赵小强^*, 任斌^*, 彭云玲^,*, 徐明霞^*, 方鹏^*, 庄泽龙^*, 张金文^*, 曾文静^*, 高巧红^*, 丁永福^*, 陈奋奇^*甘肃省干旱生境作物学重点实验室 / 甘肃农业大学农学院, 甘肃兰州 730070

Epistatic and QTL × environment interaction effects for ear related traits in two maize (Zea mays) populations under eight watering environments

ZHAO Xiao-Qiang^*, REN Bin^*, PENG Yun-Ling^,*, XU Ming-Xia^*, FANG Peng^*, ZHUANG Ze-Long^*, ZHANG Jin-Wen^*, ZENG Wen-Jing^*, GAO Qiao-Hong^*, DING Yong-Fu^*, CHEN Fen-Qi^*Gansu Provincial Key Laboratory of Aridland Crop Science / College of Agronomy, Gansu Agricultural University, Lanzhou 730070, Gansu, China

通讯作者: ^* 彭云玲, E-mail: pengyunlingpyl@163.com

收稿日期:2018-08-16接受日期:2018-12-24网络出版日期:2019-01-15

基金资助:

本研究由国家重点研发计划项目.2018YFD0100203-4 中国科学院“西部之光”项目.20180504 甘肃省重点研发计划.18YF1NA071 甘肃省自然科学基金项目.18JR3RA189 甘肃省重大科技专项.17ZD2NA016

Received:2018-08-16Accepted:2018-12-24Online:2019-01-15

Fund supported:

This study was supported by the National Key R&D Project.2018YFD0100203-4 Chinese Academy of Sciences “Light of West China” Program.20180504 Lanzhou Sci & Technol Project (2018-1-103), the Key R&D Program of Gansu, China .18YF1NA071 the National Science Foundation of Gansu Province.18JR3RA189 the Key Science and Technology Projects in Gansu Province.17ZD2NA016

作者简介 About authors
赵小强,E-mail:zhaoxq3324@163.com。













摘要
深入剖析干旱胁迫条件下玉米穗部性状的遗传机制可为玉米抗旱高产分子育种提供参考依据。以大穗型旱敏感自交系TS141为共同亲本, 分别与小穗型强抗旱自交系廊黄和昌7-2杂交, 构建了含有202个(LT_POP)和218个(CT_POP)家系的F_2:3群体, 在8种水旱环境下进行单穗重、穗轴重、穗粒重、百粒重、出籽率及穗长等6个穗部性状的表型鉴定, 并采用复合区间作图法(CIM)和基于混合线性模型的复合区间作图法(MCIM)对其进行单环境和多环境联合数量性状位点(QTL)分析。结果表明, 采用CIM法, 单环境下在2套F_2:3群体间检测到62个穗部性状QTL, 其中干旱胁迫环境下检测到38个QTL, 进一步在2套F_2:3群体多个干旱胁迫环境下检测到10个稳定表达的QTL (sQTL), 分别位于Bin 1.01-1.03、Bin 1.03-1.04、Bin 1.05、Bin 1.07、Bin 1.07-1.08、Bin 2.04、Bin 4.08、Bin 5.06-5.07、Bin 6.05和Bin 9.04-9.06。采用MCIM法, 联合分析定位到54个穗部性状联合QTL, 其中24个表现显著的QTL与环境互作(QTL×E), 17对参与了显著的加性与加性/显性(AA/AD)上位性互作, 其表型贡献率较低。这些研究结果可为系统地剖析玉米穗部性状的分子遗传机制提供理论依据; 且这2套F_2:3群体多个环境下检测到的sQTL可作为穗部性状改良的重要候选染色体区段, 用于图位克隆或抗旱高产分子育种, 但要注重环境及上位性互作效应的影响。
关键词： 玉米;干旱;穗部性状;QTL;QTL与环境互作;上位性

Abstract
Exploring genetic mechanisms of ear related traits in maize (Zea mays) under drought stress is important in maize molecular breeding for drought tolerance and high yield. Two F_2:3 populations, namely 202 F_2:3 families (LT_POP) and 218 F_2:3 families (CT_POP) derived from the common male parent TS141 with drought-sensitive and larger ear and female parents Langhuang/Chang 7-2 with higher drought tolerance and small ear were used to investigate ear weight (EW), cob weight (CW), grain weight (GW), 100-kernel weight (KW), kernel ratio (KR), and ear length (EL) under eight watering environments, and then to analyze quantitative trait locus (QTL) in a single environment by composite interval mapping (CIM) and in the eight environments by mixed linear model based on composite interval mapping (MCIM). Sixty-two QTLs for ear related traits were detected in two F_2:3 populations in a single environment by CIM, among than 38 QTLs were mapped under water-stressed environments, and further analysis showed that ten stable QTLs (sQTLs) were simultaneously identified in two F_2:3 populations under multiple water-stressed environments, these sQTLs were located in Bin 1.01-1.03, Bin 1.03-1.04, Bin 1.05, Bin 1.07, Bin 1.07-1.08, Bin 2.04, Bin 4.08, Bin 5.06-5.07, Bin 6.05, and Bin 9.04-9.06. Fifty-four joint QTLs for ear related traits were identified in the eight environments by joint analysis with MCIM, among than 24 had significant QTL by environment interaction (QTL×E), and 17 significant epistatic interactions with additive by additive/dominance (AA/AD) effects, with less phenotypic variation. These results lay a foundation for systematically revealing molecular genetic mechanism of ear related traits, these sQTLs detected in two F_2:3 populations under multiple environments are important genomic regions that could be used in positional cloning and molecular breeding for drought tolerance and high yield, however, more attention should be paid to the effects of environment or epistatic interaction.
Keywords：maize (Zea mays);drought;ear correlative traits;QTL;QTL by environment interaction (QTL×E);epistasis


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本文引用格式
赵小强, 任斌, 彭云玲, 徐明霞, 方鹏, 庄泽龙, 张金文, 曾文静, 高巧红, 丁永福, 陈奋奇. 8种水旱环境下2个玉米群体穗部性状QTL间的上位性及环境互作效应分析[J]. 作物学报, 2019, 45(6): 856-871. doi:10.3724/SP.J.1006.2019.83059
ZHAO Xiao-Qiang, REN Bin, PENG Yun-Ling, XU Ming-Xia, FANG Peng, ZHUANG Ze-Long, ZHANG Jin-Wen, ZENG Wen-Jing, GAO Qiao-Hong, DING Yong-Fu, CHEN Fen-Qi. Epistatic and QTL × environment interaction effects for ear related traits in two maize (Zea mays) populations under eight watering environments[J]. Acta Crops Sinica, 2019, 45(6): 856-871. doi:10.3724/SP.J.1006.2019.83059


玉米(Zea mays)是高效的C₄粮食作物, 蕴藏着巨大的产量潜力, 并在维护粮食安全、促进畜牧业和工业发展中具有举足轻重的作用。玉米对干旱比较敏感, 干旱缺水不仅影响玉米正常生长发育, 干扰其生理生化代谢, 而且严重影响产量和品质^[1,2]。因此, 提高玉米的抗旱能力进而增加其产量已成为玉米育种者努力的方向^[3]。玉米的穗部性状是典型的数量遗传性状, 受多个微效基因的调控, 由于其遗传力较高, 并在产量形成中具有重要作用, 因而穗部性状作为抗旱性改良指标在抗旱分子育种研究中得到了极大关注^[4,5]。近年来, 借助分子标记技术鉴定干旱胁迫下调控玉米穗部性状的基因组区域为进一步深入剖析其遗传机制提供了可行的手段。

目前, 已有相关研究对干旱胁迫下玉米穗部性状进行了遗传剖析。谭巍巍等^[3]以掖478×黄早四和齐319×黄早四构建了2套F_2:3群体, 在干旱胁迫和正常水分条件下检测到75个调控穗长、穗粗、轴粗、穗行数、行粒数、穗粒重和单穗重的QTL。Guo等^[6]利用5003×P138组合的重组自交系(recombinant inbred lines, RILs)群体在干旱胁迫条件下定位到3个调控穗行数的QTL。Lu等^[7]采用综3×87-1的RILs群体在干旱和正常灌溉条件下挖掘到5个调控穗长、行粒数和单穗重的“一致性”QTL。Sabadin等^[8]通过L-08-05F×L-14-4B组合的F_2:3群体在5种水分环境下检测到29个调控雌穗个数、单穗重、穗长、穗粗、穗行数和行粒数的QTL。甚至, 随着****对玉米穗部性状QTL定位的不断推进, 部分相关功能基因已被克隆。Calderón等^[9]以W22×CIMMYT8759构建的重组染色体近等基因系(recombinant chromosome nearly isogenic lines, RCNILs)在第1染色体上对1个调控行粒数的QTL (KRN1.4)精细定位, 并找到了1个调控玉米花序及行粒数发育的indeterminate spikelet1 (ids1)基因。Liu等^[10]以H12×H12^NX531组合的F₂群体, 在第4染色体Unbranched3 (UB3)基因的下游发现了一段约3 kb的区间调控UB3的表达进而调控玉米穗行数的变异。李雪华等^[11]采用meta-QTL (mQTL)分析, 干旱胁迫条件下在玉米第6染色体的Bin 6.05-6.06处检测到1个调控10株穗数的mQTL, 并发现此区间存在1个参与渗透调节的pmg1 (phosphoglycerate mutase1)基因。通过整合比较以往研究结果不难发现, 虽然这些QTL解析了不同水旱环境下穗部性状的部分遗传特征并成功克隆到一些功能基因, 但仍不足以全面剖析不同水分环境下玉米穗部性状的遗传机制和基因调控网络。因此, 在更为丰富的遗传背景及水旱环境下检测更多调控玉米穗部性状的QTL, 并分析QTL与环境互作(QTL by environment interaction, QTL×E)及上位性互作位点, 可为深入剖析干旱胁迫下玉米穗部性状的遗传机制、相应候选基因的确定与分离、开展抗旱高产分子育种提供更多有益参考。本研究构建了2套F_2:3群体, 在多种干旱胁迫和正常供水环境下对单穗重、穗轴重、穗粒重、百粒重、出籽率、穗长6个穗部性状进行QTL定位, 并分析QTL×E及上位性互作位点, 以期全面剖析其遗传机制, 为玉米抗旱高产分子育种提供参考。

