Inheritance of Flowering Time in Apricot (<Emphasis Type="Italic">Prunus armeniaca</Emphasis> L.) and Analysis of Linked Quantitative Trait Loci (QTLs) using Simple Sequence Repeat (SSR) Markers

Time of flowering was studied during 3 years in a BC1 apricot progeny of 73 seedlings derived from a cross between the F1 selection “Z506-07” (“Orange Red” × “Currot”) and the Spanish cultivar “Currot”. Results indicated a quantitative inheritance of flowering time in apricot with an influence of juvenility and environmental conditions (chill accumulation) on the evaluation and expression of this trait. Genetic maps consisting of 11 linkage groups for both parents representing the eight chromosomes of apricot were developed using 46 apricot and peach simple sequence repeat (SSR-microsatellites) markers and were used for the identification of quantitative trait loci (QTL). QTL analysis for flowering time allowed the identification of one significant QTL on the linkage group 5 (G5) of “Z506-07”, and explaining most of the phenotypic variation. Two microsatellite loci (UDAp-423r and AMPA-105) were found to be tightly linked to this important agronomic trait. Finally, we discuss the stability of the QTL described during the 3 years of the study and the development of efficient marker-assisted selection strategies applied to apricot and other Prunus breeding programs.     

Mapping of Candidate Genes Involved in Bud Dormancy and Flowering Time in Sweet Cherry (Prunus avium)

The timing of flowering in perennial plants is crucial for their survival in temperate climates and is regulated by the duration of bud dormancy. Bud dormancy release and bud break depend on the perception of cumulative chilling during endodormancy and heat during the bud development. The objectives of this work were to identify candidate genes involved in dormancy and flowering processes in sweet cherry, their mapping in two mapping progenies ‘Regina’ × ‘Garnet’ and ‘Regina’ × ‘Lapins’, and to select those candidate genes which co-localized with quantitative trait loci (QTLs) associated with temperature requirements for bud dormancy release and flowering. Based on available data on flowering processes in various species, a list of 79 candidate genes was established. The peach and sweet cherry orthologs were identified and primers were designed to amplify sweet cherry candidate gene fragments. Based on the amplified sequences of the three parents of the mapping progenies, SNPs segregations in the progenies were identified. Thirty five candidate genes were genetically mapped in at least one of the two progenies and all were in silico mapped. Co-localization between candidate genes and QTLs associated with temperature requirements and flowering date were identified for the first time in sweet cherry. The allelic composition of the candidate genes located in the major QTL for heat requirements and flowering date located on linkage group 4 have a significant effect on these two traits indicating their potential use for breeding programs in sweet cherry to select new varieties adapted to putative future climatic conditions.     

QTL analysis of fruit components in the progeny of a Rennell Island Tall coconut (Cocos nucifera L.) individual

We investigated the genetic factors controlling fruit components in coconut by performing QTL analyses for fruit component weights and ratios in a segregating progeny of a Rennell Island Tall genotype. The underlying linkage map of this population was already established in a previous study, as well as QTL analyses for fruit production, which were used to complement our results. The addition of 53 new markers (mainly SSRs) led to minor amendments in the map. A total of 52 putative QTLs were identified for the 11 traits under study. Thirty-four of them were grouped in six small clusters, which probably correspond to single pleiotropic genes. Some additional QTLs located apart from these clusters also had relatively large effects on the individual traits. The QTLs for fruit component weight, endosperm humidity and fruit production were found at different locations in the genome, suggesting that efficient marker-assisted selection for yield can be achieved by selecting QTLs for the individual components. The detected QTLs descend from a genotype belonging to the “Pacific” coconut group. Based on the known molecular and phenotypic differences between “Pacific” and “Indo-Atlantic” coconuts, we suggest that a large fraction of coconut genetic diversity is still to be investigated by studying populations derived from crosses between these groups. Electronic Supplementary Material Supplementary material is available for this article at and is accessible for authorized users.     

