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普通小麦氮素利用效率相关性状全基因组关联分析

本站小编 Free考研考试/2021-12-26
靳义荣,1,**, 刘金栋,2,**, 刘彩云1, 贾德新1, 刘鹏,1,*, 王雅美,2,*1德州市农业科学研究院, 山东德州 253015
2中国农业科学院深圳农业基因组研究所, 广东深圳 518124

Genome-wide association study of nitrogen use efficiency related traits in common wheat (Triticum aestivum L.)

JIN Yi-Rong,1,**, LIU Jin-Dong,2,**, LIU Cai-Yun1, JIA De-Xin1, LIU Peng,1,*, WANG Ya-Mei,2,* 1Dezhou Academy of Agricultural Sciences, Dezhou 253015, Shandong, China
2Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, Guangdong, China

通讯作者: *刘鹏, E-mail: liup9@163.com; 王雅美, E-mail: wangyamei@caas.cn

同等贡献(Contributed equally to this work)
收稿日期:2020-03-24接受日期:2020-09-13网络出版日期:2021-03-12
基金资助:山东省西部经济隆起带基层科技人才支持计划项目.XB2018FW029
山东省重点研发计划项目.2017GNC10107
山东省自然科学基金项目.ZR2017BC015
山东省农业科学院农业科技创新工程项目资助.CXGC2016B01


Received:2020-03-24Accepted:2020-09-13Online:2021-03-12
Fund supported: Support Project for Grassroots Scientific and Technological Talents of Shandong Western Economic Swells up Belt.XB2018FW029
Key Research and Development Program of Shandong Province (2017GNC10107).2017GNC10107
Natural Science Foundation of Shandong Province (ZR2017BC015), .ZR2017BC015
Agricultural Science and Technology Innovation Project of Shandong Academy of Agricultural Sciences.CXGC2016B01

作者简介 About authors
靳义荣, E-mail: jyr2014@163.com;

刘金栋, E-mail: liujindong_1990@163.com







摘要
氮元素在粮食作物生长和发育过程起着不可替代的作用。发掘氮素利用效率相关基因对于提升小麦产量、减少环境污染具有重要意义。植株根系构型(root system architecture, RSA)代表着根系的结构及空间造型, 显著受氮素水平影响。本研究在正常供氮和缺氮两种氮素水平下, 对160份来自黄淮冬麦区和北部冬麦区普通小麦品系的根系构型相关性状 (总根长、总根表面积、总根体积、平均根直径和根尖数)进行统计, 并结合小麦660K SNP (single nucleotide polymorphism)芯片基因分型数据对根系相关性状的相对值进行全基因组关联分析, 以期发掘氮素利用效率相关位点。本研究检测到34个与氮素利用效率显著关联的SNP位点, 可解释6.9%~15.4%的表型变异。关联位点在所有染色体均有分布, 主要集中于1A、2B、3B、5B、6A、6B和7A染色体。11个位点与已报道位点重叠或接近, 其他23个位点可能为新的位点。另外, 在3B染色体上发现一个编码E3泛素连接酶的候选基因。
关键词： 普通小麦;根系构型;氮素利用效率;全基因组关联分析;660K SNP芯片

Abstract
Nitrogen application plays an important role in plant growth and development. Exploring genetic loci related to nitrogen use efficiency is of great significance for improving wheat yield and reducing environmental pollution. Root system architecture (RSA) determined the composition of plant root system, and significantly affected by nitrogen level. Under different nitrogen levels (deficiency and normal), 160 winter wheat accessions from the Huanghuai valley and Northern winter wheat region were counted for their root architecture-related traits (total root length, total root surface area, total root volume, average root diameter, and root tip number). Genotype was analyzed using 660K SNP (single nucleotide polymorphism) data. Genome-wide association study (GWAS) was employed to identify the relevant loci for nitrogen use efficiency. A total of 34 associated loci were detected, which explained 6.9%-15.4% of the phenotypic variation. These loci distributed on all chromosomes and mainly centered on chromosomes 1A, 2B, 3B, 5B, 6A, 6B, and 7A, respectively. Among the loci detected in this study, 11 loci overlapped or were close to the reported ones, while the other 23 might be novel loci. In addition, we explored a candidate gene encoding the E3 ubiquitin ligase. This study is of great significance for understanding the genetic mechanism of nitrogen utilization and breeding high-yield wheat varieties.
Keywords：common wheat;root architecture;nitrogen use efficiency;GWAS;660K SNP chip


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本文引用格式
靳义荣, 刘金栋, 刘彩云, 贾德新, 刘鹏, 王雅美. 普通小麦氮素利用效率相关性状全基因组关联分析[J]. 作物学报, 2021, 47(3): 394-404. doi:10.3724/SP.J.1006.2021.01024
JIN Yi-Rong, LIU Jin-Dong, LIU Cai-Yun, JIA De-Xin, LIU Peng, WANG Ya-Mei. Genome-wide association study of nitrogen use efficiency related traits in common wheat (Triticum aestivum L.)[J]. Acta Agronomica Sinica, 2021, 47(3): 394-404. doi:10.3724/SP.J.1006.2021.01024


氮元素在植物正常生长发育过程中发挥着重要作用。但是, 长期以来, 小麦生产过程中氮肥施用量严重超出实际需求量, 氮肥利用率只有21.2%~ 35.9%。大量损失的氮素进入大气和地下水中, 严重污染水环境。另一方面, 贫困地区田间氮肥施用水平较低, 小麦产量长期处于较低水平。开展小麦氮素利用效率, 选育氮高效的新品种是提高产量和品质, 降低环境危害的有效途径。

根系作为植物最为重要的地下器官, 在植株生长发育过程中发挥着重要作用[1,2,3]。根系构型(root system architecture, RSA)主要指根系的结构及空间造型, 包括多个相关性状, 例如总根长、总根表面积、总根体积、根尖数和平均根直径等[3,4], 是影响作物产量、品质和抗性最为重要的因素之一, 与氮素吸收和转运等生理特性密切相关[5,6,7]。植株根系长度, 表面积和体积决定着植株氮素的吸收利用效率[3]。拥有大直径木质部导管的粗根系更利于穿透深层次土壤, 提高养分吸收效率[3]。另外, 根尖数增加可有效提升根表面积和体积, 影响养分利用效率[6,8]。根系的根长、直径和吸收面积等性状与千粒重、粒长、粒宽等产量性状呈显著正相关[3]。因此, 根系构型是植物氮素利用能力的体现[6,8]。

氮素利用效率是典型的复杂数量性状, 由多基因共同控制。QTL定位(quantitative trait locus)又称连锁分析, 是研究数量性状遗传功能的常用方法。在大田条件下定位氮素利用效率相关性状的QTL位点, 具有试验周期长, 且易受环境条件影响的缺点。相较于传统的连锁分析, 全基因组关联分析(genome-wide association study, GWAS)以自然群体为材料, 来源广泛 (野生种、地方品种、现代品种和高代品系), 基因多态性较高, 无需构建双亲群体, 具有发掘效率高、分辨率高且成本较低的优点。近年来, 小麦SNP技术快速发展, 国内外已开发出多款小麦SNP芯片, 如9K、90K、660K和820K芯片, 为GWAS分析提供了极为丰富且覆盖度较高的基因型数据。迄今为止, 基于SNP标记的GWAS分析在小麦加工品质[9,10]、生理[11,12]和抗病性[9,13-14]等相关性状基因的发掘研究中已广泛使用。

植物中拥有复杂的氮响应机制, 在不同的氮素水平条件下会引起植物形成一系列的生理和生化变化。NH4+可通过IAA、GA或乙烯等方式生成信号作用于下游调控蛋白以调节植物根系生长[1,2]。苗期根部性状作为表型进行氮素利用效率位点挖掘已在相关研究中广泛应用。Lian等[15]在差异氮素含量水平下对水稻苗期根系组成性状开展QTL定位, 发现仅个别QTL在多个环境下共同存在, 认为不同氮素环境下根系相关性状的定位可用于解析植物氮素利用效率遗传机制。An等[16]以小麦为研究材料, 在大田试验条件下分别对氮素吸收效率和苗期根部性状进行遗传定位, 发现多个苗期根系发育相关性状的QTL与田间氮吸收的QTL位点一致。以上研究表明, 对植株根系生长发育相关性状进行探究, 可有效反映植株氮素利用效率的遗传特征, 解析氮素利用效率遗传机制。通过大田或苗期水培实验, 在不同的氮素水平下已发掘一批影响小麦植株根系、植株生长以及产量相关性状的QTL位点, 主要集中于小麦2A、2B、4A、5A、7A和7B染色体上[16,17,18,19]。

我国黄淮冬麦区和北部冬麦区是小麦最为重要的产区, 是保障我国粮食安全的重要基础。本研究以160份来自于黄淮和北部冬麦区的育成品种、高代品系和地方品种为材料, 在正常供氮和缺氮两种氮素水培环境下研究不同基因型苗期根系构型相关性状, 并结合小麦660K芯片进行GWAS分析, 以发掘氮元素利用效率相关基因, 为培育高氮素利用效率小麦品种提供参考。

1 材料与方法

1.1 试验材料

采用160份主要来自于黄淮和北部冬麦区的小麦地方品种(20份)、育成品种(76份)和高代育种品系(64份)在不同氮素水平水培条件下进行氮素利用效率相关性状的GWAS分析(附表1)。

