环境、基因型及其互作对小麦主要品质性状的影响

  为了解环境(E)、基因型(G)及其互作(G×E)对小麦(Triticum aestivum)主要品质性状的影响效应, 连续两年进行了2组不同试验: 试验1在河南省5个不同纬度点分别种植强筋、中筋和弱筋6个小麦品种, 其品质性状的基因型差异相对较大; 试验2采用9个品种(多为中筋类型), 分别种植于我国主产麦区的8个省份, 其环境差异相对较大。研究结果表明, 2组试验中所有品质性状的基因型差异均达5%或1%的显著水平。试验2中所有品质性状的地点变异均达1%的极显著水平, 而试验1中仅蛋白质含量、湿面筋含量、沉降值、吸水率和延伸性的地点变异显著, 其多数加工品质性状的地点变异不显著。试验1中所以品质性状的地点×基因型互作均不显著; 而试验2中籽粒硬度、灰分、吸水率、形成时间、稳定时间和最大抗延伸阻力存在显著的地点×基因型互作。2组试验结果给我们的启示是: 1)基因型对多数品质性状的影响是第一位的, 因此生产中品种选择对获得理想的加工品质至关重要。2)地点对多数品质性状影响明显, 但其效应大小与试验的环境差异性有关。3)基因型与环境的互作效应明显小于基因型或环境主效应, 且受试验材料(基因型)与环境差异的影响。4)年际间多数品质性状有显著差异, 主要与灌浆期降雨、光照及温度条件有关; 过多降雨、较少日照时数及较低日均温对强筋小麦品质形成不利。     

烤烟不同生育时期产量性状的遗传效应分析

以烤烟高产品种竖把老母鸡和台烟7号与低产品种TI245配制成2个杂交组合,分别获得6个世代植株(P1、P2、F1、F2、B1、B2),运用主基因+多基因混合遗传多世代联合法对2个组合不同生育时期的产量性状进行遗传分析。研究结果表明,2个组合的产量性状在性状建成中发挥调控作用的基因数目与基因效应存在发育阶段性差异。基因加性效应与显性效应存在于整个发育过程中,且效应程度在不同生育时期存在明显差异,同时加性×加性、显性×显性互作效应在产量性状的发育过程中呈间断性表达。基因遗传力在不同生育时期存在差异,现蕾期产量性状的表型变异受基因遗传效应的决定作用高于其他2个生育时期,受环境因素影响小,且不同组合间的基因效应也存在显著差异,组合Ⅱ的遗传效应明显高于组合Ⅰ。不同组合的烤烟产量性状在不同生育时期主基因+多基因遗传效应差异显著,受环境影响程度不同,因此在育种工作中既要考虑基因效应,又要注意环境影响。     

AMMI模型在旱地春小麦稳定性分析中的应用

基因型与环境的互作(GEI)决定了作物在多变环境下性状的稳定性。AMMI模型是一种将方差分析和主成分分析结合于一体,能更有效分析GEI、进而评价基因型稳定性和环境对基因型差异分辨力的有力工具。利用AMMI模型对10个品种(系)、13个试点组成的全国旱地春小麦区域试验产量资料分析表明,试点间平均产量变幅为396.6~4050.2 kg.hm-2,现代品种间的平均产量变幅为1318.6~2315.6 kg.hm-2;基因型间、环境间和GEI引起的产量变异达到极显著水平,三者的变异平方和分别占总处理平方和的6.2%、70.3%、23.5%,表明环境和GEI对产量变化的影响远大于基因型。用前3个代表了90.8%GEI信息的显著主成分计算基因型稳定性参(Di)和试点分辨力(Dj),基因型间Di最大相差达3倍、而试点间Dj最大相差19倍;属于高产、稳产的品种有:定西35、西旱1号、定丰889,而在这两方面均表现最差的品种为蒙麦35号。有些品种对某些试点有特殊适应性,局部推广价值也大。     