1 材料与方法

1.1 试验材料

参照课题组前期的试验结果^{[12,13,14,15]}, 以大穗型旱敏感玉米自交系TS141为同一父本, 小穗型强抗旱自交系廊黄和昌7-2为母本, 组配杂交种廊黄× TS141 (F_1LT)和昌7-2×TS141 (F_1CT)。通过自交构建2套F₂作图群体并自交衍生获得2套相应的F_2:3定位群体, 分别含202 (LT_POP)和218 (CT_POP)个家系。其中自交系TS141属于Reid系, 廊黄和昌7-2属于四平头系^{[16,17,18,19]}。

1.2 田间试验及数据处理

2014年4月中旬将亲本廊黄和TS141, 及其相应的F₁杂交种(F_1LT)和F_2:3群体(LT_POP)种植于甘肃武威(37.97°N, 102.63°E, 海拔1508 m)和甘肃张掖(38.83°N, 106.93°E, 海拔1536 m)。2015年4月中旬将亲本昌7-2和TS141, 及其相应的F₁杂交种(F_1CT)和F_2:3群体(CT_POP)种植于甘肃古浪(37.67°N, 102.63°E, 海拔1785 m)和甘肃景泰(37.18°N, 104.03°E, 海拔1640 m)。在4个试验点(武威、张掖、古浪和景泰)均设置干旱胁迫(玉米大喇叭口前期至花期结束不供水, 其他生育时期每隔20 d供水一次)和正常供水(玉米全生育期内只要降水不足时就及时补水)处理^[2], 对每一处理均采用完全随机区组设计, 3次重复, 单行区, 行长6 m, 株距30 cm, 行距60 cm, 每行20株, 种植密度为55,580株 hm^-2。4个试验点玉米生育期内气象数据见图1, 由于4个试验点年蒸发量较大而降水较低, 为了便于供水, 因此, 采用平膜覆盖地表及滴灌供水, 其他管理同一般大田。

图1

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图14个试验点(武威、张掖、古浪和景泰)气象数据

Fig. 1Meteorological data in four experimental sites (Wuwei, Zhangye, Gulang, and Jingtai)



参考石云素等^[20]制定的《玉米种质资源描述规范与数据标准》, 在玉米成熟期(9月底)从每行选择长势整齐一致的亲本、F₁杂交种及F_2:3家系各10株考察单穗重(ear weight, EW)、穗轴重(cob weight, CW)、穗粒重(grain weight per ear, GW)、百粒重(100-kernel weight, KW)、出籽率(kernel ratio, KR; KR=穗粒重/单穗重×100%^[12])和穗长(ear length, EL)。参照Zhao等^[19]的方法, 计算每一穗部性状在干旱胁迫环境下的变化率(rate of change of each trait under water-stressed environment, RC)。

$\text{RC}=\left( 1-{{T}_{\text{S}}}/{{T}_{\text{W}}} \right)\times 100%$

式中, ${{T}_{\text{S}}}$表示干旱胁迫环境下相应性状的测定值, ${{T}_{\text{W}}}$表示正常供水环境下相应性状的测定值。采用IBM SPSS19.0 (SPSS Inc., Chicago, IL, USA)软件中GLM-Univariate模型对2套F2:3群体6个穗部性状进行方差分析, 并分析其Pearson表型相关系数(rp)。参照Zhao等[18]的方法估算相应穗部性状间的遗传相关系数(${{r}_{\text{g}}}$), 即:

\[{{r}_{\text{g}}}=\text{CO}{{\text{V}}_{\text{g}xy}}/\sqrt{\sigma _{\text{g}x}^{2}\times \sigma _{\text{g}y}^{\text{2}}}\]

式中, $\text{CO}{{\text{V}}_{\text{g}xy}}$表示x与y性状间的协方差, $\sigma _{\text{g}x}^{2}$和$\sigma _{\text{g}y}^{2}$分别表示x和y性状的遗传方差。参照Knapp等[21]的方法估算相应穗部性状的广义遗传力(broad- sense heritability, H2)。${{H}^{2}}=\sigma _{\text{G}}^{2}/(\sigma _{\text{G}}^{2}+\sigma _{\text{GE}}^{2}/n+{{\sigma }^{2}}/nr)\times 100%$

式中, $\sigma _{\text{G}}^{\text{2}}$、$\sigma _{\text{E}}^{2}$、$\sigma _{\text{GE}}^{\text{2}}$和${{\sigma }^{2}}$分别表示基因型方差、环境方差、基因型与环境互作方差及误差, n (n = 2)代表环境个数, r (r = 3)代表重复数。参照赵小强等[12]的方法衡量相应穗部性状的杂种优势。

F₁杂种优势指数(F₁ heterosis index) HI = F₁/MP × 100%

相对杂种优势(relative heterosis) RH = (F₁ - MP)/F₁ × 100%

中亲优势(mid-parent heterosis) MH = (F₁ - MP)/ MP × 100%

超亲优势(over-parent heterosis) OH = (F₁ - P_H)/ P_H × 100%

F_2:3优势降低率(F_2:3 advantage reduction rate) ARR = (F_2:3 - F₁)/F₁ × 100%

式中, MP为双亲平均值, P_H为高值亲本。

1.3 穗部性状QTLs检测

本课题组前期已经构建了2套相应F₂群体的遗传连锁图谱^[18,19]。采用Windows QTLs Cartographer 2.5软件(http://ststgen.ncsu.edu/qtlcart/WQTLcart.htm)的复合区间作图法(composite interval mapping, CIM)对单个水分环境下的穗部性状进行QTL定位。通过1000次随机抽样确定LOD (LOD > 3.0)阈值^[22]。参照Stuber等^[23]的方法估算基因的作用方式, |d/a| = |显性效应值/加性效应值|; A (加性效应)的|d/a| = 0~ 0.20; PD (部分显性)的|d/a| = 0.21~0.80; D (显性效应)的|d/a| = 0.81~1.20; OD (超显性效应)的|d/a| > 1.20。参照McCouch等^[24]的方法命名QTL。

采用QTL Network 2.0软件(http://ibi.zju.edu.cn/software/qtlnetwork/)的基于混合线性模型的复合区间作图法(mixed linear model based on composite interval mapping, MCIM)联合多个水分环境进行联合QTL定位, 并分析QTL×E及QTL上位性互作效应。设窗口大小为10 cM, Permutation次数为1000次, 用F值(P < 0.05)表示QTL的显著性^[25]。若QTL中间插入字母“J”表示该QTL只在联合分析时出现, 而在单环境检测下未出现。根据Tuberosa等^[26]的标准检测稳定表达的sQTL (stable QTL), 即在不同群体或环境间检测到同一性状的QTL, 标记区间相同或置信区间(confidence interval, CI)重叠(距离< 20 cM), 则被认定为一个sQTL。

2 结果与分析

2.1 不同水分环境下2套F_2:3群体穗部性状的表型鉴定

同一水分环境下, 6个穗部性状在双亲间差异较大(表1), 说明亲本TS141与廊黄或昌7-2间存在丰富的穗部性状遗传变异可对其杂交后代群体进行数量性状遗传分析。与4种正常供水环境(E1、E3、E5、E7)相比, 4种干旱胁迫环境(E2、E4、E6、E8)下3个亲本(廊黄、昌7-2、TS141)、2个F₁杂交种(F_1LT、F_1CT)和2套F_2:3群体(LT_POP、CT_POP)的6个穗部性状均显著降低(表1), 其平均降低程度(RC)为穗轴重(17.0%)>百粒重(13.0%)>穗长(12.1%)>穗粒重(9.9%)>单穗重(9.6%)>出籽率(3.9%)(图2), 表明干旱胁迫能够显著抑制玉米产量因子, 导致玉米严重减产。此外, 8种不同水分环境下, 2套F_2:3群体6个穗部性状的峰度和偏度均基本介于-1.0~1.0间(表1), 呈典型的数量遗传特性, 适合对其进行QTL分析。

Table 1
表1
表18种水分环境下F_2:3群体(LT_POP/CT_POP) 6个穗部性状的表型值
Table 1Phenotypic value of six ear related traits in F_2:3 population (LT_POP/CT_POP) under eight watering environments

