Characterization of the main effects,epistatic effects and their environmental interactions of QTLs on the genetic basis of yield traits in rice

Main effects, epistatic effects and their environmental interactions of QTLs are all important genetic components of quantitative traits. In this study, we analyzed the main effects, epistatic effects of the QTLs, and QTL by environment interactions (QEs) underlying four yield traits, using a population of 240 recombinant inbred lines from a cross between two rice varieties tested in replicated field trials. A genetic linkage map with 220 DNA marker loci was constructed. A mixed linear model approach was used to detect QTLs with main effects, QTLs involved in digenic interactions and QEs. In total, 29 QTLs of main effects, and 35 digenic interactions involving 58 loci were detected for the four traits. Thirteen QTLs with main effects showed QEs; no QE was detected for the QTLs involved in epistatic interactions. The amount of variations explained by the QTLs of main effect were larger than the QTLs involved in epistatic interactions, which in turn were larger than QEs for all four traits. This study illustrates the ability of the analysis to assess the genetic components underlying the quantitative traits, and demonstrates the relative importance of the various components as the genetic basis of yield traits in this population.     

Mapping quantitative trait Loci interactions from the maternal and offspring genomes

The expression of most developmental or behavioral traits involves complex interactions between quantitative trait loci (QTL) from the maternal and offspring genomes. The maternal-offspring interactions play a pivotal role in shaping the direction and rate of evolution in terms of their substantial contribution to quantitative genetic (co)variation. To study the genetics and evolution of maternal-offspring interactions, a unifying statistical framework that embraces both the direct and indirect genetic effects of maternal and offspring QTL on any complex trait is developed. This model is derived for a simple backcross design within the maximum-likelihood context, implemented with the EM algorithm. Results from extensive simulations suggest that this model can provide reasonable estimation of additive and dominant effects of the QTL at different generations and their interaction effects derived from the maternal and offspring genomes. Although our model is framed to characterize the actions and interactions of maternal and offspring QTL affecting offspring traits, the idea can be readily extended to decipher the genetic machinery of maternal traits, such as maternal care. Our model provides a powerful means for studying the evolutionary significance of indirect genetic effects in any sexually reproductive organisms.     

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.     

Functional genetics of intraspecific ecological interactions in Arabidopsis thaliana

Studying the genetic basis of traits involved in ecological interactions is a fundamental part of elucidating the connections between evolutionary and ecological processes. Such knowledge allows one to link genetic models of trait evolution with ecological models describing interactions within and between species. Previous work has shown that connections between genetic and ecological processes in Arabidopsis thaliana may be mediated by the fact that quantitative trait loci (QTL) with 'direct' effects on traits of individuals also have pleiotropic 'indirect' effects on traits expressed in neighbouring plants. Here, we further explore these connections by examining functional relationships between traits affected directly and indirectly by the same QTL. We develop a novel approach using structural equation models (SEMs) to determine whether observed pleiotropic effects result from traits directly affected by the QTL in focal individuals causing the changes in the neighbours' phenotypes. This hypothesis was assessed using SEMs to test whether focal plant phenotypes appear to mediate the connection between the focal plants' genotypes and the phenotypes of their neighbours, or alternatively, whether the connection between the focal plants' genotypes and the neighbours' phenotypes is mediated by unmeasured traits. We implement this analysis using a QTL of major effect that maps to the well-characterized flowering locus, FRIGIDA. The SEMs support the hypothesis that the pleiotropic indirect effects of this locus arise from size and developmental timing-related traits in focal plants affecting the expression of developmental traits in their neighbours. Our findings provide empirical insights into the genetics and nature of intraspecific ecological interactions. Our technique holds promise in directing future work into the genetic basis and functional relationship of traits mediating and responding to ecological interactions.     