烤烟主要农艺性状的遗传与相关分析

利用包括基因型与环境互作的加性-显性遗传模型,对14个烤烟品种(系)及其配制的41个杂交组合在4个环境下的7个农艺性状表现进行遗传分析。结果表明,株高、节距、腰叶宽主要受加性效应控制,叶数、腰叶长受显性×环境互作效应影响最大,茎围以加性×环境互作效应、显性×环境互作效应为主,产量以加性效应、显性×环境互作效应为主。适应当地生态条件的品种（系）具有较高的正向加性效应。许多组合的显性主效应及在各试验点的显性×环境互作效应在方向上不尽一致,杂交组合的选配宜针对特定的生态环境进行。性状相关分析表明,大多数成对性状的各项相关系数为正值,且多以加性遗传相关为主,可利用株高对产量进行间接选择。
     

五个箭筈豌豆品系基因型与环境互作效应及农艺性状稳定性

通过 2 a、4个区试点和 5种基因型及其互作效应研究 ,运用混合线性模型和 MINQU E(1)法 ,对箭豌豆包括株高在内的9个农艺性状可塑性进行评价 ,揭示了年份和区试点的生态环境效应、基因型与生态环境互作效应对各农艺性状的可塑性。结果表明 ,种子产量和千粒重等性状的基因型与生态环境互作效应达到了极显著水平 (p <0 .0 1和 p<0 .0 0 1)。生态环境分量 (年份、区试点、年份×区试点 )对各农艺性状的可塑性贡献较大 ,同时在不同生态环境间各农艺性状间差异达到了显著水平 (p <0 .0 5)。其中区试点分量对各农艺性状的可塑性贡献最大 ,各农艺性状在 4个区试点之间差异达到了显著水平 (p<0 .0 1) ,肃南和天祝的 2个区试点的牧草干重和种子产量等重要农艺性状的平均值显著大于另 2个区试点。各农艺性状在不同年份间差异达到了极显著水平 (p<0 .0 1) ,2 0 0 2年各农艺性状的平均值显著优于 2 0 0 1年。牧草干重和种子产量数量性状与气候因子的相关分析表明 ,5～ 8月份的月均温对牧草干重和种子产量的影响作用较大 ,较高的温度有利于牧草干重和种子产量的提高 ;7月份的降水量与牧草干重和种子产量存在一定程度的正相关关系。品系 2 556和 2 560在进行了基因型与环境互作效应稳定性评价后 ,4个区试点两年间都     

短季棉早熟性的遗传效应及其与环境互作研究

以熟期不同的9个棉花品种为亲本,按部分双列杂交配制46个组合的F1、F2,在3个不同生态环境条件下,研究了7个早熟相关性状的遗传效应及其与环境互作。结果表明:短季棉7个早熟相关性状的遗传均以加性效应为主,同时存在着显性效应,对于播种-现蕾、播种-开花和现蕾-开花还存在着上位性效应;短季棉各早熟性状的遗传效应与环境互作显著。生育期、播种-开花的狭义遗传率均较高,分别为66.1%和49.1%,且与环境互作效应较小,而果枝始节和播种-现蕾的遗传率最低,分别为19.8%和18.8%,且与环境互作达到极显著水平,现蕾-开花、开花-吐絮和株高这3个性状的遗传率及其与环境互作居中。由此说明:早熟性的遗传受环境影响较大,在生态条件差异较大的育种地点,以果枝始节和播种-现蕾作为早熟性指标进行异地选择是不可靠的,而以生育期、开花期为早熟性选择指标是比较可行的。     

