引用本文
司二静, 张宇, 汪军成, 孟亚雄, 李葆春, 马小乐, 尚勋武, 王化俊. . 大麦农艺性状与SSR标记的关联分析. 作物学报, 2015, 41(7): 1064-1072
SI Er-Jing, ZHANG Yu, WANG Jun-Cheng, MENG Ya-Xiong, LI Bao-Chun, MA Xiao-Le, SHANG Xun-Wu, WANG Hua-Jun. . Association Analysis between SSR Markers and Agronomic Traits in Barley. ACTA AGRONOMICA SINICA, 2015, 41(7): 1064-1072  
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Copyright©2015, 作物学报编辑部
作物学报编辑部
大麦农艺性状与SSR标记的关联分析
司二静1,2, 张宇1,2, 汪军成1,2, 孟亚雄1,2, 李葆春1,3, 马小乐1,2, 尚勋武2, 王化俊1,2,*
1甘肃省干旱生境作物学重点实验室 / 甘肃省作物遗传改良与种质创新重点实验室, 甘肃兰州 730000
2甘肃农业大学农学院, 甘肃兰州 730000
3甘肃农业大学生命科学技术学院, 甘肃兰州 730000
* 通讯作者(Correspondence author): 王化俊, E-mail: whuajun@yahoo.com

第一作者联系方式: E-mail: sierjing@163.com

收稿日期:2014-11-14 接受日期:2015-04-02 网络出版日期:2015-05-04
基金:本研究由国家自然科学基金项目(31171558), 国家现代农业产业技术体系建设专项(CARS-05)和甘肃省财政厅专项基金(035-041047)资助
摘要

为了解大麦亲本材料遗传特性和主要农艺性状特征, 采用156份不同来源的大麦材料, 在86个多态性SSR位点上检测遗传多样性, 同时对7个农艺性状在两试验点作表型鉴定, 利用GLM和MLM模型进行分子标记与表型性状的关联分析。结果共检测出392个等位变异, 平均每个标记4.6个, PIC值变异范围为0.0612~0.8560。群体遗传结构分析将156份材料分为2个亚群。利用GLM模型分析结果表明, 与株高、穗长、芒长、穗粒数和千粒重5个性状相关联的标记有18个, 单个标记对表型变异的解释率为4.81%~20.75%; 利用MLM模型分析, 与株高、穗长、芒长、分蘖数、穗粒数和千粒重6个性状相关联的标记有14个, 单个标记对表型变异的解释率范围为6.64%~31.55%。这些关联标记对后续研究有参考价值。

关键词: 大麦; SSR; 群体结构; 农艺性状; 关联分析
Association Analysis between SSR Markers and Agronomic Traits in Barley
SI Er-Jing1,2, ZHANG Yu1,2, WANG Jun-Cheng1,2, MENG Ya-Xiong1,2, LI Bao-Chun1,3, MA Xiao-Le1,2, SHANG Xun-Wu2, WANG Hua-Jun1,2,*
1 Gansu Provincial Key Laboratory of Aridland Crop Science / Gansu Key Laboratory of Crop Improvement & Germplasm Enhancement, Lanzhou 730070, China
2 College of Agronomy, Gansu Agricultural University, Lanzhou 730070, China
3 College of Life Science and Technology, Gansu Agricultural University, Lanzhou 730070, China
Abstract

This study aimed at understanding the population structure of barley parent materials and identifying SSR markers associated with plant height, spike length, awn length, tiller number, effective tiller number, grain number per spike and thousand-grain weight. A total of 392 alleles were identified in 156 accessions using 86 polymorphic SSR markers with an average of 4.6 alleles per locus. The polymorphic information content ranged from 0.0612 to 0.8560. The 156 genotypes were divided into two populations according to structure analysis with SSR data. Eighteen markers were found to be associated with plant height, spike length, awn length, grain number per spike, and thousand-grain weight using GLM (General Linear Model), and the phenotypic variation explained by a single marker ranged from 4.81% to 20.75%. Fourteen markers were found to be associated with plant height, spike length, awn length, effective tiller number, grain number per spike, and thousand-grain weight using MLM (Mixed Linear Model), and the phenotypic variation explained by a single marker ranged from 6.64% to 31.55%. These associated markers provide a basis for future research.

