基于图像处理的冬小麦植被覆盖率测定及其遗传解析

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肖永贵, 刘建军, 夏先春, 陈新民, sup 3, 何中虎. 基于图像处理的冬小麦植被覆盖率测定及其遗传解析. 作物学报, 2013, 39(11): 1935-1943[XIAO Yong-Gui, LIU Jian-Jun, XIA Xian-Chun, CHEN Xin-Min, Matthew REYNOLDS, HE Zhong-Hu. Genetic Analysis of Vegetative Ground Cover Rate in Winter Wheat Using Digital Imaging. 作物学报, 2013, 39(11): 1935-1943] 复制到剪切板

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作物学报编辑部

基于图像处理的冬小麦植被覆盖率测定及其遗传解析

肖永贵¹, 刘建军², 夏先春¹, 陈新民¹, sup 3, 何中虎^1,^4,^*

¹中国农业科学院作物科学研究所/国家小麦改良中心, 北京100081

²山东省农业科学院作物研究所, 山东济南 250100

³ CIMMYT, Apartado Postal 6-641, 06600 México, DF Mexico

⁴国际玉米小麦改良中心(CIMMYT)中国办事处, 北京 100081

^* 通讯作者(Corresponding author): 何中虎, E-mail:zhhecaas@163.com

收稿日期:2013-06-24

基金:本研究由国家自然科学基金项目(31161140346和31201207)和旱区作物逆境生物学国家重点实验室(西北农林科技大学)开放课题资助。

摘要

植被覆盖率是反映植株生长势的重要生理性状, 在旱作地区尤为重要。图像处理技术能够快速有效地对苗期和孕穗期植被覆盖率进行量化分析。以28份山东小麦主栽品种和品系为材料, 在240株 m^-2和360株 m^-2密度下, 连续2年测定了孕穗前不同发育阶段的植被覆盖率, 并利用921个DArT标记和83个SSR标记分析了与植被覆盖率相关的遗传区段。结果表明, 不同密度下, 冬小麦植被覆盖率在越冬期、返青期和孕穗期存在显著差异, 而拔节期基本一致。拔节期植被覆盖率与春季最高分蘖数、抽穗后群体叶面积指数、单位面积穗数和籽粒产量均呈显著正相关,r = 0.73~0.76 (P<0.01), 表明拔节期植被覆盖率可用于预测上述性状。共检测出12个遗传区段与植被覆盖率相关联, 大部分区段直接参与调控苗期和孕穗期的生长势。10个遗传区段与已报道的苗期性状、产量性状及抗病位点一致, 其中5BL、6AS和6BL染色体上携带的植被覆盖率相关遗传区段与已报道的苗期比叶面积和生物量等位点完全相同。建议将植被覆盖率作为生长势量化指标, 用于育种选择和遗传研究。

关键词: 普通小麦; 植被覆盖率; 关联分析; 遗传区段

Genetic Analysis of Vegetative Ground Cover Rate in Winter Wheat Using Digital Imaging

XIAO Yong-Gui¹, LIU Jian-Jun², XIA Xian-Chun¹, CHEN Xin-Min¹, Matthew REYNOLDS³, HE Zhong-Hu^1,^4,^*

¹ Institute of Crop Science / National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), Beijing 100081, China

² Crop Research Institute, Shandong Academy of Agricultural Sciences, Jinan 250100, China

³ CIMMYT, Apartado Postal 6-641, 06600 México, DF Mexico

⁴ CIMMYT-China Office, c/o CAAS, Beijing 100081, China

Abstract

Vegetative vigour is an important physiological trait and selection for greater seedling vigour is a goal of breeding programs, especially in rain-fed regions. This study aimed to identify the genetic variation of early vigour, determine the agronomic traits most closely associated with seedling growth, and detect the major gene-containing region of early vigour in winter wheat. Twenty-eight cultivars and advanced lines at two planting densities (240 plants m^-2 and 360 plants m^-2) were grown in Jinan during 2009-2010 and 2010-2011 cropping seasons, with randomized complete block design of three replications. Whole-genome association mapping was employed to identify the chromosome region controlling early vigour using 921 Diversity Array Technology (DArT) and 83 SSR markers. Early vigour was evaluated with vegetative ground cover rate via implementation of photographic image analysis, whereby computer analysis was used to determine percentage ground cover. Significant differences of ground cover rate between two planting densities were detected in pre-winter period, greening-up and booting stages, but not in early stem elongation stage. Ground cover rate in stem elongation stage was significantly and positively associated with maximum tiller number (r = 0.76,P< 0.01), leaf area index (r = 0.74,P< 0.01), spike number (r = 0.73,P< 0.01), and grain yield (r = 0.73,P< 0.01). Twelve gene-containing regions for vegetative ground cover rate were detected in two seasons. Most of the regions conditioning the vegetative ground cover rate were not affected by the developmental stages. Ten gene-containing regions identified were consistent with previously reported QTLs for seedling traits, grain yield and disease resistance. Three regions on 5BL, 6AS, and 6BL were the same as previously reported loci for seedling traits. Therefore, there is sufficient genetic variation to increase early vigour in winter wheat, and early vigour could be quickly measured through digital image analysis.

Keyword: Common wheat; Vegetative ground cover rate; Association study; Gene-containing region

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引言

植株生长势是小麦苗期地上部分生长发育的综合表现, 与品种适应性和产量潜力密切相关。在干旱环境下, 提高植株生长势能够显著增加生物学产量和籽粒产量^[1,2], 因而地中海地区和澳大利亚西部等干旱地区一直将提高小麦植株生长势作为重要耐旱指标^[3,4], 并选育出一批苗期繁茂且耐旱高产的品种^[2]。即便在灌溉地区, 植株生长势也是育种家选择的重要性状, 因此加强该性状的遗传研究, 探索简单易行的测定方法, 发掘相关基因位点对高产广适品种选育具有重要意义。

小麦胚大小、比叶面积(叶片面积与其重量比值)和苗期生物量是决定植株生长势的关键性状^[5,6]。Rebetzke等^[3,7]认为, 植株生长势相关性状存在较大遗传变异, 主要受基因型影响, 广义遗传力为59%~ 88%。控制水稻苗期生长势的遗传位点主要分布在第1、第3、第6和第7染色体上^[8,9,10]。大麦较小麦具有较好的幼苗生长势, 控制大麦苗期生长势相关性状的基因位点主要分布在2H、4H和5H染色体上^[11,12,13]。Genc等^[14]在盐分胁迫下发现, 4A、5A、5B、5D和6A染色体上携带控制苗期生物量相关性状的重要基因位点。Spielmeyer等^[15]在6A染色体上定位了一个控制胚芽鞘长短和幼苗叶片宽度的主效QTL, 分别解释8%和14%的表型变异, 与标记 NW3106 紧密连锁。此外, 矮秆基因 Rht8 对幼苗生长势也有重要影响^[16]。尽管在春小麦上已发掘了一些与生长势相关的QTL, 但在冬小麦遗传背景下的相关研究较少。

传统的小麦植株生长势测定主要依靠目测, 不仅需要丰富的育种经验, 而且难以量化不同基因型间的差异^[17]。根据植物冠层光谱反射原理研制的野外光谱辐射计(FieldSpec UV/VNIR), 可有效估测幼苗生长特性, 但由于价格昂贵、便携性能差等原因, 在实际育种中应用很少^[18,19]。数码相机具有灵敏的色彩传感器, 能够用于测定植株生长特性^[20], 具有价格低廉、方便快捷、测定准确等优势, 目前已用于植物的产量估测、叶片衰老鉴定、植物病斑、氮肥营养诊断和植被覆盖^[21]等性状测定。在测定小麦植被覆盖率时, 所获取的植株数码图像是地表综合特征的二维表现形式, 可包括生长势相关的胚芽鞘长短、生长速度、比叶面积、分蘖数和干物质积累量等性状^[19]。Preussa等^[21]认为植被覆盖率可作为鉴定植株生长势的指标, 用于反映苗期和孕穗期的生长特性。

山东省位于黄淮冬麦区北片, 小麦面积和总产皆居全国第2位, 年播种面积约400万公顷, 总产约220亿千克。本研究在墒情适宜、适时播种条件下, 设置两个密度种植, 研究山东当前主要栽培品种及苗头品系的苗期和孕穗期植株生长势, 并利用921个DArT标记和83个SSR标记对植被覆盖率进行关联分析, 旨在估测品种间生长势遗传差异, 发掘控制孕穗以前植株生长势的遗传片段, 为培育高产广适型新品种提供理论依据。

