Envirotyping within a multi-environment trial allowed identifying genetic determinants of winter oilseed rape yield plasticity

Envirotyping within a multi-environment trial allowed identifying genetic determinants of winter oilseed rape yield plasticity | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Envirotyping within a multi-environment trial allowed identifying genetic determinants of winter oilseed rape yield plasticity Erwan CORLOUER, Christopher SAUVAGE, Magalie LEVEUGLE, Nathalie NESI, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3788902/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract A main challenge for rapeseed consists in maintaining seed yield while adapting to climate changes and contributing to environmental-friendly cropping systems. Breeding for plasticity and cultivar adaptation is one of the keys to meet this challenge. Genetic diversity for plasticity is the expression of Genotype x environment interaction. Therefore, we propose to identify the genetic determinant of seed yield G×E interaction for winter oilseed rape using GWAS coupled with a multi-environmental trial and to interpret them in the light of environmental characteristics. Thanks to a comprehensive characterization of a multi-environmental trial using 79 indicators, 4 contrasting envirotypes were defined and used to identify interactive and stable seed yield (SY) QTL. A total of four QTL were detected for SY, among which, QA09 and QC09a, were stable (detected at the multi-environmental trial scale or for different envirotypes and environments); and one, QA07a, was specifically detected into the most stressed envirotype. The analysis of the molecular diversity at QA07a showed a lack of genetic diversity within modern lines compared to older cultivars bred before the selection for low glucosinolate content. The results were discussed in comparison to other studies and methods as well as in the context of breeding programs. Brassica napus Seed yield GWAS QTL stability plasticity MET Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The main challenge for agriculture consists in ensuring food security while adapting to climate changes and contributing to eco-friendly farming systems. Both will be met through cultural practices adaptations and the development of new crop varieties (Lobell et al. 2018). Indeed, crops must face the effects of climate change characterized by an increase in temperature and CO 2 concentration but also by the increase of intense climatic phenomena ( e.g. , droughts, floods or frosts; Bell et al. 2004 ). In addition, agroecological practices have emerged as a way to produce more food in a sustainable manner by enhancing ecology-based practices (Wezel et al. 2014 ) and reducing the use of pesticide and chemical inputs. These practices include the use of natural biological control of pests, cover crops, intercropping, or cultivar mixtures, in addition to new soil management practices (reduced tillage, …) that increases the complexity of plant-environment interactions (Lamichhane et al. 2018 ). Faced with these profound changes, the question of crop adaption to ensure stable performance under fluctuating agricultural and climatic conditions arises. New crop varieties must then be adapted to a wide range of pedoclimatic and environmental conditions, meaning they must present phenotypic plasticity (Nicotra et al. 2010 ). The phenotypic plasticity corresponds to the range of phenotypic variation observed for a dedicated genotype under a variety of environmental conditions (Nicotra et al. 2010 ; El-Soda et al. 2014 ). Phenotypic plasticity differences exist among genotypes indicating the possibility to breed for stability, however this remains complex. Indeed, the observed phenotype can be expressed as the sum of a genetic effect (G), an environmental effect (E) defined here as the combination of pedoclimatic conditions and cultural practices, and the G×E interaction, corresponding to the modification of the phenotypic plasticity between genotypes (Becker and Leon 1988 ; Malosetti et al. 2016 ; van Eeuwijk et al. 2016 ). Breeding programs must therefore consider the G×E interaction to breed for new varieties better adapted to fluctuating environments (Cooper et al. 2020 ; Snowdon et al. 2021 ). Classically, breeding programs rely on average performance of genotypes grown under multi-environmental and multi-annual field trials. Although this allows passive selection for a small adaptive effect (Snowdon et al. 2021 ), this methodology does not allow a direct access to the genetic determinism of QTL×E interactions that underly plasticity (Garin et al. 2020 ). A first approach to unravel the genetic determinism of QTL×E consists in a comparison of QTL detected using a population trialed across a multi environmental network. This method is efficient to access to the QTL×E interaction but the results remain difficult to interpret (Garin et al. 2020 ). To gain power in detecting QTL associated to plant adaptation, stability indicators have been developed and used in genetic analyses of plasticity. For instance, ecovalence indices (Wricke 1962 ; dos Santos Silva et al. 2021 ) or AMMI stability values are used to describe genotypic contribution to G×E and to characterize the adaptability versus stability in different environments (Purchase et al. 2000; Bouchet et al. 2016 ; Lozada and Carter, 2020). Genotypic reaction norms to environmental gradient are also commonly used, they correspond for instance to Finlay and Wilkinson's regression slope (1963) (see Diouf et al. 2020 ; Xavier et al. 2018 for application examples). More recently, linear mixed models have been developed to consider simultaneously all environments and genotypes in a multi-environment trial (MET) (van Eeuwijk et al. 2010 ; 2016 ) and have opened new avenues to detect G×E interactions and access its genetic determinism (Malosetti et al. 2008 ; Happ et al. 2021). However, without a comprehensive environmental characterization of the MET, the QTL involved in plasticity are difficult to interpret in terms of the underlying mechanisms of plant adaptation. Rapeseed is a major worldwide crop cultivated mainly for its seeds oil and meal production, presenting an estimated annual production of 71.3 Mt in 2021 (FAOSTAT 2023). Farmers have more and more difficulties to maintain rapeseed seed yield under adverse environmental conditions ( e.g. , drought during sowing, insect damages during fall, nutritional constraints and abiotic stresses). This leads to a reduction of the cultivated surfaces (-21% between 2016 and 2022, AGRESTE 2023). Seed yield (SY) is a complex trait defined by multiple components as plant population density, the number of pods per plant, the number of seeds per pods or the seed weight (Diepenbrock 2000 ). The potential seed yield of winter oilseed rape is determined since the end of the fall but depends on many factors and stresses occurring all along the crop life cycle as abiotic stresses (temperature, water, radiation), biotic stresses (pest, pathogens and weeds) or nutritional stresses, particularly with nitrogen (Rathke et al. 2006 ). All these constraints on winter oilseed rape seed yield elaboration result in an important environmental effect and G×E interaction that explained around 10% of the seed yield variation under French conditions (Bouchet et al. 2016 ; Corlouer et al. 2019 ). Numerous studies reported the genetic determinant of SY in rapeseed (reviewed by Delourme et al. 2018 ) and reported a high number of QTL (Shi et al. 2009 ; Raboanatahiry et al. 2018 ), thus confirming SY as highly polygenic. The comparison between QTL detected in different environments revealed also QTL×E interactions, with some QTL being characterized as “stable” ( i.e. detected across all environments) and QTL being characterized as “interactive” ( i.e. specific to an environmental condition, Bouchet et al. 2016 ). However, the QTL specificity observed in a MET is rarely associated with the identification of the environmental features causing the observed adaptation. In this context, this study aims at identifying the genetic determinants of seed yield G×E interaction in winter oilseed rape and interpret them in the light of environmental characteristics. We based our strategy on the analysis of winter oilseed rape accessions experimented across a multi environment trial (MET-47) consisting in 47 environments representing the diversity of French growing conditions. First, we developed a comprehensive characterization of the MET-47 to identify the limiting factors that occurred. Then, to identify the genetic determinant of the G×E interaction, a panel of 173 accessions was experimented in a sub-MET of 22 environments out of the 47 (MET-22). Envirotypes were defined among the MET-22 and corresponded to the clustering of the 22 environments according to their limiting factors pattern. Then GWAS analyses were carried out using BLUE (Best Linear Unbiased Estimator) obtained for each genotype and each envirotype to identify QTL specific of G×E. Finally, a genetic diversity analysis was carried out to decipher the potential impact of breeding for seed quality on the reduction of genetic diversity at those detected QTL that may limit phenotypic plasticity. Materials and methods Field network description Field experiments were run in a multi environment trial (MET-47) consisting of 47 environments defined as combinations of ‘year × location × nitrogen (N) fertilization’ in France between 2011 and 2018 (Supplemental Data 1). Each individual trial was conducted using classical crop management for winter oilseed rape (WOSR) with comprehensive protection against weeds, pests and pathogens. Optimal N fertilization was estimated using the balance sheet method (Rémy and Hébert 1977 ; Parnaudeau et al. 2009 ) for a target yield of 3.5 t.ha − 1 and applied in a subset of 19 environments defined as high N (N + ). In contrast, a low N fertilization regime was applied in the remaining 28 environments defined as low N (N − ) that corresponded to the N + regime lowered by 80–100 kg.ha − 1 of N (Supplemental Data 1). Each environment was designed as a randomized complete block design with two to four replicates depending on the environment, with an individual plot area ranging from 6.75 m 2 to 14 m 2 . Mature dry seeds were harvested when the vegetative parts were fully senescent and the seeds were dark and hard. The targeted traits were the seed yield (SY in q.ha − 1 ) determined for each genotype in each trial and adjusted to 0% water content and 0% impurities, as well as the seed number (SN in seeds.m − 2 ) calculated according to the SY and the thousand seed weight (SN = SY×100 000/TSW). Plant material The WOSR genotypes ‘Aviso’ and ‘Montego’ were trialed over the whole MET-47 and therefore considered as probe genotypes for environmental characterization as reported by Corlouer et al. ( 2019 ). A diversity panel of 173 WOSR accessions (hereafter referred to as P173) was scored for SY and SN over a subset of 22 environments (11 N + and 11 N − , called hereafter MET-22) run over the 2013–2014 and 2014–2015 growing seasons. The P173 accessions originated primarily from Western Europe and mostly represented commercial varieties released from 1959 to 2010 (Supplemental Data 2) with 31 accessions of the ‘++’ type (high contents in both erucic acid and glucosinolates; high C22:1, high GSL), 15 accessions of the ‘0+’ type (low C22:1, high GSL), 1 accession of the ‘+0’ type (high C22:1, low GSL), and 126 accessions of the ‘00’ type (low C22:1, low GSL) of which 46 were elite lines. Seeds were provided by the BrACySol Biological Resource Center or private seed companies. The P173 population was genotyped using the Brassica 60K Illumina Infinium™ array (Clarke et al. 2016 ) and an exome sequence capture assay (Leveugle et al. 2015 ). A total of 217,805 SNP were scored and validated for the current study using a threshold of 2.5% for the minor allele frequency (MAF) and of 10% for the missing values. The missing genotyping data were inferred using Beagle v3 software (Browning and Browning 2009 ). All the SNP were physically anchored onto the latest Brassica napus reference genome of Darmor- bzh (Rousseau-Gueutin et al. 2020 ). Environmental characterization and envirotyping Pedoclimatic indicators were calculated as described by Corlouer et al. ( 2019 ) and estimated for each of the key periods of the winter oilseed rape growing cycle. Briefly, these periods were fixed on meteorological data and the phenology of the probe genotypes. They covered fall (F), climatic winter (CW), bolting (B), seed number fixation (P300), reserve allocation to the pod (P600) and seed growth (P1000) stages. The 70 indicators correspond to four main categories: water stress, temperature, radiation and vernalization conditions (Corlouer et al. 2019 ). In addition, new indicators were developed to consider the contrasting N nutrition status (Supplemental Data 3). These included indicators related to the plant N status such as the nitrogen nutrition index (NNI; Colnenne et al. 1998) as well as the aerial dry biomass (ADB) and the aerial nitrogen quantity (AN), all scored at bolting stage (BBCH50, Lancashire et al. 1991) with a minimum delay of two weeks after the latest N supply. These values were measured on plants collected in the field on a surface of 1 m² and are expressed in quantity per hectare. On the other hand, indicators related to the N fertilization management were also considered such as the total amount of N supplied to the crop (N total , in kg.ha − 1 ), and the water regime at the time of N supply was assessed considering the maximum number of days without rainfall during the 10 days preceding the N supply (DBI, dryness before input), the number of days without rainfall over 10 days after the N supply (DAI, dryness after input), the sum of the rainfalls over 10 days after the N supply (RF, rainfall), the number of days of leaching over 10 days after the N supply (RO, run off), and the mean value of the water soil content at the time of the N supply (from 3 days before up to 10 days after the input) expressed in percentage of the maximal water soil content (WSC_I). Given that N fertilization was provided in one, two or three applications depending on the local pedoclimatic conditions, we defined a unique indicator for each trial that considers the number of N supplies, calculated as following: $$Final indicator={\sum }_{i=0}^{n}{Indicator}_{i}\times \frac{{N}_{i}}{{N}_{total}}$$ 1 Where Indicator i is the given indicator (DBI, DAI, RF, RO or WSC_I) calculated at the N supply i , N i is the amount of N brought to the environment at the application i , and N total the total N amount considering all the N applications. When no N fertilization was applied, the final indicators were fixed to 0. Missing indicators were imputed using the missMDA package (Josse and Husson 2016 ). A total of 79 indicators (Supplemental Data 3) were considered in the present study. To go further into the analysis of the G×E, an envirotyping was carried out according to the methodology proposed in Corlouer et al. ( 2019 ). Briefly, a univariate PLS regression analysis was run at the MET-47 scale by regressing the mean seed yield of Aviso and Montego for each of the 47 environments on the 79 environmental indicators. A variable selection was performed to keep the reduced set of indicators that best explained seed yield variation, hereafter referred as limiting factors. Then, a hierarchical clustering was performed on the environments of the MET-22 to define envirotypes according to their pattern of limiting factors. Phenotypic data analyses Trait analysis was run using three different mixed linear models that were fitted using the ‘lme4’ (Bates et al. 2015 ) and ‘lmerTest’ packages (Kuznetsova et al. 2017 ), and corresponded to the three scales of analysis used in this study, i.e. the whole MET-22 scale, the envirotype scale (cluster of different environments presenting the same pattern of limiting factors), or the single environment scale. A first linear mixed model (2) was fitted at the MET-22 scale including an effect of the envirotype to evaluate the impact of the envirotyping on the variance repartition. $${Y}_{ijkl}= \mu +{G}_{i}+{En}_{l}+{En}_{l\left(k\right)}+{G}_{i}\times {En}_{l}+{G}_{i}\times {En}_{l\left(k\right)}+\underset{\_}{{R}_{j(l\times k)}}+\underset{\_}{{\epsilon }_{ijkl}}$$ 2 where \({Y}_{ijkl}\) is the phenotypic value, \(\mu\) is the population mean, \({G}_{i}\) stands for the effect of genotype i, \({En}_{l}\) for the envirotype l, \({En}_{l\left(k\right)}\) for the environment k nested in the envirotype l, \({R}_{j(l\times k)}\) for the replicate j, nested in the environment k and envirotype l; and \({\epsilon }_{ijkl}\) is the residual. Then, at the single environment scale models (3) and (4) were used, and at the envirotype and at the MET-22 scale models (5) and (6) were used to study the genotypic effect, to calculate the corresponding Best Linear Unbiased Estimators (BLUE), and to estimate trait heritability. These models are presented below. Model (3) was fitted at the scale of each environment: $${Y}_{ij}=\mu +{G}_{i}+\underset{\_}{{R}_{j}}+\underset{\_}{{\epsilon }_{ij}}$$ 3 where \({Y}_{ij}\) is the phenotypic value, \(\mu\) is the population mean, \({G}_{i}\) stands for the effect of genotype i, \({R}_{j}\) for the replicate j and \({\epsilon }_{ij}\) is the residual. The replicate effect and the residual were declared as random. The heritability was calculated as: $${h}^{2}=\frac{{\sigma }_{G}^{2}}{{\sigma }_{G}^{2}+\raisebox{1ex}{${\sigma }_{\epsilon }^{2}$}\!\left/ \!\raisebox{-1ex}{$r$}\right.}$$ 4 where \({\sigma }_{G}^{2}\) is the genetic variance, \({\sigma }_{\epsilon }^{2}\) the residual variance and r the number of replicates per genotype. Model (5) was applied at the envirotype or at the MET-22 scale: $${Y}_{ijk}= \mu +{G}_{i}+{E}_{k}+{G}_{i}\times {E}_{k}+\underset{\_}{{R}_{j\left(k\right)}}+\underset{\_}{{\epsilon }_{ijk}}$$ 5 where \({Y}_{ijk}\) is the phenotypic value, \(\mu\) is the population mean, \({G}_{i}\) stands for the effect of genotype i, \({E}_{k}\) for the environment k, \({R}_{j\left(k\right)}\) for the replicate j, nested in the environment k and \({\epsilon }_{ijk}\) is the residual. The corresponding heritability was defined as follow with all terms declared as random in Eq. ( 5 ): $${h}^{2}=\frac{{\sigma }_{G}^{2}}{{\sigma }_{G}^{2}+\raisebox{1ex}{${\sigma }_{G\times E}^{2}$}\!\left/ \!\raisebox{-1ex}{$t$}\right.+\raisebox{1ex}{${\sigma }_{\epsilon }^{2}$}\!\left/ \!\raisebox{-1ex}{$r*e$}\right.