Mapping QTLs and QTL × environment interaction for CIMMYT maize drought stress program using factorial regression and partial least squares methods

The study of QTL × environment interaction (QEI) is important for understanding genotype × environment interaction (GEI) in many quantitative traits. For modeling GEI and QEI, factorial regression (FR) models form a powerful class of models. In FR models, covariables (contrasts) defined on the levels of the genotypic and/or environmental factor(s) are used to describe main effects and interactions. In FR models for QTL expression, considerable numbers of genotypic covariables can occur as for each putative QTL an additional covariable needs to be introduced. For large numbers of genotypic and/or environmental covariables, least square estimation breaks down and partial least squares (PLS) estimation procedures become an attractive alternative. In this paper we develop methodology for analyzing QEI by FR for estimating effects and locations of QTLs and QEI and interpreting QEI in terms of environmental variables. A randomization test for the main effects of QTLs and QEI is presented. A population of F₂ derived F₃ families was evaluated in eight environments differing in drought stress and soil nitrogen content and the traits yield and anthesis silking interval (ASI) were measured. For grain yield, chromosomes 1 and 10 showed significant QEI, whereas in chromosomes 3 and 8 only main effect QTLs were observed. For ASI, QTL main effects were observed on chromosomes 1, 2, 6, 8, and 10, whereas QEI was observed only on chromosome 8. The assessment of the QEI at chromosome 1 for grain yield showed that the QTL main effect explained 35.8% of the QTL + QEI variability, while QEI explained 64.2%. Minimum temperature during flowering time explained 77.6% of the QEI. The QEI analysis at chromosome 10 showed that the QTL main effect explained 59.8% of the QTL + QEI variability, while QEI explained 40.2%. Maximum temperature during flowering time explained 23.8% of the QEI. Results of this study show the possibilities of using FR for mapping QTL and for dissecting QEI in terms of environmental variables. PLS regression is efficient in accounting for background noise produced by other QTLs.     

Multi-environment QTL mixed models for drought stress adaptation in wheat

Many quantitative trait loci (QTL) detection methods ignore QTL-by-environment interaction (QEI) and are limited in accommodation of error and environment-specific variance. This paper outlines a mixed model approach using a recombinant inbred spring wheat population grown in six drought stress trials. Genotype estimates for yield, anthesis date and height were calculated using the best design and spatial effects model for each trial. Parsimonious factor analytic models best captured the variance–covariance structure, including genetic correlations, among environments. The 1RS.1BL rye chromosome translocation (from one parent) which decreased progeny yield by 13.8 g m⁻² was explicitly included in the QTL model. Simple interval mapping (SIM) was used in a genome-wide scan for significant QTL, where QTL effects were fitted as fixed environment-specific effects. All significant environment-specific QTL were subsequently included in a multi-QTL model and evaluated for main and QEI effects with non-significant QEI effects being dropped. QTL effects (either consistent or environment-specific) included eight yield, four anthesis, and six height QTL. One yield QTL co-located (or was linked) to an anthesis QTL, while another co-located with a height QTL. In the final multi-QTL model, only one QTL for yield (6 g m⁻²) was consistent across environments (no QEI), while the remaining QTL had significant QEI effects (average size per environment of 5.1 g m⁻²). Compared to single trial analyses, the described framework allowed explicit modelling and detection of QEI effects and incorporation of additional classification information about genotypes. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Unifying Genetic Canalization,Genetic Constraint,and Genotype-by-Environment Interaction: QTL by Genomic Background by Environment Interaction of Flowering Time in Boechera stricta