The QTL analysis on maternal and endosperm genome and their environmental interactions for characters of cooking quality in rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L<Emphasis Type="Italic">.</Emphasis>)

Investigations to identify quantitative trait loci (QTLs) governing cooking quality traits including amylose content, gel consistency and gelatinization temperature (expressed by the alkali spread value) were conducted using a set of 241 RIL populations derived from an elite hybrid cross of “Zhenshan 97” × “Minghui 63” and their reciprocal backcrosses BC₁F₁ and BC₂F₁ populations in two environments. QTLs and QTL × environment interactions were analyzed by using the genetic model with endosperm and maternal effects and environmental interaction effects on quantitative traits of seed in cereal crops. The results suggested that a total of seven QTLs were associated with cooking quality of rice, which were subsequently mapped to chromosomes 1, 4 and 6. Six of these QTLs were also found to have environmental interaction effects.     

Advanced backcross-QTL analysis in spring barley (H. vulgare ssp. spontaneum) comparing a REML versus a Bayesian model in multi-environmental field trials

A common difficulty in mapping quantitative trait loci (QTLs) is that QTL effects may show environment specificity and thus differ across environments. Furthermore, quantitative traits are likely to be influenced by multiple QTLs or genes having different effect sizes. There is currently a need for efficient mapping strategies to account for both multiple QTLs and marker-by-environment interactions. Thus, the objective of our study was to develop a Bayesian multi-locus multi-environmental method of QTL analysis. This strategy is compared to (1) Bayesian multi-locus mapping, where each environment is analysed separately, (2) Restricted Maximum Likelihood (REML) single-locus method using a mixed hierarchical model, and (3) REML forward selection applying a mixed hierarchical model. For this study, we used data on multi-environmental field trials of 301 BC₂DH lines derived from a cross between the spring barley elite cultivar Scarlett and the wild donor ISR42-8 from Israel. The lines were genotyped by 98 SSR markers and measured for the agronomic traits “ears per m2,” “days until heading,” “plant height,” “thousand grain weight,” and “grain yield”. Additionally, a simulation study was performed to verify the QTL results obtained in the spring barley population. In general, the results of Bayesian QTL mapping are in accordance with REML methods. In this study, Bayesian multi-locus multi-environmental analysis is a valuable method that is particularly suitable if lines are cultivated in multi-environmental field trials. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Study of tree architecture of apple (<Emphasis Type="Italic">Malus</Emphasis> × <Emphasis Type="Italic">domestica</Emphasis> Borkh.) by QTL analysis of growth traits

Apple tree architecture is naturally very diverse, but for fruit production, certain tree habits are more desirable than others. Here we describe the results of a QTL analysis performed to study the genetic control of growth traits in apple. This was carried out on the progeny of a cross between two apple cultivars of contrasting tree architectures. “Telamon” has a columnar tree form and “Braeburn” has a more standard, “normal” growth habit. The growth traits were measured on the F ₁ seedlings of the Telamon × Braeburn population for two consecutive years of growth on own roots and for the first year of growth on M9 rootstock. QTL analysis was carried out using either the Kruskal–Wallis method or the Multiple QTL Method. For all but one growth characteristic, significant QTLs were detected. A major cluster of QTLs was located in the Co gene region of “Telamon”, confirming the major influence of the Co gene on tree architecture, although this influence changed as the plant material aged and was generally more pronounced for rootstock-grown plants. Additional QTL results suggest the occurrence of genes with pleiotropic effects on tree architecture. The observed QTL instability over different years and for different root systems indicates that the genetic control of tree architecture is largely influenced by environmental factors and probably changes as the tree matures. Finally, a major influence of the root system on all the traits determining tree architecture was clearly demonstrated.     

Genetic basis of adaptation: flowering time in Arabidopsis thaliana

 We have mapped QTLs (quantitative trait loci) for an adaptive trait, flowering time, in a selfing annual, Arabidopsis thaliana. To obtain a mapping population we made a cross between an early-summer, annual strain, Li-5, and an individual from a late over-wintering natural population, Naantali. From the backcross to Li-5 298 progeny were grown, of which 93 of the most extreme individuals were genotyped. The data were analysed with both interval mapping and composite interval mapping methods to reveal one major and six minor QTLs, with at least one QTL on each of the five chromosomes. The QTL on chromosome 4 was a major one with an effect of 17.3 days on flowering time and explaining 53.4% of the total variance. The others had effects of at most 6.5 days, and they accounted for only small portions of the variance. Epistasis was indicated between one pair of the QTLs. The result of finding one major QTL and little epistasis agrees with previous studies on flowering time in Arabidopsis thaliana and other species. That several QTLs were found was expected considering the large number of possible candidate loci. In the light of the suggested genetic models of gene action at the candidate loci, epistasis was to be expected. The data showed that major QTLs for adaptive traits can be detected in non-domesticated species. Received: 15 January 1997/Accepted: 21 February 1997     

