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.     

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。     

Evaluation of genomic approaches for marker‐based improvement of lodging tolerance in triticale

Lodging tolerance is an important agronomic trait as it can have a severe negative impact on grain yield and quality. Here, we used a large mapping population of 647 doubled haploid triticale lines derived from four families to dissect the genetic architecture underlying lodging tolerance and to assess different approaches for a genomics‐based improvement of the trait. The plants were evaluated for lodging in two environments and genotyped with 1710 genomewide DArT markers. We observed a large genotypic variation for lodging and transgressive segregation in all families. Employing two complementary QTL mapping approaches, we identified both main effect and epistatic QTL. Using cross‐validation, we showed that the proportion of genotypic variance explained by the detected QTL is low, thus limiting the efficiency of marker‐assisted selection to improve this trait. By contrast, the cross‐validated predictive ability of genomic prediction was approximately twice as high as that of the QTL‐based selection approaches. In conclusion, our results show that lodging tolerance is a complex trait that can be improved by classical breeding but also assisted by marker‐based approaches.     

Combining ability and heterosis under pest epidemics in a broad-based global wheat-breeding population

Wheat breeders rarely apply population improvement schemes or select parental sources according to combining ability and heterotic patterns. They rely on pedigree selection methods for breeding new cultivars. This experiment was undertaken to assess the advantages of using diallel crosses to define combining ability and understand heterosis in a broad‐based wheat‐breeding population across different environments affected by yellow rust. Sixty‐four genotypes derived from a full diallel mating scheme were assessed for grain yield in two contrasting growing seasons at two locations for two consecutive years. Parental genotypes showed significant combining ability for grain yield that was affected by yellow rust and genotype‐by‐environment (GE) interactions, both of which affected heterosis for grain yield. Significant GE interactions suggested that decentralized selection for specific environments could maximize the use of this wheat germplasm. Cultivar effects and specific heterosis were the most important factors influencing grain yield. Some crosses capitalized on additive genetic variation for grain yield. This research shows the power of available quantitative breeding tools to help breeders choose parental sources in a population improvement programme.     

Detection of additive and additive×environment interaction effects of QTLs for yield‐component traits of rice using single‐segment substitution lines (SSSLs)

Additive effects (A) and additive‐by‐environment interactions (A×E) for five rice yield components were analysed using 20 SSSLs under mixed linear model methodology. Thirty‐one QTLs were detected. Different yield components have different QTL‐by‐environment (Q×E) interaction patterns. No A×E interaction effects were detected for the four QTLs for panicle number (PN). Four QTLs detected for spikelets per panicle (SPP) had A×E interactions. Five of seven QTLs detected for grains per panicle (GPP), two of 10 QTLs detected for 1000‐grains weight (GWT) and three of six QTLs detected for seed set ratio (SSR) showed significant A×E interaction. Most of these QTLs were distributed in clusters across the genome. The complexity of linkage and pleiotropy of these QTLs plus environmental effect may result in the diversity of the yield phenotype in the SSSLs. Only S19 exhibited a significant increase in yield with a predicted gain by 281.58 kg ha^?1. The results may be useful to design a better breeding strategy that takes advantage of QTL‐by‐environment interaction effects in each of the SSSLs.     

Crop breeding for salt tolerance in the era of molecular markers and marker‐assisted selection

Crop salt tolerance (ST) is a complex trait affected by numerous genetic and non‐genetic factors, and its improvement via conventional breeding has been slow. Recent advancements in biotechnology have led to the development of more efficient selection tools to substitute phenotype‐based selection systems. Molecular markers associated with genes or quantitative trait loci (QTLs) affecting important traits are identified, which could be used as indirect selection criteria to improve breeding efficiency via marker‐assisted selection (MAS). While the use of MAS for manipulating simple traits has been streamlined in many plant breeding programmes, MAS for improving complex traits seems to be at infancy stage. Numerous QTLs have been reported for ST in different crop species; however, few commercial cultivars or breeding lines with improved ST have been developed via MAS. We review genes and QTLs identified with positive effects on ST in different plant species and discuss the prospects for developing crop ST via MAS. With the current advances in marker technology and a better handling of genotype by environment interaction effects, the utility of MAS for breeding for ST will gain momentum.     

