大豆产量及主要农艺性状QTL的上位性互作和环境互作分析

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梁慧珍, 余永亮, 杨红旗, 张海洋, 董薇, 李彩云, 杜华, 巩鹏涛, 刘学义, 方宣钧. 大豆产量及主要农艺性状QTL的上位性互作和环境互作分析. 作物学报, 2014, 40(1): 37-44
LIANG Hui-Zhen, YU Yong-Liang, YANG Hong-Qi, ZHANG Hai-Yang, DONG Wei, LI Cai-Yun, GONG Peng-Tao, LIU Xue-Yi, FANG Xuan-Jun. Epistatic Effects and QTL × Environment Interaction Effects of QTLs for Yield and Agronomic Traits in Soybean. Acta Agronomica Sinica, 2014, 40(1): 37-44 复制到剪切板

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大豆产量及主要农艺性状QTL的上位性互作和环境互作分析

梁慧珍¹, 余永亮¹, 杨红旗¹, 张海洋¹, 董薇¹, 李彩云¹, 杜华¹, 巩鹏涛², 刘学义³, 方宣钧⁴

¹河南省农业科学院芝麻研究中心, 河南郑州 450002

²东北油田盐碱植被恢复与重建教育部重点实验室 / 东北林业大学盐碱地生物资源环境研究中心, 黑龙江哈尔滨 150040

³山西省农业科学院经济作物研究所, 山西汾阳 032200

⁴海南省热带农业资源开发利用研究所, 海南三亚 572025

第一作者联系方式: E-mail:lhzh66666@163.com, Tel: 0371-65751589

收稿日期:2013-09-16

基金:本研究由河南省科技创新杰出人才计划(114200510002)和国家转基因生物新品种培育科技重大专项(2009ZX08018-001B, 2011ZX08004-005)资助。

摘要

以栽培大豆晋豆23为母本, 半野生大豆灰布支黑豆ZDD2315为父本杂交衍生的F_2:15和F_2:16的447个RIL家系为遗传群体, 绘制SSR遗传图谱, 采用混合线性模型方法, 对2年大豆小区产量及主要农艺性状进行加性QTL、加性×加性上位互作及环境互作分析。结果检测到9个与小区产量、茎粗、有效分枝、主茎节数、株高、结荚高度相关的QTL, 分别位于J_2、I、M连锁群上, 其中小区产量、茎粗、株高、有效分枝和主茎节数QTL的加性效应为正值, 说明增加这些性状的等位基因来源于母本晋豆23。同时, 检测到7对影响小区产量、茎粗、株高和结荚高度的加性×加性上位互作效应及环境互作效应的QTL, 共发现14个与环境存在互作的QTL。上位效应和QE互作效应对大豆小区产量及主要农艺性状的遗传影响较大。大豆分子标记辅助育种中, 既要考虑起主要作用的QTL, 又要注重上位性QTL, 才有利于性状的稳定表达和遗传。

关键词: 大豆; 小区产量; 农艺性状; QTL与环境互作效应; 上位互作效应

Epistatic Effects and QTL × Environment Interaction Effects of QTLs for Yield and Agronomic Traits in Soybean

LIANG Hui-Zhen¹, YU Yong-Liang¹, YANG Hong-Qi¹, ZHANG Hai-Yang¹, DONG Wei¹, LI Cai-Yun¹, GONG Peng-Tao², LIU Xue-Yi³, FANG Xuan-Jun⁴

¹ Sesame Research Center, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China

² Key Laboratory of Ministry of Education for Saline-alkali Vegetation Ecology Restoration in Oil Field (SAVER) /Alkali Soil Natural Environmental Science Center (ASNESC), Northeast Forestry University, Harbin 150040, China

³ Institute of Industrial Crops, Shanxi Academy of Agricultural Sciences, Fenyang 032200, China

⁴Hainan Institute of Tropical Agriculture Resources, Sanya 572025, China

Abstract

Improving seed yield is an important goal of soybean breeding programs. In this investigation, a soybean SSR genetic linkage map constructed by a total of 447 recombinant inbred lines (RILs) derived from a cross between Jindou 23 (cultivar, female parent) and ZDD2315 (semi-wild, male parent) and the mixed linear model was used to identify the QTLs of yield and other QTLs for major agronomic traits in a two-year experiment. Nine QTLs bearing additive effects for pod position, plant height, node number on main stem, branch number, stem thickness and yield per plot were mapped in the linkage groups J_2, I, and M. The QTLs of yield per plot, stem thickness, plant height, branch number and node number on main stem showed positive additive effects donated by Jindou 23. Seven pairs of epistatic effects QTLs for pod position, plant height and stem thickness were detected, which had an interaction with environments. The results indicated that the epistatic effects and the environmental factors played an important role in yield per plot and agronomic traits in soybean. It will be very important to pay attention to not only QTLs with major effects but also those with epistatic effects in soybean molecular marker-assisted breeding in considering the stability expression and inheritance of the agronomic traits.

Keyword: Soybean; Yield per plot; Agronomic traits; QTL #x000d7;environment interaction; Epistatic effect

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大豆具有较高的营养价值, 广泛应用于工业与食品。长期以来, 大豆产量偏低的问题, 一直困扰着大豆产业发展。大豆产量是受多基因控制的综合数量性状, 容易受遗传、生理、环境、形态以及农艺性状的影响^{[ 1]}。近年来, 分子遗传学和分子标记技术快速发展, 构建出了高密度遗传连锁图谱, 推动了数量性状的QTL基因定位研究进程^{[ 2]}。根据SoyBase大豆数据库(http://soybeanbreederstoolbox.org/)最新公布的数据, 对大豆产量及相关性状的QTL定位已经超过350个, 并且几乎覆盖所有的连锁群。以往对大豆产量和相关农艺性状的研究, 主要集中于QTL的定位及各座位间的互作, 而较少涉及QTL间的上位性和环境互作效应, 这主要是受到了遗传分析模式研究和分析软件研发的限制^{[ 3, 4, 5, 6, 7, 8]}。朱军^{[ 9]}采用混合线性模型, 扩展了以往的广义遗传模型分析方法, 使基因和环境的互作效应无偏分析变成了现实。Wang等^{[ 10]}据此开发出了QTL和QE互作分析软件。研究人员对玉米^{[ 11, 12]}、水稻^{[ 13, 14]}、小麦^{[ 15, 16]}等作物进行了QTL与环境互作分析, 对大豆蛋白质、脂肪和异黄酮^{[ 17, 18, 19]}等已有研究报道, 但对产量特别是小区产量诸性状QTL间的上位性和环境互作效应研究较少。受遗传背景和环境条件的影响, 前人对大豆产量和相关性状定位出的QTL, 在用于分子标记时, 经常出现屏蔽。同时, QTL间的上位性效应对小区产量诸性状表型值的组成具有重要作用。本研究对2011—2012年种植的大豆晋豆23×半野生大豆灰布支黑豆重组自交系进行小区产量及主要农艺性状QTL定位和加性效应、加性×加性上位互作效应及环境互作效应分析, 以期用于指导实践中的分子标记辅助育种工作。

1 材料与方法

1.1 供试材料

以栽培大豆晋豆23为母本, 半野生大豆灰布支黑豆(ZDD2315)为父本所衍生的447个株系的F_2:15和F_2:16重组自交系作为性状遗传分析的供试群体。

1.2 田间试验

以随机区组设计, 3次重复, 行长4 m, 行距0.4 m, 株距0.13 m。周边设6行保护行, 消除边行优势。2011和2012年连续2年在河南省农业科学院现代农业科技试验示范基地(河南省原阳县)种植, 一播全苗, 按照大田模式进行田间管理。成熟后, 将4行区全部收获计产。按照《中国大豆品种志》的标准调查小区产量(yield per plot, YLD)、结荚高度(pod position, PP)、株高(plant height, PH)、主茎节数(node number on main stem, NNMS)、分枝数(branch number, BN)、百粒重(100-seed weight, SW)、茎粗(stem thickness, ST) 7个性状。

1.3 表型数据分析

分别取2011和2012两年测量数据的平均值, 利用DPS软件^{[ 20]}对各性状考察并作相关性分析。

1.4 遗传连锁图谱的构建与QTL分析

本研究群体的遗传连锁图谱已经由王珍等^{[ 21, 22]}构建完成。图谱全长2047.6 cM, 包括27个连锁群, 232个标记位点^{[ 22]}。把不同年份作为环境因子处理, 根据两环境的表型数据, 利用QTLNetwork 2.0^{[ 23]}软件中的混合线性模型复合区间作图法(MCIM), 选取临界阈值 P=0.05, 检测每个环境下的QTL、加性效应、加性×加性上位互作效应及环境互作效应。当QTL效应 P≤0.05时, 认为QTL存在; 如果临近位点间图距小于5 cM, 就初步认定是同一个QTL。参照McCouch^{[ 24]}方式命名QTL。