利用双单倍体群体剖析水稻产量及其相关性状的遗传基础

主效QTL、上位性效应和它们与环境的互作(QE)都是数量性状的重要遗传因素。利用籼粳交珍汕97／武育粳2号F1植株上的花药进行组织培养得到的190个双单倍体群体和179个微卫星标记,通过两年两重复田间试验,采用混合线性模型方法分析了9个控制水稻产量及其相关性状的遗传效应,得到57个主效QTL,41对上位性互作,8对QTL与环境的互作和7对上位性效应与环境的互作。单个主效QTL解释这些性状1．3％～25．8％的表型方差。各性状QTL的累积表型贡献率达11．5％～66．8％。大多数性状之间具有显著的表型相关性,相关性较高的性状之间常具有较多共同或紧密连锁的QTL。结果表明,基因的多效性或紧密连锁可能是性状相关的重要遗传基础。     

Genetic architecture of complex traits and accuracy of genomic prediction: coat colour, milk-fat percentage, and type in Holstein cattle as contrasting model traits

Prediction of genetic merit using dense SNP genotypes can be used for estimation of breeding values for selection of livestock, crops, and forage species; for prediction of disease risk; and for forensics. The accuracy of these genomic predictions depends in part on the genetic architecture of the trait, in particular number of loci affecting the trait and distribution of their effects. Here we investigate the difference among three traits in distribution of effects and the consequences for the accuracy of genomic predictions. Proportion of black coat colour in Holstein cattle was used as one model complex trait. Three loci, KIT, MITF, and a locus on chromosome 8, together explain 24% of the variation of proportion of black. However, a surprisingly large number of loci of small effect are necessary to capture the remaining variation. A second trait, fat concentration in milk, had one locus of large effect and a host of loci with very small effects. Both these distributions of effects were in contrast to that for a third trait, an index of scores for a number of aspects of cow confirmation ("overall type"), which had only loci of small effect. The differences in distribution of effects among the three traits were quantified by estimating the distribution of variance explained by chromosome segments containing 50 SNPs. This approach was taken to account for the imperfect linkage disequilibrium between the SNPs and the QTL affecting the traits. We also show that the accuracy of predicting genetic values is higher for traits with a proportion of large effects (proportion black and fat percentage) than for a trait with no loci of large effect (overall type), provided the method of analysis takes advantage of the distribution of loci effects.     

Male-limited evolution suggests no extant intralocus sexual conflict over the sexually dimorphic cuticular hydrocarbons of <Emphasis Type="Italic">Drosophila melanogaster</Emphasis>

Sexually dimorphic traits are likely to have evolved through sexually antagonistic selection. However, recent empirical data suggest that intralocus sexual conflict often persists, even when traits have diverged between males and females. This implies that evolved dimorphism is often incomplete in resolving intralocus conflict, providing a mechanism for the maintenance of genetic variance in fitness-related traits. We used experimental evolution in Drosophila melanogaster to directly test for ongoing conflict over a suite of sexually dimorphic cuticular hydrocarbons (CHCs) that are likely targets of sex-specific selection. Using a set of experimental populations in which the transmission of genetic material had been restricted to males for 82 generations, we show that CHCs did not evolve, providing experimental evidence for the absence of current intralocus sexual conflict over these traits. The absence of ongoing conflict could indicate that CHCs have never been the target of sexually antagonistic selection, although this would require the existing dimorphism to have evolved via completely sexlinked mutations or as a result of former, but now absent, pleiotropic effects of the underlying loci on another trait under sexually antagonistic selection. An alternative interpretation, and which we believe to be more likely, is that the extensive CHC sexual dimorphism is the result of past intralocus sexual conflict that has been fully resolved, implying that these traits have evolved genetic independence between the sexes and that genetic variation in them is therefore maintained by alternative mechanisms. This latter interpretation is consistent with the known roles of CHCs in sexual communication in this species and with previous studies suggesting the genetic independence of CHCs between males and females. Nevertheless, direct estimates of sexually antagonistic selection will be important to fully resolve these alternatives.     