The potential of genomic‐assisted breeding to improve Fusarium head blight resistance in winter durum wheat

Durum wheat is the most important tetraploid wheat mainly used for semolina and pasta production, but is notorious for its high susceptibility to Fusarium head blight (FHB). Our objectives were to identify and characterize quantitative trait loci (QTL) in winter durum and to evaluate the potential of genomic approaches for the improvement of FHB resistance. Here, we employed an international panel of 170 winter and 14 spring durum lines, phenotyped for Fusarium culmorum resistance at five environments. Heading date, plant height and mean FHB severity showed significant genotypic variation with high heritabilities and FHB resistance was negatively correlated with both heading date and plant height. The dwarfing gene Rht‐B1 significantly affected FHB resistance and the genome‐wide association scan identified eight additional QTL affecting FHB resistance, explaining between 1% and 14% of the genotypic variation. A genome‐wide prediction approach yielded only a slightly improved predictive ability compared to marker‐assisted selection based on the four strongest QTL. In conclusion, FHB resistance in durum wheat is a highly quantitative trait and in breeding programmes may best be tackled by classical high‐throughput recurrent phenotypic selection that can be assisted by genomic prediction if marker profiles are available.     

Genotype × environment interaction and yield stability in a cowpea-based cropping system

Twenty-four cowpea genotypes were evaluated under sole cropping or additive series intercropping with sorghum from 2004 to 2005 at four sites representative of the Guinea and Sudan savannah ecologies in Ghana. The aim was to determine whether cowpea breeding programs that emphasize selection under sole-crop conditions have the potential to produce cultivars that are effective under additive series intercropping. Genotype × cropping systems interaction was significant for days to 50% flowering but not for grain yield, biomass and other studied traits. Genotypic yield reaction to cropping systems indicated that bridging the yield gap between sole cropping and intercropping systems is best addressed by agronomic interventions that reduce stress on intercrop cowpea rather than by selecting for specifically adapted genotypes for intercropping. Significant genotype × environment interactions were observed for all traits when data was pooled over cropping systems. Partitioning of the genotype × environment interaction variance indicated that days to 50% flowering was dominated by heterogeneity of genotypic variance, whereas genotype × environment interactions for grain yield and biomass was mainly due to imperfect correlations. Large differences in genotypic yield stability were observed as estimated by the among-environment variance, regression of yield on the environmental index, Kataoka’s index, and by partitioning of genotype × environment interaction sum of squares into components attributable to each genotype. The results suggest that in regions where genotype × environment interaction for yield frequently causes re-ranking across environments, genotypes with the least contribution to the interaction sum of squares are likely to be most productive. On the whole, the results support the contention that breeding under sole-crop conditions has the potential to produce cultivars effective under intercropping conditions.     

Response of Mediterranean Tall Fescue Cultivars to Contrasting Agricultural Environments and Implications for Selection

Extensive livestock is a basic socio‐economic feature of the Mediterranean region whose environmental and economic sustainability depends on the ability of forage resources to withstand climatically stressful conditions. Perennial forages such as tall fescue can be a valuable alternative to annuals, if they can survive across successive summer droughts. Three‐year dry matter yield and plant survival of five cultivars of Mediterranean‐type tall fescue were evaluated in six sites of Algeria, France, Italy, Morocco and Portugal, with the following objectives: (i) modelling adaptive responses and targeting cultivars as a function of environmental factors associated with genotype × location interaction; and (ii) defining plant ideotypes, adaptation strategies and opportunities for international co‐operation for regional breeding programmes. Site mean yield and winter temperatures were positively correlated, whereas sward persistence was positively correlated to lower site heat and drought stress. Cultivar adaptation was adequately modelled by factorial regression as a function of site spring–summer (April–September) drought stress (long‐term potential evapotranspiration minus actual water available) for yield, and annual drought stress for final persistence. Specific‐adaptation responses to high‐ or low‐stress environments emerged which were consistent with drought‐stress levels of cultivar selection environments. However, the wide‐adaptation response of cultivar Flecha suggested that breeding for wide adaptation can be feasible.     