水稻生物学产量及其构成性状的QTL定位

QTL的加性效应、加性×加性上位性效应及它们与环境的互作效应是数量性状的重要遗传分量.利用IR64/Azucena的125个DH品系为群体,分析了水稻生物学产量及其两个构成性状干草产量和谷粒产量的遗传组成.用基于混合模型的复合区间作图(MCIM)方法进行QTL定位.检测到12个位点有加性主效应,27个位点涉及双位点互作,18个位点存在环境互作.结果表明水稻生物学产量和它的两个构成性状普遍存在上位性效应和QE互作效应.此外,还探讨了性状间相关的遗传基础.发现4个QTLs和一对上位性QTLs可能与生物学产量与干草产量之间的正相关有关.3个QTL可能与干草产量与谷粒产量之间的负相关有关.这些结果可能部分地解释了这3个性状相关的遗传原因.通过对水稻生物学产量及其两个构成性状所定位QTL的分析,加深了对数量性状QTL的认识.首先,QTL的上位性效应和QE互作效应是普遍存在的;其次,QTL的多效性或紧密连锁可能是遗传相关的原因,当QTL对两个性状作用的方向相同时可导致正向遗传相关,反之则为负向遗传相关,当有些QTL表现为同向作用而另一些QTL表现为反向作用时,则可削弱性状间的遗传相关性;第三,复合性状的QTL效应可分解为其组成性状的QTL效应,如果QTL对各组成性状的效应方向相反而相互抵消,可使复合性状的QTL效应不易被检测;第四,加性效应的QTL常参预构成上位性效应,而具有上位性效应的QTL并非都有加性主效应,表明忽略上位性的QTL定位方法会降低检测QTL的功效;最后,鉴别不同类型的QTL效应有利于指导育种实践,选择主效QTL适用于多环境,QE互作QTL适用于特定环境,对上位性QTL应强调选择基因组合而并非单个基因.     

云南粳稻碾磨品质性状稳定性分析

利用AMMI模型对2年5点12个粳稻品种的糙米率、精米率和整精米率进行了稳定性分析,并以碾磨品质性状的表型值及其相应的稳定性参数(Di)为指标,对供试品种进行聚类分析和评价.结果表明,糙米率、精米率和整精米率在不同品种和环境间的差异以及品种×环境互作效应均达极显著水平;碾磨品质性状的稳定性随品种和环境不同而变化较大,其稳定性顺序为糙米率>精米率>整精米率.综合考虑糙米率、精米率和整精米率及其稳定性,云粳优14号、滇元1号、云粳18号和滇元2号的碾磨品质和稳定性好,可作育种亲本,以改良水稻品种的碾磨品质及其稳定性.     

甘蓝型油菜主要农艺性状的遗传模型和基因效应分析

本文以甘蓝型优质油菜(81008×Tower)正反交各12个世代的资料,按照4种不同的遗传模型,对包括单株产量在内的8个农艺性状的遗传方式进行了详尽地分析。结果表明:所有性状的遗传皆不符合简单的加性-显性模型,上位性效应普遍存在,且对世代平均数有较大影响;单株产量及几个产量性状的遗传均受重复型互作基因的控制,杂种优势需利用三基因或多基因间的互作效应。     

对应用几种统计模型评价甘蔗品种稳定性的初步比较

利用广东省2009年甘蔗品种区域试验产量数据,对线性回归模型、AMMI模型和LR-PCA模型在评价甘蔗品种稳定性方面的应用进行了初步比较,结果发现,回归法计算简便、直观,AMMI模型和LR-PCA模型的分析结果则更全面、深入,而这两种模型之间仍存在着一定差异.实际操作中,在根据不同的数据资料选择相适宜的分析方法的同时,也可以采用不同的方法进行分析,通过比较选择较为合理的结果.     