性状Trait		环境 Env.		双亲 Parents					F1杂交种 F1 hybrid			LTPOP群体 LTPOP population
				廊黄 Langhuang		TS141			F1LT			均值 Mean			变幅 Range			变异系数 CV (%)			偏度Skewness			峰度Kurtosis
EW		E1		71.33±6.89		111.30±9.72			184.27±7.03			149.81±43.25			52.85-285.91			28.87			0.315			0.313
(g)		E2		66.58±7.95		100.67±8.44			163.51±6.79			133.19±51.83			43.27-265.43			38.92			0.453			-0.327
		E3		88.29±10.43		128.85±6.45			197.80±6.74			163.19±41.49			70.21-281.87			25.42			0.086			-0.338
		E4		84.14±6.77		110.56±8.01			169.14±7.83			152.76±51.41			52.27-276.94			33.65			0.382			-0.577
CW		E1		12.53±1.65		29.90±2.15			33.71±2.81			27.65±9.43			9.47-53.31			34.11			0.326			-0.490
(g)		E2		9.85±1.23		21.81±1.89			26.58±2.44			25.11±8.23			7.91-50.34			32.77			0.230			-0.051
		E3		15.75±2.05		33.78±3.13			37.83±3.15			30.79±10.85			10.87-60.03			35.22			0.636			-0.141
		E4		11.66±2.78		23.04±2.61			31.09±3.07			28.17±9.56			8.50-51.23			33.92			0.294			-0.410
GW		E1		59.81±4.70		82.40±4.82			150.56±5.11			123.16±33.98			65.07-208.15			27.59			0.815			0.206
(g)		E2		56.73±3.96		71.92±3.17			136.93±5.09			108.22±24.72			61.14-186.27			22.84			-0.192			-0.887
		E3		64.54±4.51		85.07±4.66			159.97±4.85			132.46±25.05			68.94-207.92			18.91			-0.620			0.753
		E4		60.48±5.03		73.52±4.49			138.05±4.23			125.01±23.36			65.31-194.58			18.68			0.904			0.727
KW		E1		19.70±2.51		25.15±1.67			40.06±3.40			27.05±7.52			18.40-50.15			27.80			0.853			0.728
(g)		E2		15.80±1.38		20.05±1.41			33.95±4.18			25.65±6.39			12.00-45.50			24.91			0.308			-0.376
		E3		20.55±1.62		32.70±1.72			43.97±3.79			33.80±7.20			13.75-60.25			21.30			0.617			-0.407
		E4		25.70±1.70		24.80±2.03			31.20±3.73			25.90±6.45			13.35-45.50			24.90			0.467			-0.339
性状Trait			环境 Env.		双亲 Parents				F1杂交种 F1 hybrid			LTPOP群体 LTPOP population
					廊黄 Langhuang		TS141		F1LT			均值 Mean			变幅 Range			变异系数 CV (%)			偏度Skewness			峰度Kurtosis
KR		E1		83.43±1.84		77.34±1.67		83.93±2.01			81.59±4.94			70.63-91.08			6.05			0.711			-0.095
		E2		81.02±1.35		73.08±1.55		82.15±1.84			78.80±5.79			65.97-88.84			7.35			-0.093			0.268
		E3		85.14±1.42		79.62±1.73		85.92±1.85			83.01±5.05			71.89-92.51			6.08			-0.216			0.670
		E4		82.26±1.61		73.98±1.89		83.93±1.79			80.56±5.93			68.05-89.93			7.36			-0.326			-0.431
EL		E1		9.23±1.68		15.33±1.74		20.17±1.40			15.74±2.72			9.18-22.78			17.29			-0.069			0.000
(cm)		E2		8.05±1.21		13.10±1.46		18.83±2.05			14.82±3.08			8.20-21.30			20.74			-0.203			-0.855
		E3		9.55±2.02		15.62±1.31		20.84±1.87			15.81±2.92			9.10-23.20			18.46			0.073			-0.089
		E4		8.72±1.04		12.60±1.76		17.68±1.92			15.07±2.52			8.21-19.80			16.69			-0.497			0.047
				双亲 Parents				F1杂交种 F1 hybrid			CTPOP群体 CTPOP population
				昌7-2 Chang 7-2		TS141		F1CT			均值 Mean			变幅 Range			变异系数 CV (%)			偏度Skewness			峰度Kurtosis
EW		E5		63.68±4.57		107.64±7.99		235.32±7.15			121.00±39.89			27.07-224.56			32.97			0.528			0.041
(g)		E6		59.05±6.22		96.47±6.80		215.76±6.98			111.20±38.62			22.34-211.91			34.73			0.596			0.152
		E7		54.79±3.16		101.26±5.43		228.43±7.68			105.30±38.29			13.20-200.00			36.37			-0.033			-0.191
		E8		50.00±2.69		90.43±5.90		205.37±8.03			90.03±41.93			12.07-198.34			46.26			0.238			-0.220
CW		E5		6.86±3.30		21.41±3.88		30.28±1.89			17.51±6.71			4.24-41.21			38.33			0.577			0.619
(g)		E6		5.90±5.28		17.68±4.72		26.77±2.01			15.81±5.84			4.16-35.99			36.94			0.701			1.037
		E7		6.22±2.19		18.64±3.61		28.95±1.68			16.81±5.74			5.09-32.56			34.14			0.258			-0.119
		E8		5.03±3.00		15.11±2.45		23.72±1.74			14.49±6.12			4.03-32.08			42.26			0.425			-0.368
GW		E5		56.82±4.01		86.25±3.77		205.07±7.56			104.49±33.19			25.17-184.38			31.76			-0.911			0.150
(g)		E6		52.12±3.65		73.79±4.09		189.99±6.70			95.39±28.05			19.78-180.21			29.41			0.228			1.006
		E7		49.57±4.22		82.63±4.73		199.48±5.79			89.87±30.82			10.92-174.89			34.29			0.858			0.417
		E8		43.97±3.99		70.32±4.36		180.55±6.04			77.54±31.21			9.95-173.24			40.25			0.325			0.271
KW		E5		15.15±1.24		23.40±2.14		34.77±2.13			25.20±6.27			6.15-46.14			24.88			0.275			0.761
(g)		E6		13.95±1.90		20.10±1.00		29.94±1.78			22.00±7.50			5.05-45.60			34.09			0.435			1.069
		E7		14.05±1.05		21.21±1.12		31.26±1.69			22.80±6.29			8.40-42.71			27.58			0.483			0.407
		E8		10.80±1.76		14.97±1.33		28.78±1.55			21.60±5.37			6.15-44.43			24.86			0.438			0.696
KR		E5		87.22±2.16		80.19±2.32		88.36±1.69			85.50±4.89			73.47-90.80			5.72			0.916			0.188
		E6		85.81±2.02		73.73±1.85		86.03±1.57			83.17±5.63			65.13-87.96			6.77			-0.470			0.735
		E7		84.64±1.90		79.42±2.13		84.75±1.96			84.04±5.30			71.90-91.83			6.31			0.438			-0.307
		E8		80.09±2.11		70.68±2.26		80.77±2.01			79.99±6.37			62.38-90.48			7.96			0.691			-0.740
EL		E5		8.15±2.36		13.15±1.77		21.86±2.04			13.55±2.50			7.30-21.20			18.47			0.522			0.903
(cm)		E6		7.02±2.69		10.44±3.10		17.99±1.21			12.49±2.32			6.80-20.60			18.59			0.164			0.455
		E7		7.08±1.57		12.53±2.85		19.78±11.36			12.91±2.14			7.30-20.30			16.57			0.357			0.723
		E8		6.00±2.31		10.48±2.08		16.04±1.02			12.01±2.59			5.70-18.30			21.52			-0.283			-0.218

EW: ear weight; CW: cob weight; GW: grain weight per ear; KW: 100-kernel weight; KR: kernel ratio; EL: ear length. LTPOP/CTPOP: F_2:3 population derived from Langhuang×TS141/Chang 7-2×TS141. E1/E2: well-watered/water-stressed environment at Wuwei; E3/E4: well- watered/water-stressed environment at Zhangye; E5/E6: well-watered/water-stressed environment at Gulang; E7/E8: well-watered/water- stressed environment at Jingtai. CV: coefficient of variation.
EW: 单穗重; CW: 穗轴重; GW: 穗粒重; KW: 百粒重; KR: 出籽率; EL: 穗长。LT_POP/CT_POP: 廊黄×TS141/昌7-2×TS141构建的F_2:3群体。E1/E2: 武威正常供水/干旱胁迫环境处理; E3/E4: 张掖正常供水/干旱胁迫环境处理; E5/E6: 古浪正常供水/干旱胁迫环境处理; E7/E8: 景泰正常供水/干旱胁迫环境处理。CV: 变异系数。

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2个F₁杂交种(F_1LT、F_1CT)与其亲本相比, 除出籽率的正向超亲优势(0.9%)表现不明显外, 其余5个穗部性状均显著大于高值亲本, 表现为明显的正向超亲优势, 其平均超亲优势介于117.8% (穗粒重)~35.8% (穗轴重)(图2); 此外, 6个穗部性状的F₁杂种优势指数、相对杂种优势、中亲优势及F_2:3优势降低率均一致表现为穗粒重(256.3%、59.7%、156.3%和-34.6%)>单穗重(236.3%、55.8%、136.3%和-33.9%)>穗轴重(196.1%、47.5%、96.1%和-26.9%)>穗长(185.2%、45.5%、85.2%和-26.3%)>百粒重(176.7%、42.0%、76.7%和-25.4%)> 出籽率(105.2%、5.5%、5.8%和-2.8%)(图2), 说明玉米F₁杂交种6个穗部性状的杂种优势大小不一, 且自交后出现不同程度的杂种优势衰退现象。

图2

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图26个穗部性状RC和杂种优势分析缩写见表1。Abbreviations correspond with these given in Table 1.