}$$ 6 where \({\sigma }_{G}^{2}\) is the genetic variance, \({\sigma }_{G\times E}^{2}\) the G×E interaction variance, \({\sigma }_{\epsilon }^{2}\) the residual variance, e the number of environment and r the number of replicates per genotype. GWAS analyses The BLUE defined at each scale (environment, envirotype, MET-22) using models (3) and (5) for SY and SN were used to perform the genetic analyses. GWAS analyses were conducted using the FastLMM algorithm (Lippert et al. 2011 ) using a kinship matrix calculated for each linkage group as described by Rincent et al. ( 2014 ) and following the Astle algorithm (Astle and Balding 2009 ). A Bonferroni detection threshold was set to 10%, based on a corrected SNP population as proposed by the simpleM method developed by Gao et al. ( 2008 ). This method was based on the composite linkage disequilibrium (CLD) correlation between SNP. The CLD was used to calculate the effective number of independent tests (M eff ). In this study, the M eff was calculated and fixed to 19,886 SNP. We defined the threshold of highly significant associated SNP as: $$t=-{\text{log}}_{10}\left(\raisebox{1ex}{${\alpha }_{G}$}\!\left/ \!\raisebox{-1ex}{${M}_{eff}$}\right.\right)$$ 7 where t is the value of the value of the threshold, \({\alpha }_{G}\) is the threshold of the p -value and was fixed to 0.1 and M eff was the effective population of SNP. The final threshold used was t = 5.30. Genetic diversity analysis A genetic diversity was conducted at the associated loci detected through GWAS. This evaluated the potential impact of a major breeding event based on the selection of varieties without glucosinolates in seeds. Therefore, two populations were confronted: a first population (GSL+) composed by genotypes with high contents of glucosinolate in seeds (> 18 µmol.g − 1 ) (46 WOSR, “++” and “0+”) and a second population (GSL-) composed by the remaining accessions of the P173 that present low glucosinolate content (< 18 µmol.g − 1 ) (127 WOSR, “+0” and “00”) (Supplemental Data 2). The nucleotide divergence statistic (π) and the mean Fst (Weir and Cockerham, 1984 ) for the whole P173 and for the GSL + population and the GSL- population were calculated using VCFtools v0.1.13 software (Danecek et al. 2011 ) for a window of 10 kb around each genomic region of interest. Results N stress was not the only factor impacting seed yield A first overlook of the MET-47 was carried out by looking at the relationship between the mean values of seed yield and NNI for Aviso and Montego per environment (Fig. 1 ). When focusing on environments defined as N − , it is to be noticed than NNI values ranged from 0.63 to 1.26, with NNI values exceeding 1.0 for 7 N − -environments. Moreover, in two N − environments (Lou18_N − and Chr18_N − ) the observed SY was even higher that the targeted value for N + conditions. Thus, these results demonstrated the difficulty of implementing a nitrogen stress under field conditions. When considering the N + environments, it can be observed that the targeted seed yield of 35 q.ha − 1 was achieved in 13 environments out of 19. However, in these 13 environments, NNI was always higher than 1 (except for LR13_N + ), indicating the presence of other environmental factors that did impact seed yield elaboration. These results illustrated the need to consider a comprehensive environmental description of the trials to detect the factors that indeed affected SY. Identification of 13 environmental indicators limiting seed yield MET-47 offered a unique chance to get insights into the SY most limiting factors. For that purpose, the set of 79 indicators was used to run a PLS regression analysis on the mean SY variation of Aviso and Montego for each of the 47 environments. SY limiting factors corresponded to a set of 13 indicators (see Table 1 for a precise description). Predicted seed yields were strongly correlated to the observed seed yields (R² = 0.87, RMSE = 2.6). These 13 indicators covered the whole crop growing cycle from the climatic winter (CW) to the reserve allocation to the pod period (P600). Six indicators referred to the temperature (TMN_CW, TMAX_B, TMAX_FLO, HT_FLO, TMAX_P300 and TMIN_P600), two to the water status (WSC_MAX and WD_P600), one to the radiation (SSR_FLO), one to vernalization conditions (VERN) and three to nitrogen status (NNI, N total and DAI; Table 2 ). Finally, the study of the Pearson correlations (Table 2 , Supplemental Fig. 1) showed important correlations between the 13 identified indicators and indicators related to the reproductive periods of winter oilseed rape (FLO to P1000) and to the climatic winter period (Table 1 ), highlighting the key role of these periods on the elaboration of the final seed yield. Table 1 Seed yield limiting indicators across the MET-47 Indicators Description Correlated indicators* TMN_CW Mean temperature during CW - TMAX_B Maximal temperature registered during B - TMAX_FLO Maximal temperature registered during FLO SSR_CW (0.67); LT_B (-0.69); LT_FLO (-0.66) HT_FLO Number of days with high temperature (> 25°C) at FLO TMIN_CW (-0.7); TMN_FLO (0.66); LGDD_FLO (0.66); TMN_P600 (0.7) TMAX_P300 Maximal temperature registered during P300 LGDD_CW (0.68); SSR_CW (0.67); LSR_CW (0.7); LT_FLO (-0.68); HT_P300 (0.68) TMIN_P600 Minimal temperature registered during P600 TMIN_FLO (0.73); TMN_FLO (0.7); LT_FLO (-0.67); LGDD_FLO (0.7); TMN_P600 (0.78) WD_P600 Number of days with WSC = 0 during P600 HT_F (0.66); WS_FLO (0.7); WSC_P300 (-0.68); WSC_P600 (-0.66) WSC_MAX Maximal water soil capacity TMN_F (-0.67) SSR_FLO Sum of solar radiation during FLO LSR_FLO (-0.67); TMAX_P1000 (0.69); HT_P1000 (0.65) VERN Optimal vernalization treatment LT_CW (0.65) NNI Nitrogen nutrition index AN (0.65); N total (0.66) N total Nitrogen input NNI (0.66) DAI Number of days of dryness after N supply - *Value of the Pearson correlation coefficient was indicated between brackets Table 2 Results of the mixed linear model applied on the MET-22 to evaluate the impact of the envirotyping on variance repartition. Data had been analyzed using linear model (2): \({Y}_{ijkl}= \mu +{G}_{i}+{En}_{l}+{En}_{l\left(k\right)}+{G}_{i}\times {En}_{l}+{G}_{i}\times {En}_{l\left(k\right)}+\underset{\_}{{R}_{j(l\times k)}}+\underset{\_}{{\epsilon }_{ijkl}}\) ; with the genotype (G), the envirotype (En), the environment nested in the envirotype (En l(k) or En×E), the genotype by envirotype interaction (G×En), the genotype by environment by envirotype interaction (G i ×En l(k)) or G×En×E) and the block effect nested in the environment, nested in the envirotype (R) and compared with the results of linear model (5): \({Y}_{ijk}= \mu +{G}_{i}+{E}_{k}+{G}_{i}\times {E}_{k}+\underset{\_}{{R}_{j\left(k\right)}}+\underset{\_}{{\epsilon }_{ijk}}\) , with the genotype (G), the environment (E), the genotype by environment interaction (G×E) and the block (R) nested in the environment. Trait Models % Variance Models (5) G E G×E R ε SY 24.7 55.5 5.8 3.8 10.1 Model (2) G En En×E G×En G×En×E R ε SY 20 44.4 19.2 1.2 3.9 3.1 8.2 MET-22 included four contrasting envirotypes The MET-22 is a subset of the MET-47 where 173 genotypes were trialed, allowing the study of the G×E interactions. The mean seed yield of the MET-22 was 30.35 q.ha − 1 (29.40 q.ha − 1 for the MET-47), and the mean NNI across the MET-22 was 1.06 (1.02 for the MET-47). The MET-22 description using the limiting indicators previously identified presented a similar profile to the MET-47 (Supplemental Fig. 2). A hierarchical clustering was performed on the environments of the MET-22 to define envirotypes according to their pattern of limiting indicators. Four envirotypes were defined (EA, EB, EC and ED; Fig. 2 , Supplemental Data 1). Envirotype EA is composed of three N + environments and one N − environment. The mean seed yield observed across EA was 37.6 q.ha − 1 and the mean NNI was 1.29. This envirotype can therefore be defined as non N stressed and high yielding. The analysis of the limiting indicators pattern showed that EA, when compared to the mean MET-22, is characterized by lower temperature during winter, lower heat stress and solar radiation at flowering (HT_FLO), a higher temperature during the seed number fixation period (TMAX_P300) and a higher NNI (Fig. 3 ). In contrast, envirotype ED was characterized by the lowest mean seed yield (18.5 q.ha − 1 ) and a mean NNI of 0.91 suggesting that the environments of this envirotype have been more stressed. ED gathered three N − environments and one N + environment. The limiting indicators pattern of envirotype ED is characterized by poor vernalization conditions (VERN), higher temperature during winter (TMN_CW), lower SSR_FLO and TMAX_P300 than the mean MET-22 (Fig. 3 ). Finally, the two remaining envirotypes, EB and EC, presented mean seed yields of 28.4 q.ha − 1 and 27.2 q.ha − 1 respectively while the mean MET-22 seed yield was 30.34 q.ha − 1 . Envirotype EB was composed of eight environments (three N − and five N + ) and envirotype EC of six environments (four N − and two N + ). Envirotypes EC and EB distinguished from each other according to NNI (1.22 for EB and 0.92 for EC), N total , VRN, and conditions at flowering (HT_FLO and SSR_FLO). The profiles of limiting indicators showed that TMAX_FLO, HT_FLO, SSR_FLO, TMAX_P300, WSC_MAX, TMN_CW were lower in envirotype EB compared to the MET-22. Envirotype EC was characterized by higher HT_FLO, SSR_FLO, WSC_MAX and TMN_CW while DAI, NNI, N total , TMAX_B and VERN were lower than observed for the MET-22 (Fig. 3 ). Our results demonstrated that the 22 environments of the MET-22 could be classified in 4 envirotypes: envirotype EA that can be considered as not stressed, envirotype ED considered as the most stressed, while envirotypes EB and EC showed moderate stress and mean SY similar to the mean SY observed at the MET-22 scale, but with a different pattern of limiting factors. Envirotyping explained up to 70% of environmental variation and 24.6% of G×E To evaluate the impact of envirotyping on the variance distribution of the E and G×E effects, a linear model (2) was applied to SY. At the MET-22 scale, the envirotype effect (En) explained a main part of the environmental effect (70% for SY) as shown by the comparison of models (2) and (5) (Table 2 ). Genotype by envirotype interaction (G×En) explained 24.6% of the G×E interaction observed at the MET-22 scale. Within each envirotype, the G×E variation was reduced in envirotype EA (2.3% of total variation) when compared to the MET-22 (5.8%), but increased for envirotypes EB (8.7%) and particularly for ED (15.9%) (Table 3 ). As SY and SN were highly correlated (r = 0.94, p -value < 0.001) the envirotyping based on SY was used to study SN variation and its repartition. Similar results were observed and are reported on Table 4 . Table 3 Results of the mixed linear model applied for the MET-22 and for each envirotype considering all trials as confounded (linear model (5)) and heritabilities estimation. Linear model (5):, with the genotype (G), the environment (E), the genotype by environment interaction (G×E) and the block (R) nested in the trial. Heritabilities (h²) were estimated using Eq. ( 6 ): % Variance (a) Trait Group Nb of environment (b) G E G×E R ε h² SY MET-22 22 24.7 55.5 5.8 3.8 10.1 0.98 Envirotype EA 4 37.7 48.3 2.3 3.8 7.8 0.96 Envirotype EB 8 38.5 34 8.7 4.8 14.1 0.95 Envirotype EC 6 45.7 8.7 5.9 10.8 29 0.94 Envirotype ED 4 28.9 34.8 15.9 5.3 15 0.83 SN MET-22 18 33 40.4 8.1 4.9 13.5 0.98 Envirotype EA 2 46.9 34.1 8.1 1.1 9.9 0.88 Envirotype EB 7 49 27 9.4 2.1 12.5 0.96 Envirotype EC 6 43.8 6.9 9.1 13.7 26.5 0.93 Envirotype ED 3 29.6 32.5 11.5 7.4 19 0.81 (a) Percentage of variance explained by each parameter (b) Number of trials considered for the evaluation of the different effects and estimation of heritabilities Table 4 Heritabilities estimated for each trait and each environment of the MET-22. Components of the heritabilities were obtained using the linear model (3): ; Heritabilities (h²) were calculated according to Eq. ( 4 ): h² Envirotype Environment SY SN EA Dij15_N − 0.90 - Dij15_N + 0.91 - Ver15_N + 0.91 - Yeb15_N + 0.92 0.93 EB Ch14_N + 0.93 0.93 Pre15_N − 0.81 0.85 Pre15_N + 0.80 0.85 Sel14_N − 0.94 0.93 Sel14_N + 0.95 0.93 Sel15_N + 0.63 - Ver14_N + 0.91 0.9 Ver15_N − 0.86 - EC LR15_N − 0.85 0.88 LR15_N + 0.87 0.9 Md15_N − 0.67 0.53 Md15_N + 0.74 0.65 Ver14_N − 0.77 0.71 Yeb15_N − 0.95 0.96 ED Ch14_N − 0.81 0.78 Pre14_N − 0.68 0.75 Pre14_N + 0.87 0.87 Sel15_N − 0.96 - Heritabilities were calculated for SY and SN for each environment, for each envirotype and for the mean MET-22. Into more details, SY and SN were highly heritable (0.98 for the MET-22 for both traits) and heritabilities ranged from 0.83 to 0.96 for SY and from 0.81 to 0.86 for SN depending on the envirotype (Table 3 ). At the environment scale, heritabilities ranged from 0.63 to 0.96 for SY and from 0.53 to 0.86 for SN (Table 4 ). For most of the environments, SY and SN were highly heritable. Most of the SY-related QTL detected at the environment scale were specific of a single environment Comparison of SY-QTL detected for each environment is a first step to decipher the genetic determinism of G×E. Therefore, the BLUE obtained for each genotype and each environment using model (3) were used as input for GWAS analysis. A total of 87 SNP were detected considering all the analyses performed for each trait and each environment (Supplemental Data4). Theses SNP were grouped into 11 QTL related to SY (QA03, QA05a, QA07a, QA07b, QA09, QC02, QC03a, QC04, QC08, QC09a and QC09b) and four QTL related to SN (QA05b, QA07a, QA09 and QA10) (Table 5 ). For almost all loci, the most frequent allele in the population was the favorable one except for QA03, QA07a and QC09b. Considering both traits, it is worth noting that all QTL were specific to a single environment except QA07a, QA09, QA10, QC02, QC04 and QC09a. QA07a, QA10, QC02, QC04 were only detected in two environments. The QA09 and QCA09a were detected in a large range of environments with 6 environments for QA09 and 7 for QC09a, respectively. Table 5 Description of the QTL detected for seed yield and seed number stability/instability. A QTL is defined as a region that can gather different individual QTL detected at different scales (MET, envirotype, environment; Supplemental Data 4) with overlapping positions. The range of the individual positions observed for a dedicated QTL is indicated in the “Positions” column. The positions refer to the reference genome Darmor- bzh v10 version (Rousseau-Gueutin et al, 2020 ). The “Traits” column refers to Seed Yield (SY) or Seed Number (SN), the environments, envirotypes and MET-22 indicated the different scales for which the considered QTL was detected. Name Chromosome Positions Traits Environments Envirotypes MET-22 QA03 A03 6 452 989 SY Pre15_N +(EB) - - QA05a A05 2 078 385–2 080 519 Sy Chr14_N +(EB) - - QA05b A05 3 630 228 SN Pre14_N +(ED) - - QA07a A07 138 830–1 009 271 SN LR15_N +(EC) / Pre14_ N −(ED) ED - 138 830–1 009 271 SY LR15_N +(EC) QA07b A07 20 033 023–20 033 025 SY LR15_N +(EC) - - QA09 A09 3 933 991–4 504 817 SY/SN Yeb15_N +(EA) /Ver14_N +(EB) Ch14_N −(ED) /Ch14_N +(EB) Dij15_N −(EA) / LR15_N +(EC) EA / ED Yes QA10 A10 2 515 307 SN Pre14_N −(ED) / Ver14_N −(EC) - - QC02 C02 60 063 320–60 615 209 SY Ver14_N +(EB) / Yeb15_N +(EA) - - QC03a C03 20 140 168–20 809 663 SY Sel15_N +(EB) - - QC03b C03 23 645 808 SN - EC - QC04 C04 6 033 108–6 289 953 SY Dij15_N +(EA) / Pre15_N −(EB) - - QC08 C08 37 326 511–37 326 814 SY Ver14_N +(EB) - - QC09a C09 2 184 690–4 511 229 SN - EA / EB - QC09a C09 2 184 690–4 511 229 SY Ver14_N +(EB) /Sel14_N −(EB) Md15_N +(EC) / Dij15_N −(EA) EA / EB / EC Yeb15_N +(EA) /Ch14_N +(EB) LR15_N +(EC) QC09b C09 51 377 310 SY Ver14_N −(EC) - - Envirotyping highlighted 5 QTL BLUE calculated for each genotype and for each of the four envirotypes were used for GWAS analysis. This method offers the opportunity to identify QTL involved in the G×E interaction. In this way, the detected QTL will be linked to the envirotype characterization ran upon through their profile of limiting indicators. QTL QC09a (Table 5 ) was detected for SY and SN and for 3 envirotypes (EA, EB and EC). It was also detected for SY in 7 environments as previously mentioned (Table 5 , Supplemental Data 4). It explained between 9.5% and 16.7% of SY variation depending on environment or envirotype; and 20.4% of SN variation for envirotype EA and 18.8% of SN variation for envirotype EB. The major allele was favorable to increase both traits (Supplemental Data 4). The SN QTL QC03b (Table 5 ) was specifically detected in envirotype EC. It was not detected at the environmental scale. This QTL explained 21% of the SN variation (R²=0.21) in envirotype EC and the major allele was the favorable one. However, confidence in this QTL is low as it is composed of a single SNP (Supplemental Data 4). QA07a, was detected for SN in envirotype ED on chromosome A07 (Table 5 ). This QTL was also detected in environment LR15_N + (EC) and environment Pre14_N − (ED) for SN and for SY in LR15_N + (EC) (Table 5 ). This QTL explained 19.5% of the SN variation in envirotype ED and the minor allele of the population was the favorable one. (Supplemental Data 4). QA07a could be considered as specific to stressed conditions. Lastly, the QA09 and QC09a QTL, that were detected for a wide range of environments, were also detected for several envirotypes: EA and ED for QA09 and EA, EB and EC for QC09a. QA09 was the unique QTL detected across the MET-22 BLUE obtained for each genotype at the whole MET-22 scale were used to detect consistent QTL controlling SY and SN across environments. A set of 15 SNP were detected for SY on chromosome A09 (Supplemental data 4) and consisted in the QTL QA09 (Table 5 ). QA09 explained 12% of the SY variation and the favorable allele was the major allele. As previously shown, QA09 was also detected for SY at the envirotype scale for EA and ED (Table 5 ), and at the environment scale in 6 environments that belonged to EA (2), EB (2), EC (1) and ED (1) (Table 5 , Supplemental Data 4). The same analysis carried out for SN revealed that locus QA09 was also detected, but only in envirotype EA. Thus, QA09 was qualified as a stable effect genomic region controlling SY and SY components across a wide range of environmental conditions. Two stable QTL (QA09 and QC09a) and an interactive QTL (QA07a) were revealed thanks to a multi-scale QTL detection (environment, envirotype, MET) Finally, the QTL analysis revealed two loci (QA09 and QC09a) that can be characterized as stable and a locus (QA07a) that can be characterized as interactive. QA09 was detected across the MET-22, in 6 environments and two envirotypes and QC09a was detected in three envirotypes and 7 individual environments. QA07a was specifically detected for the most stressed envirotype (ED) and could be a good candidate for breeding programs. The allelic diversity underlying this QTL must be further investigated to evaluate the genetic diversity available at this locus for breeding purposes. Ten other QTL were detected at the environment scale but could not be linked to a limiting factor profile. To go further into the characterization of the three main QTL detected, a mixed linear model was fitted to estimate the QTL×E interaction using the data obtained for the whole MET-22 and both for SY and SN. (Table 6 ). The QA09×E interaction explained 31.9% and 34.5% of the total variance for SY and SN respectively, whereas the genotype effect