Natural populations exhibit substantial variation in quantitative traits. A quantitative trait is typically defined by its mean and variance, and to date most genetic mapping studies focus on loci altering trait means but not (co)variances. For single traits, the control of trait variance across genetic backgrounds is referred to as genetic canalization. With multiple traits, the genetic covariance among different traits in the same environment indicates the magnitude of potential genetic constraint, while genotype-by-environment interaction (GxE) concerns the same trait across different environments. While some have suggested that these three attributes of quantitative traits are different views of similar concepts, it is not yet clear, however, whether they have the same underlying genetic mechanism. Here, we detect quantitative trait loci (QTL) influencing the (co)variance of phenological traits in six distinct environments in Boechera stricta, a close relative of Arabidopsis. We identified nFT as the QTL altering the magnitude of phenological trait canalization, genetic constraint, and GxE. Both the magnitude and direction of nFT''s canalization effects depend on the environment, and to our knowledge, this reversibility of canalization across environments has not been reported previously. nFT''s effects on trait covariance structure (genetic constraint and GxE) likely result from the variable and reversible canalization effects across different traits and environments, which can be explained by the interaction among nFT, genomic backgrounds, and environmental stimuli. This view is supported by experiments demonstrating significant nFT by genomic background epistatic interactions affecting phenological traits and expression of the candidate gene, FT. In contrast to the well-known canalization gene Hsp90, the case of nFT may exemplify an alternative mechanism: Our results suggest that (at least in traits with major signal integrators such as flowering time) genetic canalization, genetic constraint, and GxE may have related genetic mechanisms resulting from interactions among major QTL, genomic backgrounds, and environments.     

Gene-environment interactions in complex diseases: genetic models and methods for QTL mapping in multiple half-sib populations

An interval quantitative trait locus (QTL) mapping method for complex polygenic diseases (as binary traits) showing QTL by environment interactions (QEI) was developed for outbred populations on a within-family basis. The main objectives, within the above context, were to investigate selection of genetic models and to compare liability or generalized interval mapping (GIM) and linear regression interval mapping (RIM) methods. Two different genetic models were used: one with main QTL and QEI effects (QEI model) and the other with only a main QTL effect (QTL model). Over 30 types of binary disease data as well as six types of continuous data were simulated and analysed by RIM and GIM. Using table values for significance testing, results show that RIM had an increased false detection rate (FDR) for testing interactions which was attributable to scale effects on the binary scale. GIM did not suffer from a high FDR for testing interactions. The use of empirical thresholds, which effectively means higher thresholds for RIM for testing interactions, could repair this increased FDR for RIM, but such empirical thresholds would have to be derived for each case because the amount of FDR depends on the incidence on the binary scale. RIM still suffered from higher biases (15-100% over- or under-estimation of true values) and high standard errors in QTL variance and location estimates than GIM for QEI models. Hence GIM is recommended for disease QTL mapping with QEI. In the presence of QEI, the model including QEI has more power (20-80% increase) to detect the QTL when the average QTL effect is small (in a situation where the model with a main QTL only is not too powerful). Top-down model selection is proposed in which a full test for QEI is conducted first and then the model is subsequently simplified. Methods and results will be applicable to human, plant and animal QTL mapping experiments.     

A statistical model for testing the pleiotropic control of phenotypic plasticity for a count trait

The differences of a phenotypic trait produced by a genotype in response to changes in the environment are referred to as phenotypic plasticity. Despite its importance in the maintenance of genetic diversity via genotype-by-environment interactions, little is known about the detailed genetic architecture of this phenomenon, thus limiting our ability to predict the pattern and process of microevolutionary responses to changing environments. In this article, we develop a statistical model for mapping quantitative trait loci (QTL) that control the phenotypic plasticity of a complex trait through differentiated expressions of pleiotropic QTL in different environments. In particular, our model focuses on count traits that represent an important aspect of biological systems, controlled by a network of multiple genes and environmental factors. The model was derived within a multivariate mixture model framework in which QTL genotype-specific mixture components are modeled by a multivariate Poisson distribution for a count trait expressed in multiple clonal replicates. A two-stage hierarchic EM algorithm is implemented to obtain the maximum-likelihood estimates of the Poisson parameters that specify environment-specific genetic effects of a QTL and residual errors. By approximating the number of sylleptic branches on the main stems of poplar hybrids by a Poisson distribution, the new model was applied to map QTL that contribute to the phenotypic plasticity of a count trait. The statistical behavior of the model and its utilization were investigated through simulation studies that mimic the poplar example used. This model will provide insights into how genomes and environments interact to determine the phenotypes of complex count traits.     