2 结果与分析

2.1 亲本及家系性状的表型分析

通过对群体亲本两地表型数据统计(表1), 发现2个亲本在7个性状间差异较大, 为QTL分析提供了较好的遗传背景。RIL群体的最小值和最大值之间差异明显, 表型数据的随机误差项服从正态分布, 满足QTL分析要求。

表1 RIL群体亲本和群体家系性状描述统计分析 Table 1 Phenotypic variation of all traits in RIL and the parents

2.2 相关性分析

表2表明, 不同环境条件下的相关分析结果基本一致。小区产量与结荚高度、株高、主茎节数、分枝数、百粒重和茎粗均呈现显著或极显著正相关, 茎粗在2012年表现出与株高、主茎节数、分枝数、结荚高度、百粒重极显著正相关, 2011年表现出与株高、主茎节数、分枝数极显著正相关。其他各性状间均显著或极显著正相关。说明小区产量诸性状之间存在着既相互影响又相互制约的关联关系, 在实际育种工作中为表型相关选择提供了参考。

表2 7个性状之间的相关性系数 Table 2 Correlation coefficients between seven traits

2.2 小区产量及主要农艺性状的QTL与环境互作效应

联合分析2年的结荚高度、株高、主茎节数、分枝数、百粒重、茎粗、小区产量性状数据, 共检测到9个QTL (表3和图1)。检测到7个性状的加性效应为-0.6633~5.3910, 对性状贡献率为1.14%~13.01%, 其中, 小区产量、茎粗、株高、有效分枝和主茎节数均表现遗传正效应, 即增加其性状的等位基因来源于母本晋豆23。除百粒重外, 其他性状与环境互作效应贡献率为0.02%~9.74%。所有性状( qPP_M_1和 qYLD-J_2-1除外)与环境互作的贡献率均小于其自身的加性效应贡献率。

表3 各个性状的QTL及QTL与环境互作 Table 3 QTL and QE interaction for all traits

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	图1 检测到的QTL及加性效应QTL在连锁群上的分布Fig. 1 Location of QTLs and epistatic effects QTLs on linkage groups

小区产量、茎粗和有效分枝的QTL均位于J_2染色体, 加性效应值分别为0.4894、3.61和7.59, 贡献率分别为3.06%、3.61%和7.59%; 3个性状均与环境之间存在互作, 互作贡献率分别为9.74%、1.62%和4.95%。小区产量的加性贡献率小于互作贡献率, 说明环境对产量的影响较大。

共检测到2个主茎节数QTL, 分布在J_2和N上, 加性效应值分别为1.1541和1.0368, 贡献率分别为13.01%和9.21%; 2个QTL均与环境存在互作效应, 互作贡献率分别为1.43%和1.12%。

共检测到2个株高QTL, 分布在J_2和I上, 加性效应值分别为5.3910和4.5808, 贡献率分别为7.78%和6.64%; 2个QTL均与环境存在互作效应, 互作贡献率分别为0.45%和0.02%, qPH-I-1与环境的互作贡献率较小。共检测到2个结荚高度QTL, 分布在J_2和M上, 加性效应值分别为0.7097和-0.6633, 贡献率分别为2.77%和1.14%; 2个QTL均与环境存在互作效应, 互作贡献率分别为1.58%和1.48%。主茎节数、株高和结荚高度分析结果表明加性贡献率大于互作贡献率, 说明基因比环境对这3个因子的影响显著。

2.3 小区产量诸性状的上位互作效应

共检测到7对影响小区产量、结荚高度、株高和茎粗的加性×加性上位互作效应的QTL (表4)。2对小区产量QTL上位性效应的效应值分别为-0.1858和-0.1944, 贡献率分别为2.00%和3.27%, 效应值均为负值, 表现出重组型大于亲本型。2对QTL均存在上位互作效应, 互作贡献率分别为2.30%和4.42%。1对茎粗QTL上位性效应的效应值

为-0.0286, 贡献率为3.32, 效应值为负值, 为重组型大于亲本型。这对QTL存在上位互作效应, 互作贡献率为1.38%。3对结荚高度QTL上位性效应的效应值分别为0.7538、0.5989和0.5878, 贡献率分别为1.11%、0.99%和1.51%, 效应值均为正值, 为亲本型大于重组型。3对QTL均存在上位互作效应, 互作贡献率分别为1.16、1.08%和0.82%。1对株高QTL上位性效应的效应值为4.8742, 贡献率为5.45, 效应值为正值, 为亲本型大于重组型。这对QTL存在上位互作效应, 互作贡献率为1.14%。

表4 各个性状加性×加性上位互作效应QTL Table 4 Epistatic effects QTL of additive × additive for all traits

3 讨论

3.1 “小区产量”与“产量”作为个体值定位的选择

小区产量可以理解为构成性状, 产量是复合形状, 本研究选择“小区产量”作为个体值进行QTL定位, 研究结果可能更为有效。首先, 复合性状比构成性状受到更多的QTL控制, QTL的连锁关系和遗传效应更加复杂。因而, QTL作图功效明显下降, 使用复合性状作图的意义不大^{[ 25]}。其次, 遗传力 h²= V_A/ V_P, V_A和 V_P均为个体值的方差。产量是复合性状, 往往是不准确的。即使采用, 也由于竞争的作用而意义不大。第三, 构成性状和复合性状在遗传研究和实际育种中的使用, 应考虑到二者的研究目标不尽相同。遗传研究的目的是探索控制目标性状基因的遗传规律, 相对“产量”(复合性状)而言, “小区产量”(构成性状)遗传模型较为简单, 受较少的QTL控制, 更易于进行单个QTL遗传研究; 而作物育种的目的, 是要尽可能多地选择优良基因和有利基因组合。和多性状选择指数^{[ 26]}类似, “产量”作为复合性状, 在育种工作中可以同时选择相互之间有影响的多个有利等位基因, 提高实际育种工作效率。因此, 选用“小区产量”更适合于QTL作图研究, 在育种中“产量”则更为常用。

3.2 联合数据分析对结果的影响

本研究检验出的QTL贡献率值在各年份间大小不同, 联合数据分析结果往往出现QTL贡献率值低于不考虑QTL与环境互作的结果。2年间大豆小区产量诸性状的QTL与环境互作, 共检测到9个加性效应QTL, 相对数量较少, 且每个性状均有1个QTL定位在J染色体上, 呈现出很大的集中性, 这与前人的研究结果有所不同。同时, 有1个结荚高度QTL定位在M连锁群上, 各有1个株高和主茎节数QTL定位在I连锁群上, 与已有研究结果类似^{[ 27]}, 又呈现出较好的稳定性和真实性。加性效应的9个QTL贡献率总体表现较低, 分布在1.44%~13.01%之间, 其中4个QTL的贡献率低于4%, 只有一个大于10%。为了验证结果的可靠性, 选用WinQTL Cart 2.5复合区间作图法中Zmapqtl方法的Model 6进行了QTL分析。设定QTL的显著性水平为0.05, 当LOD>2.5时, 即认定该位置存在一个QTL。结果共定位出5个结荚高度QTL, 分别位于A2、C2、J_1和J_2上; 7个株高QTL, 分别位于A2、B1、C1、C2、I和M上; 3个茎粗QTL, 分别位于A2、C2和J_2上; 6个小区产量QTL, 分别位于A2、C1、C2、J_2、N和M上; 分别于与 QTLNetwork 2.0 定位结果中的结荚高度J_2、株高I、茎粗J_2和小区产量J_2相吻合; 同时, 用WinQTLCart 2.5定位出2个主茎节数QTL, 分别位于C2和H_1上、3个有效分枝QTL, 分别位于C1、C2和O上、2个粒厚QTL, 分别位于A2和C2上, 与 QTLNetwork 2.0 定位结果不一致(表5)。

表5 用WinQTLCart检测出的各个性状QTL位置及遗传效应 Table 5 Putative QTL and their genetic effects for all traits detected by WinQTLCart

综上所述, 我们认为QTLNetwork 2.0分析结果应该是可靠的。分析结果中定位结果较少、贡献率偏低产生的原因可能是分析软件综合考虑了加性、上位性以及与环境互作效应, 以往的研究中考虑与环境的互作效应较少。