explained only 13.2% and 20.1% of the total variance for SY and SN respectively. The QC09a×E interaction explained 31.9% and 38.5% of the total variance for SY and SN respectively, whereas the genotype effect explained only 12.3% and 18.7% of the total variance for SY and SN respectively. For the QA07a, that was qualified as interactive, the QTL×E effect explained less than 1% of variance for both SY and SN, this can be related to the overall small effect of this QTL at the MET-22 scale. Table 6 Results of the mixed linear model applied at the MET-22 scale to test the effect of the QTL×E or the QTL×En interaction. The three QTL QA09, QC09a and QA07a (described in Table 5 ) were considered. The following mixed Linear model was used \({Y}_{ijkl}= \mu +{G}_{i}+{E}_{k}+{marker}_{j}\times {E}_{k}+\underset{\_}{{R}_{l\left(k\right)}}+\underset{\_}{{\epsilon }_{ijkl}}\) , with the genotype (G), the environment (E), the QTL (marker), the genotype by environment interaction effect (G×E) and the block effect (R) nested in the trial., as proposed by Happ et al (2021). The QTL effect is tested using the marker presenting the highest -log( p -value) within its confidence interval. In each case, the percentage of variance explained by the considered effect is indicated. Seed Yield (SY) Seed Number (SN) QA09 QC09a QA07a QA09 QC09a QA07a Genotype 13.2% 12.3% 24.3% 20.1% 18.7% 31.9% Trial 35.7% 35.7% 56% 19.4% 16.7% 41.4% Block × Trial 4.1% 4.2% 3.7% 5.4% 5.3% 4.9% QTL × Trial 31.9% 31.9% 0.3% 34.5% 38.5% 0.9% residual 15.2% 15.9% 15.6% 20.7% 20.8% 20.9% Diversity analysis at QA07a revealed a lack of favorable allele in the modern-grown varieties The composition of the panel P173 (GSL + vs GSL- lines) allowed highlighting the recent history of winter oilseed rape breeding, including rapid selection for low glucosinolate contents in seeds, and estimating its impact on genetic diversity at the whole genome scale, as well as at the scale of previously detected QTL. The F ST analysis showed a slight differentiation between the GSL + and GSL- populations (F ST value of 0.078 at the whole genome scale, Table 7 ). However, the genetic differentiation was higher when considering specific chromosomes such as A09 and C09, already known to carry genes controlling seed glucosinolate pathway, but also for chromosomes A08, C02 and C03 (F ST values > 0.1) (Table 7 ). No specific pattern was observed on the A07 chromosome harboring the interactive QA07a QTL for SN. Table 7 Mean nucleotide diversity (π) and mean F ST statistics per chromosome and per population. Mean π Mean F ST P173 GSL+ GSL- GSL + vs GSL- Whole genome 1.69e − 04 1.77e − 04 1.61e − 04 0.078 Sub-genome A 1.81e − 04 1.92e − 04 1.70e − 04 0.070 Sub-genome C 1.58e − 04 1.62e − 04 1.51e − 04 0.086 A01 1.34e − 04 1.63e − 04 1.19e − 04 0.058 A02 1.55e − 04 1.67e − 04 1.47e − 04 0.055 A03 2.03e − 04 2.26e − 04 1.89e − 04 0.057 A04 1.65e − 04 1.69e − 04 1.60e − 04 0.060 A05 1.99e − 04 2.06e − 04 1.88e − 04 0.064 A06 2.17e − 04 2.28e − 04 2.06e − 04 0.052 A07 2.03e − 04 2.10e − 04 1.98e − 04 0.046 A08 1.96e − 04 1.91e − 04 1.81e − 04 0.159 A09 1.45e − 04 1.46e − 04 1.30e − 04 0.128 A10 1.76e − 04 1.86e − 04 1.69e − 04 0.039 C01 1.73e − 04 1.22e − 04 1.85e − 04 0.056 C02 1.31e − 04 1.46e − 04 1.21e − 04 0.111 C03 1.85e − 04 1.64e − 04 1.85e − 04 0.107 C04 1.43e − 04 1.82e − 04 1.23e − 04 0.087 C05 1.40e − 04 1.68e − 04 1.33e − 04 0.045 C06 1.60e − 04 1.85e − 04 1.42e − 04 0.098 C07 1.75e − 04 1.68e − 04 1.70e − 04 0.075 C08 1.59e − 04 1.77e − 04 1.54e − 04 0.046 C09 1.29e − 04 1.49e − 04 1.16e − 04 0.125 P173 corresponds to the whole diversity set (173 accessions) GSL + corresponds to the 46 accessions of the P173 with high contents of glucosinolate in seeds (> 18 µmol.g − 1 ). GSL- corresponds to the 127 accessions of the P173 with low contents of glucosinolate in seeds (< 18 µmol.g − 1 ). The nucleotide diversity index π was calculated at the whole genome scale and for each chromosome for both panel GSL + and GSL-. Special attention was given to GSL- since this germplasm is more connected to the elite germplasm currently used by breeders. A scan of π values was also carried out targeting detected QTL regions. On average, lower nucleotide diversity was observed in the GSL + lines than in the GSL- lines excepted for chromosomes A07, A09, A10, C01, C07 and C08 (Table 7 ). However, the study of the π index at QTL QA07a showed a higher diversity in the GSL + lines than in the GSL- (Fig. 4 ). These results showed a higher nucleotide diversity in GSL + and consequently a lack of diversity in the GSL- germplasm, corresponding here to a deficit of the favorable allele. Thus, GSL + germplasm may be an interesting source of genetic diversity to improve WOSR seed yield in unfavorable environments such as envirotype ED. In contrast, at the QTL QA09, a higher π value was observed in the GSL- illustrating a gain of nucleotide diversity induced by breeding at this locus (Fig. 4 ). Discussion Based on a comprehensive environmental characterization of a MET composed of 22 locations, we identified a group of environments (ED) characterized by a combination of stresses (poor vernalization conditions, N stress, and low temperature and radiation during flowering and grain filling period) that drastically impacted seed yield. In addition, we identified a QTL specific to these conditions (QA07a). This QTL was characterized by a reduced genetic diversity in the modern germplasm, when compared to older cultivars. Our analysis also highlighted two stable QTL controlling seed yield (QA09 and QC09a) that were expressed whatever the environmental conditions, as well as ten other QTL specifically detected in one or two single environments of the MET. The question of crop adaptation and the underlying genetic determinism of crop plasticity is clearly a major challenge for agriculture for the coming decades. To answer this question tools are being developed and the number of related studies raised drastically during the last years (van Euwijk et al. 2010; 2016). However, to our knowledge, only few studies dedicated to the analysis of Brassica napus genetic determinism of plasticity has been reported yet. The recent studies of yield stability specifically targeted the response of seed yield to water deficit (Zandberg et al; 2022 ; Raman et al. 2023 ) but did not address combination of different agro-pedoclimatic limiting factors. To address yield stability and G×E interaction, authors usually test the QTL detected across different environmental conditions (Li et al. 2016 ; Lu et al. 2017 ; Zou et al. 2022 ). However, the proposed experimental designs are often limited to 4 to 6 environments and G×E genetic determinism is addressed by the confrontation of QTL detected for single environment to QTL detected across all the environments (Wang et al. 2018 ; Zheng et al. 2017 ; Lu et al. 2017 ; Li et al. 2020 ; Arifuzzaman et al. 2019 ; Pal et al. 2021 ; Sun et al. 2016 ; Gajardo et al. 2015 ) or by the meta-analysis of QTL detected for each environment using linkage analyses (Xie et al. 2020 ; Deng et al. 2019 ). The agro-pedoclimatic description of the MET-22 environments supplemented by a clustering of the MET-22 according to the main limiting factors, allowed explaining up to 70% of the environmental effect and 24.6% of the G×E effect affecting seed yield. At the envirotype scale, the G×E part was reduced in the non-limiting envirotype EA (2.3%), but not in the most stressed envirotype ED (15.9%), when compared to MET-22 (5.8%). This result may be linked to the fact that the limiting factors used to group MET-22 were detected at the MET-47 scale. Indeed, although MET-22 and MET-47 present similar profiles in terms of limiting factors, slight differences were observed with regards to TMN-CW, N total and DAI indicators. From an analytical point of view, this resulted in a loss of power in the decomposition of the G×E effect at the MET-22 scale. However, from an agronomic point of view, this resulted in a more representative list of limiting factors at the MET scale representing Western European rapeseed growing areas, leading to envirotypes more representative of future growing conditions. Overall, the envirotyping-based methodology allowed prioritizing 3 QTL involved in the genetic determinism of seed yield plasticity across the 16 QTL detected. These 3 QTL have also been reported by Bouchet and coworkers ( 2016 ) genetic germplasm related to the P173 population used here. The stable QTL QA09 was also reported in different studies for seed-yield related traits as summed up by Raboanatahiry et al. ( 2018 ), whereas no-colocalization was found in the literature for QC09a. The interactive QTL QA07a may correspond to the QTL detected previously for branching number (Zhao et al. 2016 ) or thousand seed weight and plant height (Quijada et al. 2006 ; Udall et al. 2006 ). The 13 remaining SY-related QTL were only detected in one to two environments. This pattern of QTL specificity to a unique environment is widely reported in literature (Bouchet et al. 2016 ; Garin et al. 2020 ) and offers a first approach to QTL×E interactions, but the robustness of these specific QTL is also questionnable. The methodology we proposed in this study was helpful to prioritize QTL and link the QTL with agro-pedoclimatic scenarii. The low level of coincidence between our results and the QTL reported in the literature can result from differences in terms of genetic material used for GWAS or linkage studies. Indeed, most of studies reporting QTL for SY-related traits were carried out using spring type germplasm experimented in the field under short growth cycle conditions, unlike the winter oilseed rape germplasm used for this study. Winter and spring germplasms went through separated breeding history that can explain the differences of QTL detected, driven through selection for different breeding targets. Thus, broadening the genetic diversity considered for GWAS may help identifying additional seed yield related QTL. For example, adding semi-winter accessions, more likely to withstand western Europe growing conditions, is a promising way to increase the population resolution by reducing the extent of linkage disequilibrium. A second way to improve the power of GWAS consists in increasing the number of genomic markers. However, recent genomics advances already led to a common use of resolutive genotyping resources such as the 60K Illumina array in most of the genetic studies (Clarke et al. 2016 ). Here, we developed and used a novel and dense genomic resource based on whole genome exome capture to characterize the population (Leveugle et al. 2015 ). This resource provided 217,805 SNP covering the entire genome and therefore increased the resolution of the QTL regions as a higher number SNP were detected per genomic region. Our results identified this genomic resource as highly valuable to describe and capture the LD pattern of the P173 WOSR panel. This consists in an important resource, especially when dealing with complex traits such as Seed Yield or G×E genetic determinism. To facilitate interpretation of G×E and QTL specificity, approaches have already been proposed based on clustering of environments within a MET for other species. This involves grouping individual environments into mega-environments such that genotypes exhibit similar behaviors in a mega-environment and may differ between mega-environments. In fact, G×E is primarily explained by differences between mega-environments, rather than by G×E within a mega-environment. Thus, Moreau et al. ( 2004 ) proposed a clustering of environments according to the G×E interaction matrix and were able to explain the specificity of QTL according to climatic or water stress conditions. More recently, Millet et al. ( 2016 ) and Touzy et al. ( 2019 ) proposed to cluster the environments of a MET according to drought and/or heat scenarii and then to study the pattern of QTL effects from one scenario to another. Most of SY-related QTL presented significant interaction with the climatic scenario and for some of them, the favorable allele changed according to the scenario (Millet et al. 2016 ). Our study supports the interest of clustering the environments to identify and interpret QTL effects. In this study, we identified QTL (QA07a, QC03b) present in given envirotypes (ED and EC respectively) and absent for others, a pattern also revealed in maize by Millet et al. ( 2016 ) and Touzy et al. ( 2019 ) when opposing contrasting scenarii. However, as opposed to Millet et al. ( 2016 ), we didn’t record any change of the favorable allele at a given QTL, depending on the envirotype. These two last studies focused their environments clustering on a priori defined stresses (water and temperature) and developed a targeted characterization of the environments of the MET. For winter oilseed rape, the 11-months growth cycle makes it more difficult to focus on a dedicated stress and we therefore choose an alternative method that reports a posteriori the main combinations of environmental factors that did impact seed yield elaboration. Even if nitrogen input was managed to be one of the main limiting factors, it was clearly shown that it had to be considered in combination with others stress (radiation, temperature, …) occurring during the crop cycle, thus making ineffective an a priori clustering based on nitrogen indicators only. Moreover, Ravier et al. ( 2017 ) showed that N defiencies, even intense, do not always affect seed yield, especially if they occur early during the wheat growth cycle. This method was also successfully used to identify limiting factors occurring over a MET for barley (Beillouin et al. 2018 ) and highlights the need for indicators to account for potential stresses occurring in the field. In this study the envirotype ED corresponded to the combination of limiting factors that impacted the most seed yield. Its limiting factors targeted different phases of the crop cycle: winter (fulfillment of vernalization requirements, high temperatures during winter), bolting (N fertilization), flowering (lack of solar radiation) and grain filling (lower temperature). This high impacting combination of stress was observed for the 4 environments of envirotype ED. Individually, each of these factors has already been shown to impact seed yield: thermal stress during flowering affects flower fertility, pod number and seed number (Morrison 1993 ; Angadi et al. 2000 ; Young et al. 2004 ); a poor vernalization conditions could lead to delayed or no flowering (Ferreira et al. 1995 ; Chandler et al. 2005 )d limitation is also known to impact seed yield (Rathke et al. 2006 ). Here we were able to identify that these different stresses co-occurred in the field and truly impact seed yield. Excepting N stress, the main limiting factors in ED corresponded to climatic factors. We suggest that combining envirotyping and QTL analysis must be considered an effective approach, enabling the identification of both agro-pedoclimatic indicators and QTL involved in G×E interaction, shedding light on yield plasticity determinism. QTL with the highest effects and qualified as "stable" may nevertheless present significant QTL×E interactions. However, they are detected in most environments and envirotypes, with the favorable allele being the same whatever the environment considered, which underlines their interest in improving yield for a large range of environmental conditions. Specific QTL such as QA07a presented smaller effect and were not significantly involved in QTL×E at the whole MET-22 scale. However, this QTL presented a specific interest for a specific combination of limiting agro-pedoclimatic conditions that was observed in the envirotype ED. The analyses performed at the MET-22 scale or directly at the single environment scale were not consistent to highlight this genomic region. The molecular diversity as this locus indicated that the GSL- cultivars did not fix the favorable allele, demonstrating the interest of the GSL + cultivars as valuable source of genetic diversity for improving seed yield and its stability. For further breeding programs, there is a particular interest in validating the four envirotypes described in the present study for a wider range of agro-pedoclimatic conditions. Indeed, a posteriori analysis of larger climatic datasets at the same locations, coupled with crop physiological models, such as AZODYN-colza (Jeuffroy et al. 2006 ), may conduct to an estimation of the frequency of these four specific envirotypes across growing seasons. Envirotype ED can consist in a new target environment for breeding if it occurs rather frequently within the rapeseed production area. This strategy could also help redesigning multi-environment trials for WOSR breeding, for instance by discarding redundant locations, reducing experimental costs and by maximizing the opportunities of desired envirotypes/pedoclimatic scenario within a MET. The envirotyping approach can also lead to an estimation of a similarity matrix of a MET locations, according to their limiting factors pattern. This can be a clue to identify accurate match between dedicated genotypes and environments (Resende et al. 2021 ) leading, notably, to better product placement for the seed industry. Declarations Acknowledgements The authors would like to thank the technical staff of the UMR IGEPP for collecting the field data as well as for management of samples at Le Rheu (Elise Alix, Bernard Moulin, Solenn Guichard, Alina Tollenaere, Tiffany Bourlet), as well as Cécile Baron who handled the genotyping data, and Mathieu Rousseau-Gueutin who provided genomic support concerning the Darmor-bzh v10 genome version. The authors would also like to thank the “Domaine de la Motte” Experimental Unit (INRAE Bretagne Normandie, Domaine de la Motte, 35650 Le Rheu) for the provision of the experimental plots, the cultural interventions and the agri-environmental data recorded and used for this study. The authors are also grateful to the partners of the RAPSODYN project (Innolea, Limagrain Europe, Lidea, MAS seeds, Syngenta, RAGT, Terres Inovia) that provided the field data at the MET scale. Author contribution statement AL and NN conceived and designed the analyses, planned the experiments, collected the data, contributed to the interpretation of the results and supervised the work. EC analyzed the data, interpreted the results and wrote the manuscript with inputs from all co-authors. CS contributed to the data collection, ran the diversity analyses. ML ran the genomic work, analyzed the exome capture data. All authors discussed the results, contributed, edited and validated the final manuscript. Funding This research was supported by two national collaborative projects untitled GENERGY (ANR-07-GPLA-016) funded by the French National Research Agency (ANR) and RAPSODYN (ANR-11-BTBR-0004) funded by the program “Investments for the Future”. Data Availability The datasets generated and analyzed in this study are available using the following link during the review process (https://entrepot.recherche.data.gouv.fr/privateurl.xhtml?token=27961ecd-f375-43e0-952d-23610e7ceb7d) and will be freely available with a DOI if the article is accepted. Conflict of interest statement The authors declare no conflicts of interest. References Angadi SV, Cutforth HW, Miller PR, Mcconkey BG, Entz MH, Brandt SA (2000) Response of three Brassica species to high temperature stress during reproductive growth. Can J Plant Sci 80:693–702. https://doi.org/10.4141/P99-152 Arifuzzaman M, Oladzadabbasabadi A, McLean P, Rahman M (2019) Shovelomics for phenotyping root architectural traits of rapeseed/canola ( Brassica napus L.) and genome-wide association mapping. Mol Genet Genomics 294:985–1000. https://doi.org/10.1007/s00438-019-01563-x Astle W, Balding DJ (2009) Population structure and cryptic relatedness in genetic association studies. 