Fig. 2RC (rate of change for each trait under water-stressed environment) and heterosis analysis of six ear related traits



联合方差分析表明, 构建的2套F_2:3群体6个穗部性状在基因型间、环境间、基因型与环境互作间均达到P < 0.01或P < 0.05差异显著水平(表2), 说明玉米的6个穗部性状除显著受自身的遗传调控外, 还显著受水分环境及其互作间的影响。且多种水分环境下6个穗部性状的广义遗传力均较高, 介于77.03%~90.95% (表2), 表明玉米的6个穗部性状受遗传本质的影响较大。

玉米6个穗部性状表型和遗传相关分析表明, 在不同水分环境下, 玉米的单穗重与穗轴重、穗粒重及穗长间极显著(P < 0.01)正向表型/遗传相关, 与出籽率间极显著负向表型/遗传相关; 穗轴重与穗长间极显著正向表型/遗传相关, 与穗粒重、百粒重及出籽率间极显著负向表型/遗传相关; 穗粒重与百粒重、出籽率及穗长间极显著正向表型/遗传相关; 百粒重与出籽率间极显著正向表型/遗传相关, 与穗长间极显著负向表型/遗传相关; 出籽率与穗长间极显著正向表型/遗传相关(表3), 说明不同水旱环境下玉米的各个穗部性状彼此高度遗传和表型关联, 并在不同水分环境影响下协同作用, 最终形成玉米产量。

2.2 单水分环境下2套F_2:3群体穗部性状的QTL分析

采用CIM法, 2套F_2:3群体8种水旱环境下总共检测到62个穗部性状QTL, 分布于玉米的10个连锁群中, 单环境下单个QTL的表型贡献率介于4.00% (LT_POP在E1环境下的qCW-Ch.4-1)~15.65% (LT_POP在E2环境下的qEW-Ch.1-2)(图3)。其中有关单穗重的13个QTL, 44.4%、22.2%、7.4%和26.0%的单穗重分别受加性(A)、部分显性(PD)、显性(D)和超显性(OD)等遗传效应调控; 有关穗轴重的11个

QTL, 55.2%、17.2%和27.6%的穗轴重分别受A、PD和D等遗传效应调控; 有关穗粒重的13个QTL, 25.0%、35.7%、17.9%和21.4%的穗粒重分别受A、PD、D和OD等遗传效应调控; 有关百粒重的7个QTL, 35.3%、47.1%和17.6%的百粒重分别受A、PD和OD等遗传效应调控; 有关出籽率的9个QTL, 66.7%、19.0%和14.3%的出籽率分别受A、PD和D等遗传效应调控; 有关穗长的9个QTL, 50.0%、35.0%和15.0%的穗长分别受PD、D和OD等遗传效应调控。


Table 2
Table 2Complex variance for six ear related traits in F_2:3 populations （LT_pop/CT_pop）under eight watering envrionments

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Table 2
Table 2Phenoty and genetic correlation analysis among six ear relaits in F_2:3 populations （LT_pop/CT_pop）

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图3

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图3采用CIM和MCIM法不同水分环境下F_2:3群体（LT_pop/CT_pop）6个穗部性状QTL在染色体上的分布

Fig.3Distribution of QTLs for six ear related traits in in F_{2:3 populatiom} (LT_pop) under different watering environments by CIM and MCIM



干旱胁迫环境下2套F_2:3群体间总共检测到38个(占61.3%)穗部性状QTL (图3), 说明干旱胁迫能够激活部分玉米穗部性状QTL/基因的表达。2套F_2:3群体在多个干旱胁迫环境下总共检测到10个稳定表达的sQTL (图3), 即umc2224-bnlg1484 (Bin 1.01- 1.03)、bnlg1484-umc1917 (Bin 1.03-1.04)、bmc1627/ umc1356-umc1278 (Bin 1.07)、bnlg1018-bnlg1909/ bnlg1613 (Bin 2.04)处分别调控穗轴重、出籽率、穗长等的4个sQTL; 及umc2025-umc1395 (Bin 1.05)、bnlg1025-mmc0041 (Bin 1.07-1.08)、umc2041-umc2188 (Bin 4.08)、umc2216-umc1072 (Bin 5.06-5.07)、umc2040-bnlg1174a (Bin 6.05)、umc1120-umc2346 (Bin 9.04-9.06)处同时调控多个穗部性状的6个“一因多效”sQTL, 说明玉米的这些Bin区域6个穗部性状在不同环境下普遍呈紧密连锁遗传。

2.3 不同水分环境下2套F_2:3群体穗部性状的联合QTL及QTL×E分析

采用MCIM法, 2套F_2:3群体在8种水旱环境下进行了QTL联合分析, 总共检测到54个穗部性状联合QTL, 单个联合QTL的加性效应表型贡献率(h²_A)介于2.79% (LT_POP的qGW-Ch.8-1)~12.95% (LT_POP的qEW-J10-1), 其中25个(46.3%)联合QTL仅在联合分析中被检测到(图3和表4)。

Table 4
表4
表4多环境下采用MCIM法对F_2:3群体(LT_POP/CT_POP) 6个穗部性状联合QTL及QTL×E分析
Table 4Joint QTLs and QTL×E for six ear related traits detected in F_2:3 population (LT_POP/CT_POP) across multiple environments with MCIM

性状Trait	QTL	Chr.	QTL位置 QTL position			A	AE1/ AE5	AE2/ AE6	AE3/ AE7	AE4/ AE8	h2A (%)	h2AE (%)
			cM	Mb	标记区间 Marker interval
LTPOP群体 LTPOP population
EW	qEW-Ch.1-2	1	60.7	0.05	umc2025-umc1395	-2.07					12.56
	qEW-J2-1	2	89.2	4.40	bnlg1520-umc1736	-1.55		-0.81	-0.95		9.43	7.08
	qEW-Ch.4-1	4	183.6	46.16	umc2041-umc2287	-0.98					7.18
	qEW-Ch.9-1	9	54.4	16.69	umc1120-umc2134	-1.70					6.33
	qEW-J10-1	10	2.7	0.26	umc1319-bnlg1451	1.41	0.66	0.89			12.95	6.59
CW	qCW-Ch.1-1	1	35.8	22.79	umc2224-bnlg1484	1.09		0.86			10.20	8.12
	qCW-Ch.2-1	2	23.0	2.71	umc1555-umc1024	0.30					4.98
	qCW-J2-1	2	87.5	4.40	bnlg1520-umc1736	-1.37		-0.79	-0.65		9.03	6.84
	qCW-Ch.4-1	4	179.9	46.16	umc2041-umc2287	0.24					6.16
	qCW-Ch.9-1	9	67.3	23.41	umc1120-umc2346	-1.58					8.54
GW	qGW-J1-1	1	114.2	5.03	phi308707-umc1847	-1.20	-0.64	-0.37	0.45		8.14	5.01
	qGW-J2-1	2	10.5	0.39	umc2363-umc2403	-0.53					4.85
	qGW-J2-2	2	101.8	0.24	bnlg1520-umc1736	-1.55		-0.86	-0.40	-0.71	9.31	6.86
	qGW-Ch.4-1	4	181.9	1.32	umc2041-umc2287	-1.31					8.32
	qGW-Ch.8-1	8	44.2	0.01	bnlg1863-umc2075	-0.35		-0.26		-0.17	2.79	1.24
KW	qKW-Ch.1-2	1	114.2	5.03	phi308707-umc1847	-1.06	-0.63	-0.20	-0.48		8.02	2.87
	qKW-Ch.4-1	4	181.0	4.16	umc2041-umc2287	-1.28					8.17
	qKW-J6-1	6	94.9	20.76	bnlg2191-mmc0523	0.72					5.48
	qKW-Ch.6-1	6	119.7	11.12	umc2040-bnlg1174a	0.41					3.56
性状Trait	QTL	Chr.	QTL位置 QTL position			A	AE1/ AE5	AE2/ AE6	AE3/ AE7	AE4/ AE8	h2A (%)	h2AE (%)
			cM	Mb	标记区间 Marker interval
KR	qKR-Ch.1-1	1	40.7	1.56	bnlg1484-umc1917	1.11					11.69
	qKR-J1-1	1	95.4	2.30	bnlg1025-mmc0041	0.60			0.37		5.30	2.73
	qKR-Ch.6-1	6	119.8	0.03	umc2040-bnlg1174a	0.47					3.51
	qKR-Ch.7-1	7	110.5	1.01	umc1708-umc1768	0.65					5.37
	qKR-J8-1	8	88.3	0.54	umc2356-umc1607	0.84	0.26	0.43		-0.31	8.14	2.20
EL	qEL-Ch.9-1	9	66.5	23.41	umc1120-umc2346	0.64					5.36
	qEL-Ch.10-1	10	50.1	2.85	umc1345-umc2016	0.82					6.49
CTPOP群体 CTPOP population
EW	qEW-Ch.1-1	1	138.4	17.57	bnlg1025-mmc0041	-0.90					5.06
	qEW-J2-1	2	124.3	4.40	bnlg1520-umc1736	-1.76			-0.88	-0.74	10.93	6.00
	qEW-Ch.5-1	5	236.0	2.96	umc2216-umc1072	1.03					4.71
	qEW-Ch.6-1	6	81.3	22.97	mmc0523-umc2141	-0.77					4.84
	qEW-J8-1	8	8.8	4.96	umc1327-bnlg1194	1.19		0.95			4.90	4.63
	qEW-J10-1	10	4.9	0.26	umc1319-bnlg1451	1.01		0.81	0.58	0.66	10.25	7.11
CW	qCW-Ch.1-1	1	27.1	22.79	umc2224-bnlg1484	1.10		0.73		0.58	6.49	4.57
	qCW-J1-1	1	159.0	19.78	phi308707-umc2289	1.13	-0.79	-0.43	0.68		5.40	4.34
	qCW-J5-1	5	113.8	15.76	umc1226-umc1815	-0.88			-0.54		4.92	3.59
	qCW-Ch.8-1	8	107.0	32.89	umc2218-umc2356	-1.20					7.93
	qCW-Ch.9-1	9	51.8	16.69	umc1120-umc2134	-1.82					9.29
	qCW-J10-1	10	3.3	0.26	umc1319-bnlg1451	1.07	0.55				6.05	4.18
GW	qGW-J1-1	1	154.9	17.51	mmc0041-phi308707	-2.03	-0.84	-0.66	-0.41	-0.68	11.07	5.50
	qGW-J2-1	2	38.6	2.01	umc2363-umc1024	-0.88					7.31
	qGW-J2-2	2	127.1	0.24	bnlg1520-umc1736	-1.63	-0.89	-0.41	-0.64	-0.50	9.95	4.19
	qGW-Ch.4-1	4	120.3	0.66	umc2041-umc2188	-1.77					10.14
	qGW-J6-1	6	97.4	0.03	umc2040-bnlg1174a	-1.05					8.96
	qGW-J8-1	8	40.7	0.01	bnlg1863-umc2075	-0.84	-0.36	-0.52			7.20	4.47
KW	qCW-J1-1	1	155.0	17.51	mmc0041-phi308707	-0.78	-0.58				7.10	6.03
	qKW-Ch.4-1	4	`120.5	0.66	umc2041-umc2188	-1.02					8.95
	qKW-Ch.6-1	6	81.0	22.97	mmc0523-umc2141	0.44					4.06
KR	qKR-J1-1	1	139.3	2.30	bnlg1025-mmc0041	0.51	0.26	0.39			4.15	2.93
	qKR-J6-1	6	88.1	0.17	umc2141-umc2040	0.58					4.20
	qKR-J7-1	7	118.0	1.01	umc1708-umc1768	0.73					6.12
	qKR-Ch.8-1	8	114.9	1.96	umc2356-phi233376	0.62	0.37	0.18			5.04	2.11
EL	qEL-J1-1	1	154.6	17.51	mmc0041-phi308707	-0.79	-0.66				7.04	6.18
	qEL-Ch.4-1	4	120.3	0.66	umc2041-umc2188	-1.32					9.16
	qEL-Ch.10-1	10	142.6	0.75	bnlg1839-umc1249	0.36					3.41