Keyword: Barley; SSR; Population structure; Agronomic trait; Association analysis

大麦是第四大禾谷类作物, 具有早熟、耐旱、耐瘠和耐盐等特点, 在世界各地广泛栽培种植。传统育种中的高强度选择压力导致遗传基础逐渐狭窄, 目前在很多作物上已成为品种改良的瓶颈之一[1], 丰富基因资源多样性、广泛挖掘有利基因近来也越来越受关注。关联分析(association analysis), 又称连锁不平衡作图(LD mapping)或关联作图(association mapping), 是一种以连锁不平衡为基础, 自然群体为材料, 检测群体内处于连锁不平衡状态的标记或候选基因的遗传变异与目标性状显著关联频率的方法[2, 3, 4]。关联分析对供试群体候选基因的检测或者分子标记扫描, 可以获得丰富的基因位点及其等位基因信息, 从而能够在大量种植资源中鉴定对目标性状有正向贡献的优异等位基因[5]。关联分析方法已在小麦[6, 7]、玉米[8, 9, 10]、水稻[11, 12]、马铃薯[13]、大麦[14, 15, 16, 17, 18]等作物上广泛应用。在大麦上, Ivandic等[14]对来自以色列、土耳其和伊朗的39份野生大麦进行关联分析, 检测出与开花期相关联的SSR标记。Ivandic等[19]运用33个SSR标记对野生大麦的耐水分胁迫和白粉病抗感特性进行关联分析, 检测出SSR标记Bmac181 (4H)和Bmac316 (6H)分别与水分胁迫和白粉病抗性极显著相关联。Wu等[15]收集了188份西藏野生大麦, 通过关联分析, 找到与耐盐相关联SNP标记。Kraakman等[20, 21]利用236个AFLP标记与春大麦的产量和产量稳定性进行关联分析, 结果显示与大麦产量相关联标记有8个, 与产量稳定性相关联的有5个, 此外还将AFLP和SSR标记与148份春大麦的部分性状进行LD作图, 找到与农艺相关性状(抽穗期和株高)、抗性(叶锈抗性和大麦黄矮病抗性)和形态特征(小穗轴长度、浆片大小)相关的标记。Roy等[22]运用DArT和SNP标记对大麦斑点病抗性进行关联分析, 找到13个大麦斑点病抗性QTL, 解释率幅度为2.3%~3.9%。赖勇等[23]利用57个SSR标记对113份大麦材料进行关联分析, 运用GLM和MLM两种关联分析模型, 分别获得9个与株高、穗长、芒长、穗粒数和小穗着生密度相关联, 6个与株高、芒长和小穗着生密度相关联的标记。这些研究表明, 运用合适的分子标记, 可以对拥有的大麦材料进行标记与性状的关联分析, 寻找相关联标记为后期的辅助育种提供一定的依据。

本研究利用156个大麦育种亲本构成自然群体, 通过SSR标记与目标性状的关联分析, 进一步寻找和确定与产量相关性状关联的SSR标记, 旨在丰富重要农艺性状的分子标记, 为大麦遗传研究和分子标记辅助育种提供依据。

1 材料与方法
1.1 试验材料

156份大麦材料, 由甘肃农业大学甘肃省干旱生境作物学重点实验室/甘肃省作物遗传改良与种质创新重点实验室麦类种质创新课题组和甘肃省农业科学院大麦研究所提供, 主要是国内不同省份材料和国外栽培品种(见附表)。

1.2 农艺性状鉴定

2014年3月将156份大麦材料分别种植于甘肃省武威市黄羊河农场大麦原种场和张掖市农业科学院大田亲本圃, 点播种植每份材料一行, 行长2.0 m, 行间距为0.25 m。采用常规田间管理, 至成熟期从每份材料随机选取5株对株高、穗长、芒长、分蘖数、有效分蘖数和穗粒数进行鉴定, 取平均值, 六棱大麦品种穗粒数除以3, 与二棱材料统一标准后用于关联分析, 室内测定千粒重。

1.3 SSR标记分析

室内种植试验材料, 采用CTAB法[24, 25]从黄化苗嫩叶中提取每份大麦材料基因组DNA, 经紫外分光光度计法检测其质量和浓度, 置-20℃冰箱保存。参考GrainGenes 2.0网站Korff等[26]构建的遗传图谱, 选取均匀分布于大麦1H~7H染色体的SSR标记102个, PCR扩增程序为95℃ 5 min; 94℃ 50 s, 64~55℃ (touch-down PCR) 50 s, 72℃ 50 s, 10个循环; 94℃ 50 s, 55℃ 50 s, 72℃ 50 s, 30个循环; 72℃ 10 min, 4℃保存。扩增产物经8%聚丙烯酰胺凝胶电泳, 银染显色后照相。

