Genotype × environment interaction and genetic improvement for yield and yield stability of rainfed durum wheat in Iran

The genotype × environment (GE) interaction influences genotype selection and recommendations. Consequently, the objectives of genetic improvement should include obtaining genotypes with high potential yield and stability in unpredictable conditions. The GE interaction and genetic improvement for grain yield and yield stability was studied for 11 durum breeding lines, selected from Iran/ICARDA joint program, and compared to current checks (i.e., one durum modern cultivar and two durum and bread wheat landraces). The genotypes were grown in three rainfed research stations, representative of major rainfed durum wheat-growing areas, during 2005–09 cropping seasons in Iran. The additive main effect and multiplicative interaction (AMMI) analysis, genotype plus GE (GGE) biplot analysis, joint regression analysis (JRA) (b and S²di), six stability parameters derived from AMMI model, two Kang’s parameters [i.e., yield-stability (YSi) statistic and rank-sum], GGE distance (mean performance + stability evaluation), and two adaptability parameters [i.e., TOP (proportion of environments in which a genotype ranked in the top third) and percentage of adaptability (Ad)] were used to analyze GE interaction in rainfed durum multi-environment trials data. The main objectives were to (i) evaluate changes in adaptation and yield stability of the durum breeding lines compared to modern cultivar and landraces (ii) document genetic improvement in grain yield and analyze associated changes in yield stability of breeding lines compared to checks and (iii) to analyze rank correlation among GGE biplot, AMMI analysis and JRA in ranking of genotypes for yield, stability and yield-stability. The results showed that the effects due to environments, genotypes and GE interaction were significant (P < 0.01), suggesting differential responses of the genotypes and the need for stability analysis. The overall yield was 2,270 kg ha^?1 for breeding lines and modern cultivar versus 2,041 kg ha^?1 for landraces representing 11.2 % increase in yield. Positive genetic gains for grain yield in warm and moderate locations compared to cold location suggests continuing the evaluation of the breeding material in warm and moderate conditions. According to Spearman’s rank correlation analysis, two types of associations were found between the stability parameters: the first type included the AMMI stability parameters and joint regression parameters which were related to static stability and ranked the genotypes in similar fashion, whereas the second type consisted of the rank-sum, YSi, TOP, Ad and GGED which are related to dynamic concept of stability. Rank correlations among statistical methods for: (i) stability ranged between 0.27 and 0.97 (P < 0.01), was the least between AMMI and GGE biplot, and highest for AMMI and JRA and (ii) yield-stability varied from 0.22 (between GGE and JRA) to 0.44 (between JRA and AMMI). Breeding lines G8 (Stj3//Bcr/Lks4), G10 (Ossl-1/Stj-5) and G12 (modern cultivar) were the best genotypes in terms of both nominal yield and stability, indicating that selecting for improved yield potential may increase yield in a wide range of environments. The increase in adaptation, yield potential and stability of breeding lines has been reached due to gradual accumulation of favorable genes through targeted crosses, robust shuttle breeding and multi-locational testing.     

Analysis of genotype-by-environment interaction for grain yield of rainfed durum wheat genotypes in warm winter areas of Iran

This study was performed for pattern analysis of genotype-by-environment (GE) interaction on 20 durum wheat genotypes grown in 15 testing environments during 2004–06 in Iran. Combined analysis of variance showed significant genotypes (G), environments (E), and GE interactions (P < 0.01), with environmental main effects being the predominant source of variation, followed by GE interaction. The results showed various patterns of genotype responses to different environment groups and assisted in structuring the durum wheat testing locations with identification of two major-environment groups with high genotype discrimination ability. The locations (Gachsaran and Ilam) corresponding to warm and semi-arid aresa were similar in genotype discrimination and showed no association with the other testing locations (Gonbad, Moghan, and Khoramabad) representing the Mediterranean area, indicating they differ in rankings of genotypes. The top-yielding genotypes, G13, G14 and G9, were highly adapted to warm and semi-arid environments, but those corresponding to the Mediterranean area had a high ability to discriminate the genotypes G16, G11, and Saimareh. The stability and adaptability of specific genotypes were assessed by plotting their nominal grain yields at specific environments in an ordination biplot, which aided in the identification of environment groups. Appropriate check genotypes for all environments or for specific environments were also identified. Pattern analysis allowed a sensible and useful summarization of GE interaction data set and helped to facilitate selecting superior genotypes for target-growing sites.     