A: additive effect of QTL; AE1-AE4: additive by designated environment (1-4 represent E1, E2, E3, and E4 in LT_POP; respectively); AE5-AE8: additive by designated environment (5-8 represent E5, E6, E7, and E8 in CT_POP; respectively). h²_A: phenotypic variance explained by the QTL across all environments; h²_AE: phenotypic variance explained by the additive by designated environment interaction effect. Abbreviations correspond with these given in Table 1.
A: 加性效应; AE1~AE4: 加性与环境互作效应(1~4分别表示LT_POP群体中E1、E2、E3和E4等环境); AE5~AE8: 加性与环境互作效应(5~8分别表示CT_POP群体中E5、E6、E7和E8等环境)。h²_A: 加性效应表型贡献率; h²_AE: 加性与环境互作表型贡献率。英文缩写见表1。

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此外, 24个(44.4%)联合QTL参与了QTL×E (P < 0.05), 主要分布于第1、第2、第5、第8和第10染色体上, 单个QTL×E的加性与环境互作表型贡献率(h²_AE)介于1.24% (LT_POP的qGW-Ch.8-1)~7.11% (CT_POP的qEW-J10-1)(表4)。2套F_2:3群体间总共检测到6个稳定表达的QTL×E (表4), 分别位于umc2224-bnlg1484 (Bin 1.01-1.03)、phi308707- umc1847 (Bin 1.10)/mmc0041-phi308707 (Bin 1.08- 1.10)/bnlg1025-mmc0041 (Bin 1.07-1.08)、bnlg1520- umc1736 (Bin 2.09)、bnlg1863-umc2075 (Bin 8.03)、umc2356-umc1607 (Bin 8.06-8.07)和umc1319-bnlg 1451 (Bin 10.01)处。

2.4 不同水分环境下2套F_2:3群体穗部性状的QTL间上位性效应分析

不同水分环境下2套F_2:3群体穗部性状QTL间的上位性互作效应分析中总共检测到17对加性与加性/显性上位性(AA/AD) QTL (P < 0.05)(表5)。其中单穗重间检测到2对AD互作, 分别位于Bin 2.09与Bin 4.08-4.09、Bin 1.07-1.08与Bin 6.04-6.05处, AD上位性互作表型贡献率(h²_AD)介于4.27%~6.92%; 穗轴重间检测到3对AA互作, 分别位于Bin 1.01- 1.03与1.10、Bin 2.09与Bin 4.08、Bin 1.10-4.09处, AA上位性互作表型贡献率(h²_AA)介于3.85%~5.14%间; 穗粒重间检测到1对AA和1对AD互作, 分别位于Bin 2.02-2.04与Bin 6.05、Bin 2.02-2.04与Bin 8.03处, h²_AA和h²_AD分别为5.94%和3.61%~4.93%; 百粒重间检测到1对AD互作, 位于Bin 1.08-1.10与Bin 6.04处, h²_AD为4.40%~6.88%; 出籽率间检测到4对AA互作, 分别位于Bin 1.07-1.08与Bin 8.06-8.07、Bin 6.05与Bin 7.04、Bin 6.05与Bin 8.06-8.07处, h²_AA介于2.24%~6.01%; 穗长间检测到1对AA互作, 位于Bin 1.08-1.10与Bin 4.08处, h²_AA为4.96%。


Table 2
Table 2Epistatic interactions among QTLS for six ear related traits in F_2:3 population （LT_pop/CT_pop）under multiple environments

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3 讨论

3.1 水分对玉米穗部性状的影响及穗部性状的育种应用

在全球对玉米需求量的持续刚性增加及水资源短缺不可逆转的趋势下, 通过遗传改良提高玉米的抗旱性并最终增加产量已成为全球玉米生产亟待解决的问题之一。生殖生长阶段水分亏缺时, 玉米的雌穗发育迟缓, 小花受精率降低, 籽粒结实率下降, 空秆率增加, 籽粒发育受阻, 穗长、单穗重、穗轴重、穗粒重、百粒重、穗粒数和产量等严重降低^[2-3,14,27]。本研究也得到类似的研究结果, 即干旱胁迫后抗旱性差异较大的3份玉米亲本自交系及其构建的2个F₁杂交种和2套F_2:3群体的6个穗部性状均显著降低, 其降低程度为穗轴重>百粒重>穗长>穗粒重>单穗重>出籽率, 表明穗轴重、百粒重和穗长的下降是导致玉米产量降低的主要原因。Robinson等^[28]研究表明, 玉米育种中通过间接选择遗传力较高的穗粗、穗长、穗行数、百粒重等穗部性状, 进而培育出优良品种的效率远高于对其产量的直接选择。本研究表明, 8种不同水旱环境下2套F_2:3群体6个穗部性状受本身遗传特性影响较大, 其广义遗传力为77.03%~90.95%。此外, 2个F₁杂交种的单穗重、穗轴重、穗粒重、百粒重、穗长呈明显的正向超亲优势, 其值显著大于高值亲本, 而出籽率表现为较弱的正向超亲优势; 且2个F₁杂交种的F₁杂种优势指数及相对杂种优势与F_2:3群体的优势降低率表现一致, 均为穗粒重>单穗重>穗轴重>穗长>百粒重>出籽率, 这与李忠南等^[29]、赵小强等^[12]和Zhao等^[18]的研究结果一致。因此, 选择优良穗部性状的玉米基因型时需要注重对其基础材料的选择, 同时注意对父本、中亲和高亲的选择, 并兼顾杂种优势和杂种优势的衰退, 进而提高育种选择效率。

剖析不同水旱环境下玉米穗部性状的遗传机制, 可为开展抗旱高产分子育种提供理论支持。本研究共检测到62个穗部性状QTL, 分布于玉米的10个连锁群中。穗轴重和出籽率QTL主要受加性遗传效应调控, 其加性遗传效应分别占55.2%和66.7%, 单穗重、穗粒重和百粒重QTL主要受非加性遗传效应调控, 其非加性遗传效应分别占55.6%、75.0%和64.7%, 而穗长QTL受两种遗传效应调控, 各占50.0%, 与前人^[2-5,12]的研究结果一致。因此, 在不同水旱环境下改良玉米的穗轴重、出籽率和穗长时应注重对其加性效应的累加, 同时还要考虑非加性效应的影响, 而对单穗重、穗粒重和百粒重而言, 组配杂交种时应特别注重双亲的相互作用及杂种优势的影响。