Genetic Relationships Among Panicle Characteristics of Rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.) Using Unconditional and Conditional QTL Analyses

Using mixed-model-based composite interval mapping and conditional statistical methods, we studied quantitative trait loci (QTLs) with epistatic effects and QTLs by environment interaction effects for rice seed set percent (SSP), filled grain number per plant (FGP), and panicle length (PL). A population of 241 recombinant inbred lines was used which was derived from a cross between “Zhenshan 97” and “Minghui 63.” Its linkage map included 221 molecular markers. Our QTL analysis detected 28, 25, and 32 QTLs for SSP, FGP, and PL, respectively. Each QTL explained 1.37%∼13.19% of the mean phenotypic variation. A comparison of conventional and conditional mapping provided information about the genetic control system involved in the synthetic process of SSP, FGP, and PL at the level of individual QTLs. Conditional QTLs with reduced (or increased) effects were identified for SSP, which were significantly influenced by FGP or PL. Some QTLs could express independently for the given traits, thereby providing possibilities for simultaneous improvement of SSR and PL, and SSR and FGP. Epistasis was more sensitive to environmental conditions than were additive effects.     

Molecular detection of QTLs for agronomic and quality traits in a doubled haploid population derived from two Canadian wheats (Triticum aestivum L.)

Development of high-yielding wheat varieties with good end-use quality has always been a major concern for wheat breeders. To genetically dissect quantitative trait loci (QTLs) for yield-related traits such as grain yield, plant height, maturity, lodging, test weight and thousand-grain weight, and for quality traits such as grain and flour protein content, gluten strength as evaluated by mixograph and SDS sedimentation volume, an F₁-derived doubled haploid (DH) population of 185 individuals was developed from a cross between a Canadian wheat variety “AC Karma” and a breeding line 87E03-S2B1. A genetic map was constructed based on 167 marker loci, consisting of 160 microsatellite loci, three HMW glutenin subunit loci: Glu-A1, Glu-B1 and Glu-D1, and four STS-PCR markers. Data for investigated traits were collected from three to four environments in Manitoba, Canada. QTL analyses were performed using composite interval mapping. A total of 50 QTLs were detected, 24 for agronomic traits and 26 for quality-related traits. Many QTLs for correlated traits were mapped in the same genomic regions forming QTL clusters. The largest QTL clusters, consisting of up to nine QTLs, were found on chromosomes 1D and 4D. HMW glutenin subunits at Glu-1 loci had the largest effect on breadmaking quality; however, other genomic regions also contributed genetically to breadmaking quality. QTLs detected in the present study are compared with other QTL analyses in wheat.     

Salt tolerance in Lycopersicon spp. VII. Pleiotropic action of genes controlling earliness on fruit yield

 The change from vegetative to reproductive development (earliness) in Lycopersicon chesmannii line L2 was delayed for 20 weeks when compared to other Lycopersicon species under greenhouse conditions. The interspecific hybrid of L. chesmannii L2 and L. esculentum E9, a cherry tomato cultivar, also showed this delay in reproductive development. The distribution of this character in the F₂-derived population showed a bimodal shape, plants could be scored easily as “early” or “late” in two nutrient conditions (optimum and high salinity). A QTL with major effects on earliness was detected in salinity, which explained 35.6% of the phenotypic variation. The effect of this QTL greatly diminished under control conditions, indicating differences in the genetic control of earliness between treatments. ACC synthase or phytochrome B2 are the products of candidate genes for such a major QTL. Other QTLs with minor effects, and epistatic interactions, are also involved in earliness under both conditions. A “late” F₂ subpopulation yielded twice as much as an “early” F₂; conversely, “early” plants were taller than “late” plants, regardless of the treatment. QTL analysis, carried out in both subpopulations, showed that yield differences may be explained by chesmannii alleles showing negative additive effects at some QTLs only in the “early” subpopulation. The effect of population subdivision on QTL analysis was investigated by computer simulations to show sample-size or random effects; thus, important pleiotropic or regulatory effects of genes controlling earliness on yield that affect QTL analysis, have been reveiled. Therefore alleles controlling earliness in L. chesmannii have to be taken into account for a more efficient utilization of the genetic resources of this species. Received: 30 June 1998 / Accepted: 31 August 1998     