Quantitative trait loci affecting delta13C and response to differential water availibility in Arabidopsis thaliana

Phenotypic plasticity is an important response mechanism of plants to environmental heterogeneity. Here, we explored the genetic basis of plastic responses of Arabidopsis thaliana to water deficit by experimentally mapping quantitative trait loci (QTL) in two recombinant inbred populations (Cvi x Ler and Ler x Col). We detected genetic variation and significant genotype-by-environment interactions for many traits related to water use. We also mapped 26 QTL, including six for carbon isotope composition (delta13C). Negative genetic correlations between fruit length and fruit production as well as between flowering time and branch production were corroborated by QTL colocalization, suggesting these correlations are due to pleiotropy or physical linkage. Water-limited plants were more apically dominant with greater root:shoot ratios and higher delta13C (higher water-use efficiency) when compared to well-watered plants. Many of the QTL effects for these traits interacted significantly with the irrigation treatment, suggesting that the observed phenotypic plasticity is genetically based. We specifically searched for epistatic (QTL-QTL) interactions using a two-dimensional genome scan, which allowed us to detect epistasis regardless of additive genetic effects. We found several significant QTL-QTL interactions including three that exhibited environmental dependence. These results provide preliminary evidence for proposed genetic mechanisms underlying phenotypic plasticity.     

Sex-specific genotype-by-environment interactions for cuticular hydrocarbon expression in decorated crickets, Gryllodes sigillatus: implications for the evolution of signal reliability

Phenotypic traits that convey information about individual identity or quality are important in animal social interactions, and the degree to which such traits are influenced by environmental variation can have profound effects on the reliability of these cues. Using inbred genetic lines of the decorated cricket, Gryllodes sigillatus, we manipulated diet quality to test how the cuticular hydrocarbon (CHC) profiles of males and females respond across two different nutritional rearing environments. There were significant differences between lines in the CHC profiles of females, but the effect of diet was not quite statistically significant. There was no significant genotype-by-environment interaction (GEI), suggesting that environmental effects on phenotypic variation in female CHCs are independent of genotype. There was, however, a significant effect of GEI for males, with changes in both signal quantity and content, suggesting that environmental effects on phenotypic expression of male CHCs are dependent on genotype. The differential response of male and female CHC expression to variation in the nutritional environment suggests that these chemical cues may be under sex-specific selection for signal reliability. Female CHCs show the characteristics of reliable cues of identity: high genetic variability, low condition dependence and a high degree of genetic determination. This supports earlier work showing that female CHCs are used in self-recognition to identify previous mates and facilitate polyandry. In contrast, male CHCs show the characteristics of reliable cues of quality: condition dependence and a relatively higher degree of environmental determination. This suggests that male CHCs are likely to function as cues of underlying quality during mate choice and/or male dominance interactions.     

Genotype-by-environment interactions and adaptation to local temperature affect immunity and fecundity in Drosophila melanogaster

Natural populations of most organisms harbor substantial genetic variation for resistance to infection. The continued existence of such variation is unexpected under simple evolutionary models that either posit direct and continuous natural selection on the immune system or an evolved life history "balance" between immunity and other fitness traits in a constant environment. However, both local adaptation to heterogeneous environments and genotype-by-environment interactions can maintain genetic variation in a species. In this study, we test Drosophila melanogaster genotypes sampled from tropical Africa, temperate northeastern North America, and semi-tropical southeastern North America for resistance to bacterial infection and fecundity at three different environmental temperatures. Environmental temperature had absolute effects on all traits, but there were also marked genotype-by-environment interactions that may limit the global efficiency of natural selection on both traits. African flies performed more poorly than North American flies in both immunity and fecundity at the lowest temperature, but not at the higher temperatures, suggesting that the African population is maladapted to low temperature. In contrast, there was no evidence for clinal variation driven by thermal adaptation within North America for either trait. Resistance to infection and reproductive success were generally uncorrelated across genotypes, so this study finds no evidence for a fitness tradeoff between immunity and fecundity under the conditions tested. Both local adaptation to geographically heterogeneous environments and genotype-by-environment interactions may explain the persistence of genetic variation for resistance to infection in natural populations.     