3.2 干旱胁迫下玉米穗部性状QTL/sQTL的挖掘

本研究检测到62个QTL, 在干旱胁迫环境下检测到38个QTL。说明调控玉米穗部性状的QTL在不同水旱环境下会发生改变, 有些在正常供水环境下表达的QTL在干旱胁迫环境下被抑制了, 而有些在正常供水环境下不表达的QTL在干旱胁迫环境的诱导下被激活了, 因此QTL/基因在不同水旱环境下的特异性表达直接导致了玉米穗部性状表型上的差异。Tuberosa等^[26]研究发现由于不同性状间存在共同的遗传机制, 导致“一因多效”或控制不同性状的基因普遍紧密连锁。本研究也在Bin 1.05、Bin 1.07-1.08、Bin 4.08、Bin 5.06-5.07、Bin 6.05和Bin 9.04-9.06处检测到多个“一因多效”穗部性状QTL, 与表型及遗传相关分析结果高度一致, 暗示玉米染色体的这些Bin区域协同作用, 共同调控玉米穗部相关性状的遗传。此外, 作为典型的复杂数量遗传性状, 玉米穗部性状的QTL定位因不同遗传背景、生态环境或定位方法而不尽相同, 所以直接将这些QTL应用于玉米育种实践中尚存在一定难度。本研究总共检测到10个稳定表达的sQTL, 可为干旱胁迫环境下玉米穗部性状遗传机制阐述、QTL精细定位、基因克隆及抗旱分子育种提供有益参考。

位于umc2224-bnlg1484 (Bin 1.01-1.03)处存在1个调控穗轴重的sQTL, 位于bnlg1484-umc1917 (Bin 1.03-1.04)处存在1个调控出籽率的sQTL。赵小强等^[12]利用2套F₂群体也在此区间bnlg1484附近检测到1个同时调控单穗重、穗轴重和出籽率的sQTL。Messmer等^[30]利用1套RILs群体还在Bin 1.03和Bin 1.04处定位到2个调控玉米百粒重的QTL。此外, 采用meta分析法, 于多个干旱胁迫环境下Zhao等^[2]在Bin 1.02-1.03 (umc1166-bnlg439)处挖掘到1个同时调控单穗重、穗轴重、穗长和产量的meta-QTL(mQTL), 并在此区间检测到glutamine synthetase 6 (gln6)、heat shock protein 26 (hsp26)和heat shock protein (hsp70) 3个候选基因, 其中gln6可以显著提高植物的耐盐性, 而hsp26和hsp70参与了植物的耐热和抗旱性。李雪华等^[11]也在Bin 1.03 (umc11a)处检测到1个同时调控产量和10株穗数的mQTL, 说明Bin 1.03附近是调控玉米多个穗部性状及产量的重要“一因多效”或富集位点, 在不同水分环境下通过协调调控单株穗数、单穗重、穗轴重、出籽率和穗长等的发育, 影响玉米产量的形成。于umc2025-umc1395 (Bin 1.05)处存在1个同时调控单穗重、穗粒重和百粒重的sQTL。马金亮等^[31]利用1套F_2:3群体的多年表型鉴定, 在Bin 1.05 (umc1676-umc1703)处挖掘到1个调控出籽率的sQTL, 暗示Bin 1.05处可能是调控玉米穗部性状的重要富集区域, 可能包含调控玉米穗部性状的重要功能基因。于bmc1627/umc1356-umc1278 (Bin 1.07)处存在1个调控出籽率的sQTL, 于bnlg1025- mmc0041 (Bin 1.07-1.08)处存在1个同时调控单穗重、穗轴重、穗粒重和百粒重的sQTL。马金亮等^[31]在Bin1.07-1.08 (umc1122-bnlg643)处挖掘到1个调控出籽率的sQTL。张伟强等^[32]在Bin 1.07 (umc2236)附近定位到1个调控出籽率的主效QTL。Li等^[33]也在Bin 1.07-1.08 (bnlg1556-phi039)区间检测到1个同时调控穗长和行粒数的mQTL, Wang等^[34]也在Bin 1.07处检测到1个调控产量的mQTL, 印证了这一Bin区域是重要的玉米穗部性状“一因多效”位点。于umc2041-umc2188 (Bin 4.08)处挖掘到1个同时调控单穗重、穗粒重、百粒重和穗长的sQTL。多个水旱环境下Zhao等^[2]在Bin 4.08 (umc2041-umc2287)处检测到1个同时调控单穗重、穗轴重、百粒重和穗长的sQTL, 进一步分析在umc2287附近发掘到1个调控穗轴重的mQTL, 并预测到1个H⁺-ATPase基因。于umc2040-bnlg1174a (Bin 6.05)处检测到1个同时调控穗粒重、百粒重、出籽率和穗长的sQTL。赵小强等^[12]在同一区间的umc2040-bnlg1174处还检测到1个调控雌穗个数的sQTL。利用meta分析法, Wang等^[34]在Bin 6.05处也检测到了2个同时调控雌穗个数、穗粒重和产量的mQTLs, 以及1个调控玉米雌雄穗发育的iguleless3 (Ig3)基因。于umc1120-umc2346 (Bin 9.04-9.06)处存在1个同时调控单穗重、穗轴重、穗粒重、百粒重和穗长的sQTL。Zhao等^[2]、赵小强等^[12]和兰进好等^[35]也在此区间内同样检测到多个调控单穗重、穗轴重、穗长、百粒重、穗行数和穗粒重的QTL。另外, 本研究还检测到2个新的sQTL, 位于bnlg1018 (Bin 2.04)附近的调控穗长, 位于umc2216-umc1072 (Bin 5.06-5.07)处的同时调控单穗重和穗粒重, 这2个Bin位点可为干旱胁迫环境下玉米穗部性状遗传基因剖析提供新信息。

3.3 玉米穗部性状QTL×E效应

Zhuang等^[36]分析表明基因与环境互作是影响数量性状的重要因素之一, 因此在不同环境下定位到的QTL结果不尽相同。本研究中也存在类似的研究结果, 但又与前人研究存在较大差异。如谭巍巍等^[37]利用2套F_2:3群体在多年多点的联合分析中只检测到8个穗部性状(分别为1、2和5个穗行数、穗长和穗粒重) QTL×E (P < 0.05)位点, 主要分布于第1、第6、第7、第8和第10染色体。本研究分析表明, 44.4%的QTL参与了QTL×E (P < 0.05), 主要分布于第1、第2、第5、第8和第10染色体。另外, 部分QTL×E位点能在多个环境中被检测到, 如位于Bin 1.01-1.03 (umc2224-bnlg1484)区间调控穗轴重的QTL, 能在LT_POP群体的E1、E2、E3和E4等4种环境下被检测到, 又同时与E2环境存在较强的互作关系, 还在CT_POP群体的E5、E6和E8等3种环境下被检测到, 又同时与E6和E8等环境存在较强的互作关系, 其平均主效h²_A为8.35%, 明显大于其平均h²_AE的6.35%。位于bnlg1863- umc2075 (Bin 8.03)区间调控穗粒重的QTL, 在LT_POP群体的E3和E4等两种环境下被检测到, 又同时与E2和E4等环境存在较强的互作关系, 还在CT_POP群体中与E5和E6等环境存在较强的互作关系, 其平均主效h²_A为5.00%, 远大于其平均h²_AE的2.86%。这与谭巍巍等^[37]的研究结果类似, 即2种环境下在第1染色体umc1009-umc1331区间定位到的qGW1-1-2, 又同时与3种环境存在显著的互作关系。出现这种现象的原因主要体现在2个方面^[38,39], 一是可能QTL本身的表型贡献率较大, 易在不同环境中被检测到, 二是可能存在较强的QTL×E, 显著影响QTL在不同环境中的检测结果。对于这些既能在多环境下被检测到又同时与环境存在显著互作的QTL, 能否成为分子标记辅助选择或图位克隆的候选染色体区段, 关键在于这些QTL×E是否有利。因此, 如何利用玉米穗部性状的这些QTL×E将是今后研究的重点。

3.4 玉米穗部性状QTL的上位性互作效应

Hagiwara等^[40]指出QTL的上位性互作效应是复杂数量性状遗传和杂种优势形成的重要遗传基础。常立国等^[41]采用178×K12构建1套RILs群体检测到15对调控出籽率的AA上位性QTL。谭巍巍等^[37]利用掖478×黄早4和齐319×黄早四构建2套F_2:3群体检测到18对调控穗长、穗粗、穗行数和穗粒重的AA、AD、DA、DD上位性QTL。本研究也同样对2套F_2:3群体的6个穗部性状在8种不同水分环境下检测到17对AA/AD上位性QTL。综合分析这些研究结果发现, AA、AD、DA及DD上位性QTL在玉米穗部性状中普遍存在, 也从一定程度上阐述了玉米穗部性状基因位点间的内在关系, 并在遗传中可能与主效QTL发挥同等重要的作用。因此, 今后应在更为丰富的遗传背景下有效地检测和利用穗部性状上位性QTL, 为玉米穗部性状分子遗传机制剖析提供有益参考。

4 结论

干旱胁迫下玉米的6个穗部性状均显著降, 降低程度为穗轴重>百粒重>穗长>穗粒重>单穗重>出籽率, 表明穗轴重、百粒重和穗长的下降是导致玉米产量降低的主要原因; F₁杂交种的单穗重、穗轴重、穗粒重、百粒重、穗长呈明显的正向超亲优势, 而出籽率表现为较弱的正向超亲优势, 其杂种优势表现为穗粒重>单穗重>穗轴重>穗长>百粒重>出籽率; 采用CIM法, 单环境下在2套F_2:3群体间总共定位到62个穗部性状QTL, 其中干旱胁迫环境下检测到38个, 且在2套F_2:3群体多个干旱胁迫环境下检测到10个sQTL, 位于Bin 1.01-1.03、Bin 1.03- 1.04、Bin 1.05、Bin 1.07、Bin 1.07-1.08、Bin 2.04、Bin 4.08、Bin 5.06-5.07、Bin 6.05和Bin 9.04-9.06处, 将在一定程度上为干旱胁迫下玉米穗部性状的改良提供参考; 采用MCIM法, 联合分析中总共定位到54个穗部性状联合QTL, 其中24个表现显著的QTL×E, 17对QTL参与了显著的AA/AD上位性互作, 体现了穗部性状遗传基础的复杂性。今后应在更为丰富的遗传背景及环境下深入研究穗部性状的遗传机制, 应用于玉米分子育种。

The authors have declared that no competing interests exist.