Use of trial clustering to study QTL × environment effects for grain yield and related traits in maize

A population of 300 F_3:4 lines derived from the cross between maize inbred lines F2 and F252 was evaluated for testcross value in a large range of environmental conditions (11 different locations in 2 years: 1995 and 1996) in order to study (1) the magnitude of genotype × environment and (2) the stability of quantitative trait loci (QTL) effects. Several agronomic traits were measured: dry grain yield (DGY), kernel weight, average number of kernels per plant, silking date (SD) and grain moisture at harvest. A large genotype × environment interaction was found, particularly for DGY. A hierarchical classification of trials and an additive main effects and multiplicative interaction (AMMI) model were carried out. Both methods led to the conclusion that trials could be partitioned into three groups consistent with (1) the year of experiment and (2) the water availability (irrigated vs non-irrigated) for the trials sown in 1995. QTL detection was carried out for all the traits in the different groups of trials. Between 9 and 15 QTL were detected for each trait. QTL × group and QTL × trial effects were tested and proved significant for a large proportion of QTL. QTL detection was also performed on coordinates on the first two principal components (PC) of the AMMI model. PC QTL were generally detected in areas where QTL × group and QTL × trial interactions were significant. A region located on chromosome 8 near an SD QTL seemed to play a key role in DGY stability. Our results confirm the key role of water availability and flowering earliness on grain yield stability in maize.     

Predictive and postdictive success of statistical analyses of yield trials

Summary The accuracy of a yield trial can be increased by improved experimental techniques, more replicates, or more efficient statistical analyses. The third option involves nominal fixed costs, and is therefore very attractive. The statistical analysis recommended here combines the Additive main effects and multiplicative interaction (AMMI) model with a predictive assessment of accuracy. AMMI begins with the usual analysis of variance (ANOVA) to compute genotype and environment additive effects. It then applies principal components analysis (PCA) to analyze non-additive interaction effects. Tests with a New York soybean yield trial show that the predictive accuracy of AMMI with only two replicates is equal to the predictive accuracy of means based on five replicates. The effectiveness of AMMI increases with the size of the yield trial and with the noisiness of the data. Statistical analysis of yield trials with the AMMI model has a number of promising implications for agronomy and plant breeding research programs.This research was supported by the Rhizobotany Project of the USDA-ARS     

Stability Performance of Inductively Coupled Plasma Mass Spectrometry-Phenotyped Kernel Minerals Concentration and Grain Yield in Maize in Different Agro-Climatic Zones

Deficiency of iron and zinc causes micronutrient malnutrition or hidden hunger, which severely affects ~25% of global population. Genetic biofortification of maize has emerged as cost effective and sustainable approach in addressing malnourishment of iron and zinc deficiency. Therefore, understanding the genetic variation and stability of kernel micronutrients and grain yield of the maize inbreds is a prerequisite in breeding micronutrient-rich high yielding hybrids to alleviate micronutrient malnutrition. We report here, the genetic variability and stability of the kernel micronutrients concentration and grain yield in a set of 50 maize inbred panel selected from the national and the international centres that were raised at six different maize growing regions of India. Phenotyping of kernels using inductively coupled plasma mass spectrometry (ICP-MS) revealed considerable variability for kernel minerals concentration (iron: 18.88 to 47.65 mg kg^–1; zinc: 5.41 to 30.85 mg kg^–1; manganese: 3.30 to17.73 mg kg^–1; copper: 0.53 to 5.48 mg kg^–1) and grain yield (826.6 to 5413 kg ha^–1). Significant positive correlation was observed between kernel iron and zinc within (r = 0.37 to r = 0.52, p < 0.05) and across locations (r = 0.44, p < 0.01). Variance components of the additive main effects and multiplicative interactions (AMMI) model showed significant genotype and genotype × environment interaction for kernel minerals concentration and grain yield. Most of the variation was contributed by genotype main effect for kernel iron (39.6%), manganese (41.34%) and copper (41.12%), and environment main effects for both kernel zinc (40.5%) and grain yield (37.0%). Genotype main effect plus genotype-by-environment interaction (GGE) biplot identified several mega environments for kernel minerals and grain yield. Comparison of stability parameters revealed AMMI stability value (ASV) as the better representative of the AMMI stability parameters. Dynamic stability parameter GGE distance (GGED) showed strong and positive correlation with both mean kernel concentrations and grain yield. Inbreds (CM-501, SKV-775, HUZM-185) identified from the present investigation will be useful in developing micronutrient-rich as well as stable maize hybrids without compromising grain yield.     