1 材料与方法

1.1 材料与试验设计

2009—2010和2010—2011连续两年度在山东省农业科学院作物所(山东济南, 36.66°N, 117.07°E)试验农场种植山东省主栽品种和苗头品系28份, 这些材料基本反映了目前山东省小麦品种的现状。试验田土质为黏壤土, 耕层含有机质18.4 g kg^-1、全氮1.2 g kg^-1、碱解氮135.0 mg kg^-1、速效磷(P₂O₅) 25.6 mg kg^-1、速效钾(K₂O) 209.0 mg kg^-1、pH 8.1。播种前施复合肥750 kg hm^-2 (约含氮120 kg hm^-2, 磷120 kg hm^-2, 钾60 kg hm^-2), 拔节期追施尿素225 kg hm^-2。

采取随机区组设计, 2个种植密度分别为240株 m^-2和360株 m^-2, 利用小区播种机(Wtersteiger Plotseed TC, 奥地利), 分别于2009年10月13日和2010年10月16日播种。 3次重复, 小区行距20 cm, 行长4 m, 面积为5.6 m²。于幼苗三叶期间苗, 以保证群体基本苗与计划密度一致, 全生育期浇灌越冬水、拔节水和灌浆水, 并进行田间除草。

1.2 田间数据获取

在小麦的越冬前期(12月5日)、返青期(3月5日)、拔节期(4月6日)和孕穗期(4月20日), 植株叶片无露水条件下, 利用数码相机(G11, Canon Inc., 日本), 在晴日无风天气的10:00至15:00取像, 相机调至自动拍摄模式, 关闭闪光灯, 镜头距地面高度1.2 m, 垂直于小区的中央, 取像区域排除小区两端20 cm区, 仅拍摄非边行的4行, 数字照片像素为1280 × 960。每次集中在60 min内获取全部实验照片, 避免流动云层阴影对取像的影响, 每取20张照片后, 在天空太阳对角方向获取照片1张, 作为图像空白对照, 用于矫正光线对植株图像影响的偏差。

利用Adobe PhotoShop CS3软件处理图像, 分析植株绿色部分占整个图像的比例。操作步骤如下: (1)调整图像的色相、饱和度和亮度值分别为0、+60和-20; (2)在“色彩范围”选项中使用“加号滴管”工具对图像内所有绿色像素点进行选择; (3)将所选择的绿色像素填充为“白色”, “反向”选项选取剩余的图像部分, 填充为“黑色”(图1); (4)在“分析-选择数据点-custom”选项中选定并分析图像中的“平均灰度值(mean grey value)”; (5)计算植被覆盖面积(vegetative ground cover, VGC), VGC (%)=(平均灰度值/255)×100^[19]。

	Figure Option View Download New Window
	图1 小麦越冬前(a, b)、返青期(c, d)、拔节期(e, f)和孕穗期(g, h)的生长特征和植被覆盖率处理图(良星66)Fig. 1 Photographer of field and digital ground cover at pre-winter (a, b), greening-up (c, d), jointing (e, f), and booting (g, h) stages of wheat (Liangxing 66)

小区间苗后, 在非边行区选取2个长势均匀的1 m样段, 分别在起身期和灌浆中后期测定最大分蘖和穗数。在抽穗期和灌浆期, 于晴日11:00至13:00时, 利用植物冠层分析仪(AccuPAR LP80, Decagon Devices Inc., 美国)测定小区群体叶面积指数。将收获的籽粒自然晾晒至含水量13%以下, 测定千粒重, 称取小区产量, 并折算为公顷产量。

1.3 DNA提取及DArT和SSR标记分析

从每份材料选取10个大小和色泽均匀一致的籽粒在培养皿中育苗2周, 混合剪取叶片, 利用CTAB法提取全基因组DNA, 用分光光度计(UAS-99, ACTGene Inc., 美国)检测DNA的浓度和质量, 并将DNA终浓度稀释到50~100 ng µL^-1。由澳大利亚Triticarte股份有限公司(http://www.triticarte.com.au/)完成DArT标记分析工作, 在小麦全基因组3000个标记中筛选出921个标记产生限制性多态片段信号, 并按照每条染色体分布3个以上SSR标记的原则, 筛选出83个标记用于测定供试材料的等位变异。

根据DArT和SSR标记在供试材料中的遗传变异分布, 使用STRUCTURE软件分析群体结构, K值取1~20的范围, 每个K值重复估算5次, 采用MCMC (Monte Carlo Markov Chain)模型, burn-in和run length均设置100 000, 估算出Q值为4。以SPAGeDi软件估算供试材料亲缘矩阵(kinship matrix), 并利用Tassel 3.0软件(http://www.maizegenetics.net/)的MLM模型(Mixed Linear Model)进行关联分析, 选择 P<0.05为等位变异与植被覆盖率关联阈值。利用已公布的高密度图谱(http://wheat.pw.usda.gov/ggpages/SSRclub/

GeneticPhysical/; Marone等^[31])对所有关联的标记进行染色体定位, 以10 cM内富集3个以上关联标记为标准, 判断为性状关联的遗传区段。

1.4 数据分析

利用SAS (Statistical Analysis System) 9.0软件分析不同发育阶段植被覆盖度的基本统计量, 并进行方差分析和多重比较。用Microsoft Excel 2010软件计算植被覆盖率与最大分蘖数、粒重、叶面积指数、每平方米穗数和籽粒产量的相关系数。

2 结果与分析

2.1 苗期和孕穗期植被覆盖率的方差分析

越冬前、返青、拔节和孕穗期4个发育时期的植被覆盖率在基因型、种植密度、环境(年份)和基因型与环境互作等方面差异均达0.01显著水平(表1)。除越冬期外, 返青至孕穗期的植被覆盖率在同年份不同重复间的差异均不显著。越冬、返青和孕穗期的植被覆盖率显著受种植密度影响, 差异达0.01的显著水平。说明苗期和孕穗期植被覆盖率受基因型、环境和基因型与环境互作3个因素的共同作用, 种植密度对植被覆盖率有显著影响, 但拔节期影响较小。

2.2 植被覆盖率变化趋势分析

不同基因型间的植被覆盖率在每个发育阶段存在显著差异(表2)。越冬前, 泰山23、潍麦8号、鲁麦23、济宁16、山农15和济宁13等品种植被覆盖率均较大, 为47.5%~54.4%; 泰农2413、烟农24、济麦5037和济麦6324覆盖率较小, 为33.0%~37.9%, 在2个种植密度间品种植被覆盖率变化趋势一致。越冬前植被覆盖率较高或较低的品种, 极大多数在返青期仍保持着较高或较低的覆盖率, 而且两个种植密度间趋势一致。返青后, 伴随着温度快速回升, 小麦苗期生长加剧, 拔节期植被覆盖率迅速达到70%以上, 植被覆盖率较高品种为鲁麦23、临麦4号、济宁13、淄麦12和济南17等, 越冬前和返青期覆盖率较小的品种在拔节期仍较低, 如泰农2413、济麦6324、济麦5037和烟农24等。品种在孕穗期的植被覆盖率与拔节前有明显差异, 其值较高的品种有淄麦12、泰农2413、汶农6号、良星66等, 较低的品种为临麦4号、济麦5504、济南17、临麦2号、泰山23, 在不同种植密度间差异基本一致。综合而言, 品种间植被覆盖率的差异主要受基因型影响, 拔节以前品种间幼苗生长势变化趋势基本一致, 受种植密度影响较小, 越冬前植被覆盖率大的品种到拔节期仍具有较高的覆盖率。但是, 孕穗期品种间植被覆盖率变化趋势与拔节前存在差异, 可能与品种的叶片大小、叶型、株型等综合农艺性状有关。

表1 不同发育阶段植被覆盖率的方差分析 Table 1 Mean squares of variance of vegetative ground cover rate at different developmental stages

表2 28个供试品种在不同发育阶段植被覆盖率的比较分析 Table 2 Comparison of vegetative ground cover rate of 28 varieties at different developmental stages