QTL analysis of plant development and fruit traits in pepper and performance of selective phenotyping

A QTL analysis was performed to determine the genetic basis of 13 horticultural traits conditioning yield in pepper (Capsicum annuum). The mapping population was a large population of 297 recombinant inbred lines (RIL) originating from a cross between the large-fruited bell pepper cultivar ‘Yolo Wonder’ and the small-fruited chilli pepper ‘Criollo de Morelos 334’. A total of 76 QTLs were detected for 13 fruit and plant traits, grouped in 28 chromosome regions. These QTLs explained together between 7% (internode growth time) and 91% (fruit diameter) of the phenotypic variation. The QTL analysis was also performed on two subsets of 141 and 93 RILs sampled using the MapPop software. The smaller populations allowed for the detection of a reduced set of QTLs and reduced the overall percentage of trait variation explained by QTLs. The frequency of false positives as well as the individual effect of QTLs increased in reduced population sets as a result of reduced sampling. The results from the QTL analysis permitted an overall glance over the genetic architecture of traits considered by breeders for selection. Colinearities between clusters of QTLs controlling fruit traits and/or plant development in distinct pepper species and in related solanaceous crop species (tomato and eggplant) suggests that shared mechanisms control the shape and growth of different organs throughout these species.     

Quantitative trait loci controlling vernalisation requirement, heading time and number of panicles in meadow fescue (Festuca pratensis Huds.)

The “BF14/16×HF2/7” mapping population of meadow fescue (Festuca pratensis Huds.) was characterised for number of panicles produced by non-vernalised plants in the field, vernalisation requirement (number of weeks at 6°C and 8 h photoperiod), as well as days to heading, number of panicles and proportion of shoots heading after a 12 weeks vernalisation treatment. Quantitative trait loci (QTLs) were identified and compared to QTLs and genes related to the induction of flowering in cereals and grasses. A region on chromosome 1F affected days to heading and the proportion of shoots heading. Chromosome 4F appeared to have several genes with a strong effect on vernalisation requirement. The strongest effects were located in the proximal end of 4F and may correspond to the earliness per se (eps) QTL eps6L.2 in barley and a heading time QTL in perennial ryegrass. A part of the meadow fescue orthologue of VRN1 was sequenced and mapped to another region of 4F that also had a strong effect on vernalisation requirement. The proximal end of chromosome 5F had QTLs for days to heading and proportion of heading shoots. Syntenic regions in wheat and barley contain eps-loci. A QTL for number of panicles in the field and a QTL for proportion of heading shoots were present on chromosome 6. A region on 7F affected the variation in number of panicles among plants without a vernalisation requirement, and is syntenic to regions in perennial ryegrass, barley and rice containing orthologues of Arabidopsis thaliana CO.     

Fruit size QTL analysis of an F<Subscript>1</Subscript> population derived from a cross between a domesticated sweet cherry cultivar and a wild forest sweet cherry

Maximizing fruit size is critical for profitable sweet cherry (Prunus avium L.) production. Yet, despite its importance, little is known about the genetic control of fruit size. The objective of this study was to identify quantitative trait loci (QTLs) for fruit size and two essential components of fruit size, mesocarp cell number and size. This study utilized a double pseudo-testcross population derived from reciprocal crosses between a sweet cherry cultivar with ~8 g fruit, “Emperor Francis” (EF), and a wild forest sweet cherry selection with ~2 g fruit, “New York 54” (NY). A total of 190 F₁ progeny previously utilized for the construction of the linkage maps were evaluated in 2006 and 2007 for fruit weight, length, and diameter; mesocarp cell number and length; and pit length and diameter. In 2008, a subset of this population was again evaluated for fruit weight. Correlation analysis revealed that the three fruit size traits were highly correlated with each other, and mesocarp cell number, not cell length, was correlated with fruit size. Three QTLs were identified for each fruit size trait, and one QTL was identified for mesocarp cell number. Fruit size QTLs were found on linkage group 2 on the EF map (EF 2) and linkage groups 2 and 6 on the NY map (NY 2 and NY 6). On EF 2, the cell number QTL clustered with the fruit size QTL, suggesting that the underlying basis of the fruit size increase associated with this QTL was an increase in mesocarp cell number. On NY 6, pit length and diameter QTLs clustered with those for fruit size, suggesting that the underlying morphological basis of this fruit size QTL is the difference in pit size.     