1.4 遗传结构和关联分析

采用DPS7.05软件对大麦材料在两试点农艺性状表型数据进行统计分析。将亲本间表现多态性的SSR引物扩增的每一条带记为1个位点, 以大写字母A、B、C等记录基因型。以Structure 2.3.1软件进行群体遗传结构分析, 估计最佳群体数K, 其取值范围为1~15, 将参数iterations设为10 000, burn- in period 设为100 000, 每个K值重复运行6次, 依据似然值最大原则选取合适的K值为群体数目[27], 当K值持续增大时, 参照Evanno等[28]的方法计算Δ K选择合适的K值。计算Q参数, 将其作为协变量, 以SPAGeDi-1.3d处理基因型数据获得个体间亲缘关系Kinship 矩阵, 运用Tassel 2.1软件一般线性模型(general linear model, GLM), 将Q作为协变量进行回归分析, 混合线性模型(mixed linear model, MLM)采用Q+K方法, 分析方法选择EM, 分别运用两试验点表型数据结合分子标记数据和群体结构进行标记-性状关联分析, 确定关联位点。

2 结果与分析
2.1 两试点农艺性状统计分析

各农艺性状的变异系数, 武威点为12.39%~ 37.88%, 张掖点为11.01%~36.32%。两试点均以分蘖数的变异最大, 武威点芒长变异最小, 张掖点千粒重变异最小(表1)。

表1 两试点大麦材料各农艺性状的变异 Table 1 Variations of various traits of barley at two test sites
2.2 SSR标记分析

以102对SSR引物检测156份材料, 其中86对引物表现多态性, 共检测到392个等位变异, 单个引物检测到2~10个等位变异, 平均4.6个, 以Bmac40位点的等位变异最多(10个)。标记多态信息含量(PIC)变幅为0.0612~0.8560, 平均每个标记的PIC值为0.5548, 以Bmac40的PIC值最大, GBM1140的PIC值最小(表2)。

2.3 群体遗传结构分析

选取遗传距离适中的62个SSR标记对156份供试材料进行群体结构分析, 供试大麦材料的等位变异频率特征类型数K持续增大(图1-A), 因此需要计算Δ K确定K值, 图1-B显示K为2时Δ K出现峰值, 因此选定K=2作为亚群数目, 图1-C为156份供试材料群体结构图, 2个亚群分别包含91和65份材料。

图1 基于SSR标记的156份大麦亲本材料群体遗传结构Fig. 1 Population structure of 156 parent materials based on SSR markers

表2 SSR标记多样性统计 Table 2 Diversity statistics of SSR markers
2.4 SSR标记与部分性状的关联分析

GLM分析结果显示, 两试点共有18个标记与株高、穗长、芒长、穗粒数和千粒重性状显著关联(P< 0.01), 其中9个标记与株高关联, 3个与穗长关联, 其他3个性状各有4个关联标记。各标记对农艺性状表型变异解释率的变化范围为4.81%~ 20.75%, 与株高关联的Bmag382和HVABAIP分别具有最高和最低的表型解释率。MLM分析结果显示, 两试点共14个标记与株高、穗长、芒长、分蘖数、穗粒数和千粒重显著关联(P< 0.01), 其中3个标记与株高相关联, 6个与穗长相关联, 3个与芒长相关联, 1个与分蘖数相关联, 3个与穗粒数相关联, 4个与千粒重相关联(表3)。

MLM分析结果中7个标记与在GLM分析结果中检测到的相同, 其中4个标记(Bmag0872、Bmag382、Bmag125和HVM60)在两种模型中均检测到与相同农艺性状关联, 3个标记(GMS21、Ebmac778和Bmag0500)在MLM模型中被检测到与GLM模型检测到的农艺性状不同, GMS21在武威试验点与穗长相关, EBmac778在武威试验点和张掖试验点结果中分别与穗长和千粒重相关, Bmag0500在武威试点与千粒重相关, 此外与有效分蘖数相关的标记在两种模型分析结果中均未被检测到。与株高相关的标记Bmag382在两试验点和2种模型中均被检测到, 其中在武威试点的关联检测到与株高达到极显著(P< 0.001)水平, 2种模型中解释率分别为14.89%和19.84%。GLM和MLM两种分析模型的分析结果均分别有4个标记与农艺性状极显著关联(表3)。