品种 Variety	240株 m^-2240 plants m^-2				360株 m^-2360 plants m^-2
品种 Variety	越冬前 Pre-winter	返青期 Greening up	拔节期 Jointing	孕穗期 Booting	越冬前 Pre-winter	返青期 Greening up	拔节期 Jointing	孕穗期 Booting
济麦5037 Jimai 5037	37.9 efg	24.3 mn	68.9 hi	90.8 abcdef	50.8 kl	31.6 lmn	69.2 kl	88.7 ghijkl
济麦6402 Jimai 6402	40.0 defg	24.7 mn	70.5 ghi	90.4 abcdef	54.0 ghijkl	29.1 n	71.3 hijkl	87.8 jklm
济麦5504 Jimai 5504	45.6 abcdef	35.1 defghi	75.5 abcd	86.4 fg	61.0 abcde	40.6 defghi	72.9 defghi	86.3 m
济麦6324 Jimai 6324	38.4 efg	23.7 mn	68.6 i	93.4 abc	54.6 ghijk	36.8 ghijkl	73.1 defgh	91.7 bcde
济麦19 Jimai 19	43.2 bcdef	29.8 jkl	75.5 abcd	91.9 abcde	56.8 defghi	36.4 hijklm	74.1 cdefg	89.0 ghijkl
济麦20 Jimai 20	43.9 bcdef	31.3 hijkl	73.0 defg	89. 5 cdef	58.3 bcdefg	35.3 jklm	71.9 fghij	90.5 defg
济麦22 Jimai 22	40.2 defg	32.9 efghij	73.6 defg	91.0 abcdef	57.4 cdefgh	39.4 defghij	73.3 defgh	90.0 efghi
济南17 Jinan 17	42.4 cdefg	35.1 defghi	77.3 abc	87.7 ef	54.0 ghijkl	39.9 defghij	74.6 cde	89.8 efghij
济宁13 Jining 13	47.5 abcde	37.0 def	77.4 ab	89.1 cdef	61.7 abcd	46.4 abc	77.6 b	88.1 ijklm
济宁16 Jining 16	49.3 abcd	37.9 cd	73.9 cdef	91.6 abcde	62.5 abc	38.1 fghij	74.0 cdefg	87.6 klm
良星66 Liangxing 66	46.4 abcdef	32.9 fghij	74.6 bcdef	93.1 abcd	56.1 fghij	38.7 efghij	72.6 efghij	93.7 ab
临麦2号 Linmai 2	41.2 defg	29.6 jkl	71.4 fghi	87.7 ef	53.7 hijkl	37.3 fghijk	70.3 ijkl	87.1 lm
临麦4号 Linmai 4	45.2 abcdef	37.8 cde	78.0 ab	81.6 g	52.4 ijkl	39.3 defghij	77.5 b	91.2 cdef
鲁麦21 Lumai 21	39.6 defg	27.1 klm	73.2 defg	89.2 cdef	52.2 ijkl	32.8 klmn	71.7 ghijk	88.3 hijklm
鲁麦23 Lumai 23	51.2 abc	49.5 a	78.0 a	90.4 bcdef	60.9 abcdef	49.5 ab	80.9 a	89.9 efghi
山农15 Shannong 15	47.8 abcde	36.6 defg	72.5 defg	90.0 cdef	60.4 abcdef	42.6 cdef	72.2 efghij	90.1 efghi
泰农2413 Tainong 2413	33.0 g	21.2 n	59.4 j	95.0 ab	51.2 kl	29.6 n	68.9 l	93.4 ab
泰山21 Taishan 21	38.6 efg	31.9 ghijk	74.9 abcde	93.2 abc	58.4 bcdefg	42.1 cdef	72.7 efghij	88.2 ijklm
泰山223 Taishan 223	43.7 bcdef	37.9 cd	74.6 bcdef	93.0 abcd	52.3 ijkl	41.8 cdefg	73.2 defgh	92.3 bcd
泰山23 Taishan 23	54.4 a	42.0 bc	73.9 cdef	87.3 ef	62.8 ab	44.2 bcd	71.9 fghij	89.3 fghijk
泰山24 Taishan 24	47.3 abcde	43.7 b	77.0 abcd	89.9 cdef	60.7 abcdef	50.0 a	76.4 bc	90.5 defg
潍麦8号 Weimai 8	52.8 ab	37.1 def	74.7 abcdef	93.4 abc	63.6 a	42.5 cdef	72.9 defghij	89.0 ghijkl
汶农6号 Wennong 6	36.8 fg	30.7 ijkl	72.1 efgh	93.7 abc	52.0 jkl	35.7 ijklm	73.6 defgh	93.2 abc
烟农19 Yannong 19	45.2 abcdef	35.9 defgh	75.1 abcde	88.2 def	59.3 bcdefg	41.6 cdefgh	74.5 cdef	87.0 lm
烟农23 Yannong 23	39.9 defg	26.7 lm	71.5 fghi	92.3 abcde	53.0 hijkl	31.7 lmn	70.3 jkl	89.1 ghijkl
烟农24 Yannong 24	37.3 fg	24.4 mn	68.9 hi	91.3 abcdef	49.6 l	31.3 mn	73.0 defgh	88.3 ijklm
中麦155 Zhongmai 155	38.1 efg	32.0 ghij	72.3 defg	91.8 abcde	57.1 cdefgh	36.5 ghijklm	73.0 defgh	90.4 defgh
淄麦12 Zimai 12	43.9 bcdef	33.4 defghij	77.4 ab	95.3 a	56.3 defghi	43.5 cde	75.4 bcd	95.1 a

数据后字母不同表示品种间有显著差异( P<0.05)。

Values within a column followed by different letters are significantly different at P<0.05.

表2 28个供试品种在不同发育阶段植被覆盖率的比较分析 Table 2 Comparison of vegetative ground cover rate of 28 varieties at different developmental stages

2.3 植被覆盖率与农艺和产量性状的相关分析

越冬前植被覆盖率与千粒重呈显著正相关( r = 0.50, P< 0.05), 拔节期植被覆盖率与群体最高分蘖数( r = 0.76, P< 0.01)、抽穗后叶面积指数( r = 0.74, P< 0.01)、穗数 m^-2 ( r = 0.73, P< 0.01)和籽粒产量( r = 0.73, P< 0.01)均呈极显著正相关(图3), 而返青期和孕穗期的植被覆盖率仅与抽穗后叶面积指数呈显著正相关( r = 0.51~0.73, P< 0.05), 但相关系数低于拔节期的植被覆盖率。说明春季小麦返青后植被覆盖率与群体单位面积光截获能力(叶面积指数)密切相关, 其中拔节期植被覆盖率可作为判断品种叶面积指数、单位面积穗数和产量的指标。

	Figure Option View Download New Window
	图3 拔节期植被覆盖率与最大分蘖数(a)、叶面积指数(b)、穗数(c)和籽粒产量(d)的线性关系Fig. 3 Linear regressions of vegetative ground cover rate with maximum tiller number (a), leaf area index (b), spike number (c), and grain yield (d) at early stem elongation stage

2.4 有效遗传区段解析

在小麦全基因组上共检测到与植被覆盖率显著( P<0.05)相关的标记46个, 其中8个标记 Xcfa2147 (1A)、 wPt-9154 (3A)、 Xwmc47 (4B)、 Xbarc1 (5A)、 wPt-6674 (6B)和 Xbarc54 (6D)等与多个发育阶段的植被覆盖率相关联。所关联标记与已报道的遗传图谱和基因位点对比表明, 这些标记集中于12个遗传区段, 每个遗传区段包含3~8个标记, 其中10个遗传区段中与已经报道的苗期特征性状、农艺性状、产量性状及抗病位点一致(表3)。位于1AL上 wPt0128 - wPt5316 区段和6AL上 Xgwm169 - wPt-8124 区段携带有与返青期和孕穗期的植被覆盖率关联位点; 2BS上 Xbarc2 - Xbarc7 区段和4BL上 wPt-5265 - Xwmc47 区段存在有控制春季幼苗返青后植被覆盖率的位点; 7BL 上 wPt-2305-wPt6372 区段携带的遗传区段主要控制返青期和孕穗期的植被覆盖率; 7DS上 Xcfd4 - wPt-3462 区段主要与起身前的苗期植被覆盖率相关联。此外, 在2AS、3AL、5AS、6AS、6BL和6DS染色体区段上, 均有孕穗前植被覆盖率显著的位点。所鉴定的植被覆盖率关联区段中, 大部分区段控制孕穗前植株生长发育。可见, 控制植被覆盖率的位点相对单一, 受发育阶段影响较小。

3 讨论

植被覆盖率是植株生长势的综合量化指标^[19,21]。通过数码垂直成像和简单图像处理技术能够快速量化植株生长势, 明确品种间遗传差异, 减少田间主观判断造成的偏差。本研究证实, 田间数码取像与Photoshop软件批量处理相结合, 能够快速准确获取小麦孕穗前植株生长势相关数据, 受测定环境影响小, 操作简单, 技术易掌握, 成本低, 耗时少, 每小时可测定300~450个小区, 是鉴定植株生长势的理想方法。此外, 植被覆盖率可以作为判定苗期群体光截获参数, 用于分析品种苗期光能利用效率^[19], 拔节期的植被覆盖率可作为判断和预测群体叶面积指数、每平方米穗数和产量的有效指标。

表3 苗期和孕穗期不同发育阶段的植被覆盖率的遗传区段分析 Table 3 Analysis of gene-containing region for vegetative ground cover rate in different seedling developmental stages