Small- and Large-Effect Quantitative Trait Locus Interactions Underlie Variation in Yeast Sporulation Efficiency

Quantitative trait loci (QTL) with small effects on phenotypic variation can be difficult to detect and analyze. Because of this a large fraction of the genetic architecture of many complex traits is not well understood. Here we use sporulation efficiency in Saccharomyces cerevisiae as a model complex trait to identify and study small-effect QTL. In crosses where the large-effect quantitative trait nucleotides (QTN) have been genetically fixed we identify small-effect QTL that explain approximately half of the remaining variation not explained by the major effects. We find that small-effect QTL are often physically linked to large-effect QTL and that there are extensive genetic interactions between small- and large-effect QTL. A more complete understanding of quantitative traits will require a better understanding of the numbers, effect sizes, and genetic interactions of small-effect QTL.     

The genetic basis of stay-green in rice analyzed in a population of doubled haploid lines derived from an<Emphasis Type="Italic"> indica</Emphasis> by<Emphasis Type="Italic"> japonica</Emphasis> cross

Delayed leaf-senescence, or stay-green, has been regarded as a desired characteristic for the production of a number of crops including rice. In this study, we analyzed the genetic basis of stay-green using a population of 190 doubled haploid lines from the cross between an indica parent Zhenshan 97 and a stay-green japonica parent Wuyujing 2. The population was tested in replicated field trials in 2 consecutive years, and six traits were defined to evaluate the stay-green characteristics. A genetic linkage map with 179 SSR (simple sequence repeat) marker loci was constructed. The software QTLMapper, based on a mixed linear model approach, was applied to detect QTLs, epistatic effects and their environmental interactions for these traits. A total of 46 main-effect QTLs was detected for the six traits that can be localized to 25 chromosomal regions. The individual effects of all the QTLs were small. Fifty digenic interactions were resolved that involved 66 loci distributed on all 12 chromosomes. Environmental interactions were detected for 18 of the main-effect QTLs and 14 of the epistatic interactions. Collectively, the epistatic effects and QTL by year interactions accounted for large proportions of the phenotypic variations. The results also showed that most of the stay-green traits were negatively correlated with yield and its component traits. The implications of the results in crop improvement were discussed.Communicated by C. Möllers     

A study on the minimum number of loci required for genetic evaluation using a finite locus model

For a finite locus model, Markov chain Monte Carlo (MCMC) methods can be used to estimate the conditional mean of genotypic values given phenotypes, which is also known as the best predictor (BP). When computationally feasible, this type of genetic prediction provides an elegant solution to the problem of genetic evaluation under non-additive inheritance, especially for crossbred data. Successful application of MCMC methods for genetic evaluation using finite locus models depends, among other factors, on the number of loci assumed in the model. The effect of the assumed number of loci on evaluations obtained by BP was investigated using data simulated with about 100 loci. For several small pedigrees, genetic evaluations obtained by best linear prediction (BLP) were compared to genetic evaluations obtained by BP. For BLP evaluation, used here as the standard of comparison, only the first and second moments of the joint distribution of the genotypic and phenotypic values must be known. These moments were calculated from the gene frequencies and genotypic effects used in the simulation model. BP evaluation requires the complete distribution to be known. For each model used for BP evaluation, the gene frequencies and genotypic effects, which completely specify the required distribution, were derived such that the genotypic mean, the additive variance, and the dominance variance were the same as in the simulation model. For lowly heritable traits, evaluations obtained by BP under models with up to three loci closely matched the evaluations obtained by BLP for both purebred and crossbred data. For highly heritable traits, models with up to six loci were needed to match the evaluations obtained by BLP.     