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Mol Breed 42:15. https://doi.org/10.1007/s11032-022-01281-0 Supplementary Files CorloueretalSupp.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 09 Jan, 2024 Reviewers invited by journal 08 Jan, 2024 Editor assigned by journal 21 Dec, 2023 First submitted to journal 20 Dec, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3788902","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":265897249,"identity":"cabb3196-4564-44ca-afae-f63c6dc02cc1","order_by":0,"name":"Erwan CORLOUER","email":"","orcid":"","institution":"INRAE Bretagne-Normandie: Institut National de Recherche pour l'Agriculture l'Alimentation et l'Environnement Centre Bretagne-Normandie","correspondingAuthor":false,"prefix":"","firstName":"Erwan","middleName":"","lastName":"CORLOUER","suffix":""},{"id":265897250,"identity":"9f443850-b8fd-4205-b428-fa9622609715","order_by":1,"name":"Christopher SAUVAGE","email":"","orcid":"","institution":"Syngenta France SAS","correspondingAuthor":false,"prefix":"","firstName":"Christopher","middleName":"","lastName":"SAUVAGE","suffix":""},{"id":265897251,"identity":"de50d94c-d116-4f93-a9fa-5a44a5e12545","order_by":2,"name":"Magalie LEVEUGLE","email":"","orcid":"","institution":"Groupe Limagrain","correspondingAuthor":false,"prefix":"","firstName":"Magalie","middleName":"","lastName":"LEVEUGLE","suffix":""},{"id":265897252,"identity":"d4fcf8c4-e99c-405d-828f-3bca4a074fdc","order_by":3,"name":"Nathalie NESI","email":"","orcid":"","institution":"INRAE Bretagne-Normandie: Institut National de Recherche pour l'Agriculture l'Alimentation et l'Environnement Centre Bretagne-Normandie","correspondingAuthor":false,"prefix":"","firstName":"Nathalie","middleName":"","lastName":"NESI","suffix":""},{"id":265897253,"identity":"c81fa364-6673-4a84-8448-3813207d2a95","order_by":4,"name":"Anne Laperche","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-4930-8407","institution":"L'Institut Agro Rennes-Angers","correspondingAuthor":true,"prefix":"","firstName":"Anne","middleName":"","lastName":"Laperche","suffix":""}],"badges":[],"createdAt":"2023-12-21 21:46:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3788902/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3788902/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49411297,"identity":"e120313d-9106-49f8-9075-64cfc0fcd5f6","added_by":"auto","created_at":"2024-01-10 10:25:23","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":27325,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationship between mean values of seed yield (SY, y-axis) and nitrogen nutrition index (NNI, x-axis) observed for the probe genotypes, Aviso and Montego, across the MET-47.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEmpty and plain circles correspond respectively to the N\u003csup\u003e-\u003c/sup\u003e and N\u003csup\u003e+\u003c/sup\u003e nitrogen fertilization regimes. A vertical line was added and corresponds to a NNI value of 1, above which environment are considered as not impacted by N stress. The horizontal bar corresponds to a SY of 35 q.ha\u003csup\u003e-1 \u003c/sup\u003e\u0026nbsp;which is the targeted yield for N\u003csup\u003e+\u003c/sup\u003e conditions.\u003c/p\u003e","description":"","filename":"Slide1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3788902/v1/12df9b6877d941adf16c14d6.jpg"},{"id":49411000,"identity":"740ee7c7-d372-47b8-9e71-62f0e8cc566e","added_by":"auto","created_at":"2024-01-10 10:17:23","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":60771,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEnvirotyping of the MET-22.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea\u003c/strong\u003e Dendrogram tree of the 22 environments of the MET-22 based on the PLS regression results. Mean seed yield (SY) and mean nitrogen nutrition index (NNI) values are indicated for each envirotype. \u003cstrong\u003eb\u003c/strong\u003eVariation of the inertia gain depending on the number of groups chosen for the hierarchical clustering. The dashed line represents the choice of groups number.\u003c/p\u003e","description":"","filename":"Slide2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3788902/v1/085a4b1782a250b508387990.jpg"},{"id":49410999,"identity":"49912dbf-9ecc-4c5e-bed9-0bd97155e979","added_by":"auto","created_at":"2024-01-10 10:17:23","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":33979,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEnvirotype description based on the seed yield limiting factors.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe profile of each limiting factor is represented on a 0-10 scale for each envirotype as well as for the MET-22. Abbreviations of the indicators are as following: TMN_CW: mean temperature at climatic winter, VERN: vernalization condition, TMAX_B: maximal temperature at bolting, HT_FLO: number of days with high temperature (\u0026gt;25°C) at flowering, SSR_FLO: sum of solar radiation at flowering, TMAX_P300: maximal temperature during the seed number fixation period, TMIN_P600: minimal temperature during the pod growth, WD_P600: water deficit during the pod growth, NNI: Nitrogen Nutrition Index, N\u003csub\u003etotal\u003c/sub\u003e: Amount of Nitrogen bring to the environment, DAI: number of dry days after the N\u003csup\u003e-\u003c/sup\u003e input. WSC_MAX: maximal water soil capacity.\u003c/p\u003e","description":"","filename":"Slide3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3788902/v1/fc8a8fbb9dcb4a2c667c3b51.jpg"},{"id":49411002,"identity":"b9701fe0-2e05-4d9d-aa63-2ef3007b759a","added_by":"auto","created_at":"2024-01-10 10:17:23","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":71714,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNucleotide diversity (π) under QTL QA07a and QA09.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe nucleotide diversity was calculated for all P173 population (blue curve), the GSL+ (yellow curve) and GSL- (pink curve) accessions. The associated SNP for each QTL are indicated in black. The\u003cstrong\u003e π\u003c/strong\u003e index is calculated for windows of 10 Kbp.\u003c/p\u003e","description":"","filename":"Slide4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3788902/v1/2be69edeef90b88cb80be570.jpg"},{"id":49411912,"identity":"625b07d1-931a-44cf-9797-d1bb1969dd37","added_by":"auto","created_at":"2024-01-10 10:33:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":811061,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3788902/v1/3c38716e-f00d-4111-83a4-dfce76807824.pdf"},{"id":49411003,"identity":"6be12983-64b2-478f-845b-a402a0abfd99","added_by":"auto","created_at":"2024-01-10 10:17:23","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":328934,"visible":true,"origin":"","legend":"","description":"","filename":"CorloueretalSupp.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3788902/v1/ef4ad77af4ae02e664a0061e.xlsx"}],"financialInterests":"","formattedTitle":"Envirotyping within a multi-environment trial allowed identifying genetic determinants of winter oilseed rape yield plasticity","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe main challenge for agriculture consists in ensuring food security while adapting to climate changes and contributing to eco-friendly farming systems. Both will be met through cultural practices adaptations and the development of new crop varieties (Lobell et al. 2018). Indeed, crops must face the effects of climate change characterized by an increase in temperature and CO\u003csub\u003e2\u003c/sub\u003e concentration but also by the increase of intense climatic phenomena (\u003cem\u003ee.g.\u003c/em\u003e, droughts, floods or frosts; Bell et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). In addition, agroecological practices have emerged as a way to produce more food in a sustainable manner by enhancing ecology-based practices (Wezel et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and reducing the use of pesticide and chemical inputs. These practices include the use of natural biological control of pests, cover crops, intercropping, or cultivar mixtures, in addition to new soil management practices (reduced tillage, \u0026hellip;) that increases the complexity of plant-environment interactions (Lamichhane et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Faced with these profound changes, the question of crop adaption to ensure stable performance under fluctuating agricultural and climatic conditions arises. New crop varieties must then be adapted to a wide range of pedoclimatic and environmental conditions, meaning they must present phenotypic plasticity (Nicotra et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe phenotypic plasticity corresponds to the range of phenotypic variation observed for a dedicated genotype under a variety of environmental conditions (Nicotra et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; El-Soda et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Phenotypic plasticity differences exist among genotypes indicating the possibility to breed for stability, however this remains complex. Indeed, the observed phenotype can be expressed as the sum of a genetic effect (G), an environmental effect (E) defined here as the combination of pedoclimatic conditions and cultural practices, and the G\u0026times;E interaction, corresponding to the modification of the phenotypic plasticity between genotypes (Becker and Leon \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Malosetti et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; van Eeuwijk et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Breeding programs must therefore consider the G\u0026times;E interaction to breed for new varieties better adapted to fluctuating environments (Cooper et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Snowdon et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Classically, breeding programs rely on average performance of genotypes grown under multi-environmental and multi-annual field trials. Although this allows passive selection for a small adaptive effect (Snowdon et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), this methodology does not allow a direct access to the genetic determinism of QTL\u0026times;E interactions that underly plasticity (Garin et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA first approach to unravel the genetic determinism of QTL\u0026times;E consists in a comparison of QTL detected using a population trialed across a multi environmental network. This method is efficient to access to the QTL\u0026times;E interaction but the results remain difficult to interpret (Garin et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). To gain power in detecting QTL associated to plant adaptation, stability indicators have been developed and used in genetic analyses of plasticity. For instance, ecovalence indices (Wricke \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1962\u003c/span\u003e; dos Santos Silva et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) or AMMI stability values are used to describe genotypic contribution to G\u0026times;E and to characterize the adaptability versus stability in different environments (Purchase et al. 2000; Bouchet et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Lozada and Carter, 2020). Genotypic reaction norms to environmental gradient are also commonly used, they correspond for instance to Finlay and Wilkinson's regression slope (1963) (see Diouf et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Xavier et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2018\u003c/span\u003e for application examples). More recently, linear mixed models have been developed to consider simultaneously all environments and genotypes in a multi-environment trial (MET) (van Eeuwijk et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and have opened new avenues to detect G\u0026times;E interactions and access its genetic determinism (Malosetti et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Happ et al. 2021). However, without a comprehensive environmental characterization of the MET, the QTL involved in plasticity are difficult to interpret in terms of the underlying mechanisms of plant adaptation.\u003c/p\u003e \u003cp\u003eRapeseed is a major worldwide crop cultivated mainly for its seeds oil and meal production, presenting an estimated annual production of 71.3 Mt in 2021 (FAOSTAT 2023). Farmers have more and more difficulties to maintain rapeseed seed yield under adverse environmental conditions (\u003cem\u003ee.g.\u003c/em\u003e, drought during sowing, insect damages during fall, nutritional constraints and abiotic stresses). This leads to a reduction of the cultivated surfaces (-21% between 2016 and 2022, AGRESTE 2023). Seed yield (SY) is a complex trait defined by multiple components as plant population density, the number of pods per plant, the number of seeds per pods or the seed weight (Diepenbrock \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The potential seed yield of winter oilseed rape is determined since the end of the fall but depends on many factors and stresses occurring all along the crop life cycle as abiotic stresses (temperature, water, radiation), biotic stresses (pest, pathogens and weeds) or nutritional stresses, particularly with nitrogen (Rathke et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). All these constraints on winter oilseed rape seed yield elaboration result in an important environmental effect and G\u0026times;E interaction that explained around 10% of the seed yield variation under French conditions (Bouchet et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Corlouer et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Numerous studies reported the genetic determinant of SY in rapeseed (reviewed by Delourme et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and reported a high number of QTL (Shi et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Raboanatahiry et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), thus confirming SY as highly polygenic. The comparison between QTL detected in different environments revealed also QTL\u0026times;E interactions, with some QTL being characterized as \u0026ldquo;stable\u0026rdquo; (\u003cem\u003ei.e.\u003c/em\u003e detected across all environments) and QTL being characterized as \u0026ldquo;interactive\u0026rdquo; (\u003cem\u003ei.e.\u003c/em\u003e specific to an environmental condition, Bouchet et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, the QTL specificity observed in a MET is rarely associated with the identification of the environmental features causing the observed adaptation.\u003c/p\u003e \u003cp\u003eIn this context, this study aims at identifying the genetic determinants of seed yield G\u0026times;E interaction in winter oilseed rape and interpret them in the light of environmental characteristics. We based our strategy on the analysis of winter oilseed rape accessions experimented across a multi environment trial (MET-47) consisting in 47 environments representing the diversity of French growing conditions. First, we developed a comprehensive characterization of the MET-47 to identify the limiting factors that occurred. Then, to identify the genetic determinant of the G\u0026times;E interaction, a panel of 173 accessions was experimented in a sub-MET of 22 environments out of the 47 (MET-22). Envirotypes were defined among the MET-22 and corresponded to the clustering of the 22 environments according to their limiting factors pattern. Then GWAS analyses were carried out using BLUE (Best Linear Unbiased Estimator) obtained for each genotype and each envirotype to identify QTL specific of G\u0026times;E. Finally, a genetic diversity analysis was carried out to decipher the potential impact of breeding for seed quality on the reduction of genetic diversity at those detected QTL that may limit phenotypic plasticity.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eField network description\u003c/h2\u003e \u003cp\u003eField experiments were run in a multi environment trial (MET-47) consisting of 47 environments defined as combinations of \u0026lsquo;year \u0026times; location \u0026times; nitrogen (N) fertilization\u0026rsquo; in France between 2011 and 2018 (Supplemental Data 1). Each individual trial was conducted using classical crop management for winter oilseed rape (WOSR) with comprehensive protection against weeds, pests and pathogens. Optimal N fertilization was estimated using the balance sheet method (R\u0026eacute;my and H\u0026eacute;bert \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1977\u003c/span\u003e; Parnaudeau et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) for a target yield of 3.5 t.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and applied in a subset of 19 environments defined as high N (N\u003csup\u003e+\u003c/sup\u003e). In contrast, a low N fertilization regime was applied in the remaining 28 environments defined as low N (N\u003csup\u003e\u0026minus;\u003c/sup\u003e) that corresponded to the N\u003csup\u003e+\u003c/sup\u003e regime lowered by 80\u0026ndash;100 kg.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e of N (Supplemental Data 1). Each environment was designed as a randomized complete block design with two to four replicates depending on the environment, with an individual plot area ranging from 6.75 m\u003csup\u003e2\u003c/sup\u003e to 14 m\u003csup\u003e2\u003c/sup\u003e. Mature dry seeds were harvested when the vegetative parts were fully senescent and the seeds were dark and hard. The targeted traits were the seed yield (SY in q.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) determined for each genotype in each trial and adjusted to 0% water content and 0% impurities, as well as the seed number (SN in seeds.m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e) calculated according to the SY and the thousand seed weight (SN\u0026thinsp;=\u0026thinsp;SY\u0026times;100 000/TSW).