作者已声明无竞争性利益关系。

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[3] 谭巍巍, 李永祥, 王阳, 刘成, 刘志斋, 彭勃, 王迪, 张岩, 孙宝成, 石云素, 宋燕春, 杨德光, 王天宇, 黎裕 . 在干旱和正常水分条件下玉米穗部性状QTL分析
作物学报, 2011,37:235-248.
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Tan W W, Li Y X, Wang Y, Liu C, Liu Z Z, Peng B, Wang D, Zhang Y, Sun B C, Shi Y S, Song Y C, Yang D G, Wang T Y, Li Y . QTL mapping of ear traits of maize under different water regimes
Acta Agron Sin, 2011,37:235-248 (in Chinese with English abstract).
[本文引用: 3]

[4] Nikolic A, Andjelkovic A, Dodig D, Ignjatovic-Micic D . Quantitative trait loci for yield and morphological traits in maize under drought stress
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[5] Almeida G D, Nair S, Borém A, Cairns J, Trachsel S, Ribaut J M, B?nziger M, Prasanna B M, Crossa J, Babu R . Molecular mapping across three populations reveals a QTL hotspot region on chromosome 3 for secondary traits associated with drought tolerance in tropical maize
Mol Breed, 2014,34:701-715.
DOI:10.1007/s11032-014-0068-5URLPMID:4092235 [本文引用: 2]
Identifying quantitative trait loci (QTL) of sizeable effects that are expressed in diverse genetic backgrounds across contrasting water regimes particularly for secondary traits can significantly complement the conventional drought tolerance breeding efforts. We evaluated three tropical maize biparental populations under water-stressed and well-watered regimes for drought-related morpho-physiological traits, such as anthesis-silking interval (ASI), ears per plant (EPP), stay-green (SG) and plant-to-ear height ratio (PEH). In general, drought stress reduced the genetic variance of grain yield (GY), while that of morpho-physiological traits remained stable or even increased under drought conditions. We detected consistent genomic regions across different genetic backgrounds that could be target regions for marker-assisted introgression for drought tolerance in maize. A total of 203 QTL for ASI, EPP, SG and PEH were identified under both the water regimes. Meta-QTL analysis across the three populations identified six constitutive genomic regions with a minimum of two overlapping traits. Clusters of QTL were observed on chromosomes 1.06, 3.06, 4.09, 5.05, 7.03 and 10.04/06. Interestingly, a ~8-Mb region delimited in 3.06 harboured QTL for most of the morpho-physiological traits considered in the current study. This region contained two important candidate genes viz., zmm16 (MADS-domain transcription factor) and psbs1 (photosystem II unit) that are responsible for reproductive organ development and photosynthate accumulation, respectively. The genomic regions identified in this study partially explained the association of secondary traits with GY. Flanking single nucleotide polymorphism markers reported herein may be useful in marker-assisted introgression of drought tolerance in tropical maize.

[6] Guo J F, Su G Q, Zhang J P, Wang G Y . Genetic analysis and QTL mapping of maize yield and associate agronomic traits under semi-arid land condition
Afr J Biotech, 2008,7:1829-1838.
[本文引用: 1]

[7] Lu G H, Tang J H, Yan J B, Ma X Q, Li J S, Chen S J, Ma J C, Liu Z X, 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.
DOI:10.1111/j.1744-7909.2006.00289.xURL [本文引用: 1]
Drought or water stress is a serious agronomic problem resulting in maize ( Zea mays L.) yield loss throughout the world. Breeding hybrids with drought tolerance is one important approach for solving this problem. However, lower efficiency and a longer period of breeding hybrids are disadvantages of traditional breeding programs. It is generally recognized that applying molecular marker techniques to traditional breeding programs could improve the efficiency of the breeding of drought-tolerant maize. To provide useful information for use in studies of maize drought tolerance, the mapping and tagging of quantitative trait loci (QTL) for yield and its components were performed in the present study on the basis of the principle of a mixed linear model. Two hundred and twenty-one recombinant inbred lines (RIL) of Yuyu 22 were grown under both well-watered and water-stressed conditions. In the former treatment group, plants were well irrigated, whereas those in the latter treatment group were stressed at flowering time. Ten plants of each genotype were grown in a row that was 3.00 m 0.67 m (length width). The results show that a few of the QTL were the same (one additive QTL for ear length, two additive QTL and one pair of epistatic QTL for kernel number per row, one additive QTL for kernel weight per plant), whereas most of other QTL were different between the two different water treatment groups. It may be that genetic expression differs under the two different water conditions. Furthermore, differences in the additive and epistatic QTL among the traits under water-stressed conditions indicate that genetic expression also differs from trait to trait. Major and minor QTL were detected for the traits, except for kernel number per row, underwater-stressed conditions. Thus, the genetic mechanism of drought tolerance in maize is complex because the additive and epistatic QTL exist at the same time and the major and minor QTL all contribute to phenotype under water-stressed conditions. In particular, epidemic QTL under water-stressed conditions suggest that it is important to investigate the drought tolerance of maize from a genetic viewpoint.

[8] Sabadin P K, de-Souza C L, de-Souza A P, Franco G A A . QTL mapping for yield components in a tropical maize population using microsatellite markers
Hereditas, 2008,145:194-203.
DOI:10.1111/j.0018-0661.2008.02065.xURL [本文引用: 1]
QTL mapping provides usefull information for breeding programs since it allows the estimation of genomic locations and genetic effects of chromossomal regions related to the expression of quantitative traits. The objective of this study was to map QTL related to several agronomic important traits associated with grain yield: ear weight (EW), prolificacy (PROL), ear number (NE), ear length (EL) and diameter (ED), number of rows on the ear (NRE) and number of kernels per row on the ear (NKPR). Four hundred F 2:3 tropical maize progenies were evaluated in five environments in Piracicaba, Sao Paulo, Brazil. The genetic map was previously estimated and had 117 microssatelite loci with average distance of 14 cM. Data was analysed using Composite Interval Mapping for each trait. Thirty six QTL were mapped and related to the expression of EW (2), PROL (3), NE (2), EL (5), ED (5), NRE (10), NKPR (5). Few QTL were mapped since there was high GE interaction. Traits EW, PROL and EN showed high genetic correlation with grain yield and several QTL mapped to similar genomic regions, which could cause the observed correlation. However, further analysis using apropriate statistical models are required to separate linked versus pleiotropic QTL. Five QTL (named Ew 1, Ne 1, Ed 3, Nre 3 and Nre 10) had high genetic effects, explaining from 10.8% ( Nre 3) to 16.9% ( Nre 10) of the phenotypic variance, and could be considered in further studies.

[9] Calderón C I, Yandell B S, Doebley J F . Fine mapping of a QTL associated with kernel row number on chromosome 1 of maize
PLoS One, 2016,11:e0150276.
DOI:10.1371/journal.pone.0150276URLPMID:26930509 [本文引用: 1]
The genetic factors underlying changes in ear morphology, and particularly the inheritance of kernel row number (KRN), have been broadly investigated in diverse mapping populations in maize (Zea maysL.). In this study, we mapped a region on the long arm of chromosome 1 containing a QTL for KRN. This work was performed using a set ofrecombinantchromosomenearlyisogeniclines (RCNILs) derived from a BC2S3population produced using the inbred maize line W22 and teosinte (Zea mays ssp.parviglumis) as the parents. A set of 48 RCNILs was evaluated in the field during the summer of 2013 in order to perform the mapping. A QTL for KRN was found that explained approximately 51% of the phenotypic variance and had a 1.5-LOD confidence interval of 203 kb. Seven genes are described in this interval. One of these candidate genes may have been the target of domestication processes in maize and contributed to the shift from two kernel row ears in teosinte to a highly polystichous ear in maize.