3.3 环境对大豆产量的影响

环境对大豆产量有重要影响。同一性状在不同的环境下的QTL常常表现出较大差异^{[ 28]}。遗传力越低, 受环境影响越大; QTL效应越小, 越容易易受环境条件影响^{[ 29]}。品种与环境互作的存在是品种稳定性产生的根源, 对其效应进行准确的估计是合理评估作物品种稳定性的基础^{[ 30]}。本研究共检测到9个有加性效应的QTL, 除 qPH-I-1没有互作外, 其他QTL都与环境存在互作效应, 其中 qYLD-J_2-1与环境互作贡献率明显高于加性效应的贡献率, 说明环境比基因对大豆产量的影响更大。该研究结果从理论上进一步解释了大豆产量对纬度、光照比较敏感和“百里不引豆”的主要原因。

3.4 相关性分析与QTL定位

经典数量遗传学认为, 性状相关是由于基因多效性或者基因紧密连锁引起的。本研究检测到控制小区产量、茎粗、有效分枝、主茎节数、株高、结荚高度6个性状有加性效应的9个QTL中, 每个性状均有1个QTL定位在J染色体上, 呈现出很大的集中性。株高、主茎节数、分枝数、结荚高度、百粒重均呈现极显著正相关, 说明控制这些性状的基因存在着紧密连锁。同时, 尽管控制这些性状的QTL存在紧密连锁关系, 但并不排除控制这些性状的基因独立存在。有1个结荚高度QTL定位在M连锁群上, 各有1个株高和主茎节数QTL定位在I连锁群上, 与已有研究结果类似^{[ 29]}, 说明它们在遗传上也可能是相互独立的, 具有较好的稳定性。

4 结论

检测到9个大豆小区产量诸性状的QTL, 全部表现出与环境的互作效应; 同时检测出7对上位互作位点, 说明加性效应、加性×加性上位互作效应及环境互作效应是小区产量、茎粗、有效分枝、主茎节数、株高和结荚高度等性状的重要遗传基础, 在进行分子标记辅助育种时, 既要考虑起主要作用的QTL, 又要关注其上位性QTL, 这样更有利于性状的稳定表达和遗传。

The authors have declared that no competing interests exist.

作者已声明无竞争性利益关系。

参考文献

View Option

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2004

0.0

... 大豆产量是受多基因控制的综合数量性状, 容易受遗传、生理、环境、形态以及农艺性状的影响^[1] ...

2004

3.297

0.0

Theor Appl Genet. 2004, 109(1):122 - 128 DOI:10.1007/s00122-004-1602-3

A new integrated genetic linkage map of the soybean

Q. J. Song(1) L. F. Marek(2) R. C. Shoemaker(23) K. G. Lark(4) V. C. Concibido(5) X. Delannay(5) J. E. Specht(6) P. B. Cregan(1)

1.USDA-ARS Soybean Genomics and Improvement Laboratory Beltsville MD 20705 USA
2.Iowa State University Department of Agronomy, USDA-ARS-CICG Ames IA 50011 USA
3.Iowa State University Department of Agronomy Ames IA 50011 USA
4.University of Utah Department of Biology Salt Lake City UT 84112 USA
5.Monsanto Company 800 Lindbergh Boulevard St. Louis MO 63167 USA
6.University of Nebraska Department of Agronomy Lincoln NE 68583-0915 USA

A total of 391 simple sequence repeat (SSR) markers designed from genomic DNA libraries, 24 derived from existing GenBank genes or ESTs, and five derived from bacterial artificial chromosome (BAC) end sequences were developed. In contrast to SSRs derived from EST sequences, those derived from genomic libraries were a superior source of polymorphic markers, given that the mean number of tandem repeats in the former was significantly less than that of the latter (P<0.01). The 420 newly developed SSRs were mapped in one or more of five soybean mapping populations: lsquo

Minsoy

Noir 1

Minsoy

Archer

Noir 1

Clark

Harosoy

, and A81-356022 × PI468916. The JoinMap software package was used to combine the five maps into an integrated genetic map spanning 2,523.6 cM of Kosambi map distance across 20 linkage groups that contained 1,849 markers, including 1,015 SSRs, 709 RFLPs, 73 RAPDs, 24 classical traits, six AFLPs, ten isozymes, and 12 others. The number of new SSR markers added to each linkage group ranged from 12 to 29. In the integrated map, the ratio of SSR marker number to linkage group map distance did not differ among 18 of the 20 linkage groups; however, the SSRs were not uniformly spaced over a linkage group, clusters of SSRs with very limited recombination were frequently present. These clusters of SSRs may be indicative of gene-rich regions of soybean, as has been suggested by a number of recent studies, indicating the significant association of genes and SSRs. Development of SSR markers from map-referenced BAC clones was a very effective means of targeting markers to marker-scarce positions in the genome.

... 近年来, 分子遗传学和分子标记技术快速发展, 构建出了高密度遗传连锁图谱, 推动了数量性状的QTL基因定位研究进程^[2] ...

2012

0.0

1.4522

Sci Agric Sin. 2012, 45(13):2568 - 2579 DOI:10.3864/j.issn.0578-1752.2012.13.002

Genetic Analysis and QTL Mapping of Pod-Seed Traits in Soybean Under Different Environments

不同环境下大豆荚粒性状的遗传与QTL分析

LIANG Hui-Zhen, YU Yong-Liang, YANG Hong-Qi, ZHANG Hai-Yang, DONG Wei, LI Cai-Yun, GONG Peng-Tao, LIU Xue-Yi, FANG Xuan-Jun

梁慧珍, 余永亮, 杨红旗, 张海洋, 董薇, 李彩云, 巩鹏涛, 刘学义, 方宣钧

1.河南省农业科学院芝麻研究中心，郑州 450002
2.海南省热带农业资源开发利用研究所，海南三亚 572025
3.山西省农业科学院经济作物研究所，山西汾阳 032200

【Objective】 Soybean [Glycine mar (L.) Merr.] pod-seed trait is one of the most important traits related with yields. In this paper, the relationship between soybean pod-seed traits and yields was studied, the QTLs controlling soybean pod-seed traits were mapped, and the soybean pod-seed trait molecular genetic mechanism was revealed. 【Method】Based on three years and two environments data, a recombinant line population of 474 F2:8:11 lines from a cross of Jindou 23 and ZDD2315 was used for analyzing and mapping QTLs of soybean pod-seed related traits. 【Result】The results of the molecular genetic analysis indicated that the field traits have a significant positive correlation or extreme significant positive correlation with 100-grain-weight, grain length, single grain weight, two-grain pod and three-grain pod. According to three years and two environments data, the authors mapped six QTLs of field traits related, four QTLs of 100-grain-weight, ten QTLs of single line grain weight, two QTLs of grain width, five QTLs of grain length, seven QTLs of one-grain pod, five two-grain pod, seven QTLs of three-grain pod and five QTLs of four-grain pod. 【Conclusion】The identification of consistent QTLs under different environments and in different years will be used as candidate chromosome regions, which will help to improve the efficiency of selection for pod-seed related traits in soybean through molecular marker-assisted selection.

【目的】探讨大豆荚粒性状之间以及与产量之间的相互关系及其遗传机制，并定位控制其性状的QTL。【方法】以栽培大豆晋豆23为母本，半野生大豆灰布支黑豆为父本所衍生的474个重组自交系，通过3年2个重复的试验结果，用多年多点联合分析方法对荚粒及产量等9个性状进行遗传分析及数量性状定位。【结果】相关分析结果表明，产量与百粒重、粒长、单株粒重、2粒荚、3粒荚呈现显著或极显著正相关，在三年两地共定位了6个产量QTL、4个百粒重QTL、10个单株粒重QTL、2个粒宽QTL、5个粒长QTL、7个1粒荚QTL、5个2粒荚QTL、7个3粒荚QTL和5个4粒荚QTL。【结论】在不同年份和环境下检测到的QTL，可作为荚粒性状改良的候选染色体区段，用于分子标记辅助选择，同时也要注意环境的影响。

... 以往对大豆产量和相关农艺性状的研究, 主要集中于QTL的定位及各座位间的互作, 而较少涉及QTL间的上位性和环境互作效应, 这主要是受到了遗传分析模式研究和分析软件研发的限制^{[3,4,5,6,7,8]} ...