Integration of statistical and physiological analyses of adaptation of near-isogenic barley lines

Seven near-isogenic barley lines, differing for three independent mutant genes, were grown in 15 environments in Spain. Genotype x environment interaction (G x E) for grain yield was examined with the Additive Main Effects and Multiplicative interaction (AMMI) model. The results of this statistical analysis of multilocation yield-data were compared with a morpho-physiological characterization of the lines at two sites (Molina-Cano et al. 1990). The first two principal component axes from the AMMI analysis were strongly associated with the morpho-physiological characters. The independent but parallel discrimination among genotypes reflects genetic differences and highlights the power of the AMMI analysis as a tool to investigate G x E. Characters which appear to be positively associated with yield in the germplasm under study could be identified for some environments.     

我国旱地春小麦产量及主要农艺指标的变异分析

采用4年、13个品种(系)、18个试点组成的全国旱地春小麦区域试验产量资料,通过联合方差分析和基因型及其与环境互作（GGE）双标图分析,研究了基因型、环境、基因型与环境互作效应（GEI）对产量变异的影响及品种的产量稳定性.结果表明：环境对产量变异的影响远大于基因型和GEI,环境引起的产量变异占87.5%～92.0%．互作因素中以地点×基因型的互作效应最大,基因型×年份的互作效应最小.我国旱地春小麦基因型多年多点的平均产量水平为2550 kg·hm^-2.产量三要素中,千粒重受环境的影响最小.影响产量变异的主要环境因子有：≥10 ℃年积温、生育期降雨量、平均气温、海拔、年降雨量和无霜期.产量与单位面积穗数（0.675^**）、穗粒数（0.581^**）、千粒重（0.456^**）呈极显著正相关,产量三要素间也呈正相关（0.244～0.480^**）,处于可同步提高范围.     

Identification of QTL for growth- and grain yield-related traits in rice across nine locations of Asia

Rice double-haploid (DH) lines of an indica and japonica cross were grown at nine different locations across four countries in Asia. Genotype-by-environment (G x E) interaction analysis for 11 growth- and grain yield-related traits in nine locations was estimated by AMMI analysis. Maximum G x E interaction was exhibited for fertility percentage number of spikelets and grain yield. Plant height was least affected by environment, and the AMMI model explained a total of 76.2% of the interaction effect. Mean environment was computed by averaging the nine environments and subsequently analyzed with other environments to map quantitative trait loci (QTL). QTL controlling the 11 traits were detected by interval analysis using mapmaker/qtl. A threshold LOD of >/=3.20 was used to identify significant QTL. A total of 126 QTL were identified for the 11 traits across nine locations. Thirty-four QTL common in more than one environment were identified on ten chromosomes. A maximum of 44 QTL were detected for panicle length, and the maximum number of common QTL were detected for days to heading detected. A single locus for plant height (RZ730-RG810) had QTL common in all ten environments, confirming AMMI results that QTL for plant height were affected the least by environment, indicating the stability of the trait. Two QTL were detected for grain yield and 19 for thousand-grain weight in all DH lines. The number of QTL per trait per location ranged from zero to four. Clustering of the QTL for different traits at the same marker intervals was observed for plant height, panicle number, panicle length and spikelet number suggesting that pleiotropism and or tight linkage of different traits could be the possible reason for the congruence of several QTL. The many QTL detected by the same marker interval across environments indicate that QTL for most traits are stable and not essentially affected by environmental factors.     