[10] Liu L, Du Y F, Shen X M, Li M F, Sun W, Huang J, Liu Z J, Tao Y S, Zheng Y L, Yan J B, Zhang Z X . KRN4 controls quantitative variation in maize kernel row number. PLoS Genet, 2015,11:e1005670.
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[11] 李雪华, 李新海, 郝传芳, 田清震, 张世煌 . 干旱条件下玉米耐旱相关性状的QTL一致性图谱构建
中国农业科学, 2005,38:882-890.
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Li X H, Li X H, Hao C F, Tian Q Z, Zhang S H . Consensus map of the QTL relevant to drought tolerance of maize under drought conditions
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[12] 赵小强, 方鹏, 彭云玲, 高巧红, 曾文静, 任斌 . 基于两个相关群体的玉米6个穗部性状QTL定位
农业生物技术学报, 2018,26:729-742.
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Zhao X Q, Fang P, Peng Y L, Gao Q H, Zeng W J, Ren B . QTL mapping for six ear-related traits based on two maize (
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[本文引用: 8]

[13] 赵小强, 方鹏, 彭云玲, 张金文, 曾文静, 任斌, 高巧红 . 基于两个相关群体的玉米7个主要农艺性状遗传分析和QTL定位
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Zhao X Q, Fang P, Peng Y L, Zhang J W, Zeng W J, Ren B, Gao Q H . Genetic analysis and QTL mapping for seven agronomic traits in maize (
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Abstract Morphological traits for ear leaf are determinant traits influencing plant architecture and drought tolerance in maize. However, the genetic controls of ear leaf architecture traits remain poorly understood under drought stress. Here, we identified 100 quantitative trait loci (QTLs) for leaf angle, leaf orientation value, leaf length, leaf width, leaf size and leaf shape value of ear leaf across four populations under drought-stressed and unstressed conditions, which explained 0.71%-20.62% of phenotypic variation in single watering condition. Forty-five of the 100 QTLs were identified under water-stressed conditions, and 29 stable QTLs (sQTLs) were identified under water-stressed conditions, which could be useful for the genetic improvement of maize drought tolerance via QTL pyramiding. We further integrated 27 independent QTL studies in a meta-analysis to identify 21 meta-QTLs (mQTLs). Then, 24 candidate genes controlling leaf architecture traits coincided with 20 corresponding mQTLs. Thus, new/valuable information on quantitative traits has shed some light on the molecular mechanisms responsible for leaf architecture traits affected by watering conditions. Furthermore, alleles for leaf architecture traits provide useful targets for marker-assisted selection to generate high-yielding maize varieties.

[20] 石云素, 黎裕, 王天宇 . 玉米种质资源描述规范和数据标准. 北京: 中国农业出版社, 2006, pp 1-98.
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Heritability (H) on a progeny mean basis is frequently estimated in recurrent selection experiments for the purpose of estimating the expected progress from family selection; however, appropriate measures of precision have been developed for only a few heritability estimators. The objective of this research was to develop a measure of precision for H for certain balanced linear models. Exact confidence intervals for H were derived and are not restricted to a specific experimental design. The confidence intervals were applied to sorghum [Sorghum bicolor (L.) Moench] half-sib family data.

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The detection of genes that control quantitative characters is a problem of great interest to the genetic mapping community. Methods for locating these quantitative trait loci (QTL) relative to maps of genetic markers are now widely used. This paper addresses an issue common to all QTL mapping methods, that of determining an appropriate threshold value for declaring significant QTL effects. An empirical method is described, based on the concept of a permutation test, for estimating threshold values that are tailored to the experimental data at hand. The method is demonstrated using two real data sets derived from F2 and recombinant inbred plant populations. An example using simulated data from a backcross design illustrates the effect of marker density on threshold values.

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Abstract SUMMARY: Understanding how interactions among set of genes affect diverse phenotypes is having a greater impact on biomedical research, agriculture and evolutionary biology. Mapping and characterizing the isolated effects of single quantitative trait locus (QTL) is a first step, but we also need to assemble networks of QTLs and define non-additive interactions (epistasis) together with a host of potential environmental modulators. In this article, we present a full-QTL model with which to explore the genetic architecture of complex trait in multiple environments. Our model includes the effects of multiple QTLs, epistasis, QTL-by-environment interactions and epistasis-by-environment interactions. A new mapping strategy, including marker interval selection, detection of marker interval interactions and genome scans, is used to evaluate putative locations of multiple QTLs and their interactions. All the mapping procedures are performed in the framework of mixed linear model that are flexible to model environmental factors regardless of fix or random effects being assumed. An F-statistic based on Henderson method III is used for hypothesis tests. This method is less computationally greedy than corresponding likelihood ratio test. In each of the mapping procedures, permutation testing is exploited to control for genome-wide false positive rate, and model selection is used to reduce ghost peaks in F-statistic profile. Parameters of the full-QTL model are estimated using a Bayesian method via Gibbs sampling. Monte Carlo simulations help define the reliability and efficiency of the method. Two real-world phenotypes (BXD mouse olfactory bulb weight data and rice yield data) are used as exemplars to demonstrate our methods. AVAILABILITY: A software package is freely available at http://ibi.zju.edu.cn/software/qtlnetwork

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Comparative analysis of a number of studies in drought-stressed maize (Zea mays L.) reporting quantitative trait loci (QTLs) for abscisic acid concentration, root characteristics, other morpho-physiological traits (MPTs) and grain yield (GY) reveals their complex genetic basis and the influence of the genetic background and the environment on QTL effects. Chromosome regions (e.g. near umc11 on chromosome I and near csu133 on chromosome 2) with QTLs controlling a number of MPTs and GY across populations and conditions of different water supply have been identified. Examples are presented on the use of QTL information to elucidate the genetic and physiological bases of the association among MPTs and GY. The QTL approach allows us to develop hypotheses accounting for these associations which can be further tested by developing near isogenic lines (NILs) differing for the QTL alleles. NlLs also allow for a more accurate assessment of the breeding, value of MPTs and, in some cases, may allow for the map-based cloning of the gene(s) underlying the QTL. Although QTL analysis is still time-consuming and resource-demanding, its integration with genomics and post-genomics approaches (e.g. transcriptome, proteome and metabolome analyses) will play an increasingly important role for the identification and validation of candidate genes affecting MPTs and GY.

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Estimates of genotypic and phenotypic correlations among characters are useful in planning and evaluating breeding programs. A knowledge of the correlations that exist between important characters may facilitate the interpretation of results already obtained and provide the basis for planning more efficient programs for the future. Also, correlations between important and non-important characters may reveal that some of the latter are useful as indicators of one or more of the former....

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Main conclusion The meta-QTL and candidate genes will facilitate the elucidation of molecular bases underlying agriculturally important traits and open new avenues for functional markers development...

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An F 2 and two equivalent F 3 populations of an indica-indica cross of rice, Tesanai 2/CB, were constructed and grown in different environments. The identification of quantitative trait loci (QTL) for yield components and plant height and an analysis of QTL environment interaction were conducted for three trials. Interval mapping of QTL for eight traits was employed with a threshold of LOD=2 using the computer package MAPMAKER/QTL . A total of 44 QTL were detected in 18 intervals of nine chromosomes, including 3 for the number of panicles (NP), 5 for the number of filled grains (NFG), 6 for total number of spikelets (TNS), 3 for spikelet fertility (SF), 7 for 1000-grain weight (TGWT), 5 for grain weight per plant (GWT), 8 for plant height (PH) and 7 for panicle length (PL). The numbers of QTL detected in two or three trials were 1 for NP, 1 for NFG, 1 for TNS, none for SF, 4 for TGWT, 3 for GWT, 2 for PH and 5 for PL, making a total of 17. When a QTL was detected in more than one trial the direction and magnitude of its additive effect, the dominance effect and the degree of dominance were generally in good agreement. In all three trials, QTL were frequently detected for related traits in the same intervals. The directions of additive effect of QTL for related traits in a given interval were in agreement with few exceptions, no matter whether they were detected in the same trial or not. This result suggested that pleiotropism rather than close linkage of different QTL was the major reason why QTL for different traits were frequently detected in the same intervals. When gene pleiotropism was considered, 23 of the 29 QTL for yield and its components and 9 of the 15 QTL for plant stature were detected in more than one trial. This indicated that the detection of chromosomal segments harboring QTL was hardly affected by environmental factors.

[37] 谭巍巍, 王阳, 李永祥, 刘成, 刘志斋, 彭勃, 王迪, 张岩, 孙宝成, 石云素, 宋燕春, 杨德光, 王天宇, 黎裕 . 不同环境下多个玉米穗部性状的QTL分析
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It has been theoretically proposed that multiple linked quantitative trait loci (QTLs) play a role in the accumulation of hidden variation within and between populations. In this study, the genetic bases for grain characteristics were examined by comparing two accessions representing the two rice subspecies by QTL analysis. Grain dimensions are known to be quantitative traits and to be diagnostic between these two subspecies. To enhance the power to detect QTL with small effects, after transferring a segment of chromosome 6 from an Indica type into a Japonica type of rice by repeated backcrosses, the introgressed segment was dissected by making recombinant inbred lines (RILs) which were expected to have different sizes of the introgressed segment in the same genetic background. The resulting RILs showed distinct transgression of the grain characteristics examined. Multiple QTLs controlling each of the length and breadth of seeds were detected on the introgressed segment, and showed positive and negative additive effects as well as epistatic interactions. The present study confirmed that transgressive segregation resulted from a breakdown of linkage and that the detection of QTLs was highly dependent upon the genetic effects of the neighboring QTLs, indicating the need for caution in interpreting QTL effects.

[41] 常立国, 何坤辉, 崔婷婷, 薛吉全, 刘建超 . 玉米出籽率的QTL定位及其与环境互作分析
农业生物技术学报, 2017,25:517-525.
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Chang L G, He K H, Cui T T, Xue J Q, Liu J C . QTL mapping and QTL × environment interaction analysis of kernel ratio in maize (Zea mays).
J Agric Biotechnol, 2017,25:517-525 (in Chinese with English abstract).
[本文引用: 1]