Table s1
附表1
附表1160份小麦材料及其根系构型相关表型数据
Table s1The details of the 160 common wheat accessions and their root system architecture (RSA) related traits
品种（系）
名称		类别来源氮+磷+钾 (N+P+K)磷+钾 (P+K)
总根长总根表面积平均根直径总根体积根尖数总根长总根表面积平均根直径总根体积根尖数
TRL (cm)TRS (cm2)ARD (mm)TRV (cm3)NRTTRL (cm)TRS (cm2)ARD (mm)TRV (cm3)NRT
石特14育成种196.320.20.6910.42958.0461.325.90.3870.800590.0
复壮30育成种274.219.10.5330.424141.0252.421.70.4410.512411.5
平阳27育成种102.511.40.9230.39738.0282.920.70.4830.618320.0
蚰包地方品种104.113.70.6560.27348.5439.626.00.3860.513226.5
郑州6号育成种河南208.019.80.5780.432152.5406.825.50.3950.773702.0
豫麦49育成种河南233.019.80.6660.48468.5350.822.60.3850.436565.5
白芒麦1地方品种246.322.00.6520.473112.5396.125.30.3980.633514.5
半截芒地方品种158.517.60.7440.34486.0263.921.80.4030.376278.0
老来瞎地方品种110.513.60.9810.38026.5265.422.40.3900.429272.0
西山扁穗地方品种141.415.30.5810.29372.5473.323.70.4200.528316.5
红狗豆地方品种128.415.20.7290.27740.0255.618.80.4780.497254.5
蚰子麦育成种271.621.50.7390.593121.5433.825.00.4280.534375.0
白扁穗地方品种127.514.70.8800.45847.0367.326.30.4200.705428.5
白齐麦地方品种150.414.20.7320.31082.5285.520.70.3960.395315.0
白秃子头地方品种168.117.20.5850.32881.5363.823.40.4070.531539.0
有芒扫谷旦地方品种254.619.50.6710.538142.5401.023.70.4270.493364.5
阜阳红地方品种安徽276.224.20.7430.727116.0347.324.50.3980.566487.0
蚂蚱麦地方品种219.322.30.5400.345133.0283.223.80.4210.366270.0
抢场麦地方品种101.512.90.7690.25024.5306.219.30.4020.390357.5
火麦地方品种179.716.60.4980.358112.5442.823.00.4340.519338.0
碱麦地方品种190.319.20.6420.32175.5404.025.70.3800.539375.0
大口麦地方品种138.019.60.7810.29439.0189.514.60.5060.486208.0
白条鱼地方品种155.518.00.6550.38375.0436.219.60.4190.475450.0
老齐麦地方品种228.820.40.4180.574276.5360.519.80.3900.464514.0
出山豹地方品种191.820.00.5600.572105.5409.223.30.4620.652253.0
大粒半芒地方品种231.623.70.4940.434260.0342.420.00.3920.527285.0
扁穗麦育成种214.119.00.6780.50692.5353.224.50.4160.643375.0
齐大195育成种159.417.90.8860.724117.5587.525.50.4030.543376.0
黄县大粒半芒育成种156.518.00.5170.28995.0490.122.70.4260.601357.0
泗水三八麦育成种山东350.525.70.4750.548177.0631.325.80.3920.965593.5
蚰包麦育成种197.916.40.8020.564116.0292.820.80.4360.403220.0
碧蚂1号育成种209.822.10.4810.363124.0426.522.80.3900.624488.0
烟农78育成种山东182.117.30.4980.357174.0522.425.70.4340.878475.5
济宁3号育成种山东192.219.80.8160.62492.5358.722.90.4320.504483.0
鲁麦8号育成种山东121.816.40.9510.47830.0244.320.20.5100.678362.0
鲁麦12号育成种山东231.321.60.7100.523102.5529.925.90.4040.570390.5
山农辐63育成种山东271.222.80.6090.645142.0358.724.70.4140.665431.0
山农587育成种山东194.118.40.5700.44083.5518.825.70.3870.398551.0
刑麦9号高代品系268.821.90.6040.566214.5353.825.10.4400.538616.0
鲁麦1号育成种山东406.218.80.4040.716299.0406.218.80.4040.716299.0
鲁麦22育成种山东222.819.40.7190.65397.0427.426.90.3970.663521.0
鲁麦23育成种山东116.212.60.7710.37142.0215.018.90.3700.615543.0
鲁麦16育成种山东259.320.30.7100.706128.5558.425.80.3980.666349.5
鲁麦19育成种山东203.620.10.6110.37597.0330.121.10.3740.608627.0
鲁麦20育成种山东86.311.80.9710.42428.0347.824.50.4200.651422.5
淄麦12育成种河南307.523.70.5950.746171.0288.222.80.4120.392393.0
烟辐188育成种山东264.323.30.5610.507171.5525.424.30.4210.785483.5
山农664育成种山东223.519.70.6080.42971.0373.122.10.4320.560375.0
汶农5号育成种山东179.716.10.7630.42898.5368.424.50.4310.867551.0
莱州137育成种山东267.719.40.7140.602141.0459.224.00.3870.615569.5
烟2415育成种山东238.021.00.5730.734153.5443.024.70.4350.434323.5
山农11育成种山东161.816.10.6070.352103.0276.921.80.4080.534682.5
淄麦7号育成种河南223.820.10.5790.531171.5392.922.60.4180.650578.0
金铎1号育成种237.517.90.6460.32287.5304.520.40.4820.787284.5
济南18育成种山东135.813.50.7850.33640.0269.521.60.3960.523380.0
山融3号育成种山东179.518.50.6880.391118.0388.623.40.4060.644490.5
偃麦4110育成种河南347.023.90.4950.570377.0532.323.80.3640.652678.5
良星99育成种山东282.325.10.4850.645248.5300.621.20.4050.575528.5
济麦33高代品系山东270.222.90.4990.587268.0421.824.40.4070.639442.5
济麦37高代品系山东177.318.00.7410.573133.0515.324.90.4350.554430.0
济麦0419高代品系山东172.416.70.7110.49847.5266.119.90.4710.428367.5
济麦0536高代品系山东204.720.10.7700.528396.5420.626.00.4590.688232.0
济麦08101高代品系山东368.917.40.6270.463123.0416.324.00.3750.516673.5
济麦36高代品系山东286.421.30.5790.56976.0541.925.60.4070.796441.0
济麦47高代品系山东296.724.60.4820.612153.0412.225.70.3910.372344.0
济麦49高代品系山东222.820.70.5600.488123.0411.519.50.3780.613593.0
济麦23育成种山东213.620.10.5980.521127.0286.123.70.4330.483326.5
山农15（B245)育成种山东205.521.40.7150.59195.5336.922.00.3720.592749.0
良星77育成种山东239.821.40.6980.49380.0472.124.90.4590.747789.5
鑫麦296育成种95.611.60.8350.35926.5557.125.10.4010.758650.5
郯麦98育成种山东240.723.00.5080.431138.0433.319.00.3970.536736.5
泰农19育成种山东163.414.50.6440.39471.0345.420.10.4850.684263.5
汶农14育成种山东123.117.50.8560.55652.0448.822.00.4340.511358.5
山农0431高代品系山东157.714.80.7870.47954.5450.323.70.4140.613381.0
山农11J1565高代品系山东190.020.00.5050.25074.0334.522.80.3930.738594.5
山农21育成种山东249.122.50.5960.59987.5471.024.50.3930.630427.5
山农22育成种山东171.018.90.5780.406118.0493.122.80.3990.630672.0
山农670高代品系山东196.921.10.5840.592142.5429.922.10.4240.521288.0
山农711高代品系山东324.126.00.4970.554200.5518.124.40.4290.757450.5
山农737高代品系山东277.926.30.4640.573252.5391.121.70.4660.516290.0
山农0713-2高代品系山东277.426.30.4990.548247.5568.825.40.4081.043709.5
山农11J0635高代品系山东191.617.00.6230.435204.5464.123.00.4130.757621.5
山农12J1740高代品系山东171.215.80.5630.529129.0360.420.30.4140.668299.0
烟农173育成种山东314.326.20.4980.459149.0384.823.20.4180.560463.5
存麦1号育成种287.623.40.4410.562222.0457.223.50.3970.956555.0
花5高代品系235.223.80.5120.461146.0457.523.50.3790.749730.0
花8高代品系194.323.40.6620.44376.0479.425.10.4150.679505.0
济创15高代品系山东163.617.70.8160.41456.5330.419.40.4110.494340.5
济创23高代品系山东218.723.20.4520.367151.0523.324.70.3860.610564.0
济创28高代品系山东186.420.00.6920.494104.5432.224.00.4070.529363.0
龙麦1号高代品系山东175.418.90.6330.33397.0413.422.00.3760.306401.5
洛麦21育成种河南192.919.70.6770.45683.5416.422.40.4410.825535.0
神麦1号育成种160.315.20.5220.33293.0415.921.00.3920.565349.0
郑麦366育成种河南224.117.20.5560.39093.0554.425.70.4120.812534.5
周黑麦1号育成种河南191.822.60.6310.39194.0488.623.60.4370.645315.0
周麦30育成种河南270.521.60.5980.663131.5449.924.30.4210.816441.0
周麦31高代品系河南210.020.60.6160.43080.0371.723.50.4550.583418.0
BN10F(002)加1-特1高代品系101.111.20.5950.23148.0388.425.60.4120.880403.5
兰材282高代品系河南495.725.90.4190.532290.0547.822.50.4080.702465.0
兰材633高代品系河南259.325.00.5030.395126.0527.524.10.3780.732562.0
兰材693高代品系河南318.723.90.4570.475227.0474.323.20.4080.657574.5
兰考198高代品系河南188.817.30.6670.368108.0432.122.00.4390.598563.0
兰考298高代品系河南183.919.80.6420.40184.0264.518.40.4230.536486.5
山农1565高代品系山东150.218.50.8100.46151.0485.123.80.3770.737444.0
兰考323高代品系河南180.816.50.5540.355108.0324.525.60.3970.702471.0
兰考358高代品系河南140.717.30.6600.39173.0370.123.10.4300.667359.0
兰考380高代品系河南196.020.00.4620.506192.5501.624.70.4010.644524.5
兰考381高代品系河南179.124.00.5040.455165.0512.423.70.4160.682640.5
兰考396高代品系河南162.217.30.5960.325140.5340.822.60.4460.541480.5
百农160育成种208.519.70.6730.286136.5399.826.20.4300.562365.5
河农7069育成种河南156.918.10.6710.31754.5460.222.30.4070.672518.5
河农9206育成种河南274.322.20.5560.57591.5606.225.60.3881.073532.0
冀矮1号高代品系90.013.90.6560.22932.0382.322.70.4360.655506.0
冀麦325育成种214.121.30.5510.491117.5397.320.60.4320.437334.0
金博士731高代品系140.318.40.5230.302126.0547.224.00.4380.766720.5
石矮1号高代品系220.718.60.6590.424105.5527.524.60.3830.583881.0
石新733育成种224.923.40.4730.485177.5499.024.80.4330.677543.0
刑麦13号育成种237.521.80.5120.428128.5299.021.90.5420.534256.0
舜麦1718育成种262.123.00.6400.470116.5617.626.20.3980.767576.0
徐麦31育成种江苏206.822.00.6080.627118.5539.226.10.4270.767720.0
徐麦27育成种江苏353.726.80.4260.702234.0483.223.80.4470.728519.5
徐麦856育成种江苏237.021.60.5570.644163.5558.524.90.4170.911793.0
西农979育成种346.125.00.4910.743189.5463.821.80.4060.683549.5
普冰2011高代品系226.618.30.4840.420115.0361.816.00.4540.445285.5
中植9号高代品系河南190.521.10.5780.33971.5508.523.00.4660.599629.0
宿2013高代品系195.917.30.8130.38949.5425.321.80.4490.532352.0
安农1206高代品系261.521.80.4700.538194.5459.824.60.3850.702546.0
PI27012高代品系191.320.40.5620.38389.0365.820.70.4140.584528.0
宿8802（皖麦19）育成种188.717.60.5770.46952.5564.423.70.4370.521456.0
济宁114076高代品系山东173.720.50.6570.438118.0437.725.00.4180.563474.5
泰科麦6338（1）高代品系山东251.322.30.6390.477170.5524.624.80.4040.450495.0
泰科麦50206（3）高代品系山东219.718.90.7220.36792.5429.523.50.4210.574315.5
济南4号育成种山东120.817.00.5690.448111.5301.321.00.4110.442440.5
济麦25高代品系山东249.724.00.4920.505106.5286.224.40.4260.770623.5
济麦31高代品系山东248.825.10.5220.692263.0419.326.20.4000.638655.0
鲁原502育成种山东170.017.00.5330.25945.0529.525.80.3880.745697.5
临麦6号高代品系山东114.915.20.6450.30372.5292.917.90.3770.567614.5
烟农999育成种山东181.418.60.6700.39973.5325.620.60.4120.471355.0
周麦16育成种河南181.518.30.6080.36491.0400.122.90.4590.547391.0
周麦22育成种河南146.614.40.5760.30099.5282.524.20.4080.726493.0
石H83-366高代品系166.619.50.6800.35875.5199.017.00.4390.338269.5
淮麦05155高代品系245.621.80.6560.42780.0479.524.40.4240.698870.0
峰川9号育成种239.123.40.5710.432118.0258.118.20.4250.309273.0
烟农187高代品系山东226.721.40.4790.488204.5353.022.20.3770.562490.0
LA15P315高代品系185.818.90.6660.47188.5330.924.20.4430.752571.0
LA15P330高代品系141.815.60.5980.31072.5349.022.00.4780.613345.5
济麦51高代品系山东197.916.70.5950.45893.0363.522.70.4210.486466.5
济麦52高代品系山东120.016.20.7790.32553.5322.523.20.4140.646638.0
济麦229育成种山东254.224.10.4760.420221.5375.622.20.3860.646524.5
济麦262育成种山东196.521.70.5450.500101.5326.220.80.4290.622468.0
济麦66高代品系山东198.520.30.5690.537137.5312.621.80.4120.760402.5
LJ154052高代品系168.918.30.5620.268104.5406.724.80.3630.500510.5
LJ15鉴010高代品系322.724.90.5190.584209.5481.424.00.3880.703604.0
LJ15鉴16高代品系104.612.20.6820.34291.5463.123.80.3820.917801.5
LJ15鉴71高代品系203.519.00.6070.526111.5284.321.40.4330.531248.5
济麦21育成种山东161.315.40.6150.408105.5271.318.80.3790.523490.0
济麦22育成种山东233.118.60.5240.457118.5488.826.10.3720.637490.0
济麦44高代品系山东256.225.00.5650.36436.0325.319.60.4260.679597.5
德抗961育成种133.416.20.5700.32296.5248.620.00.4820.723315.0
LJ15210高代品系237.620.30.4520.421119.5322.922.20.4120.596501.0