From Genotype �� Environment Interaction to Gene �� Environment Interaction

Historically in plant breeding a large number of statistical models has been developed and used for studying genotype × environment interaction. These models have helped plant breeders to assess the stability of economically important traits and to predict the performance of newly developed genotypes evaluated under varying environmental conditions. In the last decade, the use of relatively low numbers of markers has facilitated the mapping of chromosome regions associated with phenotypic variability (e.g., QTL mapping) and, to a lesser extent, revealed the differetial response of these chromosome regions across environments (i.e., QTL × environment interaction). QTL technology has been useful for marker-assisted selection of simple traits; however, it has not been efficient for predicting complex traits affected by a large number of loci. Recently the appearance of cheap, abundant markers has made it possible to saturate the genome with high density markers and use marker information to predict genomic breeding values, thus increasing the precision of genetic value prediction over that achieved with the traditional use of pedigree information. Genomic data also allow assessing chromosome regions through marker effects and studying the pattern of covariablity of marker effects across differential environmental conditions. In this review, we outline the most important models for assessing genotype × environment interaction, QTL × environment interaction, and marker effect (gene) × environment interaction. Since analyzing genetic and genomic data is one of the most challenging statistical problems researchers currently face, different models from different areas of statistical research must be attempted in order to make significant progress in understanding genetic effects and their interaction with environment.     

Genotype x environment interaction in QTL analysis of an intervarietal almond cross by means of genetic markers

Besides QTL location and the estimation of gene effects, QTL analysis based on genetic markers could be used to comprehensively investigate quantitative trait-related phenomena such as pleiotropy, gene interactions, heterosis, and genotype-by-environment interaction (G x E). Given that the G x E interaction is of great relevance in tree improvement, the objective of the research presented here was to study the effect of years on QTL detection for 15 quantitative traits by means of isozymatic markers in a large progeny group of an intervarietal cross of almond. At least 17 putative QTLs were detected, 3 of which had alleles with opposite effects to those predicted from the parental genotypes. Only 3 QTLs behaved homogeneously over the years. Three possible causes are discussed in relation to this lack of stability: the power of the test statistic being used, the low contribution of the QTL to the genetic variation of the trait, and a differential gene expression dependent on the year (G x E). Most cases showing lack of stability involved traits whose heritability estimates change drastically from year to year and/or whose correlation coefficients between years are low, suggesting the presence of G x E as the most likely cause. A marker-assisted selection scheme to improve late flowering and short flowering duration is suggested for an early and wide screening of the progeny.     

Verification of yield QTL through realized molecular marker- assisted selection responses in a barley cross

Verification of putative quantitative trait loci (QTL) is an essential step towards implementing the use of marker-assisted selection (MAS) in cultivar improvement. In a previous study with 150 doubled haploid lines derived from the 6-row cross Steptoe/Morex (S/M), four regions (QTL1–4) of the barley genome were associated with differential genotypic expression for grain yield across environments. The objectives of this study were to verify the value of these four QTL for selection and to compare the efficiency of alternative MAS strategies using these QTL vs. conventional phenotypic selection for grain yield. A total of 92 DHLs derived from the S/M cross that were not used in the original mapping efforts were used for QTL verification. Confirmation of QTL effects was first accomplished by assessing yield differences between individuals carrying alternative alleles at each putative locus in three environments. QTL1 on chromosome 3 was confirmed as the most important and consistent locus to determine yield across sites, with the S allele being favorable. The M allele at QTL3 on chromosome 6 was beneficial for grain yield across sites, but to a lesser degree than QTL1. Magnitudes of allele effects at QTL2 (chromosome 2) and QTL4 (chromosome 7) were highly influenced by the environment where the genotypes were grown. Verification of QTL effects was best achieved by comparing realized selection response. Genotypic (MAS) and tandem genotypic and phenotypic selection were at least as good as phenotypic selection. Consistent selection responses were detected for QTL1 alone and together with QTL3. Genotypic selection for lines carrying the S allele at QTL1 resulted in the identification of high-yielding genotypes. Selection responses increased when the M allele at QTL3 was combined with the S allele at QTL1. Significant qualitative QTL × environment interactions for QTL2 and QTL4 were detected through differential realized selection responses at different sites. Without a thorough understanding of the physiological and agronomic particulars of any QTL and the target environment, MAS for QTL showing qualitative interactions should be minimized This revised version was published online in June 2006 with corrections to the Cover Date.     