2001

3.297

0.0

Theor Appl Genet. 2001, 103(5):702 - 709 DOI:10.1007/s001220100595

Genetic mapping of QTLs conditioning soybean sprout yield and quality

S. H. Lee(1) K. Y. Park(2) H. S. Lee(1) E. H. Park(3) H. R. Boerma(4)

1.School of Plant Science, Seoul National University, Suwon 441-744, The Republic of Korea KR
2.National crop Experiment Station, Suwon 441-100, The Republic of Korea KR
3.Faculty of Biological Resources, Yeungnam University, Gyongsan 712-749, The Republic of Korea KR
4.Department of Crop and Soil Sciences, The University of Georgia, Athens, GA 30602-7272, USA US

Soybean sprouts have been used as a food in the Orient since ancient times. In this study, 92 restriction fragment length polymorphism (RFLP) loci and two morphological markers (W1andT) were used to identify quantitative trait loci (QTLs) associated with soybean sprout-related traits in 100 F₂-derived lines from the cross of ’Pureunkong’×’Jinpumkong 2’. The genetic map consisted of 76 loci which covered about 756 cM and converged into 20 linkage groups. Eighteen markers remained unlinked. Phenotypic data were collected in 1996 and 1997 for hypocotyl length, percentage of abnormal seedlings, and sprout yield 6 days after germination at 20°C. Hypocotyl length was determined as the average length from the point of initiation of the first secondary root to the point of attachment of the cotyledons. The number of decayed seeds and seedlings, plus the number of stunted seedlings (less than 2-cm growth), was recorded a s abnormal seedlings. Seed weight was determined based on the 50-seed sample. Sprout yield was recorded as the total fresh weight of soybean sprouts produced from the 50-seed sample divided by the dry weight of the 50-seed sample. Four QTLs were associated with sprout yield in the combined analysis across 2 years. For the QTL linked to L154 on the Linkage Group (LG) G the positive allele was derived from Pureunkong (R²= 0.19), whereas at the other three QTLs (A089 on LG B1, A668n on LG K and B046 on LG L) the positive alleles were from Jinpumkong 2. QTLs conditioning seed weight were linked to markers A802n (LG B1), A069 (LG E), Cr321 (LG F) and A235 (LG G). At these four markers, the Jinpumkong allele increased seed weight. Markers K011n on LG B1,W1 on LG F and A757 on LG L were linked to QTLs conditioning hypocotyl length; and Bng119, K455n and K418n to QTLs conditioning the abnormal seedlings. The QTLs conditioning sprout yield were in the same genomic locations as the QTLs for seed weight identified in this population or from previously published research, indicating that QTLs for sprout yield are genetically linked to seed-weight QTLs or else that seed-weight QTLs pleiotropically condition sprout yield. These data demonstrate that effective marker-assisted selection may be feasible for enhancing sprout yield in a soybean. The transgressive segregation of sprout yield, as well as the existence of two QTLs conditioning greater than 10% of the phenotypic variation in sprout yields provides an opportunity to select for progeny lines with a greater sprout yield than currently preferred cultivars such as Pureunkong.

2004

3.297

0.0

Theor Appl Genet. 2004, 109(3):552 - 561 DOI:10.1007/s00122-004-1661-5

Seed quality QTL in a prominent soybean population

D. L. Hyten(15) V. R. Pantalone(1) C. E. Sams(1) A. M. Saxton(2) D. Landau-Ellis(1) T. R. Stefaniak(3) M. E. Schmidt(4)

1.Department of Plant Sciences The University of Tennessee 252 Ellington Plant Science Building, 2431 Joe Johnson Drive Knoxville TN 37996 USA
2.Department of Animal Science The University of Tennessee Knoxville TN 37996 USA
3.Department of Agronomy University of Kentucky Lexington KY 40546 USA
4.Department of Plant, Soil, and General Agriculture Southern Illinois University Carbondale IL 62901 USA
5.Soybean Genomics and Improvement Laboratory USDA-ARS-PSI Building 006, 10300 Baltimore Avenue Beltsville MD 20705 USA

Soybean [Glycine max (L.) Merr.] is a versatile crop due to its multitude of uses as a high protein meal and vegetable oil. Soybean seed traits such as seed protein and oil concentration and seed size are important quantitative traits. The objective of this study was to identify representative protein, oil, and seed size quantitative trait loci (QTL) in soybean. A recombinant inbred line (RIL) population consisting of 131 F₆-derived lines was created from two prominent ancestors of North American soybeans ( lsquo

Essex

and

Williams

) and the RILs were grown in six environments. One hundred simple sequence repeat (SSR) markers spaced throughout the genome were mapped in this population. There were a total of four protein, six oil, and seven seed size QTL found in this population. The QTL found in this study may assist breeders in marker-assisted selection (MAS) to retain current positive QTL in modern soybeans while simultaneously pyramiding additional QTL from new germplasm.

2001

1.641

0.0

2003

1.641

0.0

2004

3.297

0.0

Theor Appl Genet. 2004, 108(6):1131 - 1139 DOI:10.1007/s00122-003-1527-2

QTL mapping of ten agronomic traits on the soybean (Glycine max L. Merr.) genetic map and their association with EST markers

W.-K. Zhang(1) Y.-J. Wang(1) G.-Z. Luo(1) J.-S. Zhang(1) C.-Y. He(1) X.-L. Wu(1) J.-Y. Gai(2) S.-Y. Chen(1)

1.Plant Biotechnology Laboratory, Institute of Genetics and Developmental Biology The Chinese Academy of Sciences 100101 Beijing China
2.Soybean Research Institute of Nanjing Agricultural University, National Center for Soybean Improvement National Key Laboratory for Crop Genetics and Germplasm Enhancement 210095 Nanjing China

A set of 184 recombinant inbred lines (RILs) derived from soybean vars. Kefeng No.1 × Nannong 1138-2 was used to construct a genetic linkage map. The two parents exhibit contrasting characteristics for most of the traits that were mapped. Using restricted fragment length polymorphisms (RFLPs), simple sequence repeats (SSRs) and expressed sequence tags (ESTs), we mapped 452 markers onto 21 linkage groups and covered 3,595.9 cM of the soybean genome. All of the linkage groups except linkage group F were consistent with those of the consensus map of Cregan et al. (1999). Linkage group F was divided into two linkage groups, F1 and F2. The map consisted of 189 RFLPs, 219 SSRs, 40 ESTs, three R gene loci and one phenotype marker. Ten agronomic traits—days to flowering, days to maturity, plant height, number of nodes on main stem, lodging, number of pods per node, protein content, oil content, 100-seed weight, and plot yield—were studied. Usingwinqtlcart, we detected 63 quantitative trait loci (QTLs) that had LOD>3 for nine of the agronomic traits (only exception being seed oil content) and mapped these on 12 linkage groups. Most of the QTLs were clustered, especially on groups B1 and C2. Some QTLs were mapped to the same loci. This pleiotropism was common for most of the QTLs, and one QTL could influence at most five traits. Seven EST markers were found to be linked closely with or located at the same loci as the QTLs. EST marker GmKF059a, encoding a repressor protein and mapped on group C2, accounted for about 20% of the total variation of days to flowering, plant height, lodging and nodes on the main stem, respectively.

1994

0.0

... 朱军^[9]采用混合线性模型, 扩展了以往的广义遗传模型分析方法, 使基因和环境的互作效应无偏分析变成了现实 ...

1999

3.297

0.0

Theor Appl Genet. 1999, 99(7-8):1255-1264

Mapping QTLs with epistatic effects and QTL×environment interactions by mixed linear model approaches

D. L. Wang^(A1), J. Zhu^(A1), Z. K. L. Li^(A2), A. H. Paterson^(A2)

A1.Department of Agronomy, Zhejiang Agricultural University, Hangzhou, Zhejiang 310029, China e-mail: jzhu@zjau.edu.cn, CN
A2.Department of Soil and Crop Sciences, Texas A&M University, College Station, Texas 77843-2474, USA, US

A new methodology based on mixed linear models was developed for mapping QTLs with digenic epistasis and QTL×environment (QE) interactions. Reliable estimates of QTL main effects (additive and epistasis effects) can be obtained by the maximum-likelihood estimation method, while QE interaction effects (additive×environment interaction and epistasis×environment interaction) can be predicted by the-best-linear-unbiased-prediction (BLUP) method. Likelihood ratio andt statistics were combined for testing hypotheses about QTL effects and QE interactions. Monte Carlo simulations were conducted for evaluating the unbiasedness, accuracy, and power for parameter estimation in QTL mapping. The results indicated that the mixed-model approaches could provide unbiased estimates for both positions and effects of QTLs, as well as unbiased predicted values for QE interactions. Additionally, the mixed-model approaches also showed high accuracy and power in mapping QTLs with epistatic effects and QE interactions. Based on the models and the methodology, a computer software program (QTLMapper version 1.0) was developed, which is suitable for interval mapping of QTLs with additive, additive×additive epistasis, and their environment interactions.

