 (in Chinese with English abstract) [19]杨国虎. 玉米两个相关RILs群体遗传图谱构建及主要性状QTL分析. 河南农业大学博士学位论文, 河南郑州, 2011 Yang G H. Construction of Genetic Map and QTL Analysis for Main Traits Using Two Connected RIL Populations in Maize. PhD Dissertation of Henan Agricultural University, Zhengzhou, China, 2011 (in Chinese with English abstract) [20]Веденеев Г И (王富德译). 玉米数量性状的遗传控制: III. 穗行数和行粒数. 国外农学——杂粮作物, 1988, (3): 10–15 Веденеев Г И (Wang F D Trans). Genetic control of maize quantitative traits: III. Row number per ear and kernel number per row. Foreign Agron: Minor Cereals, 1988, (3): 10–15 (in Chinese) [21]王秀燕, 孙莉萍, 张建锋, 李辉, 吕文清, 张其清. F-box蛋白家族及其功能. 生命科学, 2008, 20: 807–811 Wang X Y, Sun L P, Zhang J F, Li H, Lyu W Q, Zhang Q Q. F-box proteins and their functions. Chin Bull Life Sci, 2008, 20: 807–811 (in Chinese with English abstract) [22]Aravind L, Koonin E V. The HD domain defines a new superfamily of metal-dependent phosphohydrolases. Trends Biochem Sci, 1998, 23: 469–472 [23]Yakunin A F, Proudfoot M, Kuznetsova E, Savchenko A, Brown G, Arrowsmith C H, Edwards A M. The HD domain of the Escherichia coli tRNA nucleotidyltransferase has 2’,3’-cyclic phosphodiesterase, 2’-nucleotidase, and phosphatase activities. J Biol Chem, 2004, 279: 36819–36827 [24]Palmgren M G, Axelsen K B. Evolution of P-type ATPases. Biochim Biophys Acta, 1998, 1365: 37–45 [25]金枫, 王翠, 林海建, 沈亚欧, 张志明, 赵茂俊, 潘光堂. 植物重金属转运蛋白研究进展. 应用生态学报, 2010, 21: 1875–1882 Jin F, Wang C, Lin H J, Shen Y O, Zhang Z M, Zhao M J, Pan G T. Heavy metal-transportproteins in plants: a review. Chin J Appl Ecol, 2010, 21: 1875–1882 (in Chinese with English abstract) [26]Seigneurin-Berny D, Gravot A, Auroy P, Mazard C, Kraut A, Finazzi G, Grunwald D, Rappaport F, Vavasseur A, Joyard J, Richaud P, Rolland N. HMA1, a new Cu-ATPase of the chloroplast envelope, is essential for growth under adverse light conditions. J Biol Chem, 2006, 281: 2882–2892

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