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1.2 水培试验

试验在室内水培条件下进行。采用两种营养液: 完全营养液和缺氮营养液。营养液配制参考Hoagland营养液[20], 根据小麦苗期生长需求略有改动。完全营养液组分如下: 1.8 mmol L-1 KCl、1.5 mmol L-1 CaCl2、1.0 mmol L-1 Ca(NO3)2、1.0 mmol L-1 (NH4)2SO4、0.5 mmol L-1 MgSO4、0.2 mmol L-1 KH2PO4、1.0×10-3 mmol L-1 H3BO3、1.0×10-3 mmol L-1 ZnSO4、1.0×10-3 mmol L-1 MnSO4、0.5×10-3 mmol L-1 CuSO4、0.1 mmol L-1 Fe-EDTA、1.0×10-4 mmol L-1 (NH4)6Mo7O24。缺氮营养液的组分如下: 1.8 mmol L-1 KCl、1.5 mmol L-1 CaCl2、1.0 mmol L-1 Ca(NO3)2、0.5 mmol L-1 MgSO4、0.2 mmol L-1 KH2PO4、1.0×10-3 mmol L-1 H3BO3、1.0×10-3 mmol L-1 ZnSO4、1.0×10-3 mmol L-1 MnSO4、0.5×10-3 mmol L-1 CuSO4、0.1 mmol L-1 Fe-EDTA、1.0×10-4 mmol L-1 (NH4)6Mo7O24。

挑选饱满、大小一致的小麦籽粒置于10% H2O2溶液中浸泡10 min, 去离子水冲洗3次后, 置于湿润滤纸上培养。在一叶一心期, 选取长势一致的幼苗, 固定在育苗盘中, 将育苗盘分别置于完全营养液和缺氮营养液中。通过氧气泵对营养液进行持续通气, 并且每隔3 d更换一次营养液, 培养至21 d后, 用去离子水将根部冲洗干净, 用剪刀将根和茎在分节处剪断, 分别置于自封袋中备测。

1.3 根系相关表型测定

于每份样品中随机选择10个单株, 将各单株侧根逐一分开, 在不重叠的状态下置于装有去离子水的透明根盘中, 利用WinRHIZOLA6400XL透视扫描系统对根系进行扫描, 获取各单株根系图片。然后利用WinRHIZOPro软件分析各样品的根尖数(number of root tips, NRT)、根系总长度(total root length, TRL)、根系总表面积(total root surface area, TRS)、根系总体积(total root volume, TRV)和根系平均直径(average root diameter, ARD)等表型数据, 取3次重复的平均值作为材料的表型数据值。随后, 计算2种不同氮素条件下各性状的相对值, 即相对根尖数(relative number of root tips, RNRT)、相对总根长(relative total root length, RTRL)、相对总根表面积(relative total root surface area, RTRS)、相对总根体积(relative total root volume, RTRV)和相对根平均直径(relative average root diameter, RARD)。采用SAS 9.2 (www.sas.com)软件统计分析数据, 并计算相关系数。

1.4 基因型检测

利用CTAB法提取小麦幼嫩叶片基因组DNA[21]。本研究基因型分析采用小麦660K SNP芯片 (包含630,517个SNP标记) (博奥晶典, http:// www.capitalbiotech.com/)在剔除缺失染色体定位信息和物理位置的标记后, 筛选最小等位基因频率(minor allele frequency, MAF) > 0.05和缺失率(missing rate) < 20% (PowerMarker v3.25)的标记用于下一步GWAS分析。利用多态性信息含量 (polymorphism information content, PIC)和基因多态性两个参数对黄淮冬麦区和北部冬麦区群体遗传多态性进行分析。PIC值基于PowerMarker v3.25按照以下公式计算: PIC = 1 - ∑ (pi)2。其中, pi为第i个等位基因在群体中出现的频率。基因多态性的计算也由PowerMarker v3.25完成。

1.5 主成分分析 (principal components analysis, PCA)和LD衰减分析

PCA分析通过Tassel v5.0 (https://www.maizegenetics.net/tassel)开展。PCA可视化在R环境下通过Pot3D (http://www.harrisgeospatial.com/docs/ PLOT3D. html)和Satterplot3d (http://www.sthda.com/english/ wiki/scatterplot3d-3d-graphics-r-software-and-data- visualization)程序包完成。利用Tassel v5.0计算标记间LD。