Comparison of the genetic determinism of two key phenological traits,flowering and maturity dates,in three Prunus species: peach,apricot and sweet cherry

The present study investigates the genetic determinism of flowering and maturity dates, two traits highly affected by global climate change. Flowering and maturity dates were evaluated on five progenies from three Prunus species, peach, apricot and sweet cherry, during 3–8 years. Quantitative trait locus (QTL) detection was performed separately for each year and also by integrating data from all years together. High heritability estimates were obtained for flowering and maturity dates. Several QTLs for flowering and maturity dates were highly stable, detected each year of evaluation, suggesting that they were not affected by climatic variations. For flowering date, major QTLs were detected on linkage groups (LG) 4 for apricot and sweet cherry and on LG6 for peach. QTLs were identified on LG2, LG3, LG4 and LG7 for the three species. For maturity date, a major QTL was detected on LG4 in the three species. Using the peach genome sequence data, candidate genes underlying the major QTLs on LG4 and LG6 were investigated and key genes were identified. Our results provide a basis for the identification of genes involved in flowering and maturity dates that could be used to develop cultivar ideotypes adapted to future climatic conditions.     

Influence of dent corn genetic backgrounds on QTL detection for plant-height traits and their relationships in high-oil maize

QTL mapping for plant-height traits has not been hitherto reported in high-oil maize. A high-oil maize inbred ‘GY220’ was crossed with two dent maize inbreds (‘8984’ and ‘8622’) to generate two connected F_2:3 populations. Four plant-height traits were evaluated in 284 and 265 F_2:3 families. Single-trait QTL mapping and multiple-trait joint QTL mapping was used to detect QTLs for the traits and the genetic relationship between plant height (PH) and two other plant-height traits. A total of 28 QTLs and 12 pairs of digenic interactions among detected QTLs for four traits were detected in the two F_2:3 families. Only one marker was shared between the two populations. Joint analysis of PH with ear height (EH) and PH with top height (TH) detected 32 additional QTLs. Our results showed that QTL detection for PH was dependent on the genetic background of dent corn inbreds. Multiple-trait joint QTL analysis could increase the number of detected QTLs.     

Indian rice “Kasalath” contains genes that improve traits of Japanese premium rice “Koshihikari”

Rice (Oryza sativa L.) chromosome segment substitution lines (CSSLs), in which chromosomal segments of the Indian landrace “Kasalath” replace the corresponding endogenous segments in the genome of the Japanese premium rice “Koshihikari”, are available and together cover the entire genome. Chromosome regions affecting a trait (CRATs) can be identified by comparison of phenotypes with genotypes of CSSLs. We detected 99 CRATs for 15 agronomic or morphological traits. “Kasalath” had positively acting alleles in 53 CRATs. Its CRATs increased panicle number per plant by up to 23.3%, grain number per panicle by up to 30.8%, and total grain number by up to 15.1%, relative to “Koshihikari”. CRATs were identified for grain size (grain thickness and width), with positive effects of about 5.0%. A CRAT on chromosome 8 almost doubled the weight of roots in uppermost soil layers compared to “Koshihikari”. Additionally, “Kasalath” possessed CRATs for higher lodging resistance (reduction in plant height and increase in stem diameter). In some cases, multiple CRATs were detected in the same chromosome regions. Therefore, CSSLs with these chromosome segments might be useful breeding materials for the simultaneous improvement of multiple traits. Five CRATs, one for plant height on chromosome 1, one for stem diameter on chromosome 8, and three for heading date on chromosomes 6, 7, and 8 overlapped with the corresponding QTLs that already had been mapped with back-crossed inbred lines of “Nipponbare” and “Kasalath”. In both “Koshihikari” CRATs and “Nipponbare” QTLs, “Kasalath” had similar effects. Both Y. Madoka and T. Kashiwagi have contributed equally to this article.     