... Wang等^[10]据此开发出了QTL和QE互作分析软件 ...

2012

0.0

1.8267

Acta Agron Sin. 2012, 38(8):1435 - 1442 DOI:10.3724/SP.J.1006.2012.01435

QTL Analysis of Tassel-Related Traits in Maize (Zeamays L.) Using Multiple Connected Populations

基于多个相关群体的玉米雄穗相关性状QTL分析

YANG Zhao-Zhao¹,LI Yong-Xiang¹,LIU Cheng²,LIU Zhi-Zhai¹,LI Chun-Hui¹,LI Qing-Chao¹,PENG Bo¹,ZHANG Yan¹,WANG Di¹,TAN Wei-Wei¹,SUN Bao-Cheng²,SHI Yun-Su^1,*,SONG Yan-Chun¹,WANG Tian-Yu1,LI Yu¹

杨钊钊¹，李永祥¹，刘成²，刘志斋¹，李春辉¹，李清超¹，彭勃¹，张岩¹，王迪¹，谭巍巍¹，孙宝成²，石云素^1,*，宋燕春¹，王天宇¹，黎裕¹

1 Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; 2 Institute of Food Crops, Xinjiang Academy of Agricultural Sciences, Urumqi 830000, China

Maize tassel-related traits are of importance to maize production. QTLs for three tassel-related traits were mapped in 11 RIL populations with the common parent “Huangzaosi” under a single environment and across environments to understand genetic basis of maize tassels. Meanwhile, epistasis among QTLs and QTL by environment interactions (QEI) was analyzed. Totally 15 major constitutive QTLs were detected in at least five environments. Particularly, two constitutive QTLs were detected in bin 3.04 in the Qi 319 and Lü 28 populations, which had an overlapping interval of 226.0–230.1 on the IBM 2008 Neighbors map, with a quite high phenotypic variance explained, and the meanR² of 17.4% and 14.4%, respectively. A total of 21 common QTLs were detected in more than two populations, of which five on chromosome 2, 3, 6 and 8 were found in three populations. The QTLs stably expressed under different environments or genetic backgrounds may play an important role in controlling maize tassel-related traits, and be used at the candidates for fine mapping and positional cloning.

雄穗相关性状对玉米生产至关重要。为了解析玉米雄穗相关性状的遗传机制，利用以黄早四为共同亲本组配的11个重组自交系群体，对玉米雄穗一级分枝数、雄穗主轴长和雄穗干重3个性状进行QTL分析。经过对11个群体及亲本两年三点的田间鉴定，单环境和联合环境下的玉米雄穗相关性状QTL定位，及基因型与环境互作和上位性互作分析，检测到15个在多环境下稳定表达(5个环境以上)的“环境钝感”主效QTL，其中，在染色体bin3.04区域，齐319群体和旅28群体中都定位到1个主效雄穗一级分枝数相关QTL，其平均贡献率分别为17.4%和14.4%，并且2个群体的QTL标记区间高度重叠，在IBM2008 Neighbors图谱上的重叠区间为226.0~230.1。对比不同群体结果发现，在2个群体以上都能检测到的一致性区间21个，其中在第2、第3、第6、第8染色体上的5个一致性区间在3个群体中可稳定表达。这些多环境和多个遗传背景下稳定表达的位点可作为玉米雄穗性状分子标记辅助选择、精细定位及基因克隆的候选位点。

... 研究人员对玉米^[11,12]、水稻^[13,14]、小麦^[15,16]等作物进行了QTL与环境互作分析, 对大豆蛋白质、脂肪和异黄酮^[17,18,19]等已有研究报道, 但对产量特别是小区产量诸性状QTL间的上位性和环境互作效应研究较少 ...

2008

0.0

1.4522

Sci Agric Sin. 2008, 41(11):3845 - 3850

普通玉米蛋白质、淀粉和油分含量的遗传效应分析

魏良明,戴景瑞,刘占先,鄂立柱

中国农业大学国家玉米改良中心

【目的】研究玉米蛋白质、淀粉和油分含量的遗传主效应及基因型×环境互作效应。【方法】采用谷物种子性状遗传模型和统计方法，分析10个自交系及90个F1、F2杂交组合在两个种植地点的试验数据。【结果】蛋白质、淀粉、油分含量的遗传主效应方差（VG）和基因型×环境互作方差（VGE）分别占各性状总遗传方差（VG +VGE）的38.5%和61.5%、48.2 %和51.8%、48.2%和51.8%。在遗传主效应中，蛋白质、油分含量同时受控于种子和母体效应；淀粉含量以母体效应为主，种子（胚乳）效应次之；细胞质效应相对较小。在基因型×环境互作效应中，3个品质性状均以种子、母体×环境互作为主，未发现细胞质×环境互作。蛋白质含量以互作遗传力(h2GE）为主；淀粉、油分含量以普通遗传力（h2G）为主。【结论】3个品质性状都不同程度地存在种子（胚乳或胚）、母体植株和细胞质等3套遗传体系的遗传主效应及基因型×环境互作。其中，蛋白质含量主要受基因型×环境互作效应影响；淀粉、油分含量同时受遗传主效应和基因型×环境互作效应的作用。

2004

0.0

1.8267

Acta Agron Sin. 2004, 30(9):849 - 854

Use of Permanent F2 Population to Analyze Epistasis and Their Interaction Effects with Environments for QTLs Controlling Heading Date in Rice

水稻永久F2群体抽穗期QTL的上位性及其与环境互作效应的分析

GAO Yong-Ming; ZHU Jun; SONG You-Shen; HE Ci-Xin; SHI Chun-Hai; XING Yong-Zhong

高用明;朱军; 宋佑胜;何慈信;石春海;邢永忠

Agronomy Department, Zhejiang University, Hangzhou 310029, Zhejiang

Immortalized F2 population in rice was constructed by random mating among recombinant inbred (RI) lines derived from a famous elite hybrid rice, Shanyou 63. Analysis on heading date of permanent F2 population was conducted through QTL Mapper 2.0, a software for QTL mapping. A total of 21 QTLs which

利用源于杂交水稻汕优63的重组近交系（RI），进行系间随机交配构建了水稻永久F2（IF2）群体。采用QTL作图软件QTL Mapper 2.0对IF2群体的抽穗期性状进行了分析，共发现了21个QTLs，分布于10条染色体上。对抽穗期QTL的加性效应，显性效应，加×加、加×显、和显×显上位性效应进行了估计，对遗传主效应与环境的互作效应做了预

2011

0.0

1.8267

Acta Agron Sin. 2011, 37(7):1175 - 1185 DOI:10.3724/SP.J.1006.2011.01175

Analyses of QTLs for Rice Panicle and Milling Quality Traits and Their Interaction with Environment

影响水稻穗部性状及籽粒碾磨品质的QTL及其环境互作分析

HU Xia¹,SHI Yu-Min^2,**,JIA Qian¹,XU Qin¹,WANG Yun¹,CHEN Kai¹,SUN Yong¹,ZHU Ling-Hua¹,XU Jian-Long^1,*,LI Zhi-Kang^1,3

胡霞¹,石瑜敏^2,**,贾倩¹,徐琴¹,王韵¹,陈凯¹,孙勇¹,朱苓华¹,徐建龙^1,*,黎志康^1,3

1 Institute of Crop Sciences / National Key Facility for Crop Gene Resources and Genetic Improvement, Chinese Academy of Agricultural Sciences, Beijing 100081, China; 2 Rice Institute of Guangxi Academy of Agricultural Sciences, Nanning 530007, China; 3 International Rice Research Institute, DAPO Box 7777, Metro Manila, Philippines

QTLs and their interactions with environments for yield-related traits—second branch number (SBN), spikelet number per panicle (SNP), filled grains number per panicle (FGN), 1000-grain weight (TGW) and panicle weight (PW); grain type traits—grain length (GL), width (GW) and thinkness (GT); and milling quality traits—brown rice percentage (BR), milled rice percentage (MR) and head rice percentage (HR) were identified and analyzed in the two environments, Nanning of Guangxi and Sanya of Hainan, using an introgression line population derived from the cross of recurrent parent Ce258 and a donor IR75862. There were significant differences in FGN, TGW, GL, GW and HR between the two parents. Significant correlations were found among the yield-related traits, and they were strikingly negatively correlated with TGW and GL. Most yield-related traits and grain type had no significant correlations with the three milling quality traits. A total of 57 QTLs were identified for yield-related traits, grain type and milling quality, including six for SBN, four for FGN, five for each of SNP, PW and TGW, nine for GL, seven for W, one for GT, four for BR, five for MR and six for HR, which distributed on all chromosomes except chromosome 11. Most QTLs affecting SBN, SNP and TGW clustered and distributed in the chromosomes independent of the QTLs for BR, MR and HR. Some important QTLs for the above traits were identified on chromosomes 2, 3, 4, 5, and 6 which had been previously detected many times in various genetic backgrounds and environments. One QTL affecting GL and GW in the region of RM152-RM310 on chromosome 8 was newly identified, which simultaneously increased GL and GW. The stable QTLs identified in this study are of importance for marker-assisted selection (MAS) in rice breeding programs. In addition, HR was largely affected by environment and the QTLs for HR had a significant interaction with environment. The interaction characteristics of QTL with environment and its application in MAS were deeply discussed in the paper.