Parametric and nonparametric measures of phenotypic stability in linseed (Linum usitatissimum L.)

A combined analysis with three parametricand two nonparametric measures to assess G × E interactions and stability analyses toidentify stable genotypes of linseed across18 environments in Ethiopia wereundertaken. The combined analysis ofvariance for environments (E), genotypes(G) and G × E interaction was highlysignificant (p<0.01), suggestingdifferential responses of the genotypes andthe need for stability analysis. Theparametric stability measures ofcoefficient of variability and thestability variance showed that R12-N10D wasthe most stable genotype, whereascultivars' superiority measure indicatedChilalo to be the most stable cultivar.Like most of the parametric methods, thenon-parametric measures revealed thatR12-N10D had the smallest changes in ranksand thus was the most stable genotype incontrast to R12-D24C, which was unstableand the lowest yielder. A comparison of thefive stability measures showed that thecoefficient of variability, stabilityvariance and variance of ranks were similarin assessing the relative stability of thegenotypes, whereas cultivars' superioritymeasure deviated from the others. Thestability variance and variance of rankswere significantly rank correlated, andwere the best in determining thecomparative stability of linseed genotypes.The coefficient of variability was alsorelatively better than the cultivar'ssuperiority measure. Further studies ofrepeatability tests are, however, needed todetermine the best methods. The stabilitystatistics generally identified R12-N10D,followed by Chilalo, as the most stablevarieties, whereas R12-D24C and R11-M20Gwere the least stable varieties.     

Stability of performance in lentil (Lens culinaris Medik) genotypes in Iran

The development of genotypes, which can be adapted to a wide range of diversified environment, is the ultimate goal of plant breeders in a crop improvement program. In this study, several stability methods were used to evaluate the genotype by environment interaction (GE) in 11 lentil (Lens culinaris Medik) genotypes. The genotypes were evaluated for grain yield at 4 different locations for 3 years in semi arid areas of Iran. The testing locations have different climatic and edaphic conditions providing the conditions necessary for the assessment of stability. A combined analysis of variance, stability statistics, rank correlations among stability statistics and yield stability statistic were determined. Significant differences were detected between genotypes and their GE interactions. Different univariate stability parameters were used to determine stability of the studied genotypes. The level of association among the parameters was assessed using Spearman's rank correlation. The different stability statistics which measured the different aspects of stability was substantiated by rank correlation coefficient. Rank-correlation coefficients between yield and some stability parameters were highly significant. Genotypes mean yield (Mean) was significantly correlated to the Lin and Binns stability parameter PI (r = 0.93^{* *}í) and desirability index Di (r = 0.89^{* *}í). A principal component analysis based on rank correlation matrix was performed for grouping the different stability parameters studied. In conclusion, based on most stability parameters, the genotypes G2, G5 and G9 were found to be the most stable. Results from rank correlation and principal component analysis showed that the stability variance (σ_i ²) was strongly correlated with Wricke's ecovalance, stability parameters of Plaisted and Peterson, and Plaisted.     

Yield stability and adaptability of canola (<Emphasis Type="Italic">Brassica napus</Emphasis> L.) in multiple environment trials