籽粒胚乳占粒重的80%, 是贮藏养分的组织, 也是幼苗生长和发育等所需物质及能量的重要来源。籽粒大小主要对幼苗胚芽鞘长度、第2、第3叶片长度和宽度有重要影响, 进而影响幼苗生长势。胚的大小对幼苗生长势有一定影响^[2], 通常情况下, 大籽粒品种的胚比重较高, 胚重与粒重相关系数达0.71 ( P<0.01)^[32]。Richards和Lukacs^[17]发现, 同一品种不同粒重相差10 mg时, 幼苗分蘖数、叶片面积和干物质重分别相差12%、15%和21%。种植密度对幼苗生长势也有显著影响^[2]。本研究表明, 越冬前240苗m^-2密度下, 潍麦8号和泰山23的覆盖率最大, 分别为52.8%和54.4%; 360苗 m^-2密度下, 潍麦8号和泰山23的覆盖率也最大, 分别为63.6%和62.8%。需要说明的是, 潍麦8号属大穗大粒类品种, 千粒重为52.0 g; 而泰山23的千粒重为44.3 g, 属于苗期生长发育快且分蘖多成穗率高的品种, 目测两个品种的胚大小基本相同。由此可见, 影响越冬前植被覆盖率的因素有2个, 一是品种籽粒大, 粒重高, 胚乳养分充足, 幼苗叶片宽大, 进而使植被覆盖率增大; 二是与品种自身特性有关, 如温度较敏感品种在适宜的环境下幼苗生长发育速度较快, 分蘖多, 促使越冬前生长势较旺^[21]。

小麦孕穗前植株长势和长相是育种家关注的重要性状之一。本研究表明植被覆盖率显著受基因型、环境和基因型与环境互作影响, 而且年份间差异较大, 可能与不同年份间土壤肥力、墒情及前茬作物有关^[7], 也可能与年度积温差异和发育阶段测定时期不严格有关^[3,21], 但相同发育阶段植被覆盖率大的品种在同年份间和同种植密度内基本一致。如在返青至拔节期, 植被覆盖率最高的5个品种(系)分别是泰山23、泰山24、鲁麦23、临麦4号和济宁13, 这些品种可用于提高植株生长势的改良。本研究还发现, 受基因型决定的品种生长习性对植被覆盖率有显著影响, 如叶片大小、形态、株型等, 但通过测定植被覆盖率能够反映出品种间生长势和株型的差异。

本研究的材料数目相对较少, 可能导致关联分析误差偏大, 但丰富的标记信息能为发掘植被覆盖率位点提供参考, 并且关联结果以一个遗传区段包括3个以上关联标记为筛选标准, 这样有利于提高性状遗传区段判断的准确性。全基因组水平上共发现12个遗传区段与植被覆盖率存在显著关联, 其中10个区段中与已经报道的苗期性状、产量性状及抗病位点一致(表2)。位于6AS染色体上的一个控制苗期和孕穗期生长势重要遗传区段, 与Spielmeyer等^[15]发现的苗期性状位点一致。本研究还发现6B染色体长臂上存在一个控制孕穗前植株生长势相关的重要位点, wPt-3376 附近的8个标记与其关联, Genc等^[14]也同样发现 wPt-3376 与苗期生长势性状紧密连锁。5BL上 Xbarc59 附近区段不仅与孕穗前植株生长势关联(表2), 而且参与调控苗期根系性状^[25]。此外, 位于1AL、2AS、2BS、3AL、4BS、5AS、6AL、6DS和7BL染色体上携带与孕穗前植株生长势相关联的遗传区段仍待进一步验证。

小麦生长势虽受基因型、环境和基因型与环境互作的共同影响, 但基因型仍是最关键的因素^[7], 而且基因间的加性效应占主要作用^[3]。已证实单个基因的遗传导入能够在提高苗期生长势上发挥一定的作用^[33], 但在杂交群体中存在苗期特性超亲分离现象, 说明生长势相关性状是由多微效基因控制的^[21]。因此, 针对生长势相关性状的遗传改良, 应加强对苗期长势和长相优良亲本材料的利用, 聚合微效多基因, 提高苗期生长势, 增强苗期抗逆能力, 进而提高籽粒产量。

4 结论

小麦苗期植被覆盖率可作为鉴定植株生长势的重要指标用于品种产量改良和遗传研究。拔节期的植被覆盖率能够有效反映叶面积指数和单位面积穗数, 并与籽粒产量显著相关。发现12个与植被覆盖率相关联的遗传区段, 其中有3个区段与已报道的苗期性状位点一致, 另外9个控制植被覆盖率的遗传区段有待验证。

The authors have declared that no competing interests exist.

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

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2002

1.641

0.0

... 在干旱环境下, 提高植株生长势能够显著增加生物学产量和籽粒产量^[1,2], 因而地中海地区和澳大利亚西部等干旱地区一直将提高小麦植株生长势作为重要耐旱指标^[3,4], 并选育出一批苗期繁茂且耐旱高产的品种^[2] ...

2002

1.328

0.0

. 2002, 53:1137-1145

Field evaluation of early vigour for genetic improvement of grain yield in wheat

Botwright T L , Condon A G , Rebetzke G J , Richards R A

Improved early vigour in wheat (Triticum aestivum L.) has been proposed as an important trait for increasing grain yield through greater water-use efficiency in rainfed, Mediterranean-type environments. Three years of field trials were undertaken in Western Australia at 2 sites, Merredin (low rainfall, 244 mm in the growing season) and Wongan Hills (medium rainfall, 308 mm), to examine the influence of increased early vigour on crop growth and yield. The effect of breeding for greater early vigour was tested in 1998 and 1999 using 3 high vigour and 3 low vigour BC₂:F₅ lines of the cultivar Amery backcrossed to a 'high vigour' donor. Averaged across environments, the high vigour backcross lines had a 10% increase in early vigour (i.e. leaf area/plant) at 50 DAS compared with the low vigour lines. Differences in yield across environments were associated with variation in total rainfall, rainfall distribution, and soil properties. In the wetter of the 2 years (1999), greater early vigour translated to increased yield of c.12%, averaged across environments, but there was no difference in yield in either environment in the drier year (1998). Potential deleterious effects of the recurrent parent on yield were eliminated in field trials in 1999 and 2000 by manipulating early vigour through varying grain size (25, 35, or 50 mg) or seeding density (50, 200, or 400 plants/m²) of Amery at sowing. Large grain increased the embryo size and early vigour at 50 DAS in both environments in 1999. This translated to greater biomass production at anthesis and maturity to increase grain yield at Wongan Hills in 1999. In contrast, there was no relationship between grain size, biomass production, and yield at Merredin in 1999 or at either site in 2000. Sowing density treatments also had no effect on yield in 2000. In conclusion, there is potential to increase yield of wheat by selecting for greater early vigour in a wheat breeding program. The expression of vigour in field conditions and the translation of this improvement to higher yields is, however, dependent on the environment. Current yield limitations arising through backcrossing with a high vigour, yet poor yielding donor, need to be addressed.

... 胚的大小对幼苗生长势有一定影响^[2], 通常情况下, 大籽粒品种的胚比重较高, 胚重与粒重相关系数达0 ...

... 种植密度对幼苗生长势也有显著影响^[2] ...

2004

2.474

0.0

... Rebetzke等^[3,7]认为, 植株生长势相关性状存在较大遗传变异, 主要受基因型影响, 广义遗传力为59%~ 88% ...

... 本研究表明植被覆盖率显著受基因型、环境和基因型与环境互作影响, 而且年份间差异较大, 可能与不同年份间土壤肥力、墒情及前茬作物有关^[7], 也可能与年度积温差异和发育阶段测定时期不严格有关^[3,21], 但相同发育阶段植被覆盖率大的品种在同年份间和同种植密度内基本一致 ...

... 小麦生长势虽受基因型、环境和基因型与环境互作的共同影响, 但基因型仍是最关键的因素^[7], 而且基因间的加性效应占主要作用^[3] ...

2008

0.0

1995

1.641

0.0

... 小麦胚大小、比叶面积(叶片面积与其重量比值)和苗期生物量是决定植株生长势的关键性状^[5,6] ...

1994

2.474

0.0

... 小麦胚大小、比叶面积(叶片面积与其重量比值)和苗期生物量是决定植株生长势的关键性状^[5,6] ...

1999

1.328

0.0

... Rebetzke等^[3,7]认为, 植株生长势相关性状存在较大遗传变异, 主要受基因型影响, 广义遗传力为59%~ 88% ...

... 小麦生长势虽受基因型、环境和基因型与环境互作的共同影响, 但基因型仍是最关键的因素^[7], 而且基因间的加性效应占主要作用^[3] ...

1995

2.945

0.0

... 控制水稻苗期生长势的遗传位点主要分布在第1、第3、第6和第7染色体上^[8,9,10] ...