利用优质恢复系测258为轮回亲本与粳型糯稻新品系IR75862杂交创制的BC₁F₇回交导入系群体，在广西南宁和海南三亚定位了产量相关性状(二次枝梗数、穗总粒数、穗实粒数、粒重和穗重)、粒型(粒长、宽、厚)和碾磨品质(糙米率、精米率和整精米率)的主效QTL并剖析其环境互作效应。双亲在穗实粒数、千粒重、粒长和粒宽及整精米率等性状上存在显著差异。各产量相关性状间呈极显著正相关，而与千粒重和粒长呈极显著负相关。多数产量及粒型相关性状与3种碾磨品质相关不显著。在南宁和三亚环境下检测到影响产量相关性状、粒型及碾磨品质的主效QTL共计57个，包括二次枝梗数的6个，穗实粒数4个，穗总粒数、粒重和穗重各5个，粒长9个，粒宽7个，粒厚1个，糙米率4个，精米率5个和整精米率6个，分布在除第11染色体外的所有染色体上。多数影响枝梗数、穗粒数和粒重的QTL成簇分布，而且与影响BR、MR和HR的QTL分布在不同染色体区域。在第2、第3、第4、第5和第6染色体上鉴定出影响穗粒数、粒重、粒型及碾磨品质的重要QTL，这些QTL在以往不同遗传背景和环境下被多次检测到。在第8染色体RM152~RM310区间鉴定到1个影响粒长和粒宽的新的QTL，能同步增加粒宽和粒长。鉴定出的这些稳定表达的QTL具有标记辅助选择育种的应用价值。整精米率是受环境影响最大的性状，其QTL的环境互作效应明显。对QTL的环境互作效应特点及其在品种标记辅助改良中的作用进行了深入探讨。

2010

0.0

1.8267

Acta Agron Sin. 2010, 36(2):267 - 275 DOI:10.3724/SP.J.1006.2010.00267

Analysis of QTLs Associated with Photosynthesis Characteristics in Wheat Seedlings

小麦苗期光合作用及其相关性状的QTL分析

LIANG Yan¹,ZHANG Kun-Pu²,ZHAO Liang¹,LIANG Xue¹,ZHANG Wen-Ting¹,SUN Xiao-Lin¹,MENG Qing-Wei¹,TIAN Ji-Chun^1,*,ZHAO Shi-Jie^1,*

梁燕¹,张坤普²,赵亮¹,梁雪¹,张雯婷¹,孙晓琳¹,孟庆伟¹,田纪春^1,*,赵世杰^1,*

¹ State Key Laboratory of Crop Biology, Shandong Agricultural University, Tai’an 271018, China;²Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China

For the purpose of detecting QTLs associated with photosynthetic related traits, a set of 168 doubled haploid (DH) lines derived from the cross between Huapei 3 and Yumai 57 was tested with 324 SSR markers covering the whole genome of wheat (Triticum aestivumL.). The net photosynthetic rate, gas changes, chlorophyll content, and chlorophyll fluorescence in leaves were investigated in both DH population and the parents at seedling stage. QTL analysis was carried out using QTLNetwork version 2.0 based on the mixed linear model. A total of 17 additive QTLs and 20 pairs of epistatic QTLs were detected for the photosynthetic related traits. All additive QTLs and 16 pairs of epistatic QTLs had interactions with environments. In agreement with the high correlations of phenotypes, several traits shared common QTL regions, and showed tight linkages of these QTLs or pleiotropisms. In the interval betweenXwmc215andXgdw63 on chromosome 5D, three major additive QTLs for chlorophyll a, chlorophyll b, and carotinoid contents explained the phenotypic variations by 18.23%, 10.40%, and 27.25%, respectively, whose positive alleles were all originated from Huapei 3. These QTLs are favorable for marker-assisted selection. In addition, this region was near the QTLs for net photosynthetic rate (P_n), stomatal conductance (G_s), intercellular CO₂ concentration (C_i), and the ratio ofC_ito gas conductance (C_r). The QTL forC_i on chromosome 5B was a minor locus but explained relatively great phenotypic variation in the interactions between QTLs and environments.

将小麦品种花培3号和豫麦57构建的DH群体的168个株系及其亲本，盆栽于两个环境中，利用324个SSR标记位点构建遗传图谱，对单叶净光合速率及相关参数、叶绿体色素含量和叶绿素荧光参数进行QTL定位和分析。利用基于混合线性模型的QTLNetwork 2.0，共检测到17个加性效应和20对上位性效应位点，其中所有加性效应位点和16对上位性效应位点具有环境互作效应。相关性较高的性状间有一些共同的QTL，表现出一因多效或者紧密连锁效应。在5D染色体上的Xwmc215至Xgdm63区段，检测到控制叶绿素a、叶绿素b和类胡萝卜素含量的3个主效QTL，各位点的遗传效应贡献率较大，增效基因均来源于花培3号，适用于分子标记辅助选择和聚合育种。另外，该区段与控制单叶净光合速率(P_n)、气孔导度(G_s)、胞间CO₂浓度(C_i)和胞间CO₂浓度与胞外CO₂浓度比值(C_i/C_{r的QTL的定位区间相近。位于5B染色体控制胞间CO₂浓度的QTL是个微效基因，但是QTL与两种环境的互作效应表现的遗传贡献比较大。})

2005

0.0

1.4522

Sci Agric Sin. 2005, 38(10):1951 - 1957

Mapping QTL for Seedling Root Traits in Common Wheat

小麦幼苗根系性状的QTL分析

,,,,

周晓果,景蕊莲,郝转芳,昌小平,张正斌

中国农业科学院作物品种资源研究所

A doubled haploid (DH) population with 150 lines, derived from a cross between two common wheat varieties Hanxuan10 and Lumai14, was used in this research. Several root traits including root number (RN), maximum root length (MRL), root fresh weight (RFW), root dry weight (RDW), ratio of root fresh weight to shoot fresh weight (RFW/SFW) and ratio of root dry weight to shoot dry weight (RDW/SDW) per plant of hydroponic seedlings were measured under water stress and control conditions, respectively. Quantitative trait loci (QTL) and G×E interactions for these traits were detected using mixed-model-based composite interval mapping method. A total of 11 additive QTL and 15 pairs of epistatic QTL associated with root traits were mapped on all chromosomes except 5A, 4B, 2D, 6D and 7D. Among these QTL, 3 QTL with additive effects and 2 pairs of epistatic QTL controlling root number, 3 QTL with additive effects and 3 pairs of epistatic QTL controlling maximum root length, 2 QTL with additive effects and 2 pairs of epistatic QTL controlling root fresh weight, 2 QTL with additive effects and 3 pairs of epistatic QTL controlling root dry weight, 2 pairs of epistatic QTL controlling ratio of root fresh weight to shoot fresh weight and 1 QTL with additive effects and 3 pairs of epistatic QTL controlling ratio of root dry weight to shoot dry weight were detected. One additive QTL × environment and three pairs of epistatic QTL × environment interaction effects were also identified. The possibility of using marker-assisted selecting root traits and drought tolerance in wheat was discussed.