Selecting high yielding genotypes with stable performance is the breeders’ priority but is constrained by genotype × environment (G×E) interaction. We investigated canola yield of 35 genotypes and its stability in multiple environment trials (MET) in south-western Australia and the possibility to breed broadly-adapted high yielding genotypes. The Finlay–Wilkinson (F–W) regression and factor analytic (FA) model were used to investigate the G×E interaction, yield and genotype stability and adaptability. The cross-over response in the F–W regression, substantial genetic variance heterogeneity, and the genetic correlations in the FA model demonstrated substantial G×E interaction for yield. Cluster analysis suggests low, medium and high rainfall mega-environments. F–W regression indicated that genotypes with high stability (e.g. low regression slope values) produced relatively low yield and vice versa, but also identified broadly adapted genotypes with high intercepts and steep regression slopes. The FA model provided a more detailed analysis of performance, dividing genotypes by positive, flat or negative responses to environment. In general, early flowering genotypes responded negatively to favourable environments and vice versa for late flowering genotypes. More importantly, a few early flowering hybrids with long flowering phases were consistently productive in both low and high yielding environments, showing broad adaptability. These productive hybrids were consistent with those identified earlier by high F–W intercept and slope values. Hybrids were higher yielding and more stable than open-pollinated canola, as was Roundup-Ready^® canola compared to the three other herbicide tolerance groups (Clearfield^®, Triazine tolerant, conventional). We conclude that yield stability and high yield are not mutually exclusive and that breeding for broadly adapted high yielding canola is possible.     

Changes in allele frequencies in landraces, old and modern barley cultivars of marker loci close to QTL for grain yield under high and low input conditions

Changes in alleles frequencies of marker loci linked to yield quantitative trait loci (QTL) were studied in 188 barley entries (landraces, old and modern cultivars) grown in six trials representing low and high yielding conditions in Spain (2004) and Syria (2004, 2005). A genome wise association analysis was performed per trial, using 811 DArT^® markers of known map position. At the first stage of analysis, spatially adjusted genotypic means were created per trial by fitting mixed models. At the second stage, single QTL models were fitted with correction for population substructure, using regression models. Finally, multiple QTL models were constructed by backward selection from a regression model containing all significant markers from the single QTL analyses. In addition to the association analyses per trial, genotype by environment interaction was investigated across the six trials. Landraces seemed best adapted to low yielding environments, while old and modern entries adapted better to high yielding environments. The number of QTL and the magnitude of their effects were comparable for low and high input conditions. However, none of the QTL were found within a given bin at any chromosome in more than two of the six trials. Changes in allele frequencies of marker loci close to QTL for grain yield in landraces, old and modern barley cultivars could be attributed to selection exercised in breeding, suggesting that modern breeding may have increased frequencies of marker alleles close to QTL that favour production particularly under high yield potential environments. Moreover, these results also indicate that there may be scope for improving yield under low input systems, as breeding so far has hardly changed allele frequencies at marker loci close to QTL for low yielding conditions.     

Mapping QTL for stay-green and agronomic traits in wheat under diverse water regimes

Wheat (Triticum aestivum L.) yield is directly proportional to physio-morphological traits. A high-density genetic map consisting of 2575 markers was used for mapping QTL controlling stay-green and agronomic traits in wheat grown under four diverse water regimes. A total of 108 additive QTL were identified in target traits. Among them, 28 QTL for chlorophyll content (CC) were detected on 11 chromosomes, 43 for normalized difference vegetation index (NDVI) on all chromosomes except 5B, 5D, and 7D, five for spikes per plant (NSP) on different chromosomes, nine for plant height (PH) on four chromosomes, and 23 for thousand-kernel weight (TKW) on 11 chromosomes. Considering all traits, the phenotypic variation explained (PVE) ranged from 3.61 to 41.62%. A major QTL, QNDVI.cgb-5A.7, for NDVI with a maximum PVE of 20.21%, was located on chromosome 5A. A stable and major PH QTL was observed on chromosome 4D with a PVE close to 40%. Most distances between QTL and corresponding flanking markers were less than 1 cM, and approximately one-third of the QTL coincided with markers. Each of 16 QTL clusters on 10 chromosomes controlled more than one trait and therefore could be regarded as pleiotropic regions in response to different water regimes. Forty-one epistatic QTL were identified for all traits having PVE of 6.00 to 25.07%. Validated QTL closely linked to flanking markers will be beneficial for marker-assisted selection in improving drought-tolerance in wheat.     

