The stability of the QTL detected for biomass production and composition traits was investigated based on 13 different conditions related to the staggered-start design established in each of the two locations. The QTL of both types of traits were found to be more stable for successive climatic conditions and ages considered together, compared to successive climatic conditions or successive ages considered separately. The evaluation of each climatic condition and each age was made possible based on the staggered-start design: usually, other types of designs such as ‘single-start’ designs lead to the evaluation of plants for a given year, in which the related age and climatic condition cannot be partitioned. According to our design, the biomass production traits appeared to be more stable than the biomass composition traits across the conditions evaluated. However, the stability of the QTL across both contrasted locations was found to be relatively low. The QTL clusters representing co-localizations of QTL for biomass production and/or composition traits were identified across 13 different conditions. The corresponding intervals were screened for the underlying genes that correspond to orthologous cell-wall-related genes known in sorghum and maize.
Three main points will be discussed in this section: (1) the stability of biomass production and composition traits highlighted across the ages and the climatic conditions of the successive years studied, based on the staggered-start design established in each location; (2) the clusters of QTL for biomass production and composition traits that are consistent with the moderate to high genetic correlations highlighted between these traits, and (3) the different orthologous cell-wall-related genes that are known in sorghum and maize, two relatives of miscanthus, and that were found at the flanking markers, support the interval positions of our QTL clusters.
Stable QTL of biomass production and composition traits were highlighted across climatic conditions and ages based on the staggered-start design of each location, while stable QTL were detected in both locations
In each location, stable QTL that corresponded to the QTL detected for a given trait that co-localized under at least two conditions across different climatic conditions and/or across different ages, were identified for biomass production and composition traits. The assessment of QTL stability was an important objective of the study, which is why the staggered-start design was analyzed according to two different linear mixed models, related to each climatic condition and each age. It also made it possible to consider all the genotypes of the population in each location. Regarding biomass production traits, the different climatic conditions and ages considered together in each location made it possible to highlight 59% and 39% of stable QTL in Estrées-Mons and Orléans, respectively. These results can be compared to those reported by Gifford et al.  and Dong et al. , in which each of the years studied in their experimental designs were not partitioned into age and climatic condition effects. Gifford et al.  studied 13 biomass production traits in a M. sinensis population over two successive years, among which they identified 61% of stable QTL: 22 QTL re-discovered in 2012 out of 36 QTL detected in 2011. Dong et al.  established three interconnected miscanthus populations and carried out four different QTL analysis methods, either related to CIM or stepwise analyses, which led to the detection of 288, 264, 133 and 109 QTL for 14 biomass production traits across two years. In 2013, they re-identified from 48 to 56% of the QTL that had already been detected in 2012. When climatic conditions and ages were considered separately in each location of our study, around 30% of stable QTL were found either over the years or across the ages, regardless of the location. Accordingly, these lower proportions result from the partition of each year studied into the corresponding climatic condition on the one hand, and the age on the other.
Regarding biomass composition traits, a higher proportion of stable QTL was also found across the climatic conditions and ages when they were considered together rather than separately. However, these different proportions ranged from 3–29%, which was relatively low compared to biomass production traits. Van der Weijde et al.  studied a M. sinensis population for traits related to biomass composition and conversion efficiency: they found 23% of stable QTL in two successive years: 20 out of 86 QTL were found in 2013 and 2014. These proportions confirm that, in miscanthus, the QTL of biomass composition traits seem to be less stable across different years (and ages) than those of biomass production traits.
In each location studied, the proportion of stable QTL for both types of traits across the climatic conditions and/or ages was rather low: this could be expected, as it has been previously shown that biomass production and composition traits can be affected by the variability related to plant age and environmental factors, such as the related climatic conditions that occur each year [73, 74]. However, these stable QTL mapped across the climatic conditions and/or ages would lead to the identification of relevant targets for Marker-Assisted Selection (MAS) programs. Among these, an interesting example was highlighted in LG8 (37 cM) for plant circumference (C50_cm) and aboveground biomass yield (ABM_tDMha), symbolized with a solid red triangle in Fig. 5: these stable QTL are interesting in terms of their stability over different ages and climatic conditions of successive years in Estrées-Mons, especially as they are stable for age 2, age 3 and age 4. It means that the future genetic material could be screened at a young age, in order to select individuals that show interesting alleles according to this QTL. It could thus speed up miscanthus breeding when based on an early selection of such individuals.
The proportion of stable QTL depends on environmental conditions, plant age and the genetic material considered, which is specific to each miscanthus study [37, 39, 40]. However, these last studies did not detect QTL for more than three years after establishment, and did not partition the year into age and climatic condition effects. Segura et al.  used a staggered-start design and carried out QTL mapping in order to dissect the apple tree architecture into genetic, ontogenetic and environmental effects, which made it possible to determine the genetic determinism of related traits, with regard to tree ontogeny and climatic conditions. To our knowledge, the present study uses a staggered-start design in miscanthus for the first time, in order to detect stable QTL related to different climatic conditions and/or different ages. Moreover, a staggered-start design was established in each of the two contrasted locations, which led to the detection of QTL across locations as well, as had never been done before in miscanthus.