2000

0.935

0.0

... 控制水稻苗期生长势的遗传位点主要分布在第1、第3、第6和第7染色体上^[8,9,10] ...

2004

3.297

0.0

Theor Appl Genet. 2004, 108(2):253 - 260 DOI:10.1007/s00122-003-1421-y

QTLs for Na⁺ and K⁺ uptake of the shoots and roots controlling rice salt tolerance

H.X. Lin(12) M.Z. Zhu(12) M. Yano(3) J.P. Gao(12) Z.W. Liang(4) W.A. Su(2) X.H. Hu(12) Z.H. Ren(2) D.Y. Chao(2)

1.SHARF Laboratory, Shanghai Institute of Plant Physiology and Ecology The Chinese Academy of Sciences 300 Fenglin Road 200032 Shanghai P.R. China
2.National Key Laboratory of Plant Molecular Genetics, Shanghai Institute of Plant Physiology and Ecology The Chinese Academy of Sciences 300 Fenglin Road 200032 Shanghai P.R. China
3.Department of Molecular Genetics National Institute of Agrobiological Sciences Tsukuba 305-8602 Ibaraki Japan
4.Northeast Institute of Geography and Agricultural Ecology Chinese Academy of Sciences 3195 Gaoxin Road 130012 Changchun P.R. China

An F₂ and an equivalent F₃population derived from a cross between a high salt-tolerance indica variety, Nona Bokra, and a susceptible elite japonica variety, Koshihikari, were produced. We performed QTL mapping for physiological traits related to rice salt-tolerance. Three QTLs for survival days of seedlings (SDSs) under salt stress were detected on chromosomes 1, 6 and 7, respectively, and explained 13.9% to 18.0% of the total phenotypic variance. Based on the correlations between SDSs and other physiological traits, it was considered that damage of leaves was attributed to accumulation of Na⁺in the shoot by transport of Na⁺ from the root to the shoot in external high concentration. We found eight QTLs including three for three traits of the shoots, and five for four traits of the roots at five chromosomal regions, controlled complex physiological traits related to rice salt-tolerance under salt stress. Of these QTLs, the two major QTLs with the very large effect, qSNC-7 for shoot Na⁺ concentration and qSKC-1 for shoot K⁺ concentration, explained 48.5% and 40.1% of the total phenotypic variance, respectively. The QTLs detected between the shoots and the roots almost did not share the same map locations, suggesting that the genes controlling the transport of Na⁺ and K⁺ between the shoots and the roots may be different.

... 控制水稻苗期生长势的遗传位点主要分布在第1、第3、第6和第7染色体上^[8,9,10] ...

2002

5.364

0.0

... 大麦较小麦具有较好的幼苗生长势, 控制大麦苗期生长势相关性状的基因位点主要分布在2H、4H和5H染色体上^[11,12,13] ...

1997

1.554

0.0

... 大麦较小麦具有较好的幼苗生长势, 控制大麦苗期生长势相关性状的基因位点主要分布在2H、4H和5H染色体上^[11,12,13] ...

2009

1.554

0.0

Euphytica. 2009, 169(2):187 - 196 DOI:10.1007/s10681-009-9919-2

Identification of QTLs associated with salinity tolerance at late growth stage in barley

Dawei Xue(14) Youzong Huang(1) Xiaoqi Zhang(2) Kang Wei(1) Sharon Westcott(3) Chengdao Li(3) Mingcan Chen(5) Guoping Zhang(1) Reg Lance(3)

1.Department of Agronomy, Zhejiang University, Huajiachi Campus, 310029 Hangzhou, China
2.Western Australia State Agricultural Biotechnology Centre, Murdoch University, Perth, TAS Australia
3.Department of Agriculture, 3 Baron-Hay Court, South Perth, WA 6151, Australia
4.College of Life and Environment Sciences, Hangzhou Normal University, 310036 Hangzhou, China
5.College of Agronomy, Henan University of Science and Technology, 471003 Luoyang, China

Salinity is a major abiotic stress to barley (Hordum vulgare L.) growth and yield. In the current study, quantitative trait loci (QTL) for yield and physiological components at the late growth stage under salt stress and non-stress environments were determined in barley using a double haploid population derived from a cross between CM72 (salt-tolerant) and Gairdner (salt-sensitive). A total of 30 QTLs for 10 traits, including tiller numbers (TN), plant height, spikes per line (SPL), spikes per plant (SPP), dry weight per plant, grains per plant, grain yield, shoot Na⁺ (NA) and K⁺ concentraitions (K) in shoot, and Na⁺/K⁺ ratio (NAK), were detected, with 17 and 13 QTLs under non-stress and salt stress, respectively. The phenotypic variation explained by individual QTL ranged from 3.25 to 29.81%. QTL flanked by markers bPb-1278 and bPb-8437 on chromosomes 4H was associated with TN, SPL, and SPP under salt stress. This locus may be useful in the breeding program of marker-assisted selection for improving salt tolerance of barley. However, QTLs associated with NA, K, and NAK differed greatly between non-stress and salt stress environments. It may be suggested that only the QTLs detected under salt stress are really associated with salt tolerance in barley.

... 大麦较小麦具有较好的幼苗生长势, 控制大麦苗期生长势相关性状的基因位点主要分布在2H、4H和5H染色体上^[11,12,13] ...

2010

3.297

0.0

Theor Appl Genet. 2010, 121(5):877 - 894 DOI:10.1007/s00122-010-1357-y

Sodium exclusion QTL associated with improved seedling growth in bread wheat under salinity stress

Y. Genc(12) K. Oldach(123) A. P. Verbyla(24) G. Lott(12) M. Hassan(3) M. Tester(25) H. Wallwork(13) G. K. McDonald(12)

1.Molecular Plant Breeding Cooperative Research Centre, University of Adelaide, Glen Osmond, SA 5064, Australia
2.School of Agriculture, Food and Wine, University of Adelaide, Glen Osmond, SA 5064, Australia
3.South Australian Research and Development Institute, Glen Osmond, SA 5064, Australia
4.Mathematics, Informatics and Statistics, CSIRO, Glen Osmond, SA 5064, Australia
5.Australian Centre for Plant Functional Genomics, University of Adelaide, Glen Osmond, SA 5064, Australia

Worldwide, dryland salinity is a major limitation to crop production. Breeding for salinity tolerance could be an effective way of improving yield and yield stability on saline-sodic soils of dryland agriculture. However, this requires a good understanding of inheritance of this quantitative trait. In the present study, a doubled-haploid bread wheat population (Berkut/Krichauff) was grown in supported hydroponics to identify quantitative trait loci (QTL) associated with salinity tolerance traits commonly reported in the literature (leaf symptoms, tiller number, seedling biomass, chlorophyll content, and shoot Na⁺ and K⁺ concentrations), understand the relationships amongst these traits, and determine their genetic value for marker-assisted selection. There was considerable segregation within the population for all traits measured. With a genetic map of 527 SSR-, DArT- and gene-based markers, a total of 40 QTL were detected for all seven traits. For the first time in a cereal species, a QTL interval for Na⁺ exclusion (wPt-3114-wmc170) was associated with an increase (10%) in seedling biomass. Of the five QTL identified for Na⁺ exclusion, two were co-located with seedling biomass (2A and 6A). The 2A QTL appears to coincide with the previously reported Na⁺ exclusion locus in durum wheat that hosts one activeHKT1;4 (Nax1) and one inactiveHKT1;4 gene. Using these sequences as template for primer design enabled mapping of at least threeHKT1;4 genes onto chromosome 2AL in bread wheat, suggesting that bread wheat carries moreHKT1;4 gene family members than durum wheat. However, the combined effects of all Na⁺ exclusion loci only accounted for 18% of the variation in seedling biomass under salinity stress indicating that there were other mechanisms of salinity tolerance operative at the seedling stage in this population. Na⁺ and K⁺ accumulation appear under separate genetic control. The molecular markerswmc170 (2A) andcfd080 (6A) are expected to facilitate breeding for salinity tolerance in bread wheat, the latter being associated with seedling vigour.

... Genc等^[14]在盐分胁迫下发现, 4A、5A、5B、5D和6A染色体上携带控制苗期生物量相关性状的重要基因位点 ...

... 本研究还发现6B染色体长臂上存在一个控制孕穗前植株生长势相关的重要位点, wPt-3376 附近的8个标记与其关联, Genc等^[14]也同样发现wPt-3376 与苗期生长势性状紧密连锁 ...