以小麦DH群体（旱选10号×鲁麦14）为材料，在水分胁迫及非胁迫两种条件下考察水培幼苗的单株根数、最大根长、根鲜重、根干重、根茎鲜重比及根茎干重比等根系性状。应用基于混合线性模型的复合区间作图法分析幼苗根系性状的QTL，以及基因与环境的互作。共检测到11个加性效应QTL和15对上位性互作QTL，分布在除5A、4B、2D、6D和7D以外的所有染色体上。其中3个加性效应QTL和2对上位性效应QTL控制根数；3个加性效应QTL和3对上位性效应QTL控制最大根长；2个加性效应QTL和2对上位性效应QTL控制根鲜重；2个加性效应QTL和3对上位性效应QTL影响根干重；2对上位性效应QTL控制根茎鲜重比；1个加性效应QTL和3对上位性效应QTL与根茎干重比有关。同时还分别检测到1个加性效应QTL、3对上位性效应QTL与水分环境的互作效应。对应用分子标记辅助选择幼苗抗旱优良根系性状的可能性进行了讨论。

2009

0.0

1.8267

Acta Agron Sin. 2009, 35(1):41 - 47 DOI:10.3724/SP.J.1006.2009.00041

Epistatic Effects and QE Interaction Effects of QTLs for Protein Content in Soybean

大豆蛋白质含量相关QTL间的上位效应和QE互作效应

SHAN Da-Peng^1,2,4,ZHU Rong-Seng^5,*,CHEN Li-Jun⁶,QI Zhao-Ming²,HU Guo-Hua^1,3,*,CHEN Qing-Shan^2,*

单大鹏^1,2,4,朱荣胜^5**,陈立君⁶,齐照明²,刘春燕^1,2,胡国华^1,3,*,陈庆山^2，*

1Crop Research and Breeding Center of Land-Reclamation,Harbin 150090,China;2 Soybean Research Institute, Northeast Agricultural Universtity,Harbin 150030,China;3The National Research Center of Soybean Engine ering and Technology,Harbin 150050,China; 4Heilongjiang Academy of Agricultural Sciences,Suihua Institute,Suihua 152052,China;5College of Science,Northeast Agricultural University,Harbin 150030,China;6 Heilongjiang Academy of Agricultural Sciences Crop Breeding Institute, Harbin 150086,China

Soybean [Glycine max(L). Merr.], widely grown in United States, Brazil, Argentina, and China, is one of the plant protein source. Protein content in soybean is a quantitative trait controlled by multiple genes, and Currently, SoyBase (2007) documented at least 76 QTL associated with protein content that have been mapped in many different populations and environments. The objective of the paper was to investigate epistatic effects and QE interaction effects of QTLs for protein content by mixed linear model. QTLs for soybean protein content were detected in a five-year experiment with the recombination inbred lines (RIL) population derived from a cross between Charleston and Dongnong 594. Ten QTLs with additive effects for protein content were mapped in the linkage groups B2, C2, D1a, E and N, one of which was the positive effect contributed by Charleston, the others of which were the negative effects donated byDongnong 94. Fifteen QTLs pairs with epistatic effects for protein content in the RIL were detected, accounting for 13.57% of the general phenotypic variation. There existed interaction between 9 QTLs and environment, and the general contribution to protein content was 4.47%.

利用Charleston×东农594重组自交系构建的SSR遗传图谱及混合线性模型方法对2002—2006连续5年的大豆蛋白质含量进行QTL定位，并作加性效应，加性×加性上位互作效应及环境互作效应分析。共检测到10个控制蛋白质含量的QTL，分别位于第B2、C2、D1a、E和N连锁群，其中1个表现为遗传正效应,9个表现为遗传负效应，另检测到15对影响蛋白质含量的加性×加性上位互作效应的QTL，解释该性状总变异的13.75%。环境互作检测中，发现9个QTL与环境存在互作，贡献率达到4.47%。

2008

0.0

1.8267

Acta Agron Sin. 2008, 34(6):952 - 957 DOI:10.3724/SP.J.1006.2008.00952

Epistatic Effects of QTLs and QE Interaction Effects on Oil Content in Soybean

大豆油分含量相关的QTL间的上位效应和QE互作效应

SHAN Da-Peng¹, QI Zhao-Ming¹,QIU Hong-Mei¹,SHAN Cai-Yun¹,LIU Chun-Yan¹²,HU Guo-Hua^23*,CHEN Qing-Shan^1*

单大鹏¹;齐照明¹;邱红梅¹;单彩云¹;刘春燕^1,2;胡国华^2,3,*;陈庆山^1,*

1 Soybean Research Institute, Northeast Agricultural University, Harbin 150030, Heilongjiang; 2 The Crop Research and Breeding Center of Land-Re-
clamation, Harbin 150090, Heilongjiang; 3 The National Research Center of Soybean Engineering and Technology, Harbin 150050, Heilongjiang, China

Soybean [Glycine max (L.) Merr., 2n=2x=40] is grown worldwide, especially in the United States, Brazil, Argentina, and China. Increasing oil content in soybean seeds is one of the main aims in soybean breeding. Oil content is quantitative traits controlled by multiple genes. Many researches have used molecular markers to map quantitative trait loci (QTL). Currently, SoyBase (2007) contains at least 68 QTL associated with oil content that have been mapped in many different populations and environments. The development of molecular tools has facilitated the task of identifying chromosomal regions related to particular traits. Song et al. (2004) developed an integrated genetic map spanning 2 523.6 cM across 20 linkage groups that contained 1 849 markers, including 1 015 SSRs, 709 RFLPs, 73 RAPDs, 24 classical traits, 6 AFLPs, 10 isozymes, and 12 others. In MAS, it would be more desirable to use confirmed QTL. For this purpose, it is necessary to conduct QTL, mapping studies in as many and as diverse environments and population as feasible. In this study, QTL of soybean oil content in five different years was analyzed with a recombination inbred lines (RIL) population derived from a cross between Charleston and Dongnong 594 by mixed linear model approach. 11 QTL with additive effects for oil content were mapped in the linkage groups A1, A2, B1, C2, D1a, D1b, F, H, and O. 15 QTL pairs with epistatic effects for oil content in the RIL were detected, and the general phenotypic variation was 17.84%. 9 QTL were detected from QTL× environment interaction, and the general contribution was 5.76%.

利用Charleston × 东农594重组自交系构建的SSR遗传图谱, 及混合线性模型方法对2002年到2006年连续5年的大豆油分含量进行QTL定位, 并作加性效应, 加性×加性上位互作效应及环境互作效应分析。共检测到11个控制油分含量的QTL, 分别位于第A1、A2、B1、C2、D1a、D1b、F、H和O连锁群上, 其中2个表现为遗传正效应, 9个表现为遗传负效应, 另检测到15对影响油分含量的加性×加性上位互作效应的QTL, 解释该性状总变异的17.84%。发现9个QTL与环境存在互作, 贡献率达到5.76%。

2012

0.0

1.4522

Sci Agric Sin. 2012, 45(19):3909 - 3920 DOI:10.3864/j.issn.0578-1752.2012.19.002

Identification of QTLs for Major Isoflavone Components mong Multiple Environments in Soybean Seeds

多环境条件下大豆异黄酮主要组分的QTL定位

ZHANG Jing-Ying, GE Yi-Nan, SUN Jun-Ming, HAN Fen-Xia, YU Fu-Kuan, YAN Shu-Rong, YANG Hua

张晶莹, 葛一楠, 孙君明, 韩粉霞, 于福宽, 闫淑荣, 杨华

1.中国农业科学院作物科学研究所/农业部北京大豆生物学重点实验室北京 100081

【Objective】The QTLs for major isoflavone components were identified among multiple environments by different QTL mapping methods in soybean seeds, in order to provide a theoretical basis for soybean isoflavone marker-assisted selection. 【Method】 In this study, a recombinant inbreed line population (RIL, F5∶7-8) was developed by using the cross between the isoflavone contrasting cv. LHD2 with high-isoflavone concentration (3 697.24 μg•g-1) and cv. NHZ with low-isoflavone concentration (1 816.67 μg•g-1). The polymorphism of SSR markers among the RIL population was analyzed and the major isoflavone components were determined by HPLC. 【Result】The results showed that a soybean linkage map with a total distance of 3 546.54 cM was constructed using 161 polymorphism SSR molecular markers in the RIL population. A total of 14 QTLs associated with the major isoflavone components were found by ICIM, IM, and SMA methods in the ICIMapping 3.2 software under the four environments. 【Conclusion】The three QTLs flanked by the marker interval Sat_003-Satt306, Satt070-Satt122 and Satt571-Satt270 were detected among multiple environments in more than two methods.