QTL Mapping for Frond Length and Width in Laminaria japonica Aresch (Laminarales, Phaeophyta) Using AFLP and SSR Markers

In Laminaria japonica Aresch breeding practice, two quantitative traits, frond length (FL) and frond width (FW), are the most important phenotypic selection index. In order to increase the breeding efficiency by integrating phenotypic selection and marker-assisted selection, the first set of QTL controlling the two traits were determined in F₂ family using amplified fragment length polymorphism (AFLP) and simple sequence repeat (SSR) markers. Two prominent L. japonicas inbred lines, one with “broad and thin blade” characteristics and another with “long and narrow blade” characteristics, were applied in the hybridization to yield the F₂ mapping population with 92 individuals. A total of 287 AFLP markers and 11 SSR markers were used to construct a L. japonica genetic map. The yielded map was consisted of 28 linkage groups (LG) named LG1 to LG28, spanning 1,811.1 cM with an average interval of 6.7 cM and covering the 82.8% of the estimated genome 2,186.7 cM. While three genome-wide significant QTL were detected on LG1 (two QTL) and LG4 for “FL,” explaining in total 42.36% of the phenotypic variance, two QTL were identified on LG3 and LG5 for the trait “FW,” accounting for the total of 36.39% of the phenotypic variance. The gene action of these QTL was additive and partially dominant. The yielded linkage map and the detected QTL can provide a tool for further genetic analysis of two traits and be potential for maker-assisted selection in L. japonica breeding.     

Crop model based QTL analysis across environments and QTL based estimation of time to floral induction and flowering in <Emphasis Type="Italic">Brassica oleracea</Emphasis>

Studying quantitative traits is complicated due to genotype by environment interactions. One strategy to overcome these difficulties is to combine quantitative trait loci (QTL) and ecophysiological models, e.g. by identifying QTLs for the response curves of adaptive traits to influential environmental factors. A B. oleracea DH-population segregating for time to flowering was cultivated at different temperature regimes. Composite interval mapping was carried out on the three parameters of a model describing time to flowering as a function of temperature, i.e. on the intercept and slope of the response of time to floral induction to temperature and on the duration from transition to flowering. The additive effects of QTLs detected for the parameters have been used to estimate time to floral induction and flowering in the B. oleracea DH-population. The combined QTL and crop model explained 66% of the phenotypic variation for time to floral induction and 56% of the phenotypic variation for time to flowering. Estimation of time to floral induction and flowering based on environment specific QTLs explained 61 and 41% of the phenotypic variation. Results suggest that flowering time can be predicted effectively by coupling QTL and crop models and that using crop modelling tools for QTL analysis increases the power of QTL detection.     

Mapping and characterisation of QTL × E interactions for traits determining grain and stover yield in pearl millet

A mapping population of 104 F(3) lines of pearl millet, derived from a cross between two inbred lines H 77/833-2 x PRLT 2/89-33, was evaluated, as testcrosses on a common tester, for traits determining grain and stover yield in seven different field trials, distributed over 3 years and two seasons. The total genetic variation was partitioned into effects due to season (S), genotype (G), genotype x season interaction (G x S), and genotype x environment-within-season interaction [G x E(S)]. QTLs were determined for traits for their G, G x S, and G x E(S) effects, to assess the magnitude and the nature (cross over/non-crossover) of environmental interaction effects on individual QTLs. QTLs for some traits were associated with G effects only, while others were associated with the effects of both G and G x S and/or G, G x S and G x E(S) effects. The major G x S QTLs detected were for flowering time (on LG 4 and LG 6), and mapped to the same intervals as G x S QTLs for several other traits (including stover yield, harvest index, biomass yield and panicle number m(-2)). All three QTLs detected for grain yield were unaffected by G x S interaction however. All three QTLs for stover yield (mapping on LG 2, LG 4 and LG 6) and one of the three QTLs for grain yield (mapping on LG 4) were also free of QTL x E(S) interactions. The grain yield QTLs that were affected by QTL x E(S) interactions (mapping on LG 2 and LG 6), appeared to be linked to parallel QTL x E(S) interactions of the QTLs for panicle number m(-2) on (LG 2) and of QTLs for both panicle number m(-2) and harvest index (LG 6). In general, QTL x E(S) interactions were more frequently observed for component traits of grain and stover yield, than for grain or stover yield per se.