Quantitative trait loci mapping of phenotypic plasticity and genotype-environment interactions in plant and insect performance

Community genetic studies generally ignore the plasticity of the functional traits through which the effect is passed from individuals to the associated community. However, the ability of organisms to be phenotypically plastic allows them to rapidly adapt to changing environments and plasticity is commonly observed across all taxa. Owing to the fitness benefits of phenotypic plasticity, evolutionary biologists are interested in its genetic basis, which could explain how phenotypic plasticity is involved in the evolution of species interactions. Two current ideas exist: (i) phenotypic plasticity is caused by environmentally sensitive loci associated with a phenotype; (ii) phenotypic plasticity is caused by regulatory genes that simply influence the plasticity of a phenotype. Here, we designed a quantitative trait loci (QTL) mapping experiment to locate QTL on the barley genome associated with barley performance when the environment varies in the presence of aphids, and the composition of the rhizosphere. We simultaneously mapped aphid performance across variable rhizosphere environments. We mapped main effects, QTL × environment interaction (QTL×E), and phenotypic plasticity (measured as the difference in mean trait values) for barley and aphid performance onto the barley genome using an interval mapping procedure. We found that QTL associated with phenotypic plasticity were co-located with main effect QTL and QTL×E. We also located phenotypic plasticity QTL that were located separately from main effect QTL. These results support both of the current ideas of how phenotypic plasticity is genetically based and provide an initial insight into the functional genetic basis of how phenotypically plastic traits may still be important sources of community genetic effects.     

QTL×环境互作对标记辅助选择响应的影响

基因型×环境互作是植物数量性状的普通属性和遗传育种改良的关注重点.采用Monte Carlo模拟方法研究了基因型×环境互作对标记辅助选择(Marker-assisted selection,简称MAS)响应的影响,揭示了育种上利用QTL(Quantitafivetrait locus,简称QTL)应当同时考虑其环境互作效应.存在基因型×环境互作下,MAS比普通表型选择更有效.特别以选育广适应性的品种为目标,MAS的优越性更明显.基于单个环境QTLs的MAS,QTL×环境互作效应通常降低了一般选择响应,一般选择响应累积量的降低程度与改良性状的QTL×环境互作效应大小相关.基于多个环境QTLs的MAS,不但产生较高的一般选择响应,而且获得的一般选择响应不受其QTL×环境互作效应大小的影响.但在某一特定环境下获得的总体选择响应仅与改良性状的总遗传率大小有关,普通遗传率和基因型与环境互作遗传率的相对变化对其影响很小.还比较研究了单地和穿梭选择对MAS遗传响应的影响.植物育种者应谨慎将某一环境的QTL信息用于实施另一环境的育种研究.     

QTL x environment interactions in rice. I. heading date and plant height

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

THE ECOLOGY AND GENETICS OF FITNESS IN CHLAMYDOMONAS III. GENOTYPE-BY-ENVIRONMENT INTERACTION WITHIN STRAINS

The fitness of genotypes created by crossing strains of Chlamydomonas reinhardtii was measured in axenic pure culture in a set of chemically defined environments. There was substantial and highly significant genotype-by-environment interaction, with genetic correlations between environments averaging only about +0.1 for both r and K. Higher-order interactions with combinations of environmental factors appeared to be no less important than simple interactions with single factors. The importance of genotype-by-environment interaction increased with the number of environmental factors manipulated. The linear reaction norms of genotypic score on environmental mean score varied substantially among genotypes and often intersected. There was also some evidence that nonallelic genetic interactions were present, and varied among environments. The genetic correlation of r with K also varied among environments, being significantly negative in some but not in others. These results are similar in all important respects to those previously obtained with different species, and suggest that genotype-by-environment interaction is important at all genetic scales. It is argued that they provide empirical support for a general theory of diversity, the “Tangled Bank,” based on the different response of genotypes to the range of conditions found in heterogeneous natural environments.     

Epistatic interaction is an important genetic basis of grain yield and its components in maize

A population of 294 recombinant inbred lines (RIL) derived from Yuyu22, an elite maize hybrid extending broadly in China, has been constructed to investigate the genetic basis of grain yield, and associated yield components in maize. The main-effect quantitative trait loci (QTL), digenic epistatic interactions, and their interactions with the environment for grain yield and its three components were identified by using the mixed linear model approach. Thirty-two main-effect QTL and forty-four pairs of digenic epistatic interactions were detected for the four measured traits in four environments. Our results suggest that both additive effects and epistasis (additive × additive) effects are important genetic bases of grain yield and its components in the RIL population. Only 30.4% of main-effect QTL for ear length were involved in epistatic interactions. This implies that many loci in epistatic interactions may not have significant effects for traits alone but may affect trait expression by epistatic interaction with the other loci.     