Genotype by environment interaction components underlying variations in root,sugar and white sugar yield in sugar beet (<Emphasis Type="Italic">Beta vulgaris</Emphasis> L.)

The success of plant breeding programs depends on the ability to provide farmers with genotypes with guaranteed superior performance in terms of yield across a range of environmental conditions. We evaluated 49 sugar beet genotypes in four different geographical locations in 2 years aiming to identify stable genotypes with respect to root, sugar and white sugar yields, and to determine discriminating ability of environments for genotype selection and introduce representative environments for yield comparison trials. Combinations of year and location were considered as environment. Statistical analyses including additive main effects and multiplicative interactions (AMMI), genotype main effects and genotype?×?environment interaction effects (GGE) models and AMMI stability value (ASV) were used to dissect genotype by environment interactions (GEI). Based on raw data, root, sugar and white sugar yields varied from 0.95 to 104.86, 0.15 to 20.81, and 0.09 to 18.45 t/ha across environments, respectively. Based on F-Gollob validation test, three interaction principal components (IPC) were significant for each trait in the AMMI model whereas according to F ratio (F_R) test two significant IPCs were identified for root yield and sugar yield and three for white sugar yield. For model diagnosis, the actual root mean square predictive differences (RMS PD) were estimated based upon 1000 validations and the AMMI-1 model with the smallest RMS PD was identified as the most accurate model with highest predictive accuracy for the three traits. In the GGE biplot model, the first two IPCs accounted for 60.52, 62.9 and 64.69% of the GEI variation for root yield, sugar yield and white sugar yield, respectively. According to the AMMI-1 model, two mega-environments were delineated for root yield and three for sugar yield and white sugar yield. The mega-environments identified had an evident ecological gradient from long growing season to intermediate or short growing season. Environment-focused scaling GGE biplots indicated that two locations (Ekbatan and Zarghan) were the most representative testing environments with discriminating ability for the three traits tested. Environmentally stable genotypes (i.e. G21, G28 and G29) shared common parental lines in their pedigree having resistance to some sugar beet diseases (i.e. rhizomania and cyst nematodes). The results of the AMMI model were partly in accord with the results of GGE biplot analysis with respect to mega-environment delineation and winner genotypes. The outcome of this study may assist breeders to save time and costs to identify representative and discriminating environments for root and sugar yield test trials and creates a corner stone for an accelerated genotype selection to be used in sweet-based programs.     

Assessment of genotype × environment interactions for yield and fiber quality in cotton performance trials

Plant breeding programs involving a wide range of crop plants routinely practice selection (directly or indirectly) for genotypes that display stability for a given trait or set of traits across testing environments through the genotype evaluation process. Genotype stability for trait performance is a direct measure of the presence and effect of genotype × environment interactions, which result from the differential performance of a genotype or cultivar across environments. The genotype evaluation process also requires selection of the proper field trial locations that best represent the target environments the breeding program is directed toward. In this study, we assessed the extent to which genotype × environment interactions affected agronomic performance (lint yield, gin turnout) and fiber quality (fiber length, fiber strength, uniformity index, micronaire, fiber elongation) in a series of cotton (Gossypium hirsutum) performance trials in 12 location–year environments in South Carolina. Genotype × environment interactions affecting lint yield were larger in higher yielding environments, while interactions for fiber strength were greater for genotypes with lower mean fiber strength values. Two regions within the South Carolina cotton production areas were identified as proper testing locations for lint yield performance, while testing for fiber strength can be accomplished in any location within the statewide cotton production areas. The U.S. Government's right to retain a non-exclusive, royalty-free license in and to any copyright is acknowledged.     

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.     