2007

3.297

0.0

Theor Appl Genet. 2007, 115(1):59 - 66 DOI:10.1007/s00122-007-0540-2

A QTL on chromosome 6A in bread wheat (Triticum aestivum) is associated with longer coleoptiles, greater seedling vigour and final plant height

W. Spielmeyer(1) J. Hyles(1) P. Joaquim(12) F. Azanza(12) D. Bonnett(1) M. E. Ellis(1) C. Moore(1) R. A. Richards(1)

1.CSIRO Plant Industry, GPO Box 1600, Canberra, ACT 2601, Australia
2.Syngenta Seeds S.A.S, 12, chemin de l’Hobit, B.P. 27, 31790 Saint Sauveur, France

Wheat crops with greater early vigour shade the soil surface more rapidly and reduce water loss. Evaporative losses affect water-use efficiency particularly in drier regions where most of the rainfall occurs early in the growing season before canopy closure. Greater seedling leaf area and longer coleoptiles are major determinants of increased vigour and better crop establishment. A previously developed high vigour breeding line ‘Vigour 18’ was used to establish a large recombinant inbred family and framework map to identify a QTL on chromosome 6A that accounted for up to 8% of the variation for coleoptile length, 14% of seedling leaf width and was associated with increased plant height. The SSR marker NW3106, nearest to the 6A QTL, was also associated with greater leaf width in a breeding population that was also derived from a cross involving the high vigour donor line ‘Vigour18’. The association between the NW3106 marker and coleoptile length was validated in a second breeding population which was developed using an unrelated long coleoptile donor line. The ‘Vigour18’ allele of the QTL on chromosome 6A promoted coleoptile length and leaf width during early plant growth but was also associated with increased plant height at maturity. Markers linked to the QTL are being used to increase the frequency of increased vigour and long coleoptile alleles in early generations of breeding populations.

... Spielmeyer等^[15]在6A染色体上定位了一个控制胚芽鞘长短和幼苗叶片宽度的主效QTL, 分别解释8%和14%的表型变异, 与标记NW3106 紧密连锁 ...

... 位于6AS染色体上的一个控制苗期和孕穗期生长势重要遗传区段, 与Spielmeyer等^[15]发现的苗期性状位点一致 ...

2005

1.014

0.0

Ann. Bot.. 2005, 95(4):631-9

Influence of the gibberellin-sensitive Rht8 dwarfing gene on leaf epidermal cell dimensions and early vigour in wheat (Triticum aestivum L.).

Botwright TL, Rebetzke GJ, Condon AG, Richards RA

CSIRO Plant Industry, PO Box 5, Wembley, WA 6913, Australia.

The gibberellin-insensitive Rht-B1b and Rht-D1b dwarfing genes are known to reduce the size of cells in culms, leaves and coleoptiles of wheat. Resulting leaf area development of gibberellin-insensitive wheats is poor compared to standard height (Rht-B1a and Rht-D1a) genotypes. Alternative dwarfing genes to Rht-B1b and Rht-D1b are available that reduce plant height, such as the gibberellin-responsive Rht8 gene. This study aims to investigate if Rht8 has a similar dwarfing effect on the size of leaf cells to reduce leaf area.

PMID: 15655105

... 此外, 矮秆基因Rht8 对幼苗生长势也有重要影响^[16] ...

2002

1.328

0.0

... 传统的小麦植株生长势测定主要依靠目测, 不仅需要丰富的育种经验, 而且难以量化不同基因型间的差异^[17] ...

... Richards和Lukacs^[17]发现, 同一品种不同粒重相差10 mg时, 幼苗分蘖数、叶片面积和干物质重分别相差12%、15%和21% ...

2007

1.641

0.0

... 根据植物冠层光谱反射原理研制的野外光谱辐射计(FieldSpec UV/VNIR), 可有效估测幼苗生长特性, 但由于价格昂贵、便携性能差等原因, 在实际育种中应用很少^[18,19] ...

2010

2.929

0.0

. 2010, 37:703-712

Quantifying genetic effects of ground cover on soil water evaporation using digital imaging

Mullan D J , Reynolds M P

Rapid development of leaf area and/or aboveground biomass has the potential to improve water harvest of rain fed wheat in Mediterranean-type environments through reduced soil evaporation. However, quantitative relationships between genetic differences in early ground cover and soil water evaporation have not been established. Furthermore, accurate phenotyping of ground cover and early vigour have typically been achieved via destructive sampling methods, which are too time-consuming to undertake within breeding programs. Digital image analysis has previously been identified as an alternative indirect method of analysis, whereby computer analysis is ued to determine percentage ground cover. This study uses a digital ground cover (DGC) analysis tool for high throughput screening of four large wheat populations. The DGC methodology was validated via comparisons with alternative measures of canopy cover, such as normalised difference vegetation index (NDVI) (r² = 0.69), biomass (r² = 0.63), leaf area index (r² = 0.80) and light penetration through the canopy (r² = 0.70). The wheat populations were utilised to estimate the potential variation in soil evaporation associated with genetic differences in early ground cover, which was validated using established models. Estimates of genetic differences in soil evaporation within the four populations (6.90–24.8 mm) suggest that there is sufficient genetic variation to increase water harvest through targeting faster ground cover. Implications for improved wheat yields and breeding are discussed.

... 在测定小麦植被覆盖率时, 所获取的植株数码图像是地表综合特征的二维表现形式, 可包括生长势相关的胚芽鞘长短、生长速度、比叶面积、分蘖数和干物质积累量等性状^[19] ...

... (5)计算植被覆盖面积(vegetative ground cover, VGC), VGC (%)=(平均灰度值/255)×100^[19] ...

... 3 讨论植被覆盖率是植株生长势的综合量化指标^[19,21] ...

... 此外, 植被覆盖率可以作为判定苗期群体光截获参数, 用于分析品种苗期光能利用效率^[19], 拔节期的植被覆盖率可作为判断和预测群体叶面积指数、每平方米穗数和产量的有效指标 ...

2004

0.0

0.8529

J Trit Crops. 2004, (4):108 - 112

Application of Image Processing Technology in Wheat Canopy Leaf Area Index Measuring

图像处理技术在冬小麦叶面积指数测定中的应用

王桂琴^[1];郑丽敏^[2];朱虹^[2];梁振兴^[1];廖树华^[1]

为了探讨利用图像处理技术测定冬小麦叶面积指数的可行性,在室外光照条件下,固定数码相机高度垂直拍摄冬小麦群体图像,利用图像处理技术获取小麦冠层的图像叶面积指数(ILAI),并用手工测量获得小麦群体实际叶面积指数(LAI),建立ILAI和LAI间与施肥处理有关的关系模型,对该模型的稳定性和实用性进行统计检验.试验结果表明,利用图像处理技术通过ILAI获取小麦LAI是可行的,且简便易行,测量精度和效率都很高.

... 数码相机具有灵敏的色彩传感器, 能够用于测定植株生长特性^[20], 具有价格低廉、方便快捷、测定准确等优势, 目前已用于植物的产量估测、叶片衰老鉴定、植物病斑、氮肥营养诊断和植被覆盖^[21]等性状测定 ...

2012

2.474

0.0

... Preussa等^[21]认为植被覆盖率可作为鉴定植株生长势的指标, 用于反映苗期和孕穗期的生长特性 ...

... 3 讨论植被覆盖率是植株生长势的综合量化指标^[19,21] ...

... 二是与品种自身特性有关, 如温度较敏感品种在适宜的环境下幼苗生长发育速度较快, 分蘖多, 促使越冬前生长势较旺^[21] ...

... 已证实单个基因的遗传导入能够在提高苗期生长势上发挥一定的作用^[33], 但在杂交群体中存在苗期特性超亲分离现象, 说明生长势相关性状是由多微效基因控制的^[21] ...

2007

4.007

0.0

. 2007, 177:1889-1913

Association analysis of historical bread wheat germplasm using additive genetic covariance of relatives and population structure

Crossa J , Burgueño J , Dreisigacker S , Vargas M , Herrera-Foessel S A , Lillemo M , Singh R P , Trethowan R , Warburton M , Franco J , Reynolds M , Crouch J H , Ortiz R

Linkage disequilibrium can be used for identifying associations between traits of interest and genetic markers. This study used mapped diversity array technology (DArT) markers to find associations with resistance to stem rust, leaf rust, yellow rust, and powdery mildew, plus grain yield in five historical wheat international multienvironment trials from the International Maize and Wheat Improvement Center (CIMMYT). Two linear mixed models were used to assess marker–trait associations incorporating information on population structure and covariance between relatives. An integrated map containing 813 DArT markers and 831 other markers was constructed. Several linkage disequilibrium clusters bearing multiple host plant resistance genes were found. Most of the associated markers were found in genomic regions where previous reports had found genes or quantitative trait loci (QTL) influencing the same traits, providing an independent validation of this approach. In addition, many new chromosome regions for disease resistance and grain yield were identified in the wheat genome. Phenotyping across up to 60 environments and years allowed modeling of genotype × environment interaction, thereby making possible the identification of markers contributing to both additive and additive × additive interaction effects of traits.