1.6 全基因组关联分析

应用Tassel v5.0软件中的MLM模型, 以主成分分析 (PCA)和亲缘关系 (Kinship)作为协变量, 结合筛选后的基因型数据, 对160份小麦品系根系构型相关性状进行关联分析。GWAS分析结果中, 当P≤0.001时, 认为该标记与性状显著关联。曼哈顿图和Q-Q图可视化在R环境中通过CMplot程序包 (https://github.com/YinLiLin/R-CMplot)完成。

2 结果与分析

2.1 表型数据分析

160份普通小麦材料在完全营养液中NRT、TRL、TRS、TRV和ARD的均值分别为119.97、210.23 cm、19.62 cm2、0.42 cm3和0.46 mm; 在缺氮营养液中NRT、TRL、TRS、TRV和ARD的均值分别为476.83、406.85 cm、22.97 cm2、0.62 cm3和0.62 mm (表1, 附表1, 附图1, 附图2, 附图3)。160份材料在2种营养液中RNRT、RTRL、RTRS、RTRV和RARD的均值分别为: 5.15 (0.59~16.60)、2.10 (0.92~5.83)、1.21 (0.74~2.27)、1.49 (0.95~2.52)和1.44 (0.53~3.82)。2种培养环境下根系相关性状比值均呈连续性分布, 变异范围广, 遗传基础丰富, 适于进行GWAS分析 (附图4)。对各性状开展相关性分析, 2种培养条件下根系构成相关性状比值呈显著正相关。其中, RTRL与RTRS (r = 0.718)、RARD (r = 0.383)、RTRV (r = 0.484)、RNRT (r = 0.513)呈显著正相关 (P < 0.05); RTRS与RARD (r = 0.531)、RTRV (r = 0.475)和RNRT (r = 0.492)呈显著正相关 (P < 0.05); RARD与RNRT呈显著正相关 (r = 0.514, P < 0.05); RTRV和RNRT也呈显著正相关 (r = 0.439, P < 0.05)。

Table 1
表1
表1160份普通小麦品种根系构型相关性状表型统计
Table 1Summary of root system architecture (RSA) traits under different nitrogen level in 160 common wheat accessions
处理
Treatment		性状
Trait		极小值
Min.		极大值
Max.		均值
Average		标准差
Standard deviation		变异系数
CV (%)
完全营养液
Absolute nutrient solution		TRL (cm)86.33495.67210.2364.4430.7
TRS (cm2)11.2526.8519.623.4817.7
TRV (cm3)0.400.980.420.1218.8
ARD (mm)0.230.750.460.1226.2
NRT24.50396.50119.9765.5054.6
缺氮营养液
N-deficiency
nutrient solution		TRL (cm)189.52631.29406.8594.2123.2
TRS (cm2)14.6226.8722.972.279.9
TRV (cm3)0.360.540.620.037.1
ARD (mm)0.311.070.620.1422.6
NRT208.00881.00476.83145.2030.5
相对比值
Relative ratio		RTRL0.925.832.100.7535.7
RTRS0.742.271.210.2621.2
RTRV0.952.521.490.2819.2
RARD0.533.821.440.5034.6
RNRT0.5916.605.153.0654.7
TRL: 根系总长度; TRS: 根系总表面积; TRV: 根系总体积; ARD: 根系平均直径; NRT: 根尖数; RTRL: 相对总根长; RTRS: 相对总根表面积; RTRV: 相对总根体积; RARD: 相对根平均直径; RNRT: 相对根尖数。
TRL: total root length; TRS: total root surface area; TRV: total root volume; ARD: average root diameter; NRT: number of root tips; RTRL: relative total root length; RTRS: relative total root surface area; RTRV: relative total root volume; RARD: relative average root diameter; RNRT: relative number of root tips.

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附图1

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附图1160份小麦品种根系构型相关性状表型分布（氮+磷+钾）

Fig. S1Distribution of root system architecture (RSA) related traits in 160 common wheat accessions (N+P+K)



附图2

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附图2160份小麦品种根系构型相关性状表型分布（磷+钾）

Fig. s2Distribution of root system architecture (RSA) related traits in 160 common wheat accessions (P+K)



附图3

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附图3160份小麦品种不同氮素条件下相对根系构型相关性状表型分布

Fig. s3Distribution of relative root system architecture (RSA) related traits under different nitrogen level in 160 common wheat accessions



2.2 基因型分析

为保证关联结果的准确性, 过滤掉物理位置缺失、MAF < 5%且缺失率 > 20%的SNP标记, 最终剩余223,168个标记用于进一步分析。标记覆盖所有染色体区段, 物理图谱总长度14,063.9 Mb, 平均密度为0.097 Mb/标记。A、B和D基因组长度分别为4934.5、5179.0和3950.4 Mb, 分别包含93,664 (41.9%)、108,936 (56.3%)和20,568 (9.2%)个标记。B基因组密度最高 (0.048 Mb/标记), 其次为A基因组(0.052 Mb/标记), D基因组标记密度最低(0.193 Mb/标记, 表2)。

Table 2
表2
表2160份普通小麦品系基因型数据统计
Table 2Genotype data statistics of 160 common wheat lines
染色体
Chr.		标记数目
Number of markers		长度 a
Length a
(Mb)		密度
Density
(Mb/marker)		最小等位基因频率
Minimum allele frequency (MAF)		基因多态性
Genetic
diversity		多态性信息含量
Polymorphic information content (PIC)
均值
Average		范围
Range		均值
Average		范围
Range		均值
Average		范围
Range
1A18,975594.000.0310.260.05-0.500.370.10-0.500.280.09-0.38
1B13,810689.400.0500.310.05-0.500.390.10-0.500.290.09-0.38
1D4464495.400.1110.180.05-0.490.340.10-0.500.280.09-0.38
2A2710780.700.2880.270.05-0.500.360.10-0.500.300.09-0.38
2B14,287801.200.0560.250.05-0.500.380.10-0.500.300.09-0.38
2D3604651.600.1810.320.05-0.500.360.10-0.500.300.09-0.38
3A13,177750.800.0570.260.05-0.500.360.10-0.500.270.09-0.38
3B20,116830.300.0410.270.05-0.500.400.10-0.500.310.09-0.38
3D2417615.400.2550.200.05-0.500.310.10-0.500.260.09-0.38
4A12,438744.500.0600.250.05-0.500.380.10-0.500.300.09-0.38
4B10,680673.400.0630.230.05-0.500.360.10-0.500.280.09-0.38
4D1055509.600.4830.260.05-0.490.380.10-0.500.310.09-0.38
5A11,972709.700.0590.270.05-0.500.370.10-0.500.290.09-0.38
5B23,584713.000.0300.290.05-0.500.370.10-0.500.290.09-0.38
5D2512566.000.2250.290.05-0.500.330.10-0.500.270.09-0.38
6A14,250618.000.0430.250.05-0.500.400.10-0.500.320.09-0.38
6B14,434720.900.0500.270.05-0.500.330.10-0.500.270.09-0.38
6D2544473.500.1860.250.05-0.500.360.10-0.500.270.09-0.38
7A20,807736.600.0350.270.05-0.500.320.10-0.500.290.09-0.38
7B12,025750.600.0620.280.05-0.500.360.10-0.500.280.09-0.38
7D4537638.600.1410.220.05-0.500.290.10-0.500.260.09-0.38
A genome93,6644934.500.0520.270.05-0.500.380.10-0.500.290.09-0.38
B genome108,9365179.000.0480.290.05-0.500.360.10-0.500.280.09-0.38
D genome20,5683950.400.1930.240.05-0.500.330.10-0.500.280.09-0.38
a 标记物理位置来源于中国春小麦参考基因组(RefSeq v1.0, http://www.wheatgenome.org/; IWGSC, 2018)。
a physical positions of SNP markers were based on the Chinese Spring reference genome in IWGSC (RefSeq v1.0, http://www.wheatgenome.org/; IWGSC, 2018).