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003ePlant material\u003c/h2\u003e \u003cp\u003eThe WOSR genotypes \u0026lsquo;Aviso\u0026rsquo; and \u0026lsquo;Montego\u0026rsquo; were trialed over the whole MET-47 and therefore considered as probe genotypes for environmental characterization as reported by Corlouer et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). A diversity panel of 173 WOSR accessions (hereafter referred to as P173) was scored for SY and SN over a subset of 22 environments (11 N\u003csup\u003e+\u003c/sup\u003e and 11 N\u003csup\u003e\u0026minus;\u003c/sup\u003e, called hereafter MET-22) run over the 2013\u0026ndash;2014 and 2014\u0026ndash;2015 growing seasons. The P173 accessions originated primarily from Western Europe and mostly represented commercial varieties released from 1959 to 2010 (Supplemental Data 2) with 31 accessions of the \u0026lsquo;++\u0026rsquo; type (high contents in both erucic acid and glucosinolates; high C22:1, high GSL), 15 accessions of the \u0026lsquo;0+\u0026rsquo; type (low C22:1, high GSL), 1 accession of the \u0026lsquo;+0\u0026rsquo; type (high C22:1, low GSL), and 126 accessions of the \u0026lsquo;00\u0026rsquo; type (low C22:1, low GSL) of which 46 were elite lines. Seeds were provided by the BrACySol Biological Resource Center or private seed companies. The P173 population was genotyped using the Brassica 60K Illumina Infinium\u0026trade; array (Clarke et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and an exome sequence capture assay (Leveugle et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). A total of 217,805 SNP were scored and validated for the current study using a threshold of 2.5% for the minor allele frequency (MAF) and of 10% for the missing values. The missing genotyping data were inferred using Beagle v3 software (Browning and Browning \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). All the SNP were physically anchored onto the latest \u003cem\u003eBrassica napus\u003c/em\u003e reference genome of Darmor-\u003cem\u003ebzh\u003c/em\u003e (Rousseau-Gueutin et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eEnvironmental characterization and envirotyping\u003c/h2\u003e \u003cp\u003ePedoclimatic indicators were calculated as described by Corlouer et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and estimated for each of the key periods of the winter oilseed rape growing cycle. Briefly, these periods were fixed on meteorological data and the phenology of the probe genotypes. They covered fall (F), climatic winter (CW), bolting (B), seed number fixation (P300), reserve allocation to the pod (P600) and seed growth (P1000) stages. The 70 indicators correspond to four main categories: water stress, temperature, radiation and vernalization conditions (Corlouer et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In addition, new indicators were developed to consider the contrasting N nutrition status (Supplemental Data 3). These included indicators related to the plant N status such as the nitrogen nutrition index (NNI; Colnenne et al. 1998) as well as the aerial dry biomass (ADB) and the aerial nitrogen quantity (AN), all scored at bolting stage (BBCH50, Lancashire et al. 1991) with a minimum delay of two weeks after the latest N supply. These values were measured on plants collected in the field on a surface of 1 m\u0026sup2; and are expressed in quantity per hectare. On the other hand, indicators related to the N fertilization management were also considered such as the total amount of N supplied to the crop (N\u003csub\u003etotal\u003c/sub\u003e, in kg.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), and the water regime at the time of N supply was assessed considering the maximum number of days without rainfall during the 10 days preceding the N supply (DBI, dryness before input), the number of days without rainfall over 10 days after the N supply (DAI, dryness after input), the sum of the rainfalls over 10 days after the N supply (RF, rainfall), the number of days of leaching over 10 days after the N supply (RO, run off), and the mean value of the water soil content at the time of the N supply (from 3 days before up to 10 days after the input) expressed in percentage of the maximal water soil content (WSC_I). Given that N fertilization was provided in one, two or three applications depending on the local pedoclimatic conditions, we defined a unique indicator for each trial that considers the number of N supplies, calculated as following:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$Final indicator={\\sum }_{i=0}^{n}{Indicator}_{i}\\times \\frac{{N}_{i}}{{N}_{total}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cem\u003eIndicator\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the given indicator (DBI, DAI, RF, RO or WSC_I) calculated at the N supply \u003cem\u003ei\u003c/em\u003e, \u003cem\u003eN\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the amount of N brought to the environment at the application \u003cem\u003ei\u003c/em\u003e, and \u003cem\u003eN\u003c/em\u003e\u003csub\u003e\u003cem\u003etotal\u003c/em\u003e\u003c/sub\u003e the total N amount considering all the N applications. When no N fertilization was applied, the final indicators were fixed to 0. Missing indicators were imputed using the missMDA package (Josse and Husson \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA total of 79 indicators (Supplemental Data 3) were considered in the present study. To go further into the analysis of the G\u0026times;E, an envirotyping was carried out according to the methodology proposed in Corlouer et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Briefly, a univariate PLS regression analysis was run at the MET-47 scale by regressing the mean seed yield of Aviso and Montego for each of the 47 environments on the 79 environmental indicators. A variable selection was performed to keep the reduced set of indicators that best explained seed yield variation, hereafter referred as limiting factors. Then, a hierarchical clustering was performed on the environments of the MET-22 to define envirotypes according to their pattern of limiting factors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003ePhenotypic data analyses\u003c/h2\u003e \u003cp\u003eTrait analysis was run using three different mixed linear models that were fitted using the \u0026lsquo;lme4\u0026rsquo; (Bates et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and \u0026lsquo;lmerTest\u0026rsquo; packages (Kuznetsova et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and corresponded to the three scales of analysis used in this study, \u003cem\u003ei.e.\u003c/em\u003e the whole MET-22 scale, the envirotype scale (cluster of different environments presenting the same pattern of limiting factors), or the single environment scale.\u003c/p\u003e \u003cp\u003eA first linear mixed model (2) was fitted at the MET-22 scale including an effect of the envirotype to evaluate the impact of the envirotyping on the variance repartition.\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$${Y}_{ijkl}= \\mu +{G}_{i}+{En}_{l}+{En}_{l\\left(k\\right)}+{G}_{i}\\times {En}_{l}+{G}_{i}\\times {En}_{l\\left(k\\right)}+\\underset{\\_}{{R}_{j(l\\times k)}}+\\underset{\\_}{{\\epsilon }_{ijkl}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Y}_{ijkl}\\)\u003c/span\u003e\u003c/span\u003e is the phenotypic value, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\mu\\)\u003c/span\u003e\u003c/span\u003e is the population mean, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({G}_{i}\\)\u003c/span\u003e\u003c/span\u003e stands for the effect of genotype i, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({En}_{l}\\)\u003c/span\u003e\u003c/span\u003e for the envirotype l, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({En}_{l\\left(k\\right)}\\)\u003c/span\u003e\u003c/span\u003e for the environment k nested in the envirotype l, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({R}_{j(l\\times k)}\\)\u003c/span\u003e\u003c/span\u003e for the replicate j, nested in the environment k and envirotype l; and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\epsilon }_{ijkl}\\)\u003c/span\u003e\u003c/span\u003e is the residual.\u003c/p\u003e \u003cp\u003eThen, at the single environment scale models (3) and (4) were used, and at the envirotype and at the MET-22 scale models (5) and (6) were used to study the genotypic effect, to calculate the corresponding Best Linear Unbiased Estimators (BLUE), and to estimate trait heritability. These models are presented below.\u003c/p\u003e \u003cp\u003eModel (3) was fitted at the scale of each environment:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$${Y}_{ij}=\\mu +{G}_{i}+\\underset{\\_}{{R}_{j}}+\\underset{\\_}{{\\epsilon }_{ij}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Y}_{ij}\\)\u003c/span\u003e\u003c/span\u003e is the phenotypic value, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\mu\\)\u003c/span\u003e\u003c/span\u003e is the population mean, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({G}_{i}\\)\u003c/span\u003e\u003c/span\u003e stands for the effect of genotype i, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({R}_{j}\\)\u003c/span\u003e\u003c/span\u003e for the replicate j and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\epsilon }_{ij}\\)\u003c/span\u003e\u003c/span\u003e is the residual. The replicate effect and the residual were declared as random. The heritability was calculated as:\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$${h}^{2}=\\frac{{\\sigma }_{G}^{2}}{{\\sigma }_{G}^{2}+\\raisebox{1ex}{${\\sigma }_{\\epsilon }^{2}$}\\!\\left/ \\!\\raisebox{-1ex}{$r$}\\right.}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\sigma }_{G}^{2}\\)\u003c/span\u003e\u003c/span\u003e is the genetic variance, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\sigma }_{\\epsilon }^{2}\\)\u003c/span\u003e\u003c/span\u003e the residual variance and \u003cem\u003er\u003c/em\u003e the number of replicates per genotype.\u003c/p\u003e \u003cp\u003eModel (5) was applied at the envirotype or at the MET-22 scale:\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$${Y}_{ijk}= \\mu +{G}_{i}+{E}_{k}+{G}_{i}\\times {E}_{k}+\\underset{\\_}{{R}_{j\\left(k\\right)}}+\\underset{\\_}{{\\epsilon }_{ijk}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Y}_{ijk}\\)\u003c/span\u003e\u003c/span\u003e is the phenotypic value, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\mu\\)\u003c/span\u003e\u003c/span\u003e is the population mean, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({G}_{i}\\)\u003c/span\u003e\u003c/span\u003e stands for the effect of genotype i, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({E}_{k}\\)\u003c/span\u003e\u003c/span\u003e for the environment k, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({R}_{j\\left(k\\right)}\\)\u003c/span\u003e\u003c/span\u003e for the replicate j, nested in the environment k and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\epsilon }_{ijk}\\)\u003c/span\u003e\u003c/span\u003e is the residual. The corresponding heritability was defined as follow with all terms declared as random in Eq.\u0026nbsp;(\u003cspan refid=\"Equ5\" class=\"InternalRef\"\u003e5\u003c/span\u003e):\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$${h}^{2}=\\frac{{\\sigma }_{G}^{2}}{{\\sigma }_{G}^{2}+\\raisebox{1ex}{${\\sigma }_{G\\times E}^{2}$}\\!\\left/ \\!\\raisebox{-1ex}{$t$}\\right.+\\raisebox{1ex}{${\\sigma }_{\\epsilon }^{2}$}\\!\\left/ \\!\\raisebox{-1ex}{$r*e$}\\right.}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\sigma }_{G}^{2}\\)\u003c/span\u003e\u003c/span\u003e is the genetic variance,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\sigma }_{G\\times E}^{2}\\)\u003c/span\u003e\u003c/span\u003e the G\u0026times;E interaction variance, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\sigma }_{\\epsilon }^{2}\\)\u003c/span\u003e\u003c/span\u003e the residual variance, \u003cem\u003ee\u003c/em\u003e the number of environment and \u003cem\u003er\u003c/em\u003e the number of replicates per genotype.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eGWAS analyses\u003c/h2\u003e \u003cp\u003eThe BLUE defined at each scale (environment, envirotype, MET-22) using models (3) and (5) for SY and SN were used to perform the genetic analyses. GWAS analyses were conducted using the FastLMM algorithm (Lippert et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) using a kinship matrix calculated for each linkage group as described by Rincent et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and following the Astle algorithm (Astle and Balding \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). A Bonferroni detection threshold was set to 10%, based on a corrected SNP population as proposed by the simpleM method developed by Gao et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). This method was based on the composite linkage disequilibrium (CLD) correlation between SNP. The CLD was used to calculate the effective number of independent tests (M\u003csub\u003eeff\u003c/sub\u003e). In this study, the M\u003csub\u003eeff\u003c/sub\u003e was calculated and fixed to 19,886 SNP. We defined the threshold of highly significant associated SNP as:\u003cdiv id=\"Equ7\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e\n$$t=-{\\text{log}}_{10}\\left(\\raisebox{1ex}{${\\alpha }_{G}$}\\!\\left/ \\!\\raisebox{-1ex}{${M}_{eff}$}\\right.\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003et\u003c/em\u003e is the value of the value of the threshold, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\alpha }_{G}\\)\u003c/span\u003e\u003c/span\u003e is the threshold of the \u003cem\u003ep\u003c/em\u003e-value and was fixed to 0.1 and M\u003csub\u003eeff\u003c/sub\u003e was the effective population of SNP. The final threshold used was t\u0026thinsp;=\u0026thinsp;5.30.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGenetic diversity analysis\u003c/h2\u003e \u003cp\u003eA genetic diversity was conducted at the associated loci detected through GWAS. This evaluated the potential impact of a major breeding event based on the selection of varieties without glucosinolates in seeds. Therefore, two populations were confronted: a first population (GSL+) composed by genotypes with high contents of glucosinolate in seeds (\u0026gt;\u0026thinsp;18 \u0026micro;mol.g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) (46 WOSR, \u0026ldquo;++\u0026rdquo; and \u0026ldquo;0+\u0026rdquo;) and a second population (GSL-) composed by the remaining accessions of the P173 that present low glucosinolate content (\u0026lt;\u0026thinsp;18 \u0026micro;mol.g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) (127 WOSR, \u0026ldquo;+0\u0026rdquo; and \u0026ldquo;00\u0026rdquo;) (Supplemental Data 2). The nucleotide divergence statistic (π) and the mean Fst (Weir and Cockerham, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1984\u003c/span\u003e) for the whole P173 and for the GSL\u0026thinsp;+\u0026thinsp;population and the GSL- population were calculated using VCFtools v0.1.13 software (Danecek et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) for a window of 10 kb around each genomic region of interest.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eN stress was not the only factor impacting seed yield\u003c/h2\u003e \u003cp\u003eA first overlook of the MET-47 was carried out by looking at the relationship between the mean values of seed yield and NNI for Aviso and Montego per environment (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). When focusing on environments defined as N\u003csup\u003e\u0026minus;\u003c/sup\u003e, it is to be noticed than NNI values ranged from 0.63 to 1.26, with NNI values exceeding 1.0 for 7 N\u003csup\u003e\u0026minus;\u003c/sup\u003e -environments. Moreover, in two N\u003csup\u003e\u0026minus;\u003c/sup\u003e environments (Lou18_N\u003csup\u003e\u0026minus;\u003c/sup\u003e and Chr18_N\u003csup\u003e\u0026minus;\u003c/sup\u003e) the observed SY was even higher that the targeted value for N\u003csup\u003e+\u003c/sup\u003e conditions. Thus, these results demonstrated the difficulty of implementing a nitrogen stress under field conditions. When considering the N\u003csup\u003e+\u003c/sup\u003e environments, it can be observed that the targeted seed yield of 35 q.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e was achieved in 13 environments out of 19. However, in these 13 environments, NNI was always higher than 1 (except for LR13_N\u003csup\u003e+\u003c/sup\u003e), indicating the presence of other environmental factors that did impact seed yield elaboration. These results illustrated the need to consider a comprehensive environmental description of the trials to detect the factors that indeed affected SY.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of 13 environmental indicators limiting seed yield\u003c/h2\u003e \u003cp\u003eMET-47 offered a unique chance to get insights into the SY most limiting factors. For that purpose, the set of 79 indicators was used to run a PLS regression analysis on the mean SY variation of Aviso and Montego for each of the 47 environments. SY limiting factors corresponded to a set of 13 indicators (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for a precise description). Predicted seed yields were strongly correlated to the observed seed yields (R\u0026sup2; = 0.87, RMSE\u0026thinsp;=\u0026thinsp;2.6). These 13 indicators covered the whole crop growing cycle from the climatic winter (CW) to the reserve allocation to the pod period (P600). Six indicators referred to the temperature (TMN_CW, TMAX_B, TMAX_FLO, HT_FLO, TMAX_P300 and TMIN_P600), two to the water status (WSC_MAX and WD_P600), one to the radiation (SSR_FLO), one to vernalization conditions (VERN) and three to nitrogen status (NNI, N\u003csub\u003e\u003cem\u003etotal\u003c/em\u003e\u003c/sub\u003e and DAI; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Finally, the study of the Pearson correlations (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Supplemental Fig.\u0026nbsp;1) showed important correlations between the 13 identified indicators and indicators related to the reproductive periods of winter oilseed rape (FLO to P1000) and to the climatic winter period (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), highlighting the key role of these periods on the elaboration of the final seed yield.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSeed yield limiting indicators across the MET-47\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCorrelated indicators*\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTMN_CW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean temperature during CW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTMAX_B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaximal temperature registered during B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTMAX_FLO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaximal temperature registered during FLO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSSR_CW (0.67); LT_B (-0.69); LT_FLO (-0.