【目的】利用不同定位方法，对不同环境条件下大豆异黄酮主要组分进行QTL定位研究，为大豆异黄酮分子标记辅助育种提供理论依据。【方法】以异黄酮含量有显著差异的鲁黑豆2号（3 697.24 μg•g-1）和南汇早黑豆（1 816.67 μg•g-1）为亲本构建的F5∶7-8重组自交系为材料，分析RIL群体的SSR标记多态性，结合HPLC法鉴定异黄酮主要组分含量。【结果】绘制了一张包含161个多态性SSR标记，全长3 546.54 cM的大豆遗传连锁图谱。利用ICIMapping 3.2软件的ICIM、IM和SMA 3种定位方法，共定位到4种环境下与异黄酮主要组分相关的14个QTL。【结论】3个标记区间在多个环境和多种定位方法下均被检测到，分别是Sat_003—Satt306、Satt070—Satt122和Satt571—Satt270。

2012

0.0

0.2185

... 3 表型数据分析分别取2011和2012两年测量数据的平均值, 利用DPS软件^[20]对各性状考察并作相关性分析 ...

2003

0.0

0.9082

... 4 遗传连锁图谱的构建与QTL分析本研究群体的遗传连锁图谱已经由王珍等^[21,22]构建完成 ...

2006

0.0

... 4 遗传连锁图谱的构建与QTL分析本研究群体的遗传连锁图谱已经由王珍等^[21,22]构建完成 ...

... 6 cM, 包括27个连锁群, 232个标记位点^[22] ...

2005

3.297

0.0

Theor Appl Genet. 2005, 110(7):1268 - 1274 DOI:10.1007/s00122-005-1963-2

Methods for predicting superior genotypes under multiple environments based on QTL effects

Jian Yang(1) Jun Zhu(1)

1.Institute of Bioinformatics Zhejiang University Hangzhou, Zhejiang 310029 People’s Republic of China

Methods were developed for predicting two kinds of superior genotypes (superior line and superior hybrid) based on quantative trait locus (QTL) effects including epistatic and QTL × environment interaction effects. Formulae were derived for predicting the total genetic effect of any individual with known QTLs genotype derived from the mapping population in a specific environment. Two algorithms, enumeration algorithm and stepwise tuning algorithm, were used to select the best multi-locus combination of all the putative QTLs. Grain weight per plant (GW) in rice was analyzed as a working example to demonstrate the proposed methods. Results showed that the predicted superior lines and superior hybrids had great superiorities over the F₁ hybrid, indicating large breeding potential remained for further improvement on GW. Results also showed that epistatic effects and their interaction with environments largely contributed to the superiorities of the predicted superior lines and superior hybrids. User-friendly software, QTLNetwork, version 1.0, was developed based on the methods in the present paper.

... 0^[23]软件中的混合线性模型复合区间作图法(MCIM), 选取临界阈值P=0 ...

1997

0.0

... 参照McCouch^[24]方式命名QTL ...

2010

0.0

1.8267

... 因而, QTL作图功效明显下降, 使用复合性状作图的意义不大^[25] ...

2007

0.0

... 和多性状选择指数^[26]类似, “产量”作为复合性状, 在育种工作中可以同时选择相互之间有影响的多个有利等位基因, 提高实际育种工作效率 ...

2003

3.297

0.0

Theor Appl Genet. 2003, 108(1):141 - 153 DOI:10.1007/s00122-003-1401-2

QTL × environment interactions in rice. I. Heading date and plant height

Z.K. Li(1) S.B. Yu(18) H.R. Lafitte(1) N. Huang(19) B. Courtois(110) S. Hittalmani(12) C.H.M. Vijayakumar(1) G.F. Liu(3) G.C. Wang(5) H.E. Shashidhar(4) J.Y. Zhuang(5) K.L. Zheng(5) V.P. Singh(6) J.S. Sidhu(7) S. Srivantaneeyakul(7) G.S. Khush(1)

1.Plant Breeding, Genetics, and Biochemistry Division International Rice Research Institute DAPO Box 7777 Metro Manila The Philippines
2.University of Agricultural Sciences, GKVK 560 065 Bangalore India
3.South China Agricultural University Guangzhou China
4.Indian Agricultural Research Institute New Delhi India
5.China National Rice Research Institute Hangzhou China
6.Punjab Agricultural University Ludhiana India
7.Rice Research Institute Bangkok Thailand
8.National Key Laboratory for Crop Improvement Huazhong Agricultural University Wuhan China
9.Applied Phytologics Incorporated Sacramento California USA

One hundred twenty six doubled-haploid (DH) rice lines were evaluated in nine diverse Asian environments to reveal the genetic basis of genotype × environment interactions (GEI) for plant height (PH) and heading date (HD). A subset of lines was also evaluated in four water-limited environments, where the environmental basis of G × E could be more precisely defined. Responses to the environments were resolved into individual QTL × environment interactions using replicated phenotyping and the mixed linear-model approach. A total of 37 main-effect QTLs and 29 epistatic QTLs were identified. On average, these QTLs were detectable in 56% of the environments. When detected in multiple environments, the main effects of most QTLs were consistent in direction but varied considerably in magnitude across environments. Some QTLs had opposite effects in different environments, particularly in water-limited environments, indicating that they responded to the environments differently. Inconsistent QTL detection across environments was due primarily to non- or weak-expression of the QTL, and in part to significant QTL × environment interaction effects in the opposite direction to QTL main effects, and to pronounced epistasis. QTL × environment interactions were trait- and gene-specific. The greater GEI for HD than for PH in rice were reflected by more environment-specific QTLs, greater frequency and magnitude of QTL × environment interaction effects, and more pronounced epistasis for HD than for PH. Our results demonstrated that QTL × environment interaction is an important property of many QTLs, even for highly heritable traits such as height and maturity. Information about QTL × environment interaction is essential if marker-assisted selection is to be applied to the manipulation of quantitative traits.

... 同时, 有1个结荚高度QTL定位在M连锁群上, 各有1个株高和主茎节数QTL定位在I连锁群上, 与已有研究结果类似^[27], 又呈现出较好的稳定性和真实性 ...

1991

4.007

0.0

... 同一性状在不同的环境下的QTL常常表现出较大差异^[28] ...

2004

0.0

1.1628

... QTL效应越小, 越容易易受环境条件影响^[29] ...

... 有1个结荚高度QTL定位在M连锁群上, 各有1个株高和主茎节数QTL定位在I连锁群上, 与已有研究结果类似^[29], 说明它们在遗传上也可能是相互独立的, 具有较好的稳定性 ...

2001

3.297

0.0

Theor Appl Genet. 2001, 103(1):104 - 111 DOI:10.1007/s001220000528

Effects of genetic background and environment on QTLs and epistasis for rice (Oryza sativa L.) panicle number

C. Y. Liao(1) P. Wu(1) B. Hu(1) K. K. Yi(1)

1.Department of Biological Science, College of Life Science, Huajiachi Campus, Zhejiang University, KaiXuan Road 268, Hangzhou, 310029,China e-mail: docpwu@cls.zju.edu.cn Tel. +86-571-6971130, Fax: +86-571-6971323 CN

A double-haploid (DH) population and a recombinant inbred (RI) line population, derived from a cross between a tropicaljaponica variety, Azucena, as male parent and twoindica varieties, IR64 and IR1552, as female parents respectively, were used in both field and pot experiments for detecting QTLs and epistasis for rice panicle number in different genetic backgrounds and different environments. Panicle number (PN) was measured at maturity. A molecular map with 192 RFLP markers for the DH population and a molecular map with 104 AFLP markers and 103 RFLP markers for the RI population were constructed, in which 70 RFLP markers were the same. Six QTLs were identified in the DH population, including two detected from field experiments and four from pot experiments. The two QTLs, mapped on chromosomes 1 and 12, were identical in both field and pot experiments. In the RI population, nine QTLs were detected, five QTLs from field conditions and four from the pot experiments. Three of these QTLs were identical in both experimental conditions. Only one QTL, linked to CDO344 on chromosome 12, was detected across the populations and experiments. Different epistasitic interaction loci on PN were found under different populations and in different experimental conditions. One locus, flanked by RG323 and RZ801 on chromosome 1, had an additive effect in the DH population, but epistatic effects in the RI population. These results indicate that the effect of genetic background on QTLs is greater than that of environments, and epistasis is more sensitive to genetic background and environments than main-effect QTLs. QTL and epistatic loci could be interchangeable depending on the genetic backgrounds and probably on the environments where they are identified.

... 品种与环境互作的存在是品种稳定性产生的根源, 对其效应进行准确的估计是合理评估作物品种稳定性的基础^[30] ...