Performance of cowpea varieties under <Emphasis Type="Italic">Striga gesnerioides</Emphasis> (Willd.) Vatke infestation using biplot analysis

Striga gesnerioides (Willd) Vatke, is a major destructive parasitic weed of cowpea (Vigna unguiculata (L.) Walp.) which causes substantial yield reduction in West and Central Africa. The presence of different virulent races within the parasite population contributes to significant genotype × environment interaction, and complicates breeding for durable resistance to Striga. A 3-year study was conducted at three locations in the dry savanna agro-ecology of Nigeria, where Striga gesnerioides is endemic. The primary objective of the study was to identify cowpea genotypes with high yield under Striga infestation and yield stability across test environments and to access suitability of the test environment. Data collected on grain yield and yield components were subjected to analysis of variance (ANOVA). Means from ANOVA were subjected to the genotype main effect plus genotype × environment (GGE) biplot analysis to examine the multi-environment trial data and rank genotypes according to the environments. Genotypes, environment, and genotypes × environment interaction mean squares were significant for grain yield and yield components, and number of emerged Striga plants. The environment accounted for 35.01%, whereas the genotype × environment interaction accounted for 9.10% of the variation in grain yield. The GGE biplot identified UAM09 1046-6-1 (V7), and UAM09 1046-6-2 (V8), as ideal genotypes suggesting that these genotypes performed relatively well in all study environments and could be regarded as adapted to a wide range of locations. Tilla was the most repeatable and ideal location for selecting widely adapted genotypes for resistance to S. gesnerioides.     

Molecular loci associated with seed isoflavone content may underlie resistance to soybean pod borer (Leguminivora glycinivorella)

Soybean pod borer (SPB) (Leguminivora glycinivorella (Mats.) Obraztsov) causes severe loss of soybean (Glycine max L. Merr.) seed yield and quality in some regions of the world, especially in north‐eastern China, Japan and Russia. Isoflavones in soybean seed play a crucial role in plant resistance to diseases and pests. The aim of this study was to find whether SPB resistance QTL are associated with soybean seed isoflavone content. A cross was made between ‘Zhongdou 27’ (higher isoflavone content) and ‘Jiunong 20’ (lower isoflavone content). One hundred and twelve F_5:10 recombinant inbred lines were derived through single‐seed descent. A plastic‐net cabinet was used to cover the plants in early August, and thirty SPB moths per square metre were put in to infest the soybean green pods. The results indicated that the percentage of seeds damaged by SPB was positively correlated with glycitein content (GC), whereas it was negatively correlated with genistein (GT), daidzein (DZ) and total isoflavone content (TI). Four QTL underlying SPB damage to seeds were identified and the phenotypic variation for SPB resistance explained by the four QTL ranged from 2% to 14% on chromosomes Gm7, 10, 13 and 17. Moreover, eleven QTL underlying isoflavone content were identified, and ten of them were encompassed within the same four marker intervals as the SPB QTL (BARC‐Satt208‐Sat292, Satt144‐Sat074, Satt540‐Sat244 and Satt345‐Satt592). These QTL could be useful in marker‐assisted selection for breeding soybean cultivars with both SPB resistance and high seed isoflavone content.     

Specific and General Adaptation and Yield Stability of Wheat (Triticum aestivum L.) Genotypes