Quantitative genetics and functional-structural plant growth models: simulation of quantitative trait loci detection for model parameters and application to potential yield optimization

BACKGROUND AND AIMS: Prediction of phenotypic traits from new genotypes under untested environmental conditions is crucial to build simulations of breeding strategies to improve target traits. Although the plant response to environmental stresses is characterized by both architectural and functional plasticity, recent attempts to integrate biological knowledge into genetics models have mainly concerned specific physiological processes or crop models without architecture, and thus may prove limited when studying genotype x environment interactions. Consequently, this paper presents a simulation study introducing genetics into a functional-structural growth model, which gives access to more fundamental traits for quantitative trait loci (QTL) detection and thus to promising tools for yield optimization. METHODS: The GREENLAB model was selected as a reasonable choice to link growth model parameters to QTL. Virtual genes and virtual chromosomes were defined to build a simple genetic model that drove the settings of the species-specific parameters of the model. The QTL Cartographer software was used to study QTL detection of simulated plant traits. A genetic algorithm was implemented to define the ideotype for yield maximization based on the model parameters and the associated allelic combination. KEY RESULTS AND CONCLUSIONS: By keeping the environmental factors constant and using a virtual population with a large number of individuals generated by a Mendelian genetic model, results for an ideal case could be simulated. Virtual QTL detection was compared in the case of phenotypic traits--such as cob weight--and when traits were model parameters, and was found to be more accurate in the latter case. The practical interest of this approach is illustrated by calculating the parameters (and the corresponding genotype) associated with yield optimization of a GREENLAB maize model. The paper discusses the potentials of GREENLAB to represent environment x genotype interactions, in particular through its main state variable, the ratio of biomass supply over demand.     

Identification of major QTLs and epistatic interactions for seed protein concentration in soybean under multiple environments based on a high-density map

The concentration of protein in soybean is an important trait that drives successful soybean quality. A recombinant inbred line derived from a cross between the Charleston and Dongnong594 cultivars was planted in one location across 10 years and two locations across 5 years in China (20 environments in total), and the genetic effects were partitioned into additive main effects, epistatic main effects and their environmental interaction effects using composite interval mapping and inclusive composite interval mapping models based on a high-density genetic map. Ten main-effect quantitative trait loci (QTLs) were identified on chromosomes 3, 6, 7, 13, 15 and 20 and detected in more than three environments, with each of the main-effect QTLs contributing a phenotypic variation of around 10 %. Between the intervals of the main-effect QTLs, 93 candidate genes were screened for their involvement in seed protein storage and/or amino acid biosynthesis and metabolism processes based on gene ontology and annotation information. Furthermore, an analysis of epistatic interactions showed that three epistatic QTL pairs were detected, and could explain approximately 50 % of the phenotypic variation. The additive main-effect QTLs and epistatic QTL pairs contributed to high phenotypic variation under multiple environments, and the results were also validated and corroborated with previous research, indicating that marker-assisted selection can be used to improve soybean protein concentrations and that the candidate genes can also be used as a foundation data set for research on gene function.     

Impacts of QTL × Environment Interactions on Genetic Response to Marker-Assisted Selection

Genotype × environment (GE) interaction is a common characteristic for quantitative traits, and has been a subject of great concern for breeding programs. Simulation studies were conducted to investigate the effects of GE interaction on genetic response to marker-assisted selection (MAS). In our study we demonstrated that MAS is generally more efficient than phenotypic selection in the presence of GE interaction, and this trend is more pronounced for developing broadly adaptable varieties. The utilization of different QTL information dramatically influences MAS efficiency. When MAS is based on QTLs evaluated in a single environment, the causal QTL × environment (QE) interactions usually reduce general response across environments, and the reduction in the cumulative general response is a function of the proportion of QE interactions for the trait studied. However, MAS using QTL information evaluated in multiple environments not only yields higher general response, but the general response obtained is also reasonably robust to QE interactions. The total response achieved by MAS in a specific environment depends largely on the total heritability of traits and is slightly subject to relative changes between general heritability and GE interaction heritability. Two breeding strategies, breeding experiments conducted in one environment throughout and in two environments alternately, were also examined for the implementation of marker-based selection. It was thus concluded that plant breeders should be cautious to utilize QTL information from only one environment and execute breeding studies in another.