Dissecting Genetic Architecture Underlying Seed Traits in Multiple Environments

The seeds of flowering plants develop from double fertilization and play a vital role in reproduction and supplying human and animal food. The genetic variation of seed traits is influenced by multiple genetic systems, e.g., maternal, embryo, and/or endosperm genomes. Understanding the genetic architecture of seed traits is a major challenge because of this complex mechanism of multiple genetic systems, especially the epistasis within or between different genomes and their interactions with the environment. In this study, a statistical model was proposed for mapping QTL with epistasis and QTL-by-environment (QE) interactions underlying endosperm and embryo traits. Our model integrates the maternal and the offspring genomes into one mapping framework and can accurately analyze maternal additive and dominant effects, endosperm/embryo additive and dominant effects, and epistatic effects of two loci in the same or two different genomes, as well as interaction effects of each genetic component of QTL with environment. Intensive simulations under different sampling strategies, heritabilities, and model parameters were performed to investigate the statistical properties of the model. A set of real cottonseed data was analyzed to demonstrate our methods. A software package, QTLNetwork-Seed-1.0.exe, was developed for QTL analysis of seed traits.     

Is local adaptation in Mimulus guttatus caused by trade‐offs at individual loci?

Local adaptation is considered to be the result of fitness trade‐offs for particular phenotypes across different habitats. However, it is unclear whether such phenotypic trade‐offs exist at the level of individual genetic loci. Local adaptation could arise from trade‐offs of alternative alleles at individual loci or by complementary sets of loci with different fitness effects of alleles in one habitat but selective neutrality in the alternative habitat. To evaluate the genome‐wide basis of local adaptation, we performed a field‐based quantitative trait locus (QTL) mapping experiment on recombinant inbred lines (RILs) created from coastal perennial and inland annual races of the yellow monkeyflower (Mimulus guttatus) grown reciprocally in native parental habitats. Overall, we detected 19 QTLs affecting one or more of 16 traits measured in two environments, most of small effect. We identified 15 additional QTL effects at two previously identified candidate QTLs [DIV ERGENCE (DIV)]. Significant QTL by environment interactions were detected at the DIV loci, which was largely attributable to genotypic differences at a single field site. We found no detectable evidence for trade‐offs for any one component of fitness, although DIV2 showed a trade‐off involving different fitness traits between sites, suggesting that local adaptation is largely controlled by non‐overlapping loci. This is surprising for an outcrosser, implying that reduced gene flow prevents the evolution of individuals adapted to multiple environments. We also determined that native genotypes were not uniformly adaptive, possibly reflecting fixed mutational load in one of the populations.     

Detection of epistatic interactions between exotic alleles introgressed from wild barley (H. vulgare ssp. spontaneum)

The expression of a quantitative phenotype can be controlled through genotype, environment and genotype by environment interaction effects. Further, genotype effects can be attributed to major genes, quantitative trait loci (QTL) and gene by gene interactions, which are also termed epistatic interactions. The present study demonstrates that two-way epistatic interactions can play an important role for the expression of domestication-related traits like heading date, plant height and yield. In the BC₂DH population S42, carrying wild barley introgressions in the genetic background of the spring barley cultivar Scarlett, 13, 8 and 12 marker by marker interaction effects could be detected for the traits heading date, plant height and yield, respectively. Significant allelic combinations at interacting loci coincided for heading date, plant height and yield suggesting the presence of pleiotropic effects rather than several linked QTL. The mode of epistasis observed was primarily characterised by either (1) compensatory effects, where allelic combinations from the same genotype buffered the phenotype, or (2) augmented effects, where only the combination of the exotic allele at both interacting loci caused an altered phenotype. The present study shows that estimates of main effects of QTL can be confounded by interactions with background loci, suggesting that the identification of epistatic effects is important for gene cloning and marker-assisted selection. Furthermore, interaction effects between loci and putative candidate genes detected in the present study reveal potential functional relationships, which can be used to further elucidate gene networks in barley.     