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHT_FLO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of days with high temperature (\u0026gt;\u0026thinsp;25\u0026deg;C) at FLO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTMIN_CW (-0.7); TMN_FLO (0.66); LGDD_FLO (0.66); TMN_P600 (0.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTMAX_P300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaximal temperature registered during P300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLGDD_CW (0.68); SSR_CW (0.67); LSR_CW (0.7); LT_FLO (-0.68); HT_P300 (0.68)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTMIN_P600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMinimal temperature registered during P600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTMIN_FLO (0.73); TMN_FLO (0.7); LT_FLO (-0.67); LGDD_FLO (0.7); TMN_P600 (0.78)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWD_P600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of days with WSC\u0026thinsp;=\u0026thinsp;0 during P600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHT_F (0.66); WS_FLO (0.7); WSC_P300 (-0.68); WSC_P600 (-0.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWSC_MAX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaximal water soil capacity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTMN_F (-0.67)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSSR_FLO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSum of solar radiation during FLO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLSR_FLO (-0.67); TMAX_P1000 (0.69); HT_P1000 (0.65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVERN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOptimal vernalization treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLT_CW (0.65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNNI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNitrogen nutrition index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAN (0.65); N\u003csub\u003e\u003cem\u003etotal\u003c/em\u003e\u003c/sub\u003e (0.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003csub\u003e\u003cem\u003etotal\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNitrogen input\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNNI (0.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of days of dryness after N supply\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e*Value of the Pearson correlation coefficient was indicated between brackets\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of the mixed linear model applied on the MET-22 to evaluate the impact of the envirotyping on variance repartition. Data had been analyzed using linear model (2): \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Y}_{ijkl}= \\mu +{G}_{i}+{En}_{l}+{En}_{l\\left(k\\right)}+{G}_{i}\\times {En}_{l}+{G}_{i}\\times {En}_{l\\left(k\\right)}+\\underset{\\_}{{R}_{j(l\\times k)}}+\\underset{\\_}{{\\epsilon }_{ijkl}}\\)\u003c/span\u003e\u003c/span\u003e; with the genotype (G), the envirotype (En), the environment nested in the envirotype (En\u003csub\u003el(k)\u003c/sub\u003e or En\u0026times;E), the genotype by envirotype interaction (G\u0026times;En), the genotype by environment by envirotype interaction (G\u003csub\u003ei\u003c/sub\u003e\u0026times;En\u003csub\u003el(k))\u003c/sub\u003e or G\u0026times;En\u0026times;E) and the block effect nested in the environment, nested in the envirotype (R) and compared with the results of linear model (5): \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Y}_{ijk}= \\mu +{G}_{i}+{E}_{k}+{G}_{i}\\times {E}_{k}+\\underset{\\_}{{R}_{j\\left(k\\right)}}+\\underset{\\_}{{\\epsilon }_{ijk}}\\)\u003c/span\u003e\u003c/span\u003e, with the genotype (G), the environment (E), the genotype by environment interaction (G\u0026times;E) and the block (R) nested in the environment.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrait\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c9\" namest=\"c3\"\u003e \u003cp\u003e% Variance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModels (5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eG\u0026times;E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eε\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e55.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e10.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel (2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEn\u0026times;E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eG\u0026times;En\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eG\u0026times;En\u0026times;E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eε\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMET-22 included four contrasting envirotypes\u003c/h2\u003e \u003cp\u003eThe MET-22 is a subset of the MET-47 where 173 genotypes were trialed, allowing the study of the G\u0026times;E interactions. The mean seed yield of the MET-22 was 30.35 q.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (29.40 q.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for the MET-47), and the mean NNI across the MET-22 was 1.06 (1.02 for the MET-47). The MET-22 description using the limiting indicators previously identified presented a similar profile to the MET-47 (Supplemental Fig.\u0026nbsp;2). A hierarchical clustering was performed on the environments of the MET-22 to define envirotypes according to their pattern of limiting indicators. Four envirotypes were defined (EA, EB, EC and ED; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Supplemental Data 1). Envirotype EA is composed of three N\u003csup\u003e+\u003c/sup\u003e environments and one N\u003csup\u003e\u0026minus;\u003c/sup\u003e environment. The mean seed yield observed across EA was 37.6 q.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and the mean NNI was 1.29. This envirotype can therefore be defined as non N stressed and high yielding. The analysis of the limiting indicators pattern showed that EA, when compared to the mean MET-22, is characterized by lower temperature during winter, lower heat stress and solar radiation at flowering (HT_FLO), a higher temperature during the seed number fixation period (TMAX_P300) and a higher NNI (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn contrast, envirotype ED was characterized by the lowest mean seed yield (18.5 q.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and a mean NNI of 0.91 suggesting that the environments of this envirotype have been more stressed. ED gathered three N\u003csup\u003e\u0026minus;\u003c/sup\u003e environments and one N\u003csup\u003e+\u003c/sup\u003e environment. The limiting indicators pattern of envirotype ED is characterized by poor vernalization conditions (VERN), higher temperature during winter (TMN_CW), lower SSR_FLO and TMAX_P300 than the mean MET-22 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFinally, the two remaining envirotypes, EB and EC, presented mean seed yields of 28.4 q.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 27.2 q.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e respectively while the mean MET-22 seed yield was 30.34 q.ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Envirotype EB was composed of eight environments (three N\u003csup\u003e\u0026minus;\u003c/sup\u003e and five N\u003csup\u003e+\u003c/sup\u003e) and envirotype EC of six environments (four N\u003csup\u003e\u0026minus;\u003c/sup\u003e and two N\u003csup\u003e+\u003c/sup\u003e). Envirotypes EC and EB distinguished from each other according to NNI (1.22 for EB and 0.92 for EC), N\u003csub\u003etotal\u003c/sub\u003e, VRN, and conditions at flowering (HT_FLO and SSR_FLO). The profiles of limiting indicators showed that TMAX_FLO, HT_FLO, SSR_FLO, TMAX_P300, WSC_MAX, TMN_CW were lower in envirotype EB compared to the MET-22. Envirotype EC was characterized by higher HT_FLO, SSR_FLO, WSC_MAX and TMN_CW while DAI, NNI, N\u003csub\u003etotal\u003c/sub\u003e, TMAX_B and VERN were lower than observed for the MET-22 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur results demonstrated that the 22 environments of the MET-22 could be classified in 4 envirotypes: envirotype EA that can be considered as not stressed, envirotype ED considered as the most stressed, while envirotypes EB and EC showed moderate stress and mean SY similar to the mean SY observed at the MET-22 scale, but with a different pattern of limiting factors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eEnvirotyping explained up to 70% of environmental variation and 24.6% of G\u0026times;E\u003c/h2\u003e \u003cp\u003eTo evaluate the impact of envirotyping on the variance distribution of the E and G\u0026times;E effects, a linear model (2) was applied to SY. At the MET-22 scale, the envirotype effect (En) explained a main part of the environmental effect (70% for SY) as shown by the comparison of models (2) and (5) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Genotype by envirotype interaction (G\u0026times;En) explained 24.6% of the G\u0026times;E interaction observed at the MET-22 scale. Within each envirotype, the G\u0026times;E variation was reduced in envirotype EA (2.3% of total variation) when compared to the MET-22 (5.8%), but increased for envirotypes EB (8.7%) and particularly for ED (15.9%) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). As SY and SN were highly correlated (r\u0026thinsp;=\u0026thinsp;0.94, \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001) the envirotyping based on SY was used to study SN variation and its repartition. Similar results were observed and are reported on Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of the mixed linear model applied for the MET-22 and for each envirotype considering all trials as confounded (linear model (5)) and heritabilities estimation. Linear model (5):, with the genotype (G), the environment (E), the genotype by environment interaction (G\u0026times;E) and the block (R) nested in the trial. Heritabilities (h\u0026sup2;) were estimated using Eq.\u0026nbsp;(\u003cspan refid=\"Equ6\" class=\"InternalRef\"\u003e6\u003c/span\u003e):\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c8\" namest=\"c4\"\u003e \u003cp\u003e% Variance\u003csup\u003e(a)\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrait\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNb of environment\u003csup\u003e(b)\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eG\u0026times;E\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eε\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eh\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eSY\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMET-22\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEnvirotype EA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEnvirotype EB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEnvirotype EC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEnvirotype ED\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eSN\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMET-22\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEnvirotype EA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEnvirotype EB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEnvirotype EC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e26.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEnvirotype ED\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e(a) Percentage of variance explained by each parameter\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e(b) Number of trials considered for the evaluation of the different effects and estimation of heritabilities\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHeritabilities estimated for each trait and each environment of the MET-22. Components of the heritabilities were obtained using the linear model (3): ; Heritabilities (h\u0026sup2;) were calculated according to Eq.\u0026nbsp;(\u003cspan refid=\"Equ4\" class=\"InternalRef\"\u003e4\u003c/span\u003e):\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eh\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvirotype\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnvironment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSN\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eEA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eDij15_N\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eDij15_N\u003c/b\u003e\u003csup\u003e\u003cb\u003e+\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eVer15_N\u003c/b\u003e\u003csup\u003e\u003cb\u003e+\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYeb15_N\u003c/b\u003e\u003csup\u003e\u003cb\u003e+\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e\u003cb\u003eEB\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCh14_N\u003c/b\u003e\u003csup\u003e\u003cb\u003e+\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePre15_N\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePre15_N\u003c/b\u003e\u003csup\u003e\u003cb\u003e+\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSel14_N\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSel14_N\u003c/b\u003e\u003csup\u003e\u003cb\u003e+\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSel15_N\u003c/b\u003e\u003csup\u003e\u003cb\u003e+\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eVer14_N\u003c/b\u003e\u003csup\u003e\u003cb\u003e+\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eVer15_N\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e\u003cb\u003eEC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eLR15_N\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eLR15_N\u003c/b\u003e\u003csup\u003e\u003cb\u003e+\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMd15_N\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMd15_N\u003c/b\u003e\u003csup\u003e\u003cb\u003e+\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eVer14_N\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eYeb15_N\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eED\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCh14_N\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePre14_N\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePre14_N\u003c/b\u003e\u003csup\u003e\u003cb\u003e+\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSel15_N\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eHeritabilities were calculated for SY and SN for each environment, for each envirotype and for the mean MET-22. Into more details, SY and SN were highly heritable (0.98 for the MET-22 for both traits) and heritabilities ranged from 0.83 to 0.96 for SY and from 0.81 to 0.86 for SN depending on the envirotype (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). At the environment scale, heritabilities ranged from 0.63 to 0.96 for SY and from 0.53 to 0.86 for SN (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). For most of the environments, SY and SN were highly heritable.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMost of the SY-related QTL detected at the environment scale were specific of a single environment\u003c/b\u003e \u003c/p\u003e \u003cp\u003eComparison of SY-QTL detected for each environment is a first step to decipher the genetic determinism of G\u0026times;E. Therefore, the BLUE obtained for each genotype and each environment using model (3) were used as input for GWAS analysis. A total of 87 SNP were detected considering all the analyses performed for each trait and each environment (Supplemental Data4). Theses SNP were grouped into 11 QTL related to SY (QA03, QA05a, QA07a, QA07b, QA09, QC02, QC03a, QC04, QC08, QC09a and QC09b) and four QTL related to SN (QA05b, QA07a, QA09 and QA10) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). For almost all loci, the most frequent allele in the population was the favorable one except for QA03, QA07a and QC09b. Considering both traits, it is worth noting that all QTL were specific to a single environment except QA07a, QA09, QA10, QC02, QC04 and QC09a. QA07a, QA10, QC02, QC04 were only detected in two environments. The QA09 and QCA09a were detected in a large range of environments with 6 environments for QA09 and 7 for QC09a, respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription of the QTL detected for seed yield and seed number stability/instability. A QTL is defined as a region that can gather different individual QTL detected at different scales (MET, envirotype, environment; Supplemental Data 4) with overlapping positions. The range of the individual positions observed for a dedicated QTL is indicated in the \u0026ldquo;Positions\u0026rdquo; column. The positions refer to the reference genome Darmor-\u003cem\u003ebzh\u003c/em\u003e v10 version (Rousseau-Gueutin et al, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The \u0026ldquo;Traits\u0026rdquo; column refers to Seed Yield (SY) or Seed Number (SN), the environments, envirotypes and MET-22 indicated the different scales for which the considered QTL was detected.