2012

3.297

0.0

Theor. Appl. Genet.. 2012, 124(1):207-21 DOI:10.1007/s00122-011-1699-0

Identification of genetic factors controlling kernel hardness and related traits in a recombinant inbred population derived from a soft × 'extra-soft' wheat (Triticum aestivum L.) cross.

Wang G, Leonard JM, Ross AS, Peterson CJ, Zemetra RS, Garland Campbell K, Riera-Lizarazu O

Department of Crop and Soil Science, Oregon State University, 107 Crop Science Building, Corvallis, OR 97331-3002, USA. Guomei.Wang@oregonstate.edu

Kernel hardness or texture, used to classify wheat (Triticum aestivum L.) into soft and hard classes, is a major determinant of milling and baking quality. Wheat genotypes in the soft class that are termed 'extra-soft' (with kernel hardness in the lower end of the spectrum) have been associated with superior end-use quality. In order to better understand the relationship between kernel hardness, milling yield, and various agronomic traits, we performed quantitative trait mapping using a recombinant inbred line population derived from a cross between a common soft wheat line and a genotype classified as an 'extra-soft' line. A total of 47 significant quantitative trait loci (QTL) (LOD ≥ 3.0) were identified for nine traits with the number of QTL affecting each trait ranging from three to nine. The percentage of phenotypic variance explained by these QTL ranged from 3.7 to 50.3%. Six QTL associated with kernel hardness and break flour yield were detected on chromosomes 1BS, 4BS, 5BS, 2DS, 4DS, and 5DL. The two most important QTL were mapped onto orthologous regions on chromosomes 4DS (Xbarc1118-Rht-D1) and 4BS (Xwmc617-Rht-B1). These results indicated that the 'extra-soft' characteristic was not controlled by the Hardness (Ha) locus on chromosome 5DS. QTL for eight agronomic traits occupied two genomic regions near semi-dwarf genes Rht-D1 on chromosome 4DS and Rht-B1 on chromosome 4BS. The clustering of these QTL is either due to the pleiotropic effects of single genes or tight linkage of genes controlling these various traits.

PMID: 21959906

2002

1.554

0.0

Euphytica. 2002, 126(2):275 - 282 DOI:10.1023/A:1016350509689

Quantitative and molecular characterization of heat tolerance in hexaploid wheat

J. Yang(1) R.G. Sears(1) B.S. Gill(2) G.M. Paulsen(1)

1.Department of Agronomy, Kansas State University, Manhattan, KS 66506-5501, U.S.A
2.Department of Plant Pathology, Kansas State University, Manhattan, KS 66506-5501, U.S.A

Understanding the genetic basis of tolerance to high temperature is important for improving the productivity of wheat (Triticum aestivum L.) in regions where the stress occurs. The objective of this study was to estimate inheritance of heat tolerance and the minimum number of genes for the trait in bread wheat by combining quantitative genetic estimates and molecular marker analyses. Two cultivars, Ventnor (heat-tolerant) and Karl92 (heat-susceptible), were crossed to produce F₁, F₂, and F₃populations, and their grain-filling duration (GFD) at 30/25^°C 16/8 h day/night was determined as a measure of heat tolerance. Distribution of GFD in the F₁ and F₂ populations followed the normal model (χ²,p > 0.10). A minimum of 1.4 genes with both additive and dominance effects, broad-sense heritability of 80%, and realized heritability of 96%for GFD were determined from F₂ and F₃ populations. Products from 59primer pairs among 232 simple sequence repeat (SSR) pairs were polymorphic between the parents. Two markers,Xgwm11 andXgwm293, were linked to GFD by quantitative trait loci (QTL) analysis of the F₂ population. TheXgwm11-linked QTL had only additive gene action and contributed 11% to the total phenotypic variation in GFD in the F₂population, whereas theXgwm293-linked QTL had both additive and dominance action and contributed 12% to the total variation in GFD. The results demonstrated that heat tolerance of common wheat is controlled by multiple genes and suggested that marker-assisted selection with microsatellite primers might be useful for developing improved cultivars.

2012

0.0

1.4522

Scientia Agricultura Sinica. 2012, 45(14):2793 - 2800 DOI:10.3864/j.issn.0578-1752.2012.14.002

QTLs Analysis of Wheat Seedling Traits under Salt Stress

盐胁迫下调控小麦苗期性状的QTL分析

REN Yong-Zhe, XU Yan-Hua, GUI Xiang-Wei, WANG Su-Ping, DING Jin-Ping, ZHANG Qing-Chen, MA Yuan-Song, PEI Dong-Li

任永哲, 徐艳花, 贵祥卫, 王素平, 丁锦平, 张庆琛, 马原松, 裴冬丽

1.商丘师范学院生命科学学院，河南商丘 476000
2.河南省延津县农业局，河南延津 453200

【Objective】The present study is aimed to identify QTLs associated with seedling traits under salt stress in common wheat (Triticum aestivum L.) and provide target loci and linked molecular markers for marker assisted selection of wheat salt tolerant-related traits.【Method】A hydroponic culture was carried out to evaluate wheat seedling traits (RDW, MRL, SDW, TDW) under control (CK) and salt stress (ST) conditions by using a recombinant inbred line (RIL) population derived from two Chinese wheat varieties Xiaoyan 54 and Jing 411. The relative values of each trait (ratio of ST/CK) were also calculated. 【Result】 A total of 25 QTLs for 4 traits were detected distributed on chromosomes 1A, 2A, 2D, 3A, 4A, 4B, 5B, 5D, 6B, 7A and 7B, and explained phenotypic variation ranging from 4.4% to 25.5%. Fifteen out of 25 QTLs detected in this research located on 5 genetic intervals in clusters on chromosomes 3A, 4A, 4B, 5B, and 5D and each of the other 10 loci located on different genetic intervals. A total of 5 major QTLs (explained phenotypic variation more than 10%) located on two major chromosome regions (Xgwm497.1-Xcfa2193 and Xbarc78-Xgwm350.1) on chromosomes 3A and 4A, respectively. 【Conclusion】 Most QTLs for wheat salt tolerance detected in present paper clustered on chromosomes 3A, 4A, 4B, 5B and 5D. Five major QTLs located on two chromosome regions (Xgwm497.1-Xcfa2193 and Xbarc78-Xgwm350.1) on chromosomes 3A and 4A, respectively. These chromosome regions may have a potential in the practice of marker assisted wheat breeding for improving salt-tolerance.

【目的】定位盐胁迫下调控小麦苗期性状的QTL位点，为分子标记辅助选择小麦耐盐性状提供基因位点和连锁标记。【方法】以小偃54×京411重组自交系群体为材料，在盐胁迫条件下检测调控小麦苗期MRL、 RDW、SDW和TDW及其相对性状的QTL位点。【结果】共检测到调控小麦苗期4个性状及其相对性状的25个QTL位点，分布在1A、2A、2D、3A、4A、4B、5B、5D、6B、7A和7B共11条染色体上，贡献率在4.4%—25.5%。其中有15个QTL位点成簇分布于3A、4A、4B、5B、5D染色体的5个遗传区间，其余10个QTL位点各自分布于不同的染色体区段。检测到的5个贡献率大于10%的位点分别位于3A染色体的Xgwm497.1—Xcfa2193和4A染色体的Xbarc78—Xgwm350.1。【结论】多数调控小麦耐盐性的QTL位点成簇分布于3A、4A、4B、5B和5D染色体上，3A染色体的Xgwm497.1—Xcfa2193和4A染色体的Xbarc78—Xgwm350.1携带所有5个贡献率在10%以上的QTL位点。

... 5BL上Xbarc59 附近区段不仅与孕穗前植株生长势关联(表2), 而且参与调控苗期根系性状^[25] ...

2011

1.653

0.0

2011

5.364

0.0

J. Exp. Bot.. 2011, 62(2):409-38 DOI:10.1093/jxb/erq287

Association mapping in durum wheat grown across a broad range of water regimes.

Maccaferri M, Sanguineti MC, Demontis A, El-Ahmed A, Garcia del Moral L, Maalouf F, Nachit M, Nserallah N, Ouabbou H, Rhouma S, Royo C, Villegas D, Tuberosa R

Department of Agroenvironmental Sciences and Technology, University of Bologna, I-40127 Bologna, Italy.