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2.3 PCA分析和LD衰减分析

利用Tassel v 5.0 160份小麦品系进行PCA分析, 发现160份小麦品系可分为2个亚群(图1)。其中, 亚群1包含20份农家种, 亚群2包含140份育成品种和高代品系。以第95百分位的r2值作为阈值估测LD衰减距离, 160份小麦品系全基因组水平的r2阈值分别为0.135。r2阈值与LD衰减曲线的交叉点为LD衰减距离[22]。本研究中, 全基因组平均LD衰减距离为8 Mb (图2)。

图1

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图1160份小麦品系主成分分析

Fig. 1Principal component analysis of 160 common wheat lines



图2

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图2160份小麦品系LD衰减分析

Fig. 2LD decay analysis of 160 common wheat lines



2.4 根系构型相关性状GWAS分析

GWAS分析中出现假阳性结果的最主要原因是材料背景复杂, 存在群体结构现象[23]。PCA分析表明前2个主成分分别可解释表型变异的35.4%和8.6%, 160份小麦品系可分为2个亚群, 存在显著的群体结构。结合群体结构(PCA)和亲缘关系(K)的混合线性模型(MLM)可有效降低由群体结构造成的假阳性现象[24,25]。因此, 本研究中, 为了避免由于群体结构和亲缘关系引起的假阳性, 我们采用基于Tassel v 5.0的MLM模型对不同氮素条件下根系构型相关性状比值进行GWAS分析。

基于两种不同氮素水平下根系构型相关性状的比值共计检测到86个与氮素利用效率相关性状显著关联的SNP标记, 分布于34个遗传位点(表3和图3)。其中, 7个位点与RNRT相关, 分布于1B、2B、2D、3A、3B (2)和4B染色体上, 分别解释8.4%~13.8%的表型变异; 2个位点与RARD关联, 分布于2B和3B染色体上, 分别解释9.2%和10.4%的表型变异; 7个位点与RTRL显著关联, 分布于1A、1B、2B、2D、3B和5B染色体上, 分别解释6.9%~11.9%的表型变异; 18个位点与RTRS相关, 分布于1A、1B、2A、3B、4B、5A、5B、6A、6B、6D、7A和7B染色体上, 分别解释9.2%~14.8%的表型变异; 29个位点与RTRV相关, 分布于1A、1B、1D、2A、2B、2D、3A、3B、4B、5A、5B、6A、6B、7A和7B染色体上, 分别解释9.6%~19.6%的表型变异(表3和图3)。不同氮素水平环境下根系构成相关性状比值存在较高的相关性。相应的, 在关联结果中我们发现多个相对性状有相同遗传位点, 如11个位点分别与RTRV和RTRS相关; 2个位点与RTRV、RTRS和RTRL相关; 2个位点与RTRV和RTRL相关; 1个位点与RNRT、RTRV和RTRL相关; 2个位点与RNRT和RTRV相关; 2个位点与RNRT、RTRV和RTRS相关; 1个位点与RTRV和RTRS相关。

Table 3
表3
表3与氮素利用效率显著关联(P ≤ 0.001)的SNP标记
Table 3Marker-trait associations for nitrogen use efficiency related traits (P ≤ 0.001)
性状
Trait		标记
Marker		染色体
Chromosome		位置
Position (Mb)		优异等位基因
Favorable allele		P值
P-value		表型解释变异
R2 (%)		参考文献
Reference
RNRTAX_1103704251B577.6G5.38E-049.7
RNRTAX_1094974622B756.7T9.75E-048.9
RNRTAX_1088119642D612.3C4.81E-0410.0
RNRTAX_1099334103A22.6G3.45E-048.4
RNRTAX_1099311463B117.6T3.78E-0410.2Ren et al.[29]
RNRTAX_1104381193B778.3T2.68E-0411.2
RNRTAX_1098383024B540.3T2.84E-0513.8
RARDAX_1089647192B207.1G9.08E-049.2
RARDAX_1109684533B386.4G4.06E-0410.4
RTRLAX_1089135591A515.8C9.88E-047.0
RTRLAX_1106177131B633.3T6.67E-049.6
RTRLAX_1090316902B25.5T6.79E-049.6
RTRLAX_1088588902B26.0T5.03E-0410.4
RTRLAX_1099584922B26.0G7.36E-049.4
RTRLAX_1089890392B26.1G7.36E-049.4
RTRLAX_1105830382B26.1G8.66E-049.2
RTRLAX_1105360692B26.1G4.07E-0410.2
RTRLAX_1098550572B26.1A7.73E-049.3
RTRLAX_1087581322B26.1C8.12E-049.6
RTRLAX_1099742382B785.8T9.99E-049.5
RTRLAX_1088119642D612.3C1.26E-0411.9
RTRLAX_1098920513B821.0T6.39E-0410.3
RTRLAX_1100454655B707.1C5.19E-0513.0
RTRSAX_1105437841A496.9A3.82E-0410.9
RTRSAX_1099735561A520.9C3.62E-0410.3
RTRSAX_1101112891B384.3G3.24E-0410.4
RTRSAX_1087747552A125.9G4.60E-049.9
RTRSAX_1100758293B250.6G3.91E-0410.2
RTRSAX_1106124223B403.5G2.72E-0410.6
RTRSAX_1104111873B410.1G7.86E-049.2
RTRSAX_1103888603B466.1G4.02E-0410.1
RTRSAX_1089775703B780.4G3.87E-0410.2
RTRSAX_1099153164B554.8G4.40E-0410.0
RTRSAX_1105227705A8.2T4.58E-0410.1Ren et al. [29]
RTRSAX_1100446225B59.2T3.82E-0410.2
RTRSAX_1103668425B60.2G4.52E-0410.0
RTRSAX_1094405175B504.0T4.23E-0410.0
RTRSAX_1088130775B517.9C4.56E-049.9
RTRSAX_1100454655B707.1C1.34E-0514.8
RTRSAX_1087404946A564.9G4.87E-049.9
RTRSAX_1099072716B54.4T2.13E-0411.2Ren et al. [29]
RTRSAX_1104024556D469.2G6.43E-049.6
RTRSAX_1104478286D472.3T4.04E-0410.3
RTRSAX_1104441747A722.6T1.21E-0411.8
性状
Trait		标记
Marker		染色体
Chromosome		位置
Position (Mb)		优异等位基因
Favorable allele		P值
P-value		表型解释变异
R2 (%)		参考文献
Reference
RTRSAX_1099834087B23.3G3.50E-0410.5
RTRVAX_1100035551A486.7T5.71E-049.6
RTRVAX_1105437841A496.9A3.80E-0617.9
RTRVAX_1099735561A520.9C3.38E-0616.9
RTRVAX_1101112891B384.3G3.01E-0617.0
RTRVAX_1104929021D382.2T2.82E-0516.7
RTRVAX_1087747552A125.9G2.48E-0617.4
RTRVAX_1105360692B26.1C7.23E-049.3
RTRVAX_1099742382B785.8T8.56E-049.6
RTRVAX_1095865502D632.0G6.33E-049.5
RTRVAX_1099334103A22.6G8.64E-047.2
RTRVAX_1109353793A491.9T1.29E-0514.9
RTRVAX_1099311463B117.6T8.08E-049.4
RTRVAX_1100758293B250.6G2.85E-0617.0
RTRVAX_1106124223B403.5G3.16E-0616.9Shen et al. [30]
RTRVAX_1104111873B410.1G6.65E-0512.6
RTRVAX_1103888603B466.1G2.83E-0617.1
RTRVAX_1089775703B780.4G2.61E-0617.2Shen et al. [30]
RTRVAX_1105319443B780.4T9.04E-049.0
RTRVAX_1098277843B780.7G8.90E-049.0
RTRVAX_1092797193B780.8G8.87E-049.0
RTRVAX_1088486573B781.3T4.03E-0410.2
RTRVAX_1087839003B781.5G3.93E-0410.1
RTRVAX_1098920513B821.0T1.54E-0412.0
RTRVAX_1099153164B554.8G3.27E-0616.8Ren et al. [29]
RTRVAX_1093641824B655.2T8.98E-0513.2Ren et al. [29]
RTRVAX_1105227705A8.2T2.32E-0617.5
RTRVAX_1100446225B59.2T2.11E-0617.5Ren et al. [29]
RTRVAX_1103668425B60.2G3.18E-0616.9
RTRVAX_1094405175B504.0T2.51E-0617.2
RTRVAX_1088130775B517.9C2.88E-0617.0
RTRVAX_1100454655B707.1C2.21E-0514.1Ren et al. [29]
RTRVAX_1088828776A557.9G5.68E-049.9Shen et al. [30]
RTRVAX_1087404946A564.9G3.75E-0616.8
RTRVAX_1105043516A596.0G8.66E-0512.7
RTRVAX_1099072716B54.4T3.12E-0617.0
RTRVAX_1095809576B149.9T1.18E-0619.6
RTRVAX_1093085956B485.5T1.34E-0515.2Ren et al. [29]
RTRVAX_1093647477A136.3T7.92E-0512.3
RTRVAX_1090249887A689.3T9.92E-048.9
RTRVAX_1104441747A722.6T3.97E-0410.2
RTRVAX_1099834087B23.3G3.46E-0617.3
RNRT: 相对根尖数; RARD: 相对根平均直径; RTRL: 相对总根长; RTRS: 相对总根表面积; RTRV: 相对总根体积。
RNRT: relative number of root tips; RARD: relative average root diameter; RTRL: relative total root length; RTRS: relative total root surface area; RTRV: relative total root volume.

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图3

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图3160份普通小麦品系不同氮素条件下根系相关性状相对值全基因组关联分析曼哈顿图

RNRT: 相对根尖数; RTRL: 相对总根长; RTRS: 相对总根表面积; RTRV: 相对总根体积; RARD: 相对根平均直径。
Fig. 3Manhattan plots for relative root system architecture (RSA) related traits under different nitrogen level in 160 common wheat lines

RNRT: relative number of root tips; RTRL: relative total root length; RTRS: relative total root surface area; RTRV: relative total root volume; RARD: relative average root diameter.