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChromosome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePositions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTraits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEnvironments\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEnvirotypes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMET-22\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQA03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u0026nbsp;452\u0026nbsp;989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePre15_N\u003csup\u003e+(EB)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQA05a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u0026nbsp;078\u0026nbsp;385\u0026ndash;2\u0026nbsp;080 519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChr14_N\u003csup\u003e+(EB)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQA05b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u0026nbsp;630 228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePre14_N\u003csup\u003e+(ED)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQA07a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138\u0026nbsp;830\u0026ndash;1\u0026nbsp;009 271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLR15_N\u003csup\u003e+(EC)\u003c/sup\u003e / Pre14_ N\u003csup\u003e\u0026minus;(ED)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138\u0026nbsp;830\u0026ndash;1\u0026nbsp;009 271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLR15_N\u003csup\u003e+(EC)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQA07b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u0026nbsp;033\u0026nbsp;023\u0026ndash;20\u0026nbsp;033 025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLR15_N\u003csup\u003e+(EC)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQA09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u0026nbsp;933\u0026nbsp;991\u0026ndash;4\u0026nbsp;504 817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSY/SN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYeb15_N\u003csup\u003e+(EA)\u003c/sup\u003e/Ver14_N\u003csup\u003e+(EB)\u003c/sup\u003e Ch14_N\u003csup\u003e\u0026minus;(ED)\u003c/sup\u003e/Ch14_N\u003csup\u003e+(EB)\u003c/sup\u003e Dij15_N\u003csup\u003e\u0026minus;(EA)\u003c/sup\u003e / LR15_N\u003csup\u003e+(EC)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEA / ED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQA10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u0026nbsp;515 307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePre14_N\u003csup\u003e\u0026minus;(ED)\u003c/sup\u003e / Ver14_N\u003csup\u003e\u0026minus;(EC)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQC02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60\u0026nbsp;063\u0026nbsp;320\u0026ndash;60\u0026nbsp;615 209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVer14_N\u003csup\u003e+(EB)\u003c/sup\u003e / Yeb15_N\u003csup\u003e+(EA)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQC03a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u0026nbsp;140\u0026nbsp;168\u0026ndash;20\u0026nbsp;809 663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSel15_N\u003csup\u003e+(EB)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQC03b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u0026nbsp;645 808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQC04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u0026nbsp;033\u0026nbsp;108\u0026ndash;6\u0026nbsp;289 953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDij15_N\u003csup\u003e+(EA)\u003c/sup\u003e / Pre15_N\u003csup\u003e\u0026minus;(EB)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQC08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37\u0026nbsp;326\u0026nbsp;511\u0026ndash;37\u0026nbsp;326 814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVer14_N\u003csup\u003e+(EB)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQC09a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u0026nbsp;184\u0026nbsp;690\u0026ndash;4\u0026nbsp;511 229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEA / EB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQC09a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u0026nbsp;184\u0026nbsp;690\u0026ndash;4\u0026nbsp;511 229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVer14_N\u003csup\u003e+(EB)\u003c/sup\u003e/Sel14_N\u003csup\u003e\u0026minus;(EB)\u003c/sup\u003e Md15_N\u003csup\u003e+(EC)\u003c/sup\u003e/ Dij15_N\u003csup\u003e\u0026minus;(EA)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEA / EB / EC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYeb15_N\u003csup\u003e+(EA)\u003c/sup\u003e/Ch14_N\u003csup\u003e+(EB)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLR15_N\u003csup\u003e+(EC)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQC09b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51\u0026nbsp;377 310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVer14_N\u003csup\u003e\u0026minus;(EC)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eEnvirotyping highlighted 5 QTL\u003c/h2\u003e \u003cp\u003eBLUE calculated for each genotype and for each of the four envirotypes were used for GWAS analysis. This method offers the opportunity to identify QTL involved in the G\u0026times;E interaction. In this way, the detected QTL will be linked to the envirotype characterization ran upon through their profile of limiting indicators.\u003c/p\u003e \u003cp\u003eQTL QC09a (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) was detected for SY and SN and for 3 envirotypes (EA, EB and EC). It was also detected for SY in 7 environments as previously mentioned (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Supplemental Data 4). It explained between 9.5% and 16.7% of SY variation depending on environment or envirotype; and 20.4% of SN variation for envirotype EA and 18.8% of SN variation for envirotype EB. The major allele was favorable to increase both traits (Supplemental Data 4).\u003c/p\u003e \u003cp\u003eThe SN QTL QC03b (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) was specifically detected in envirotype EC. It was not detected at the environmental scale. This QTL explained 21% of the SN variation (R\u0026sup2;=0.21) in envirotype EC and the major allele was the favorable one. However, confidence in this QTL is low as it is composed of a single SNP (Supplemental Data 4).\u003c/p\u003e \u003cp\u003eQA07a, was detected for SN in envirotype ED on chromosome A07 (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This QTL was also detected in environment LR15_N\u003csup\u003e+\u003c/sup\u003e (EC) and environment Pre14_N\u003csup\u003e\u0026minus;\u003c/sup\u003e (ED) for SN and for SY in LR15_N\u003csup\u003e+\u003c/sup\u003e (EC) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). This QTL explained 19.5% of the SN variation in envirotype ED and the minor allele of the population was the favorable one. (Supplemental Data 4). QA07a could be considered as specific to stressed conditions.\u003c/p\u003e \u003cp\u003eLastly, the QA09 and QC09a QTL, that were detected for a wide range of environments, were also detected for several envirotypes: EA and ED for QA09 and EA, EB and EC for QC09a.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eQA09 was the unique QTL detected across the MET-22\u003c/h2\u003e \u003cp\u003eBLUE obtained for each genotype at the whole MET-22 scale were used to detect consistent QTL controlling SY and SN across environments. A set of 15 SNP were detected for SY on chromosome A09 (Supplemental data 4) and consisted in the QTL QA09 (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). QA09 explained 12% of the SY variation and the favorable allele was the major allele. As previously shown, QA09 was also detected for SY at the envirotype scale for EA and ED (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), and at the environment scale in 6 environments that belonged to EA (2), EB (2), EC (1) and ED (1) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Supplemental Data 4). The same analysis carried out for SN revealed that locus QA09 was also detected, but only in envirotype EA. Thus, QA09 was qualified as a stable effect genomic region controlling SY and SY components across a wide range of environmental conditions.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTwo stable QTL (QA09 and QC09a) and an interactive QTL (QA07a) were revealed thanks to a multi-scale QTL detection (environment, envirotype, MET)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFinally, the QTL analysis revealed two loci (QA09 and QC09a) that can be characterized as stable and a locus (QA07a) that can be characterized as interactive. QA09 was detected across the MET-22, in 6 environments and two envirotypes and QC09a was detected in three envirotypes and 7 individual environments. QA07a was specifically detected for the most stressed envirotype (ED) and could be a good candidate for breeding programs. The allelic diversity underlying this QTL must be further investigated to evaluate the genetic diversity available at this locus for breeding purposes. Ten other QTL were detected at the environment scale but could not be linked to a limiting factor profile.\u003c/p\u003e \u003cp\u003eTo go further into the characterization of the three main QTL detected, a mixed linear model was fitted to estimate the QTL\u0026times;E interaction using the data obtained for the whole MET-22 and both for SY and SN. (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The QA09\u0026times;E interaction explained 31.9% and 34.5% of the total variance for SY and SN respectively, whereas the genotype effect explained only 13.2% and 20.1% of the total variance for SY and SN respectively. The QC09a\u0026times;E interaction explained 31.9% and 38.5% of the total variance for SY and SN respectively, whereas the genotype effect explained only 12.3% and 18.7% of the total variance for SY and SN respectively. For the QA07a, that was qualified as interactive, the QTL\u0026times;E effect explained less than 1% of variance for both SY and SN, this can be related to the overall small effect of this QTL at the MET-22 scale.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of the mixed linear model applied at the MET-22 scale to test the effect of the QTL\u0026times;E or the QTL\u0026times;En interaction. The three QTL QA09, QC09a and QA07a (described in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) were considered. The following mixed Linear model was used \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Y}_{ijkl}= \\mu +{G}_{i}+{E}_{k}+{marker}_{j}\\times {E}_{k}+\\underset{\\_}{{R}_{l\\left(k\\right)}}+\\underset{\\_}{{\\epsilon }_{ijkl}}\\)\u003c/span\u003e\u003c/span\u003e, with the genotype (G), the environment (E), the QTL (marker), the genotype by environment interaction effect (G\u0026times;E) and the block effect (R) nested in the trial., as proposed by Happ et al (2021). The QTL effect is tested using the marker presenting the highest -log(\u003cem\u003ep\u003c/em\u003e-value) within its confidence interval. In each case, the percentage of variance explained by the considered effect is indicated.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eSeed Yield (SY)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eSeed Number (SN)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQA09\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQC09a\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQA07a\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eQA09\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eQC09a\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eQA07a\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGenotype\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e31.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTrial\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e41.4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlock\u003c/b\u003e \u0026times; \u003cb\u003eTrial\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQTL\u003c/b\u003e \u0026times; \u003cb\u003eTrial\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e38.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eresidual\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eDiversity analysis at QA07a revealed a lack of favorable allele in the modern-grown varieties\u003c/h2\u003e \u003cp\u003eThe composition of the panel P173 (GSL\u0026thinsp;+\u0026thinsp;vs GSL- lines) allowed highlighting the recent history of winter oilseed rape breeding, including rapid selection for low glucosinolate contents in seeds, and estimating its impact on genetic diversity at the whole genome scale, as well as at the scale of previously detected QTL. The F\u003csub\u003eST\u003c/sub\u003e analysis showed a slight differentiation between the GSL\u0026thinsp;+\u0026thinsp;and GSL- populations (F\u003csub\u003eST\u003c/sub\u003e value of 0.078 at the whole genome scale, Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). However, the genetic differentiation was higher when considering specific chromosomes such as A09 and C09, already known to carry genes controlling seed glucosinolate pathway, but also for chromosomes A08, C02 and C03 (F\u003csub\u003eST\u003c/sub\u003e values\u0026thinsp;\u0026gt;\u0026thinsp;0.1) (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). No specific pattern was observed on the A07 chromosome harboring the interactive QA07a QTL for SN.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean nucleotide diversity (π) and mean F\u003csub\u003eST\u003c/sub\u003e statistics per chromosome and per population.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eMean π\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean F\u003csub\u003eST\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGSL+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGSL-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGSL\u0026thinsp;+\u0026thinsp;vs GSL-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhole genome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.69e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.77e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.61e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-genome A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.81e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.92e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.70e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSub-genome C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.58e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.62e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.51e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.34e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.63e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.19e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.55e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.67e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.47e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.03e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.26e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.89e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.65e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.69e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.60e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.99e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.06e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.88e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.17e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.28e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.06e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.03e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.10e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.98e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.96e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.91e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.81e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.45e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.46e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.30e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.76e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.86e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.69e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.73e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.22e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.85e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.31e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.46e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.21e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.111\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.85e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.64e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.85e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.43e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.82e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.23e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.40e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.68e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.33e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.60e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.85e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.42e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.75e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.68e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.70e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.59e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.77e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.54e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.29e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.49e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.16e\u003csup\u003e\u0026minus;\u0026thinsp;04\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eP173 corresponds to the whole diversity set (173 accessions)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eGSL\u0026thinsp;+\u0026thinsp;corresponds to the 46 accessions of the P173 with high contents of glucosinolate in seeds (\u0026gt;\u0026thinsp;18 \u0026micro;mol.g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eGSL- corresponds to the 127 accessions of the P173 with low contents of glucosinolate in seeds (\u0026lt;\u0026thinsp;18 \u0026micro;mol.g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe nucleotide diversity index π was calculated at the whole genome scale and for each chromosome for both panel GSL\u0026thinsp;+\u0026thinsp;and GSL-. Special attention was given to GSL- since this germplasm is more connected to the elite germplasm currently used by breeders. A scan of π values was also carried out targeting detected QTL regions. On average, lower nucleotide diversity was observed in the GSL\u0026thinsp;+\u0026thinsp;lines than in the GSL- lines excepted for chromosomes A07, A09, A10, C01, C07 and C08 (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). However, the study of the π index at QTL QA07a showed a higher diversity in the GSL\u0026thinsp;+\u0026thinsp;lines than in the GSL- (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These results showed a higher nucleotide diversity in GSL\u0026thinsp;+\u0026thinsp;and consequently a lack of diversity in the GSL- germplasm, corresponding here to a deficit of the favorable allele. Thus, GSL\u0026thinsp;+\u0026thinsp;germplasm may be an interesting source of genetic diversity to improve WOSR seed yield in unfavorable environments such as envirotype ED. In contrast, at the QTL QA09, a higher π value was observed in the GSL- illustrating a gain of nucleotide diversity induced by breeding at this locus (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eBased on a comprehensive environmental characterization of a MET composed of 22 locations, we identified a group of environments (ED) characterized by a combination of stresses (poor vernalization conditions, N stress, and low temperature and radiation during flowering and grain filling period) that drastically impacted seed yield. In addition, we identified a QTL specific to these conditions (QA07a). This QTL was characterized by a reduced genetic diversity in the modern germplasm, when compared to older cultivars. Our analysis also highlighted two stable QTL controlling seed yield (QA09 and QC09a) that were expressed whatever the environmental conditions, as well as ten other QTL specifically detected in one or two single environments of the MET.