Association mapping was used to dissect the genetic basis of drought-adaptive traits and grain yield (GY) in a collection of 189 elite durum wheat accessions evaluated in 15 environments highly different for water availability during the crop cycle (from 146 to 711 mm) and GY (from 9.9 to 67.3 q ha(-1)). For highly heritable traits (e.g. heading date, kernel weight, etc.) several significant experiment-wise marker-trait associations were detected across five or more (up to 13 for kernel weight) environments, with R(2) values ranging from ca. 5 to 10%. As to GY, significant associations (R(2) from 2.5 to 4.2%) were mostly detected in one environment only (56 markers), while decreasing rapidly from two to five environments (from 20 to three markers, respectively) and with only one marker (Xbarc197 on chr. 5A) found significant in six environments (ranging from low- to high-yielding). These results are probably due to the complex genetic basis of GY and its interaction with environmental conditions. The number of markers significantly affecting GY decreased considerably under drought conditions, suggesting a limited effectiveness of association mapping to identify loci for GY under low-moisture conditions, most likely because different genotypes can attain similar phenotypes via different morpho-physiological traits and corresponding gene networks. Our study confirmed the role of major loci for phenology previously described in biparental mapping populations, highlighted a novel set of loci for drought-adaptive traits, and provided information on the agronomic value of the alleles at such loci across a broad range of soil moisture conditions.

PMID: 21041372

2011

2.945

0.0

Plant Sci.. 2011, 181(1):65-75 DOI:10.1016/j.plantsci.2011.03.020

Enrichment of a common wheat genetic map and QTL mapping for fatty acid content in grain.

Wang YY, Sun XY, Zhao Y, Kong FM, Guo Y, Zhang GZ, Pu YY, Wu K, Li SS

State Key Laboratory of Crop Biology, Shandong Agricultural University, Tai'an 271018, Shandong, China.

DArT and SSR markers were used to saturate and improve a previous genetic map of RILs derived from the cross Chuan35050 × Shannong483. The new map comprised 719 loci, 561 of which were located on specific chromosomes, giving a total map length of 4008.4 cM; the rest 158 loci were mapped to the most likely intervals. The average chromosome length was 190.9 cM and the marker density was 7.15 cM per marker interval. Among the 719 loci, the majority of marker loci were DArTs (361); the rest included 170 SSRs, 100 EST-SSRs, and 88 other molecular and biochemical loci. QTL mapping for fatty acid content in wheat grain was conducted in this study. Forty QTLs were detected in different environments, with single QTL explaining 3.6-58.1% of the phenotypic variations. These QTLs were distributed on 16 chromosomes. Twenty-two QTLs showed positive additive effects, with Chuan35050 increasing the QTL effects, whereas 18 QTLs were negative with increasing effects from Shannong483. Six sets of co-located QTLs for different traits occurred on chromosomes 1B, 1D, 2D, 5D, and 6B.

PMID: 21600399

2012

2.852

0.0

Mol Breeding. 2012, 29(4):963 - 972 DOI:10.1007/s11032-011-9693-4

Genetic dissection of grain weight in bread wheat through quantitative trait locus interval and association mapping

R. R. Mir⁽¹⁾⁽²⁾,N. Kumar⁽¹⁾⁽³⁾,V. Jaiswal⁽¹⁾,N. Girdharwal⁽¹⁾,M. Prasad⁽⁴⁾,H. S. Balyan⁽¹⁾,P. K. Gupta

⁽¹⁾

1.Molecular Biology Laboratory, Department of Genetics and Plant Breeding, Ch. Charan Singh University, Meerut, 250004, India

2.Global-Theme Biotechnology, International Crops Research Institute for Semi-Arid Tropics (ICRISAT), Patancheru, Hyderabad, 502324, India

3.Department of Crop and Soil Sciences, Washington State University, Pullman, WA, 99164, USA

4.National Institute of Plant Genome Research (NIPGR), Aruna Asaf Ali Marg, New Delhi, 110067, India

Abstract
Genetic dissection of grain weight in bread wheat was undertaken through both genome-wide quantitative trait locus (QTL) interval mapping and association mapping. QTL interval mapping involved preparation of a framework linkage map consisting of 294 loci {194 simple sequence repeats (SSRs), 86 amplified fragment length polymorphisms (AFLPs) and 14 selective amplifications of microsatellite polymorphic loci (SAMPL)} using a bi-parental recombinant inbred line (RIL) mapping population derived from Rye Selection111 × Chinese Spring. Using the genotypic data and phenotypic data on grain weight (GW) of RILs collected over six environments, genome-wide single locus QTL analysis was conducted to identify main effect QTL. This led to identification of as many as ten QTL including four major QTL (three QTL were stable), each contributing >20% phenotypic variation (PV) for GW. The above study was supplemented with association mapping, which allowed identification of 11 new markers in the genomic regions that were not reported earlier to harbour any QTL for GW. It also allowed identification of closely linked markers for six known QTL, and validation of eight QTL reported earlier. The QTL identified through QTL interval mapping and association mapping may prove useful in marker-assisted selection (MAS) for the development of cultivars with high GW in bread wheat.

2010

3.297

0.0

Theor Appl Genet. 2010, 121(4):629 - 641 DOI:10.1007/s00122-010-1335-4

Identification of quantitative trait loci for ABA responsiveness at the seedling stage associated with ABA-regulated gene expression in common wheat

Fuminori Kobayashi(1) Shigeo Takumi(2) Hirokazu Handa(13)

1.Plant Genome Research Unit, National Institute of Agrobiological Sciences, 2-1-2 Kannondai, Tsukuba, Ibaraki 305-8602, Japan
2.Laboratory of Plant Genetics, Graduate School of Agricultural Science, Kobe University, 1-1 Rokkodai-cho, Nada-ku, Kobe, 657-8501 Japan
3.Graduate School of Life and Environmental Science, University of Tsukuba, 1-1-1 Ten-noh-dai, Tsukuba, Ibaraki 305-8572, Japan

Responsiveness to abscisic acid (ABA) during vegetative growth plays an important role in regulating adaptive responses to various environmental conditions, including activation of a number of ABA-responsive genes. However, the relationship between gene expression and responsiveness to ABA at the seedling stage has not been well studied in wheat. In the present study, quantitative trait locus (QTL) analysis for ABA responsiveness at the seedling stage was performed using recombinant inbred lines derived from a cross between common wheat cultivars showing different ABA responsiveness. Five QTLs were found to be significant, located on chromosomes 1B, 2A, 3A, 6D and 7B. The QTL with the greatest effect was located on chromosome 6D and explained 11.12% of the variance in ABA responsiveness. The other QTLs each accounted for approximately 5–8% of the phenotypic variation. Expression analyses of three ABA-responsiveCor/Lea genes,Wdhn13,Wrab15 andWrab17, showed that allelic differences in QTLs on chromosomes 2A, 6D and 7B influenced expression of these genes in seedlings treated with ABA. The 3A QTL appeared to be involved in the regulatory system ofWdhn13 andWrab15, but notWrab17. The effects of the 2A and 6D QTLs on gene expression were relatively large. The combination of alleles at the QTLs resulted in an additive or synergistic effect onCor/Lea expression. These results indicate that the QTLs influencing ABA responsiveness are associated with ABA-regulated gene expression and suggest that the QTL on chromosome 6D with the largest effect acts as a key regulator of ABA responses including seedling growth arrest and gene expression during the vegetative stage.

2012

3.297

0.0

Theor. Appl. Genet.. 2012, 125(8):1619-38 DOI:10.1007/s00122-012-1939-y

A high-density consensus map of A and B wheat genomes.

Marone D, Laidò G, Gadaleta A, Colasuonno P, Ficco DB, Giancaspro A, Giove S, Panio G, Russo MA, De Vita P, Cattivelli L, Papa R, Blanco A, Mastrangelo AM

CRA-Cereal Research Centre, SS16 km 675, 71122 Foggia, Italy.

A durum wheat consensus linkage map was developed by combining segregation data from six mapping populations. All of the crosses were derived from durum wheat cultivars, except for one accession of T. ssp. dicoccoides. The consensus map was composed of 1,898 loci arranged into 27 linkage groups covering all 14 chromosomes. The length of the integrated map and the average marker distance were 3,058.6 and 1.6 cM, respectively. The order of the loci was generally in agreement with respect to the individual maps and with previously published maps. When the consensus map was aligned to the deletion bin map, 493 markers were assigned to specific bins. Segregation distortion was found across many durum wheat chromosomes, with a higher frequency for the B genome. This high-density consensus map allowed the scanning of the genome for chromosomal rearrangements occurring during the wheat evolution. Translocations and inversions that were already known in literature were confirmed, and new putative rearrangements are proposed. The consensus map herein described provides a more complete coverage of the durum wheat genome compared with previously developed maps. It also represents a step forward in durum wheat genomics and an essential tool for further research and studies on evolution of the wheat genome.

PMID: 22872151

... Marone等^[31])对所有关联的标记进行染色体定位, 以10 cM内富集3个以上关联标记为标准, 判断为性状关联的遗传区段 ...

2011

0.0

... 71 (P [32] ...

1988

0.0