Mean grain yield performance of 12 wheat and one triticale genotypes were measured at four locations over four consecutive years, using a randomized complete block design with four replications. The genotypes used were commercial cultivars and advanced lines from different wheat breeding projects located in different areas in Iran. Two locations were in semiarid regions and the other two locations in the temperate zones. The combined analysis of variance indicated highly significant genotype-environment (GE) interactions. From combinations of locations and years three sets of environments were generated. Set I and set II, each, consisted of eight environments (two locations and four years) representing semiarid and temperature environments, respectively. Set III consisted of 16 environments including both semiarid and temperate conditions. Set I and set II were used to measure specific adaptation of the genotypes while set III was employed for measuring general adaptation. The methods of Eberhart and Russell (1966) were used for partitioning GE interactions. The mean square associated with the heterogeneity of regression was highly significant under all sets of environments. These observations indicated that a major part of GE interaction could be accounted for by differences in the regression of the individual genotypes. All the genotypes had significant regression mean square under set I, set II, and set III environments, with the exception of two genotypes under set II. However, mean yields, regression coefficients, and the mean squares associated with deviation from regression greatly varied over the sets of environments. Only three genotypes, a commercial cultivar and two new advanced line, were identified as having specific adaptation and yield stability to semiarid environments. Among all the genotypes, only a commercial cultivar was identified as adapted and stable to temperate conditions. Two of the three genotypes which were adapted to semiarid environments also showed general adaptation to set III environments. However, the mean yield of these two genotypes under semiarid conditions (set I) were significantly greater than their respective mean yields under set III environments. Thus, wider adaptability was compensated by lower mean yield. The present study indicates that, while a wide range of environments is necessary and recommended for measuring general adaptation reactions and yield stability of various genotypes, one should not ignore the possibility of finding some genotypes with specific adaptation to specific environments and thus maximizing yield production. Stable genotypes with general of specific adaptation should be utilized in breeding projects in order to develop even more desirable lines.     

Development of linkage map and mapping of QTLs for oil content and yield attributes in linseed (<Emphasis Type="Italic">Linum usitatissimum</Emphasis> L.)

Linseed (Linum usitatissimum L.) is an important oilseed as well as stem fiber crop and rich source of omega-3 fatty acid. The present study aims to develop linkage map based on Indian genotypes and utilize it for mapping QTLs for important agronomic traits. Two diverse parental genotypes (KL-213 and RKY-14) of linseed showed wide range of variability for oil content and yield attributes. These parental genotypes also showed reasonable level of SSR polymorphism (~ 9.0%). The mapping population showed normal distribution of phenotypic traits. One hundred forty-six SSR markers were mapped on 15 linkage groups with marker density ranging from 3 to 18 markers per linkage group at average distance of 14.2 cM. A total of 11 QTLs were identified for six quantitative traits. Three QTLs for capsules/plant, 2 QTLs each for plant height, seeds/capsule and oil content and 1 QTL each for branches/plant and seed weight/plant were detected. Phenotypic variability explained by these QTLs varied from 1 to 15.23%. This study provides framework linkage map of linseed using Indian genotypes, which needs to be enriched further for future application in marker assisted breeding of linseed.     

High yielding spring bread wheat germplasm for global irrigated and rainfed production systems

Global wheat (Triticum aestivum L.) production must increase 2% annually until 2020 to meet future demands. Breeding wheat cultivars with increased grain yield potential, enhanced water-use efficiency, heat tolerance, end-use quality, and durable resistance to important diseases and pests can contribute to meet at least half of the desired production increases. The remaining half must come through better agronomic and soil management practices and incentive policies. Analyses of the recent International Yield Trials indicate that grain yields of the best new entries were usually 10% higher than the local checks globally, as well as within a country across sites. Variation in yield across sites within a country/region underline the role of genotype × environment (GE) interaction and provides opportunities to select for stable genotypes, which is not often done. The lack of proper analysis undermines proper utilization of germplasm with high yield potential and stability in the national wheat breeding programs. Some of the best performers in irrigated areas were amongst the best in semiarid environments, reinforcing the fact that high yield potential and drought tolerance can be improved simultaneously. The best performing lines often had genotypic base of widely adapted genotypes Kauz, Attila, Baviacora, and Pastor, with genetic contributions from other parents including synthetic wheat. We recommend within country multilocation analysis of trial performance for a crop season to identify lines suiting particular or different locations within a country. The immediate feedback on GE interaction will also help in breeding lines for countries having substantial variation across locations and years.     