Screening oat germplasm for better adaptation to cold stress in the Southern Great Plains of the United States

Oat (Avena sativa L.) is one of the most important forage crops in the Southern Great Plains of the United States. However, it is more sensitive to cold stress than other small grains. In this study, diverse oat germplasm was evaluated for winter survival across multiple years and locations in the region. Field screening started with an observation trial of 1,861 diverse genotypes in the 2012–2013 season and was followed by four seasons of replicated trials from 2013 to 2017. Selection of good winter survivors was started in 2014–2015 season. All trials were laid out in randomized complete blocks with replications of two in 2013–2014 and 2014–2015, four in 2015–2016, and three in 2016–2017. Winter survival was scored in a 1‐to‐9 scale. Data were analysed for each year and location separately. Additive main effects and multiplicative interaction (AMMI) analysis were carried out on combined data of 35 genotypes that were commonly grown in each year and location. Highly significant (p < 0.001) variations were observed among genotypes, environments and genotype‐by‐environment interaction (GEI). The first three interaction principal components (IPCs) were highly significant (p < 0.001), explaining 96% of GEI. Broad sense heritability ranged from 46% to 93%, while heritability for all environments combined was relatively low (24.6%). At the end of the two cycles (2014/2015‐to‐2016/2017) of selection, mean winter survival was improved by more than 38% per cycle compared with the base population mean. Genotypes CIav 4390, CIav 6909 and CIav 7618 showed significantly higher winter survival than the standard checks Okay and Dallas. Genotypes CIav 4390 showed 20% and 35% improvement over the standard checks Okay and Dallas, respectively. Winter survival improvement in oat will remain a difficult task because of high GEI and low heritability. The identified superior genotypes will be used as crossing parents to transfer cold tolerance genes to other elite lines.     

Choice of shrinkage parameter and prediction of genomic breeding values in elite maize breeding populations

Genomic selection (GS) is a promising alternative to marker‐assisted selection particularly for quantitative traits. In this study, we examined the prediction accuracy of genomic breeding values by using ridge regression best linear unbiased prediction in combination with fivefold cross‐validation based on empirical data of a commercial maize breeding programme. The empirical data is composed of 930 testcross progenies derived from 11 segregating families evaluated at six environments for grain yield and grain moisture. Accuracy to predict genomic breeding values was affected by the choice of the shrinkage parameter λ², by unbalanced family size, by size of the training population and to a lower extent by the number of markers. Accuracy of genomic breeding values was high suggesting that the selection gain can be improved implementing GS in elite maize breeding programmes.     

A genome‐wide association study for partial resistance to southern corn rust in tropical maize

Southern corn rust (SCR) is a fungal disease found on corn in several countries worldwide. In Brazil, the disease can result in productivity losses of 65%, especially in areas with a history of the disease. In this study, the genetic architecture and identification of genomic regions associated with SCR resistance was investigated by performing a genome‐wide association study. Genotyping‐by‐sequencing was performed to carry out the association between single nucleotide polymorphism (SNP) markers and phenotypic data from two environments on a panel of 164 maize inbred lines. Eight SNPs were identified as significant for SCR resistance. These SNPs were colocalized with QTL regions, some of which underlie candidate resistance genes with functions that play an important role in the stress response during pathogen recognition. These candidate genes, involved in plant defense pathways, could be associated with partial resistance to SCR and provide a partial comprehensive insight into the genetic architecture of this trait. After validation of the SNPs, they will be useful for marker–assisted selection and for a better understanding of maize resistance to SCR.     

GGE叠图法─分析品种×环境互作模式的理想方法

本文介绍一种分析作物区域试验结果的方法－ＧＧＥ叠图法。首先，将原始产量数据减去各地 点的平均产量，由此形成的数据集只含品种主效应Ｇ和品种－环境互作效应ＧＥ，合称为ＧＧＥ。对ＧＧＥ 作单值分解，并以第一和第二主成分近似之。按照第一和第二主成分值将各品种和各地点放到一个平 面图上即形成ＧＧＥ叠图。借助于辅助线，可以直观回答以下问题：（１）什么是某一特定环境下最好的 品种；（２）什么是某一特定品种最适合的环境；（３）任意两品种在各环境下的表现如何；（４）试验中品 种×环境互作的总体模式是怎样的；（５）什么是高产、稳产品种；（６）什么是有利于筛选高产、稳产品 种的环境。     

Detection of epistatic and environmental interaction QTLs for leaf orientation‐related traits in maize