Epistatic pleiotropy and the genetic architecture of covariation within early and late-developing skull trait complexes in mice

The role of epistasis as a source of trait variation is well established, but its role as a source of covariation among traits (i.e., as a source of "epistatic pleiotropy") is rarely considered. In this study we examine the relative importance of epistatic pleiotropy in producing covariation within early and late-developing skull trait complexes in a population of mice derived from an intercross of the Large and Small inbred strains. Significant epistasis was found for several pairwise combinations of the 21 quantitative trait loci (QTL) affecting early developing traits and among the 20 QTL affecting late-developing traits. The majority of the epistatic effects were restricted to single traits but epistatic pleiotropy still contributed significantly to covariances. Because of their proportionally larger effects on variances than on covariances, epistatic effects tended to reduce within-group correlations of traits and reduce their overall degree of integration. The expected contributions of single-locus and two-locus epistatic pleiotropic QTL effects to the genetic covariance between traits were analyzed using a two-locus population genetic model. The model demonstrates that, for single-locus or epistatic pleiotropy to contribute to trait covariances in the study population, both traits must show the same pattern of single-locus or epistatic effects. As a result, a large number of the cases where loci show pleiotropic effects do not contribute to the covariance between traits in this population because the loci show a different pattern of effect on the different traits. In general, covariance patterns produced by single-locus and epistatic pleiotropy predicted by the model agreed well with actual values calculated from the QTL analysis. Nearly all single-locus and epistatic pleiotropic effects contributed positive components to covariances between traits, suggesting that genetic integration in the skull is achieved by a complex combination of pleiotropic effects.     

The importance of context to the genetic architecture of diabetes-related traits is revealed in a genome-wide scan of a LG/J?��?SM/J murine model

Variations in diabetic phenotypes are caused by complex interactions of genetic effects, environmental factors, and the interplay between the two. We tease apart these complex interactions by examining genome-wide genetic and epigenetic effects on diabetes-related traits among different sex, diet, and sex-by-diet cohorts in a Mus musculus model. We conducted a genome-wide scan for quantitative trait loci that affect serum glucose and insulin levels and response to glucose stress in an F₁₆ Advanced Intercross Line of the LG/J and SM/J intercross (Wustl:LG,SM-G16). Half of each sibship was fed a high-fat diet and half was fed a relatively low-fat diet. Context-dependent genetic (additive and dominance) and epigenetic (parent-of-origin imprinting) effects were characterized by partitioning animals into sex, diet, and sex-by-diet cohorts. We found that different cohorts often have unique genetic effects at the same loci, and that genetic signals can be masked or erroneously assigned to specific cohorts if they are not considered individually. Our data demonstrate that the effects of genes on complex trait variation are highly context-dependent and that the same genomic sequence can affect traits differently depending on an individual??s sex and/or dietary environment. Our results have important implications for studies of complex traits in humans.     

Simultaneous mapping of epistatic QTL in DU6i × DBA/2 mice

We have mapped epistatic quantitative trait loci (QTL) in an F₂ cross between DU6i × DBA/2 mice. By including these epistatic QTL and their interaction parameters in the genetic model, we were able to increase the genetic variance explained substantially (8.8%–128.3%) for several growth and body composition traits. We used an analysis method based on a simultaneous search for epistatic QTL pairs without assuming that the QTL had any effect individually. We were able to detect several QTL that could not be detected in a search for marginal QTL effects because the epistasis cancelled out the individual effects of the QTL. In total, 23 genomic regions were found to contain QTL affecting one or several of the traits and eight of these QTL did not have significant individual effects. We identified 44 QTL pairs with significant effects on the traits, and, for 28 of the pairs, an epistatic QTL model fit the data significantly better than a model without interactions. The epistatic pairs were classified by the significance of the epistatic parameters in the genetic model, and visual inspection of the two-locus genotype means identified six types of related genotype–phenotype patterns among the pairs. Five of these patterns resembled previously published patterns of QTL interactions.     