3 讨论

氮素是植物叶绿素、蛋白质、核酸、酶类、生物碱、植物激素以及维生素类等物质的重要组成成分, 是决定作物产量的首要因素。选育高氮素利用效率品种是维持我国小麦生产安全的重要基础。长期以来, 由于氮素利用效率评价困难, 育种家主要依据地上部表型性状等来判断品种氮素利用效率, 对地下根系的作用认识不足, 一定程度上限制了小麦高产育种的进展[3,6]。近年来, 小麦基因芯片的开发和应用, 显著促进了数量性状遗传解析的进程[9,26]。本研究利用小麦660K SNP芯片, 在不同的氮素水平下, 尝试通过GWAS对小麦苗期不同环境下的根系性状进行遗传解析, 以期发掘氮素利用效率相关重要标记和遗传位点, 预测候选基因, 为通过分子育种技术改良小麦氮素利用效率奠定基础。本研究采用的小麦品系来源广泛, 包括地方品种、高代品系和现代育成种。针对3种不同来源的小麦材料, 对其不同氮素水平条件下的根系相关性状进行分析。多重比较结果表明, 高代品系和现代育成种在不同氮素水平下根系构成相关性状均显著高于地方品种, 表明人工选择对于小麦根系构型有较大提升。小麦根系构型对于产量具有重要影响。在人工选育过程中, 育种家在聚合高产基因, 选择高产材料的同时[27], 一定程度上间接完成了小麦根系构型的选择。然而, 针对不同氮素条件下根系构型相关性状比值进行分析, 发现不同来源材料间存在差异, 但并不显著。以上结果表明育种家在高产育种过程中可以间接完成根系相关性状的选择, 但是并不能有效开展氮素利用效率相关性状的选择, 因此, 通过开展不同氮素环境下根系表型鉴定, 间接发掘氮素利用效率相关位点, 对于促进小麦高氮素利用效率选择和育种是十分必要的。基因多态性是保证关联分析结果高效、准确的前提。本研究采用160份品系的基因多态性为0.34, 与之前报道接近[28], 可基本反映黄淮和北部冬麦区的品种多样性。相较于D基因组, A和B基因组具有较高的遗传多态性, 这与前人研究结果一致[9]。本研究采用品系遗传变异丰富, 适用于GWAS分析。PCA分析表明人工选择对群体结构分层具有显著影响。群体结构可导致关联分析结果出现假阳性[28]。因此, 采用结合群体结构(PCA)和亲缘关系(K)的混合线性模型(MLM)进行GWAS, 以避免假阳性的出现。LD衰减是影响关联分析准确度的重要因素, 不同自然群体的LD衰减距离不同。一般来说, 普通小麦LD衰减距离在1.5~15.0 cM范围内[9,26]。本研究中, 全基因组LD平均衰减距离为8 Mb。全基因组的平均标记间距(0.097)远低于LD衰减距离, 标记密度可满足关联分析要求[21]。相较于双亲群体, 自然群体积累了广泛的重组事件, 提供了更多有效重组信息, 可在短时间内发现更多关联位点。除此之外, GWAS分辨率较高, 有利于遗传位点的进一步精细定位和图位克隆。本研究共计检测到34个与氮素利用效率显著关联的位点, 在21条染色体上均有分布。其中, 与RNRT关联的显著位点AX_109931146 (3B, 117.6 Mb), 与RTRS关联的显著位点AX_110522770 (5A, 8.2 Mb)、AX_109907271 (6B, 54.4 Mb), 与RTRV关联的显著位点AX_110612422 (3B, 403.5 Mb)、AX_110531944 (3B, 780.4Mb), AX_109915316 (4B, 554.8 Mb)、AX_109364182 (4B, 655.2 Mb)、AX_110044622 (5B, 59.2 Mb)、AX_110045465 (5B, 707.1 Mb)、AX_108882877 (6A, 557.9 Mb)和AX_109308595 (6B、485.5 Mb)与Ren等[29]和沈兴[30]发掘到的多个氮素利用效率相关位点重叠或一致。这些位点存在氮素利用效率相关基因的可能性较大, 但迄今尚未克隆, 无法探讨其遗传调控机制。另外, 在这些区域, 我们发现了部分生长素响应(auxin responsive gene, ARF)相关基因, 但由于置信区间较大, 仍不可确定为候选基因, 下一步将针对这些区间开展精细定位工作。除此之外, 前人针对小麦氮素利用效率遗传机制也开展了多项研究, 在小麦2A、2B、4A、5A、7A和7B染色体上发现一批控制小麦氮素利用效率相关遗传位点[16,17,18,19], 但是由于多数研究采用的标记为传统的RFLP、SSR或者DArT标记, 根据现有小麦遗传或物理图谱, 无法进行可靠的对比, 暂时无法确定是否与本研究发现位点一致。基于NCBI数据库, 利用与氮素利用效率相关性状显著关联的SNP标记的侧翼序列通过BLASTn发掘候选基因, 在3B染色体(AX_109827784)上检测到一个编码E3-泛素蛋白连接酶的候选基因。E3-泛素蛋白连接酶是一种多类型的蛋白家族, 在植物氮素吸收、根系和幼芽生长发育过程中起着十分重要的作用[31,32]。虽然基于关联标记的生物信息分析是发掘复杂农艺性状相关候选基因的有效方法, 然而, 由于小麦根系构型建成涉及到多个复杂的生理、生化过程, 后续工作将围绕候选基因的作用进行验证。氮素利用效率是一个十分复杂的农艺性状, 相关研究较为滞后, 严重制约了高产、广适品种的培育。由于氮素利用效率在田间育种时无法及时评价表型, 基于分子标记的MAS育种是提升品种氮素利用效率最为重要的途径之一。因此, 研究品种间氮素利用效率差异, 筛选高氮素利用效率品种, 对培育新的优良品种具有重要价值。本研究共计发现34个氮素利用效率关联位点。其中, 存在多个位点与多个性状关联, 如1A染色体486.7 Mb~496.9 Mb处与RTRS和RTRV均显著关联, 1A染色体515.8 Mb~ 520.9 Mb处与RTRL、RTRS和RTRV均显著关联、2B染色体25.5 Mb~26.1 Mb处与RTRL和RTRV等性状显著关联, 这些位点相对更为可靠, 可进一步转化为KASP[33,34]或STARP[35]标记, 用于分子标记辅助育种和QTL精细定位。在所有实验材料中, 徐麦856、郑麦366、济南4号、临麦6号、兰考358、山农587、济麦52、山农0431、山农22、济麦37、鲁原502、山农1565、汶农14、鲁麦20、鑫麦296等品种具有较高的氮素利用效率, 农艺性状优良, 含有较多优异等位基因(15~21个), 可作为亲本, 用以导入或聚合优异等位基因, 提升育种材料的氮素利用效率(表4)。基于以上结果, 下一步研究工作将主要集中于关联位点的验证和育种家友好型KASP标记的开发, 为提升氮素利用效率提供参考。

Table 4
表4
表4高氮素利用效率小麦品种
Table 4Wheat accessions with higher nitrogen use efficiency
品种
Cultivar		性状Trait优异等位基因数
Number of favorable alleles
RTRLRTRSRARDRTRVRNRT
徐麦856 Xumai 8562.3561.1490.7481.4154.85017
郑麦366 Zhengmai 3662.4741.4890.7412.0855.74718
品种
Cultivar		性状Trait优异等位基因数
Number of favorable alleles
RTRLRTRSRARDRTRVRNRT
济南4号 Jinan 42.4951.2380.7220.9853.95116
临麦6号 Linmai 62.5481.1770.5841.8708.47615
兰考358 Lankao 3582.6301.3360.6511.7074.91818
山农587 Shannong 5872.6741.4010.6780.9036.59915
济麦52 Jimai 522.6871.4330.5311.98811.92516
山农0431 Shannong 04312.8561.6000.5261.2806.99117
山农22 Shannong 222.8841.2060.6901.5545.69519
济麦37 Jimai 372.9071.3850.5870.9683.23320
鲁原502 Luyuan 5023.1141.5200.7292.88215.50015
山农1565 Shannong 15653.2301.2860.4661.5998.70616
汶农14 Wennong 143.6481.2540.5070.9186.89417
鲁麦20 Lumai 204.0292.0790.4321.53415.08921
鑫麦296 Xinmai 2965.8282.1640.4812.11324.54720
RTRL: 相对总根长; RTRS: 相对总根表面积; RARD: 相对根平均直径; RTRV: 相对总根体积; RNRT: 相对根尖数。
RTRL: relative total root length; RTRS: relative total root surface area; RARD: relative average root diameter; RTRV: relative total root volume; RNRT: relative number of root tips.

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4 结论

本研究利用160份小麦品系, 通过测定苗期根系构型相关性状, 间接开展氮素利用效率相关性状的GWAS分析, 共计检测到34个关联位点。发掘到的遗传位点及其关联标记可开发为KASP标记, 用于筛选高氮素利用效率小麦品种, 筛选出的品种, 可作为亲本材料应用于小麦高产育种中。

附图和附表 请见网络版: 1) 本刊网站http://zwxb. chinacrops.org/; 2) 中国知网http://www.cnki.net/; 3) 万方数据http://c.wanfangdata.com.cn/Periodical- zuowxb.aspx。

参考文献 原文顺序
文献年度倒序
文中引用次数倒序
被引期刊影响因子

Chen J G, Zhang Y, Tan Y W, Zhang M, Zhu L L, Xu G H, Fan X R. Agronomic nitrogen-use efficiency of rice can be increased by drivingOsNRT2.1 expression with the OsNAR2.1 promoter
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Sandrine R, Alain G, Laurence L. Signal interactions in the regulation of root nitrate uptake
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Maccaferri M, El-Feki W, Nazemi G, Salvi S, Canè M A, Colalongo M C, Stefanelli S, Tuberosa R. Prioritizing quantitative trait loci for root system architecture in tetraploid wheat
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Kabir M R, Liu G, Guan P F, Wang F, Khan A A, Ni Z F, Yao Y Y, Hu Z R, Xin M M, Peng H R. Mapping QTLs associated with root traits using two different populations in wheat (Triticum aestivum L.)
Euphytica, 2015,206:175-190.
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de Dorlodot S, Forster B, Pagès L, Price A, Tuberosa R, Draye X. Root system architecture: opportunities and constraints for genetic improvement of crops
Trends Plant Sci, 2007,12:474-481.
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Bishopp A, Lynch J P. The hidden half of crop yields
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Paez-Garcia A, Motes C M, Scheible W R, Chen R, Blancaflor E B, Monteros M J. Root traits and phenotyping strategies for plant improvement
Plants, 2015,4:334-355.
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Roots are crucial for nutrient and water acquisition and can be targeted to enhance plant productivity under a broad range of growing conditions. A current challenge for plant breeding is the limited ability to phenotype and select for desirable root characteristics due to their underground location. Plant breeding efforts aimed at modifying root traits can result in novel, more stress-tolerant crops and increased yield by enhancing the capacity of the plant for soil exploration and, thus, water and nutrient acquisition. Available approaches for root phenotyping in laboratory, greenhouse and field encompass simple agar plates to labor-intensive root digging (i.e., shovelomics) and soil boring methods, the construction of underground root observation stations and sophisticated computer-assisted root imaging. Here, we summarize root architectural traits relevant to crop productivity, survey root phenotyping strategies and describe their advantages, limitations and practical value for crop and forage breeding programs.