Leaf architecture traits in maize are quantitative and have been studied by quantitative trait loci (QTLs) mapping. However, additional QTLs for these traits require mapping and the interactions between mapped QTLs require studying because of the complicated genetic nature of these traits. To detect common QTLs and to find new ones, we investigated the maize traits of leaf angle, leaf flagging‐point length, leaf length and leaf orientation value using a set of recombinant inbred line populations and single nucleotide polymorphism markers. In total, 19 QTLs contributed 4.13–13.52% of the phenotypic effects to the corresponding traits that were mapped, and their candidate genes are provided. Common and major QTLs have also been detected. All of the QTLs showed significant additive effects and non‐significant additive × environment effects in combined environments. The majority showed additive × additive epistasis effects and non‐significant QTL × environment effects under single environments. Common and major QTLs provided information for fine mapping and gene cloning, and SNP markers can be used for marker‐assisted selection breeding.     

Contribution of Weather Variables to Genotype X Environment Interaction in Grain Sorghum1

Genotype × environment (GE) interactions are a major problem in plant breeding programs that involve testing in diverse environments. These interactions can reduce progress from selection. Few studies have characterized the effects of weather variables on GE interactions in sorghum (Sorghum bicolor [L.] Moench). The present investigation estimated the contribution of environmental index, (?, or mean yield of all cultivars in jth environment minus ?. xor overall mean yield for all cultivars and all environments), rainfall, minimum and maximum temperature, and relative humidity, to GE interaction. Yield means of 5 full-season and 10 medium-season grain sorghum hybrids grown during 1986—1988 at four locations were used in the study. The GE interaction was significant and partitioned into σ²_i, components assignable to each genotype. Weather variables (covariates) were used to remove heterogeneity from the GE interaction. The remainder of the GE interaction variance was partitioned into variance components (s²_i) assignable to each genotype. In both maturity groups, the environmental index removed most, although non-significant, heterogeneity from the GE interaction sums of squares. Of all weather variables, preseason and seasonal rainfall contributed most to the GE interaction sums of squares.     

Contributions to the Analysis of Genotype × Environment Interactions: Theoretical Results of the Application and Comparison of Clustering Techniques for the Stratification of Field Test Sites

For an analysis of cross-classified data sets with rows = genotypes and columns = environments (locations and/or years), existing genotype × environment interactions are of major importance. Differential responses of genotypes (environments) across environments (genotypes) are expressed by these effects. To reduce the impact of these genotype × environment interaction effects, one commonly stratifies genotypes or environments by cluster analysis techniques into homogeneous groups so that interactions within groups are minimized. The present paper presents a comprehensive overview of the numerous procedures for stratification of genotypes or environments by cluster analysis which have been proposed in the literature. In these studies, two different concepts of interaction have been used: the crossover concept of interaction [different rank orders of genotypes (environments) within environments (genotypes)] or the usual statistical concept of interaction (deviations from additivity of main effects in the linear model). For a quantitative characterization of cluster techniques and for a comparison of two different clustering procedures, two parameters are introduced and discussed: measure of resemblance for two classifications and cluster size for one classification.     

Barley adaptation and improvement in the Mediterranean basin

To study barley adaptation and improvement in the Mediterranean basin, a collection of 188 entries comprising landraces and old genotypes and current modern varieties from the Mediterranean basin and elsewhere was tested on moisture‐contrasted environments in seven Mediterranean countries, during 2004 and 2005 harvest seasons. The experimental design consisted of an unreplicated trial for all entries, augmented by four repeated checks to which a partial replicate containing a quarter of the entries was added. Best Linear Unbiased Predictions (BLUPs) representing adjusted genotypic means were generated for individual trials using a mixed model. BLUPs were used for genotype by environment interaction analysis using main effect plus genotype by environment interaction (GGE) biplots of yield ranked data and for comparisons of landraces, old and modern genotypes using analysis of variance. Mean yields ranged from near crop failure to 6 t/ha. Local landraces were better adapted to environments yielding below 2 t/ha, thus breeding has mostly benefited environments yielding above 2 t/ha where modern genotypes out yielded landraces and old cultivars by 15%. Current barley selection is leading to specifically adapted genotypes.