The evolution of genetic architecture. I. Diversification of genetic backgrounds by genetic drift

The genetic architecture of a phenotype plays a critical role in determining phenotypic evolution through its effects on patterns of genetic variation. Genetic architecture is often considered to be constant in evolutionary quantitative genetic models. However, genetic architecture may be variable and itself evolve when there are dominance and epistatic interactions among alleles at the same and different loci, respectively. The evolution of genetic architecture by genetic drift is examined here by testing the breeding value of four standard inbred mouse strains mated across a set of 26 related recombinant quasi-inbred (RqI) lines generated from the intercross of the Large (LG/J) and Small (SM/J) inbred mouse strains. Phenotypes of interest include age-specific body weights, growth, and adult body composition. If the genetic architecture of these traits has differentiated by genetic drift during the production of the RqI strains, we should observe interactions between tester strain and RqI strain. The breeding values of the tester strains will change relative to one another depending on which RqI strain they are crossed to. The study included an average of 15.1 offspring per cross, over a total of 100 different crosses. Multivariate and univariate analyses of variance indicate that there is strongly significant interaction for all traits. Interaction is more pronounced in males than in females and accounted for an average of about 40% of the explained variation in males and 30% in females. These results indicate that the genetic architecture of these traits has differentiated by genetic drift in the RqI strains since their isolation from a common founder population. Further analysis indicates that this differentiation results in changes in the order of tester strain effects so that common patterns of selection in these differentiated populations could result in the fixation of different alleles.     

Associations between blood groups, blood protein polymorphisms and breeding values for production traits in Swedish Red and White Dairy bulls

The relationships of nine blood group systems and two blood protein polymorphisms with breeding values for several production traits were examined in dairy cattle of the Swedish Red and White (SRB) breed. The material consisted of 2212 bulls; the bulls were performance tested for growth rate and their breeding values for milk yield, fat and protein content in milk were estimated from progeny tests. The direct effect of marker alleles or marker phenotypes was analysed in a multiple regression model. Several significant associations were found; many supported earlier findings. However, the contribution of the markers to the total variation of the breeding values was very small. Linkage between marker loci and production loci was studied in offspring from heterozygous sires by estimating the interactions between sire and marker alle using a model eliminating the direct effects of sire and marker alle. There were strong indications of linkage between some marker loci (e.g. B, J and Am-1 loci) and loci with large effects on production traits.     

Molecular-Marker-Facilitated Investigations of Quantitative-Trait Loci in Maize. I. Numbers, Genomic Distribution and Types of Gene Action

Individual genetic factors which underlie variation in quantitative traits of maize were investigated in each of two F2 populations by examining the mean trait expressions of genotypic classes at each of 17-20 segregating marker loci. It was demonstrated that the trait expression of marker locus classes could be interpreted in terms of genetic behavior at linked quantitative trait loci (QTLs). For each of 82 traits evaluated, QTLs were detected and located to genomic sites. The numbers of detected factors varied according to trait, with the average trait significantly influenced by almost two-thirds of the marked genomic sites. Most of the detected associations between marker loci and quantitative traits were highly significant, and could have been detected with fewer than the 1800-1900 plants evaluated in each population. The cumulative, simple effects of marker-linked regions of the genome explained between 8 and 40% of the phenotypic variation for a subset of 25 traits evaluated. Single marker loci accounted for between 0.3% and 16% of the phenotypic variation of traits. Individual plant heterozygosity, as measured by marker loci, was significantly associated with variation in many traits. The apparent types of gene action at the QTLs varied both among traits and between loci for given traits, although overdominance appeared frequently, especially for yield-related traits. The prevalence of apparent overdominance may reflect the effects of multiple QTLs within individual marker-linked regions, a situation which would tend to result in overestimation of dominance. Digenic epistasis did not appear to be important in determining the expression of the quantitative traits evaluated. Examination of the effects of marked regions on the expression of pairs of traits suggests that genomic regions vary in the direction and magnitudes of their effects on trait correlations, perhaps providing a means of selecting to dissociate some correlated traits. Marker-facilitated investigations appear to provide a powerful means of examining aspects of the genetic control of quantitative traits. Modifications of the methods employed herein will allow examination of the stability of individual gene effects in varying genetic backgrounds and environments.     

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.