基于环境互作的加性-显性-加加上位性模型的多环境指数选择(英文)

适应性和稳定性是优良品种所必须具备的基本条件。在多个环境下对多个性状进行选择一直是一个悬而未决的难题。基于加性-显性-加加互作以及它们和环境互作的遗传模型,本文提出了两类选择指数:普通选择指数和考虑基因型与环境互作的选择指数(环境互作选择指数)。其中普通选择指数可用于具有广泛适应性品种的选择,环境互作选择指数则可用于具有特定环境适应性的基因型的选择。对于自花授粉作物,本文提出了两类育种值,即普通育种值和基因型与环境互作育种值。其中,普通育种值包括上位性效应和加加上位性效应,基因型与环境互作育种值包括加性、加加上位性与环境的互作效应。应用混合线性模型估算选择指数构建中涉及的方差-协方差分量。以一组陆地棉双列杂交设计试验作为实例,演示了所提出的选择指数的构建过程。本文提出的指数选择方法可望为多环境下多个性状同步选择提供一条有效的途径。     

Identification of QTLs for agronomic traits using association mapping in lentil

A diverse panel of 96 genotypes of lentil was used in this study to identify QTL for nine agronomic traits through marker-trait association analysis. This study showed significant genetic variability among the lentil genotypes for nine agronomic traits and had medium to large broad sense heritability estimates (h² =?0.58–0.95). Screening of 534 SSR markers resulted in 266 polymorphic loci that generated 697 alleles ranging from 2 to 16 alleles per locus across the genotypes. The model-based population structure analysis identified two distinct subpopulations among lentil genotypes and each subpopulation did not show any admixture. Marker trait association (MTA) analysis following ML model resulted in the identification of 24 MTAs for nine traits at P?<?0.01. The per cent of phenotypic variation explained by each associated marker with particular agronomic trait ranged from 7.3 to 25.8%. The highest proportion of total phenotypic variation (23.1–25.8%) was explained by the QTLs controlling the primary branches/per plant. In the present study, few EST-SSR markers showed significant association with days to maturity, pods/plant, secondary branches/plant, 100 seed weight, yield/plant and reproductive duration and explained large phenotypic variation (7.3–23.8%). Hence, these markers can be used as functional markers in lentil breeding program for developing improved cultivars.     

Identification of QTL underlying the resistance of soybean to pod borer, <Emphasis Type="Italic">Leguminivora glycinivorella</Emphasis> (Mats.) obraztsov,and correlations with plant,pod and seed traits

Soybean (Glycine max L. Merr.) pod borer (Leguminivora glycinivorella (Mats.) Obraztsov) (SPB) results in severe loss in soybean yield and quality in certain regions of the world, especially in Northeastern China, Japan and Russia. The aim here was to evaluate the inheritance of pod borer resistance and to identify quantitative trait loci (QTL) underlying SPB resistance for the acceleration of the control of this pest. Used were the 129 recombinant inbred lines (RILs) of the F_5:6 derived population from ‘Dong Nong 1068’ × ‘Dong Nong 8004’ and 131 SSR markers. Correlations between the percentage of damaged seeds (PDS) by pod borer and plant, pod and seed traits that were potentially related to SPB resistance were analyzed. The results showed highly significant correlations between PDS by pod borer and plant height (PH), maturity date (MA), pod color (PC), pubescence density (PB), 100-seed weight (SW) and protein content existed. Soybeans with dwarf stem, light color of pod coat, small seeds, lower density of pubescence, early maturity and low content of protein seemed to have higher resistance to SPB. The correlated traits had potential to inhibit egg deposition and thereby to decrease the damage by SPB. Three QTL directly associated with the resistance to SPB judged by PDS at harvest were identified. qRspb-1 (Satt541–Satt253) and qRspb-2 (Satt253–Satt314) were both on linkage group (LG) H and qRspb-3 (Satt288–Satt199) on LG G. The three QTL explained 10.96, 9.73 and 11.59% of the phenotypic variation for PDS, respectively. In addition, 12 QTL that underlay 10 of 13 traits potentially related with SPB resistance were found. These QTL detected jointly provide potential for marker assisted selection to improve cultivar resistance to SPB. Guiyun Zhao, Jian Wang, and Yingpeng Han have equal contribution to the paper.     