\u003c/p\u003e \u003cp\u003eThe question of crop adaptation and the underlying genetic determinism of crop plasticity is clearly a major challenge for agriculture for the coming decades. To answer this question tools are being developed and the number of related studies raised drastically during the last years (van Euwijk et al. 2010; 2016). However, to our knowledge, only few studies dedicated to the analysis of \u003cem\u003eBrassica napus\u003c/em\u003e genetic determinism of plasticity has been reported yet. The recent studies of yield stability specifically targeted the response of seed yield to water deficit (Zandberg et al; \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Raman et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) but did not address combination of different agro-pedoclimatic limiting factors. To address yield stability and G\u0026times;E interaction, authors usually test the QTL detected across different environmental conditions (Li et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Lu et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zou et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, the proposed experimental designs are often limited to 4 to 6 environments and G\u0026times;E genetic determinism is addressed by the confrontation of QTL detected for single environment to QTL detected across all the environments (Wang et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zheng et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Lu et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Arifuzzaman et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Pal et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sun et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Gajardo et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) or by the meta-analysis of QTL detected for each environment using linkage analyses (Xie et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Deng et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e The agro-pedoclimatic description of the MET-22 environments supplemented by a clustering of the MET-22 according to the main limiting factors, allowed explaining up to 70% of the environmental effect and 24.6% of the G\u0026times;E effect affecting seed yield. At the envirotype scale, the G\u0026times;E part was reduced in the non-limiting envirotype EA (2.3%), but not in the most stressed envirotype ED (15.9%), when compared to MET-22 (5.8%). This result may be linked to the fact that the limiting factors used to group MET-22 were detected at the MET-47 scale. Indeed, although MET-22 and MET-47 present similar profiles in terms of limiting factors, slight differences were observed with regards to TMN-CW, N\u003csub\u003etotal\u003c/sub\u003e and DAI indicators. From an analytical point of view, this resulted in a loss of power in the decomposition of the G\u0026times;E effect at the MET-22 scale. However, from an agronomic point of view, this resulted in a more representative list of limiting factors at the MET scale representing Western European rapeseed growing areas, leading to envirotypes more representative of future growing conditions.\u003c/p\u003e \u003cp\u003eOverall, the envirotyping-based methodology allowed prioritizing 3 QTL involved in the genetic determinism of seed yield plasticity across the 16 QTL detected. These 3 QTL have also been reported by Bouchet and coworkers (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) genetic germplasm related to the P173 population used here. The stable QTL QA09 was also reported in different studies for seed-yield related traits as summed up by Raboanatahiry et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), whereas no-colocalization was found in the literature for QC09a. The interactive QTL QA07a may correspond to the QTL detected previously for branching number (Zhao et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) or thousand seed weight and plant height (Quijada et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Udall et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The 13 remaining SY-related QTL were only detected in one to two environments. This pattern of QTL specificity to a unique environment is widely reported in literature (Bouchet et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Garin et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and offers a first approach to QTL\u0026times;E interactions, but the robustness of these specific QTL is also questionnable. The methodology we proposed in this study was helpful to prioritize QTL and link the QTL with agro-pedoclimatic scenarii.\u003c/p\u003e \u003cp\u003eThe low level of coincidence between our results and the QTL reported in the literature can result from differences in terms of genetic material used for GWAS or linkage studies. Indeed, most of studies reporting QTL for SY-related traits were carried out using spring type germplasm experimented in the field under short growth cycle conditions, unlike the winter oilseed rape germplasm used for this study. Winter and spring germplasms went through separated breeding history that can explain the differences of QTL detected, driven through selection for different breeding targets. Thus, broadening the genetic diversity considered for GWAS may help identifying additional seed yield related QTL. For example, adding semi-winter accessions, more likely to withstand western Europe growing conditions, is a promising way to increase the population resolution by reducing the extent of linkage disequilibrium. A second way to improve the power of GWAS consists in increasing the number of genomic markers. However, recent genomics advances already led to a common use of resolutive genotyping resources such as the 60K Illumina array in most of the genetic studies (Clarke et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Here, we developed and used a novel and dense genomic resource based on whole genome exome capture to characterize the population (Leveugle et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This resource provided 217,805 SNP covering the entire genome and therefore increased the resolution of the QTL regions as a higher number SNP were detected per genomic region. Our results identified this genomic resource as highly valuable to describe and capture the LD pattern of the P173 WOSR panel. This consists in an important resource, especially when dealing with complex traits such as Seed Yield or G\u0026times;E genetic determinism.\u003c/p\u003e \u003cp\u003eTo facilitate interpretation of G\u0026times;E and QTL specificity, approaches have already been proposed based on clustering of environments within a MET for other species. This involves grouping individual environments into mega-environments such that genotypes exhibit similar behaviors in a mega-environment and may differ between mega-environments. In fact, G\u0026times;E is primarily explained by differences between mega-environments, rather than by G\u0026times;E within a mega-environment. Thus, Moreau et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) proposed a clustering of environments according to the G\u0026times;E interaction matrix and were able to explain the specificity of QTL according to climatic or water stress conditions. More recently, Millet et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and Touzy et al. (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) proposed to cluster the environments of a MET according to drought and/or heat scenarii and then to study the pattern of QTL effects from one scenario to another. Most of SY-related QTL presented significant interaction with the climatic scenario and for some of them, the favorable allele changed according to the scenario (Millet et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Our study supports the interest of clustering the environments to identify and interpret QTL effects. In this study, we identified QTL (QA07a, QC03b) present in given envirotypes (ED and EC respectively) and absent for others, a pattern also revealed in maize by Millet et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and Touzy et al. (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) when opposing contrasting scenarii. However, as opposed to Millet et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), we didn\u0026rsquo;t record any change of the favorable allele at a given QTL, depending on the envirotype. These two last studies focused their environments clustering on a priori defined stresses (water and temperature) and developed a targeted characterization of the environments of the MET. For winter oilseed rape, the 11-months growth cycle makes it more difficult to focus on a dedicated stress and we therefore choose an alternative method that reports \u003cem\u003ea posteriori\u003c/em\u003e the main combinations of environmental factors that did impact seed yield elaboration. Even if nitrogen input was managed to be one of the main limiting factors, it was clearly shown that it had to be considered in combination with others stress (radiation, temperature, \u0026hellip;) occurring during the crop cycle, thus making ineffective an \u003cem\u003ea priori\u003c/em\u003e clustering based on nitrogen indicators only. Moreover, Ravier et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) showed that N defiencies, even intense, do not always affect seed yield, especially if they occur early during the wheat growth cycle. This method was also successfully used to identify limiting factors occurring over a MET for barley (Beillouin et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and highlights the need for indicators to account for potential stresses occurring in the field.\u003c/p\u003e \u003cp\u003eIn this study the envirotype ED corresponded to the combination of limiting factors that impacted the most seed yield. Its limiting factors targeted different phases of the crop cycle: winter (fulfillment of vernalization requirements, high temperatures during winter), bolting (N fertilization), flowering (lack of solar radiation) and grain filling (lower temperature). This high impacting combination of stress was observed for the 4 environments of envirotype ED. Individually, each of these factors has already been shown to impact seed yield: thermal stress during flowering affects flower fertility, pod number and seed number (Morrison \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Angadi et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Young et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2004\u003c/span\u003e); a poor vernalization conditions could lead to delayed or no flowering (Ferreira et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Chandler et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2005\u003c/span\u003e)d limitation is also known to impact seed yield (Rathke et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Here we were able to identify that these different stresses co-occurred in the field and truly impact seed yield. Excepting N stress, the main limiting factors in ED corresponded to climatic factors.\u003c/p\u003e \u003cp\u003eWe suggest that combining envirotyping and QTL analysis must be considered an effective approach, enabling the identification of both agro-pedoclimatic indicators and QTL involved in G\u0026times;E interaction, shedding light on yield plasticity determinism. QTL with the highest effects and qualified as \"stable\" may nevertheless present significant QTL\u0026times;E interactions. However, they are detected in most environments and envirotypes, with the favorable allele being the same whatever the environment considered, which underlines their interest in improving yield for a large range of environmental conditions. Specific QTL such as QA07a presented smaller effect and were not significantly involved in QTL\u0026times;E at the whole MET-22 scale. However, this QTL presented a specific interest for a specific combination of limiting agro-pedoclimatic conditions that was observed in the envirotype ED. The analyses performed at the MET-22 scale or directly at the single environment scale were not consistent to highlight this genomic region. The molecular diversity as this locus indicated that the GSL- cultivars did not fix the favorable allele, demonstrating the interest of the GSL\u0026thinsp;+\u0026thinsp;cultivars as valuable source of genetic diversity for improving seed yield and its stability.\u003c/p\u003e \u003cp\u003eFor further breeding programs, there is a particular interest in validating the four envirotypes described in the present study for a wider range of agro-pedoclimatic conditions. Indeed, \u003cem\u003ea posteriori\u003c/em\u003e analysis of larger climatic datasets at the same locations, coupled with crop physiological models, such as AZODYN-colza (Jeuffroy et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), may conduct to an estimation of the frequency of these four specific envirotypes across growing seasons. Envirotype ED can consist in a new target environment for breeding if it occurs rather frequently within the rapeseed production area. This strategy could also help redesigning multi-environment trials for WOSR breeding, for instance by discarding redundant locations, reducing experimental costs and by maximizing the opportunities of desired envirotypes/pedoclimatic scenario within a MET. The envirotyping approach can also lead to an estimation of a similarity matrix of a MET locations, according to their limiting factors pattern. This can be a clue to identify accurate match between dedicated genotypes and environments (Resende et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) leading, notably, to better product placement for the seed industry.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp skip=\"true\"\u003eThe authors would like to thank the technical staff of the UMR IGEPP for collecting the field data as well as for management of samples at Le Rheu (Elise Alix, Bernard Moulin, Solenn Guichard, Alina Tollenaere, Tiffany Bourlet), as well as C\u0026eacute;cile Baron who handled the genotyping data, and Mathieu Rousseau-Gueutin who provided genomic support concerning the Darmor-bzh v10 genome version. The authors would also like to thank the \u0026ldquo;Domaine de la Motte\u0026rdquo; Experimental Unit (INRAE Bretagne Normandie, Domaine de la Motte, 35650 Le Rheu) for the provision of the experimental plots, the cultural interventions and the agri-environmental data recorded and used for this study. The authors are also grateful to the partners of the RAPSODYN project (Innolea, Limagrain Europe, Lidea, MAS seeds, Syngenta, RAGT, Terres Inovia) that provided the field data at the MET scale.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAL and NN conceived and designed the analyses, planned the experiments, collected the data, contributed to the interpretation of the results and supervised the work. EC analyzed the data, interpreted the results and wrote the manuscript with inputs from all co-authors. CS contributed to the data collection, ran the diversity analyses. ML ran the genomic work, analyzed the exome capture data. All authors discussed the results, contributed, edited and validated the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by two national collaborative projects untitled GENERGY (ANR-07-GPLA-016) funded by the French National Research Agency (ANR) and RAPSODYN (ANR-11-BTBR-0004) funded by the program \u0026ldquo;Investments for the Future\u0026rdquo;.\u003c/p\u003e\n\u003cp skip=\"true\"\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e \u003c/p\u003e\n\u003cp skip=\"true\"\u003eThe datasets generated and analyzed in this study are available using the following link during the review process (https://entrepot.recherche.data.gouv.fr/privateurl.xhtml?token=27961ecd-f375-43e0-952d-23610e7ceb7d)\u0026nbsp;\u003c/p\u003e\n\u003cp skip=\"true\"\u003e\u0026nbsp;and will be freely available with a DOI if the article is accepted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAngadi SV, Cutforth HW, Miller PR, Mcconkey BG, Entz MH, Brandt SA (2000) Response of three Brassica species to high temperature stress during reproductive growth. 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Mol Breed 42:15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11032-022-01281-0\u003c/span\u003e\u003cspan address=\"10.1007/s11032-022-01281-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taag","sideBox":"Learn more about [Theoretical and Applied Genetics](https://www.springer.com/journal/122)","snPcode":"122","submissionUrl":"https://submission.nature.com/new-submission/122/3","title":"Theoretical and Applied Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Brassica napus, Seed yield, GWAS, QTL, stability, plasticity, MET","lastPublishedDoi":"10.21203/rs.3.rs-3788902/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3788902/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eA main challenge for rapeseed consists in maintaining seed yield while adapting to climate changes and contributing to environmental-friendly cropping systems. Breeding for plasticity and cultivar adaptation is one of the keys to meet this challenge. Genetic diversity for plasticity is the expression of Genotype x environment interaction. Therefore, we propose to identify the genetic determinant of seed yield G\u0026times;E interaction for winter oilseed rape using GWAS coupled with a multi-environmental trial and to interpret them in the light of environmental characteristics. Thanks to a comprehensive characterization of a multi-environmental trial using 79 indicators, 4 contrasting envirotypes were defined and used to identify interactive and stable seed yield (SY) QTL. A total of four QTL were detected for SY, among which, QA09 and QC09a, were stable (detected at the multi-environmental trial scale or for different envirotypes and environments); and one, QA07a, was specifically detected into the most stressed envirotype. The analysis of the molecular diversity at QA07a showed a lack of genetic diversity within modern lines compared to older cultivars bred before the selection for low glucosinolate content. The results were discussed in comparison to other studies and methods as well as in the context of breeding programs.\u003c/p\u003e","manuscriptTitle":"Envirotyping within a multi-environment trial allowed identifying genetic determinants of winter oilseed rape yield plasticity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-10 10:17:18","doi":"10.21203/rs.3.rs-3788902/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-01-09T15:50:32+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-01-08T06:15:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-12-21T10:47:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"Theoretical and Applied Genetics","date":"2023-12-20T11:25:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-genetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taag","sideBox":"Learn more about [Theoretical and Applied Genetics](https://www.springer.com/journal/122)","snPcode":"122","submissionUrl":"https://submission.nature.com/new-submission/122/3","title":"Theoretical and Applied Genetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"dffd804b-9bfe-48f9-b381-73f67f69264e","owner":[],"postedDate":"January 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-05-28T06:41:37+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-10 10:17:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3788902","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3788902","identity":"rs-3788902","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}