Osmont K S, Sibout R, Hardtke C S. Hidden branches: developments in root system architecture
Annu Rev Plant Biol, 2007,58:93-113.
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Liu J D, He Z H, Rasheed A, Wen W E, Yan J, Zhang P Z, Wan Y X, Zhang Y, Xie C J, Xia X C. Genome-wide association mapping of black point reaction in common wheat (Triticum aestivum L.)
BMC Plant Biol, 2017,17:220.
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Dong Y, Liu J D, Zhang Y, Geng H W, Awais R, Xiao Y G, Cao S H, Fu L P, Yan J, Wen WW, Zhang Y, Jing R L, Xia X C, He Z H. Genome-wide association of stem water soluble carbohydrates in bread wheat
PLoS One, 2016,11:e0164293.
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Rasheed A, Xia X C, Ogbonnaya F, Mahmood T, Zhang Z, Mujeeb-Kazi A M, He Z H. Genome-wide association for grain morphology in synthetic hexaploid wheats using digital imaging analysis
BMC Plant Biol, 2014,14:12.
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Guo J, Shi W P, Zhang Z, Cheng J Y, Sun D Z, Yu J, Li X L, Guo P Y, Hao C Y. Association of yield-related traits in founder genotypes and derivatives of common wheat (Triticum aestivum L.)
BMC Plant Biol, 2018,18:387.
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Maccaferri M, Ricci A, Salvi S, Milner S G, Noli E, Martelli P L, Casadio R, Akhunov E, Scalabrin S, Vendramin V, Ammar K, Blanco A, Desiderio F, Distelfeld A, Dubcovsky J, Fahima T, Faris J, Korol A, Massi A, Mastrangelo A M, Morgante M, Pozniak C, N’Diaye A, Xu S, Tuberosa R. A high-density, SNP-based consensus map of tetraploid wheat as a bridge to integrate durum and bread wheat genomics and breeding
Plant Biotechnol J, 2015,13:648-663.
DOI:10.1111/pbi.12288URLPMID:25424506 [本文引用: 1]
Consensus linkage maps are important tools in crop genomics. We have assembled a high-density tetraploid wheat consensus map by integrating 13 data sets from independent biparental populations involving durum wheat cultivars (Triticum turgidum ssp. durum), cultivated emmer (T. turgidum ssp. dicoccum) and their ancestor (wild emmer, T. turgidum ssp. dicoccoides). The consensus map harboured 30 144 markers (including 26 626 SNPs and 791 SSRs) half of which were present in at least two component maps. The final map spanned 2631 cM of all 14 durum wheat chromosomes and, differently from the individual component maps, all markers fell within the 14 linkage groups. Marker density per genetic distance unit peaked at centromeric regions, likely due to a combination of low recombination rate in the centromeric regions and even gene distribution along the chromosomes. Comparisons with bread wheat indicated fewer regions with recombination suppression, making this consensus map valuable for mapping in the A and B genomes of both durum and bread wheat. Sequence similarity analysis allowed us to relate mapped gene-derived SNPs to chromosome-specific transcripts. Dense patterns of homeologous relationships have been established between the A- and B-genome maps and between nonsyntenic homeologous chromosome regions as well, the latter tracing to ancient translocation events. The gene-based homeologous relationships are valuable to infer the map location of homeologs of target loci/QTLs. Because most SNP and SSR markers were previously mapped in bread wheat, this consensus map will facilitate a more effective integration and exploitation of genes and QTL for wheat breeding purposes.

Bulli P, Zhang J, Chao S, Chen X, Pumphrey M. Genetic architecture of resistance to stripe rust in a global winter wheat germplasm collection
G3: Genes Genom Genet, 2016,8:2237-2253.
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Lian X M, Xing Y Z, Yan H, Xu C G, Li XH, Zhang Q F. QTL for low nitrogen tolerance at seedling stage identified using a recombinant inbred line population derived from an elite rice hybrid
Theor Appl Genet, 2005,112:85-96.
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An D G, Su J Y, Liu Q Y, Zhu Y G, Tong Y P, Li M J, Jing R L, Lin B, Li Z S. Mapping QTL for nitrogen uptake in relation to the early growth of wheat (Triticum aestivum L.)
Plant Soil, 2006,284:73-84.
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Quarrie S A, Steed A, Calestani C, Semikhodskii A, Lebreton C, Chinoy C, Steele N, Pljevljakusi? D, Waterman E, Weyen J, Schondelmaier J, Habash Z D, Farmer P, Saker L, Clarkson D T, Abugalieva A, Yessimbekova M, Turuspekov Y, Abugalieva S, Tuberosa R, Sanguineti M C, Hollington P A, Aragués R, Royo A, Dodig D. A high-density genetic map of hexaploid wheat (Triticum aestivum L.) from the cross Chinese Spring × SQ1 and its use to compare QTLs from grain yield across a range of environments
Theor Appl Genet, 2005,110:865-880.
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Fontaine J X, Ravel C, Pageau K, Heumez E, Dubois F, Hirel B. A quantitative genetic study for elucidating the contribution of glutamine synthetase, glutamate dehydrogenase and other nitrogen-related physiological traits to the agronomic performance of common wheat
Theor Appl Genet, 2009,119:645-662.
DOI:10.1007/s00122-009-1076-4URLPMID:19513687 [本文引用: 2]
To better understand the genetic variability for nitrogen use efficiency in winter wheat is a necessity in the frame of the present economic and ecological context. The objective of this work was to investigate the role of the enzymes glutamine synthetase (GS) and glutamate dehydrogenase (GDH), and other nitrogen (N)-related physiological traits in the control of agronomic performance in wheat. A quantitative genetics approach was developed using the Arche x Recital population of doubled haploid lines grown for 3 years in the field. GS and GDH activities, ammonium, amino acid and protein contents were measured at different stages of plant development in different organs after flowering. Significant genotypic effects were observed for all measured physiological and agronomical traits. Heading date was negatively correlated with ammonium, amino acid, protein contents and GS activity in the flag leaf lamina. Grain protein content was positively correlated with both ammonium and amino acid content, and to a lesser extent with soluble protein content and GS activity. A total of 148 quantitative trait loci (QTLs) were detected, 104 QTLs for physiological traits and 44 QTLs for agronomic traits. Twenty-six QTLs were detected for GDH activity spread over 13 chromosomes and 25 QTLs for GS activity spread over 12 chromosomes. We found only a co-localization between a QTL for GS activity and GSe, a structural gene encoding cytosolic GS on chromosome 4B. A coincidence between a QTL for GDH activity and a gene encoding GDH was also found on chromosome 2B. QTL regions combining both physiological and agronomical QTLs were mainly identified on linkage groups 2A, 2B, 2D, 5A, 5B and 5D. This approach allowed us to propose possible functions of physiological traits to explain the variation observed for agronomic traits including yield and its components.