表3 关联标记及其对表型变异的解释率 Table 3 Associated markers and the phenotypic variation explained (%)
3 讨论
3.1 群体结构分析

对种质资源进行群体遗传结构分析, 是关联作图的前提[29, 30]。群体结构是影响关联分析的一个重要因素, 群体结构分析能够减少群体分类对关联分析的影响, 避免人为因素对亚群划分的影响, 降低伪关联概率, 进而提高关联分析结果准确性[31, 32] 。本研究通过Structure软件进行群体结构分析将供试材料划分为2个亚群, 整体群体结构较简单, 有利于关联分析。计算所得Q值作为协变量纳入计算, 在一定程度上降低亚群混合造成的伪关联概率[33]。采用一般线性回归模型(GLM)和混合线性模型(MLM)进行SSR标记与两试验点的株高等农艺性状关联分析, MLM中检测到的标记数目少于GLM中的数目, 主要是因为MLM分析不仅考虑群体结构Q值, 同时还考虑亲缘关系K值, 因此MLM分析结果更可靠。

3.2 分子标记与表型性状的关联分析

本试验选用一年两点农艺性状进行关联分析, GLM分析结果中, 标记Bmag382、HVM60、GMS89和EBmac788在武威和张掖试验点均检测到与株高相关, 表明这4个株高相关标记较稳定, 其中Bmag382在MLM分析结果中武威和张掖试验点均检测到与株高相关, 该标记与Inostroza等[36]报道一致, 表明标记Bmag382与株高相关联的可靠性较高。另外, 用两种分析模型进行关联分析能够检测关联标记在不同试验点的稳定性和可靠性。

已有很多研究定位了与大麦控制株高[34, 35, 36, 37, 38, 39]、穗长[40, 41, 42, 43, 44]、芒长[42, 44]、穗粒数[34, 45]、千粒重[34, 35, 37, 44, 45, 46]和分蘖数[47, 48, 49]的QTL。株高相关QTL在大麦7条染色体上均有报道, Graingene2.0网站(http://wheat.pw. usda.gov/GG2/index.shtml)上已经鉴定与株高相关的标记有83个, 本研究通过两种分析模型共找到10个与株高相关标记, 定位在除6H以外的其他染色体上, 其中只在MLM模型中检测到的标记BMS64与Pillen等[35]和Korrf等[39]结果一致, 株高相关标记Bmag382与Inostroza 等[36]结果一致, 株高相关标记HVABAIP和HVITR1与Korrf等[39]报道结果一致, 其他检测到的株高相关标记未见相关报道。穗长相关QTL已定位到2H、3H、4H、5H和7H, Bmag125与Hori等[40]检测到的QTL相邻标记结果一致, 其他穗长相关标记未见报道, 穗长相关标记GMS21、Bmag0872和Bmag382在1H上, 需要进一步验证是伪关联还是在1H上存在新的QTL。芒长相关QTL已定位到染色体2H、3H和7H, 本研究定位的芒长相关标记位于1H、2H、4H、5H、7H, 其中2H和7H上的相关标记与Sameri等[42]和Wang等[45]的结果不一致, 而1H、4H和5H上定位到的相关标记需进一步验证可靠性。穗粒数相关QTL已定位在1H~7H, 本研究在1H、4H、6H和7H上定位到的相关标记需进一步验证可靠性。分蘖数相关QTL已定位在1H、2H、3H、4H、6H和7H, 本研究只在1H上定位到相关标记。千粒重QTL定位在各染色体均有报道, 本研究在1H、3H、4H、6H和7H上检测到相关标记, 但与前人研究结果差异较大, 需进一步验证标记的可靠性。

4 结论

用2种分析模型GLM和MLM分别检测到18个和14个标记与大麦农艺性状相关, 其中4个标记在两种模型中均被检测到与同一农艺性状相关; 3个株高相关标记和1个穗长相关标记与以往家系连锁定位相同; 1个株高相关标记与以往全基因组关联结果一致; 4个株高相关标记在两试验点均被检测到与同一农艺性状相关; 部分标记同时与2个或3个性状相关, 可能是性状间相关的遗传原因; 在2个试验点和2种分析模型中均检测到与株高相关的标记Bmag382, 可应用于分子标记辅助选择。

附表 供试大麦材料清单 Supplementary table List of barley accessions used in the study

The authors have declared that no competing interests exist.

作者已声明无竞争性利益关系。The authors have declared that no competing interests exist.

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