Use of the additive main effects and multiplicative interaction model in QTL mapping for adaptation in barley

The additive main effects and multiplicative interaction (AMMI) model has emerged as a powerful analytical tool for genotype x environment studies. The objective of the present study was to assess its value in quantitative trait locus (QTL) mapping. This was done through the analysis of a large two-way table of genotype-by-environment data of barley (Hordeum vulgare L.) grain yields, where the genotypes constituted a genetic population suitable for mapping studies. Grain yield data of 150 doubled haploid lines derived from the Steptoe x Morex cross, and the two parental lines, were taken by the North American Barley Genome Mapping Project (NABGMP) at 16 environments throughout the barley production areas of the USA and Canada. Four regions of the genome were responsible for most of the differential genotypic expression across environments. They accounted for approximately 50% of the genotypic main effect and 30% of the genotype x environment interaction (GE) sums of squares. The magnitude and sign of AMMI scores for genotypes and sites facilitate inferences about specific interactions. The parallel use of classification (cluster analysis of environments) and ordination (principal component analysis of GE matrix) techniques allowed most of the variation present in the genotype x environment matrix to be summarized in just a few dimensions, specifically four QTLs showing differential adaptation to four clusters of environments. Thus, AMMI genotypic scores, when the genotypes constituted a population suitable for QTL mapping, could provide an adequate way of resolving the magnitude and nature of QTL x environment interactions.Ignacio Romagosa was on sabbatical leave from the University of Lleida and the Institut de Recerca i Tecnologia Agroalimentàries, Lleida, Spain, when this study was conducted     

Full and reduced models for yield trials

Summary Empirical results routinely demonstrate that the reduced Additive Main effects and Multiplicative Interaction (AMMI) model achieves better predictive accuracy for yield trials than does the full treatment means model. It may seem mysterious that treatment means are not the most accurate estimates, but rather that the AMMI model is often more accurate than its data. The statistical explanation involves the Stein effect, whereby a small sacrifice in bias can produce a large gain in accuracy. The corresponding agricultural explanation is somewhat complex, beginning with a yield trial's design and ending with its research purposes and applications. In essence, AMMI selectively recovers pattern related to the treatment design in its model, while selectively relegating noise related to the experimental design in its discarded residual. For estimating the yield of a particular genotype in a particular environment, the AMMI model uses the entire yield trial, rather than only the several replications of this particular trial, as in the treatment means model. This use of more information is the source of AMMI's gain in accuracy.This research was supported by the Rhizobotany Project of the USDA-ARS     

Improving efficiency of breeding for higher crop yield

Summary Exclusive selection for yield raises, the harvest index of self-pollinated crops with little or no gain in total bipmass. In addition to selection for yield, it is suggested that efficient breeding for higher yield requires simultaneous selection for yield's three major, genetically controlled physiological components. The following are needed: (1) a superior rate of biomass accumulation. (2) a superior rate of actual yield accumulation in order to acquire a high harvest index, and (3) a time to harvest maturity that is neither shorter nor longer than the duration of the growing season. That duration is provided by the environment, which is the fourth major determinant of yield. Simultaneous selection is required because genetically established interconnections among the three major physiological components cause: (a) a correlation between the harvest index and days to maturity that is usually negative; (b) a correlation between the harvest index and total biomass that is often negative, and (c) a correlation between biomass and days to maturity that is usually positive. All three physiological components and the correlations among them can be quantified by yield system analysis (YSA) of yield trials. An additive main effects and multiplicative interaction (AMMI) statistical analysis can separate and quantify the genotype × environment interaction (G × E) effect on yield and on each physiological component that is caused by each genotype and by the different environment of each yield trial. The use of yield trials to select parents which have the highest rates of accumulation of both biomass and yield, in addition to selecting for the G × E that is specifically adapted to the site can accelerate advance toward the highest potential yield at each geographical site. Higher yield for many sites will raise average regional yield. Higher yield for multiple regions and continents will raise average yield on a world-wide basis. Genetic and physiological bases for lack of indirect selection for biomass from exclusive selection for yield are explained.     

杂交水稻结实率稳定性的遗传分析

本文采用三套同核异质不育系与５个恢复力不同的恢复系按Ｐ×Ｑ模式配组、分５期播种的试验设计,在ＡＭＭＩ模型分析结实率稳定性的基础上,进一步剖析了不育胞质、保持系、恢复系及其工作的各种遗传效应。