Cui F, Fan X, Zhao C, Zhang W, Chen M, Ji J, Li J. A novel genetic map of wheat: utility for mapping QTL for yield under different nitrogen treatments
BMC Genet, 2014,15:57.
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California Agric Exp Station Circular, 1950,347:1-32.
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Saghai-Maroof M A, Soliman K M, Jorgensen R A, Allard R W L. Ribosomal DNA spacer-length polymorphisms in barley: Mendelian inheritance, chromosomal location, and population dynamics
Proc Natl Acad Sci USA, 1984,81:8014-8018.
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Breseghello F, Sorrells M E. Association mapping of kernel size and milling quality in wheat (Triticum aestivum L.) cultivars
Genetics, 2006,172:1165-1177.
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Kump K L, Bradbury P J, Wisser R J, Buckler E S, Belcher A R, Oropeza-Rosas M A, Balint-Kurti P J. Genome-wide association study of quantitative resistance to southern leaf blight in the maize nested association mapping population
Nat Genet, 2011,43:163.
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Zhu C, Gore M, Buckler E S, Yu J. Status Prospects of association mapping in plants
Plant Genome, 2008,1:5-20.
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Poland J A, Bradbury P J, Buckler E S, Nelson R. Genome-wide nested association mapping of quantitative resistance to northern leaf blight in maize
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Hao C Y, Wang Y Q, Chao S M, Li T, Liu H X, Wang L F, Zhang X Y. The iSelect 9K SNP analysis revealed polyploidization induced revolutionary changes and intense human selection causing strong haplotype blocks in wheat
Sci Rep (UK), 2017,4:41247.
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Wang Y M, Hou J, Liu H, Li T, Wang K, Hao C Y, Liu H, Zhang X Y. TaBT1, affecting starch synthesis and thousand kernel weight, underwent strong selection during wheat improvement
J Exp Bot, 2019,70:1497-511.
DOI:10.1093/jxb/erz032URLPMID:30753656 [本文引用: 1]
BRITTLE1 (BT1), responsible for unidirectional transmembrane transport of ADP-glucose, plays a pivotal role in starch synthesis of cereal grain. In this study, we isolated three TaBT1 homoeologous genes located on chromosomes 6A, 6B, and 6D in common wheat. TaBT1 was mainly expressed in developing grains, and knockdown of TaBT1 in common wheat produced a decrease in grain size, thousand kernel weight (TKW), and grain total starch content. High diversity was detected at the TaBT1-6B locus, with 24 polymorphic sites forming three haplotypes (Hap1, Hap2, and Hap3). Association analysis revealed that Hap1 and Hap2 were preferred haplotypes in modern breeding, for their significant correlations with higher TKW. Furthermore, beta-glucuronidase (GUS) staining and enzyme activity assays in developing grains of transgenic rice with exogenous promoters indicated that the promoters of Hap1 and Hap2 showed stronger driving activity than that of Hap3. Evolutionary analysis revealed that BT1 underwent strong selection during wheat polyploidization. In addition, the frequency distribution of TaBT1-6B haplotypes revealed that Hap1 and Hap2 were preferred in global modern wheat cultivars. Our findings suggest that TaBT1 has an important effect on starch synthesis and TKW, and provide two valuable molecular markers for marker assisted selection (MAS) in wheat high-yield breeding.

Chen X J, Min D H, Tauqeer A Y, Hu Y G. Genetic diversity, population structure and linkage disequilibrium in elite Chinese winter wheat investigated with SSR markers
PLoS One, 2012,7:e44510.
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Ren D, Fang X, Jiang P, Zhang G, Hu J, Wang X, Wang H. Genetic architecture of nitrogen-deficiency tolerance in wheat seedlings based on a nested association mapping (NAM) population
Front Plant Sci, 2018,9:845.
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Genetic divergence for nitrogen utilization in germplasms is important in wheat breeding programs, especially for low nitrogen input management. In this study, a nested association mapping (NAM) population, derived from

沈兴. 小麦氮营养特性的基因型差异及其遗传解析. 山东农业大学硕士学位论文,
山东泰安, 2018. pp 28-44.
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Shen X. Genotypic Differences and Genetic Analysis in Nitrogen Nutritional Characteristics of Wheat. MS Thesis of Shandong Agricultural University
Tai’an, Shandong, China, 2018. pp 28-44 (in Chinese with English abstract).
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Craig A, Ewan R, Mesmar J, Gudipati V, Sadanandom A. E3 ubiquitin ligases and plant innate immunity
J Exp Bot, 2009,60:1123-1132.
DOI:10.1093/jxb/erp059URLPMID:19276192 [本文引用: 1]
In yeast and in animals the ubiquitin-proteasome system (UPS) is responsible for removing or modifying most abnormal peptides and also short-lived cellular regulators. The UPS therefore influences many processes such as the cell cycle, signal transduction, transcription, and stress responses including defence. In recent years, similar regulatory roles have been identified in plants. In Arabidopsis, mutations in the ubiquitin-proteasome pathway block development, circadian rhythms, photomorphogenesis, floral homeosis, hormone responses, senescence, and pathogen invasion. Plants have evolved an armoury of defence mechanisms that allow them to counter infection. These encompass both basal responses, triggered by recognition of conserved pathogen-associated molecular patterns, and pathogen-specific responses, mediated via pathogen- and plant-specific gene-for-gene recognition events. The role of E3 ubiquitin ligases in mediating plant defence signalling is reviewed and examples where pathogens impinge on the host's ubiquitination machinery acting as molecular mimics to undermine defence are also highlighted.

Park G G, Park J J, Yoon J M, Yu S N. An GH A RING finger E3 ligase gene, Oryza sativa Delayed Seed Germination 1 (OsDSG1), controls seed germination and stress responses in rice
Plant Mol Biol, 2010,74:467-478.
DOI:10.1007/s11103-010-9687-3URLPMID:20878348 [本文引用: 1]
Oryza sativa Delayed Seed Germination 1 (OsDSG1), causing a recessive null mutation. Overexpression of the gene enhanced seed germination. OsDSG1 is most similar to Arabidopsis AIP2, an E3 ligase targeting ABI3.Yeast two-hybrid experiments showed that our OsDSG1 binds to OsABI3, indicating that OsDSG1 is a rice ortholog of AIP2. Self-ubiquitination assay indicated that bacterially expressed OsDSG1 protein has E3 ubiquitin ligase activity. Real-time PCR analysis revealed that OsDSG1 was expressed in leaves and roots, and strongly in developing seeds. In addition to the delayed-germination phenotype, mutant plants were shorter and had greater tolerance to high-salt and drought stresses. In the osdsg1 mutant, transcript levels of ABA signaling genes and ABA responsive genes were significantly increased. By contrast, expressions of OsGAMYB and its downstream genes that encode hydrolytic enzymes were markedly reduced. These observations support that OsDSG1 is a major regulator of ABA signaling in germinating seeds. Finally, we observed that the germination rates of various rice cultivars depended upon the transcript levels of OsDSG1 and other ABA-signaling genes.]]>

Semagn K, Babu R, Hearne S, Olsen M. Single nucleotide polymorphism genotyping using Kompetitive Allele Specific PCR (KASP): overview of the technology and its application in crop improvement
Mol Breed, 2014,33:1-14.
DOI:10.1007/s11032-013-9917-xURL [本文引用: 1]

Rasheed A, Hao Y F, Xia X C, Khan A, Xu Y B, Varshney R K, He Z H. Crop breeding chips and genotyping platforms: progress, challenges, and perspectives
Mol Plant, 2017,10:1047-1064.
DOI:10.1016/j.molp.2017.06.008URLPMID:28669791 [本文引用: 1]
There is a rapidly rising trend in the development and application of molecular marker assays for gene mapping and discovery in field crops and trees. Thus far, more than 50 SNP arrays and 15 different types of genotyping-by-sequencing (GBS) platforms have been developed in over 25 crop species and perennial trees. However, much less effort has been made on developing ultra-high-throughput and cost-effective genotyping platforms for applied breeding programs. In this review, we discuss the scientific bottlenecks in existing SNP arrays and GBS technologies and the strategies to develop targeted platforms for crop molecular breeding. We propose that future practical breeding platforms should adopt automated genotyping technologies, either array or sequencing based, target functional polymorphisms underpinning economic traits, and provide desirable prediction accuracy for quantitative traits, with universal applications under wide genetic backgrounds in crops. The development of such platforms faces serious challenges at both the technological level due to cost ineffectiveness, and the knowledge level due to large genotype-phenotype gaps in crop plants. It is expected that such genotyping platforms will be achieved in the next ten years in major crops in consideration of (a) rapid development in gene discovery of important traits, (b) deepened understanding of quantitative traits through new analytical models and population designs, (c) integration of multi-layer -omics data leading to identification of genes and pathways responsible for important breeding traits, and (d) improvement in cost effectiveness of large-scale genotyping. Crop breeding chips and genotyping platforms will provide unprecedented opportunities to accelerate the development of cultivars with desired yield potential, quality, and enhanced adaptation to mitigate the effects of climate change.

Long Y M, Chao W S, Ma G J, Xu S S, Qi L L. An innovative SNP genotyping method adapting to multiple platforms and throughputs
Theor Appl Genet, 2016,130:597-607.
DOI:10.1007/s00122-016-2838-4URLPMID:27942775 [本文引用: 1]
KEY MESSAGE: An innovative genotyping method designated as semi-thermal asymmetric reverse PCR (STARP) was developed for genotyping individual SNPs with improved accuracy, flexible throughputs, low operational costs, and high platform compatibility. Multiplex chip-based technology for genome-scale genotyping of single nucleotide polymorphisms (SNPs) has made great progress in the past two decades. However, PCR-based genotyping of individual SNPs still remains problematic in accuracy, throughput, simplicity, and/or operational costs as well as the compatibility with multiple platforms. Here, we report a novel SNP genotyping method designated semi-thermal asymmetric reverse PCR (STARP). In this method, genotyping assay was performed under unique PCR conditions using two universal priming element-adjustable primers (PEA-primers) and one group of three locus-specific primers: two asymmetrically modified allele-specific primers (AMAS-primers) and their common reverse primer. The two AMAS-primers each were substituted one base in different positions at their 3' regions to significantly increase the amplification specificity of the two alleles and tailed at 5' ends to provide priming sites for PEA-primers. The two PEA-primers were developed for common use in all genotyping assays to stringently target the PCR fragments generated by the two AMAS-primers with similar PCR efficiencies and for flexible detection using either gel-free fluorescence signals or gel-based size separation. The state-of-the-art primer design and unique PCR conditions endowed STARP with all the major advantages of high accuracy, flexible throughputs, simple assay design, low operational costs, and platform compatibility. In addition to SNPs, STARP can also be employed in genotyping of indels (insertion-deletion polymorphisms). As vast variations in DNA sequences are being unearthed by many genome sequencing projects and genotyping by sequencing, STARP will have wide applications across all biological organisms in agriculture, medicine, and forensics.
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