Genotype-environment interactions and phenotypic stability analyses of linseed in Ethiopia

Seed yield of 10 linseed genotypes, tested in a randomized block design with four replications across 18 environments of Ethiopia was analysed using different stability models. The objectives were to assess genotype‐environment (G‐E) interactions, determine stable genotypes, and to compare the stability parameters. Year by location and location variability were the dominant source of interactions. The stability analyses identified ‘R12‐N10D’, ‘Chilalo’ and ‘P13611’ב10314D’ as more stable genotypes, while ‘R11‐N1266’, ‘R10‐N27G’ and ‘R12‐D24C’ were specifically adapted to some environments. The highly significant rank correlations found among the deviations from regression, additive main effects multiplicative interaction, stability values, coefficients of determination, and stability variances indicated their close similarity and effectiveness in detecting stable genotypes over a range of Ethiopian environments.     

Non-parametric measures of phenotypic stability in chickpea genotypes (<Emphasis Type="Italic">Cicer arietinum</Emphasis> L.)

Multi-environment trials (MET) play an important role in selecting the best cultivars and/or agronomic practices to be used in future years at different locations by assessing a cultivar's stability across environments before its commercial release. Objective of this study is to identify chickpea (Cicer arietinum L.) genotypes that have high yield and stable performance across different locations. The genotypes were developed by various breeders at different research institutes/stations of Iran and the International Center for Agricultural Research in Dray Areas (ICARDA), Syria. Several statistical methods were used to evaluate phenotypic stability of these test chickpea genotypes. The 17 genotypes were tested at six different research stations for two years in Iran. Three non-parametric statistical test of significance for genotype × environment (GE) interaction and ten nonparametric measures of stability analyses were used to identify stable genotypes across the 16 environments. The non-parametric measures (Kubinger, Hildebrand and Kroon/Van der) for G × E interaction were highly significant (P < 0.01), suggesting differential responses of the genotypes to the test environments. Based on high values of nonparametric superiority measure (Fox et al. 1990) and low values of Kang's (1988) rank-sum stability parameters, Flip 94-123C was identified as the most stable genotype. These non parametric parameters were observed to be associated with high mean yield. However, the other nonparametric methods were not positively correlated with mean yield and were therefore not used in classifying the genotypes. Simple correlation coefficients using Spearman’s rank correlation, calculated using ranks was used to measure the relationship between the stability parameters. To understand the nature of relationships among the nonparametric methods, a hierarchical cluster analysis based on non weighted values of genotypes, was performed. The 10 stability parameters fell into three groups.     

水稻种子衰老相关基因定位

利用ZS97×MH63组合衍生的160份重组自交系进行种子衰老遗传分析。每份自交系3批种子,即在武汉室温条件下存放3年的自然衰老种子,收获3个月后的种子,收获3个月后再经过高温、高湿加快衰老处理的种子,供25℃条件下考查发芽率和发芽势来衡量种子衰老状况。复合区间作图方法进行了2批衰老种子的发芽势与发芽率QTL定位。自然     

Mixed models including environmental covariables for studying QTL by environment interaction

The study of the phenotypic responses of a set of genotypes in their dependence on the environment has always been an important area of research in plant breeding. Non-parallelism of those responses is called genotype by environment interaction (GEI). GEI especially affects plant breeding strategies, when the phenotypic superiority of genotypes changes in relation to the environment. The study of the genetic basis of GEI involves the modelling of quantitative trait locus (QTL) expression in its dependence on environmental factors. We present a modelling framework for studying the interaction between QTL and environment, using regression models in a mixed model context. We integrate regression models for QTL main effect expression with factorial regression models for genotype by environment interaction, and, in addition, take care to model adequately the residual genetic variation. Factorial regression models describe GEI as differential genotypic sensitivity to one or more environmental covariables. We show how factorial regression models can be generalized to make also QTL expression dependent on environmental covariables. As an illustrative example, we reanalyzed yield data from the North American Barley Genome Project. QTL by environment interaction for yield, as identified at the 2H chromosome could be described as QTL expression in relation to the magnitude of the temperature range during heading. This revised version was published online in July 2006 with corrections to the Cover Date.