Finally, when considering the different years (i.e. climatic conditions) and ages together across locations, maximum proportions of 12% and 16% of stable QTL were highlighted for biomass production and composition traits, respectively. These different stable QTL are interesting for miscanthus breeding programs, as they express themselves for different climatic conditions, ages and locations. As for the stable QTL identified for different climatic conditions and/or different ages in each location, these QTL could also be relevant targets for MAS programs: the example in LG4 (125 cM) is interesting (symbolized with an empty triangle in Fig. 5), as a QTL related to plant stem number (PSNb) is stable across the two locations at age 2, meaning that it expresses itself in two contrasted locations for the same age. The fact that the proportion of these stable QTL was not higher than what had been found elsewhere, indicates that other environmental effects interact with the genetic determinism of biomass production and composition traits across locations. The different climatic and soil conditions could explain that, as the staggered-start design was established in a deep loam soil in Estrées-Mons, while the staggered-start design in Orléans was established in a sandy soil. In addition, the climate in Estrées-Mons is more influenced by the ocean than in Orléans: the differences in climatic conditions between both locations are presented according to different periods of the plant cycle in Raverdy et al. (submitted to BioEnergy Research). Moreover, 53 additional progenies were grown in Estrées-Mons compared to Orléans (Fig. 1): the genetic variability was therefore not totally similar between the two locations. This can also be a reason why the genotypic variations (R2) explained by the QTL found in Orléans were mainly higher than those explained by the QTL found in Estrées-Mons. Moreover, significant genotype x location interactions could explain variable proportions of stable QTL across locations. In a study comparing different miscanthus species across five different locations in Europe, Clifton-Brown et al.  and Lewandowski et al.  indeed highlighted genotype × location interactions for biomass production and biomass composition traits, respectively. The establishment effect can also impact QTL detection power, as miscanthus is mature from around two to three years  or five years after establishment : a substantial number of QTL was found from young to old plants in our study, which suggests that the effect due to the establishment may be limited within the location (i.e. in our study, age 1 was defined in 2015 for G1 plants established in 2014 and in 2016 for G2 plants established in 2015). However, the different establishment conditions between locations could also influence the proportion of stable QTL found across locations. Tejera et al.  used a staggered-start design and showed that the M. × giganteus yield response to fertilization was influenced by establishment conditions in each location but not by the plant age.
In this study, each staggered-start design makes it possible to highlight a higher proportion of stable QTL for a range of climatic conditions and ages considered together rather than separately. However, the stability of QTL under these conditions is higher for biomass production traits than for biomass composition traits, studied together for the first time in a miscanthus mapping population. Across locations, some stable QTL were stable despite different environmental conditions such as climate, soil and establishment effects. This brings new insights into miscanthus breeding, as stable QTL are needed from different genetic material evaluated across different ages and climatic conditions: the comparison of stable QTL between studies would lead to the identification of the most significant genomic regions associated with biomass production and composition traits.
Clusters of QTL for biomass production and composition traits were consistent with the moderate to high genetic correlations highlighted between these traits
The QTL clusters identified for biomass production and composition traits were in agreement with the moderate to high genetic correlations highlighted between the traits. The QTL clusters related to biomass production traits were identified in LG4, LG7 and LG18. They were made up of QTL that overlapped at similar positions, for traits such as canopy height, total plant height, plant circumference and aboveground biomass yield. The corresponding significant and moderate to high genetic correlations suggest that QTL overlapping is not random. Moreover, the stability of the QTL cluster is shown in LG18, as QTL were detected in 2018 and at ages 2 and 4 in Estrées-Mons. This is possible based on each staggered-start design evaluated over five years in two locations. Gifford et al.  identified QTL clusters in LG3 and LG6, that were re-identified in two subsequent years and that were consistent with high genetic and phenotypic correlations as well. These clusters were made up of QTL related to the plant circumference, stem diameter, plant stem number, aboveground biomass yield or characteristics of the leaves such as leaf width, length and area. These QTL identified for leaf-related traits are interesting: as canopy height refers to the height of the different leaves of the plant that contributes to yield-building, the QTL identified for canopy height in our study can in fact be related to different phenotypic characteristics of the leaves in our population. However, none of their different QTL clusters were common to our QTL clusters. Dong et al.  highlighted different QTL clusters found in their three miscanthus interconnected populations: these clusters were related to plant height, plant circumference, stem characteristics such as its volume and density, and the aboveground biomass yield. They were identified in various LG according to the population considered, and were in agreement with the moderate to high phenotypic and genetic correlations between these traits. For one of their populations originating from a cross between a M. sinensis and a M. sacchariflorus cultivar, they identified QTL clusters in LG4 and LG7: these LGs were common to the LGs in which we found QTL clusters for the same type of traits related to plant height, plant circumference and aboveground biomass yield. However, an investigation of the QTL cluster positions found in their study would be based on the alignment with the M. sinensis reference genome in order to confirm if the same genomic regions are involved.
Regarding biomass composition traits, we identified QTL clusters in LG4, LG5, LG13, LG15 and LG16, which were also in agreement with the significant moderate to high correlations between these traits. The stability of the clusters is also highlighted because two clusters were found in 2017 and for age 2 in Orléans. This is made possible based on the staggered-start design as well. They were located in LG15 and made up of traits related to cellulose, hemicelluloses, lignin and ADF contents. The co-localization of QTL related to ADF content with those related to cellulose and lignin content is not surprising, as ADF content represents the sum of cellulose and lignin contents . Van der Weijde et al.  identified a major QTL cluster for traits related to conversion efficiency and composition traits: this cluster was located on chromosome 6 according to the Sorghum bicolor reference genome that was used in the construction of their two parental miscanthus genetic maps. In miscanthus, the corresponding chromosomes are chromosomes 11 and 12, as the miscanthus genome has been shown to be the result of chromosomal duplication and fusion based on the sorghum genome [36, 38, 45]. However, none of our QTL clusters are common with their QTL clusters, because we did not identify QTL clusters in LG11 and LG12.
Our study was conducted by considering biomass production and composition traits together: this made it possible to identify a QTL cluster in LG15, which was made up of both biomass production and composition traits. The corresponding traits were canopy height, NDF (%DM) and hemicellulose content: the moderate and significant correlations of canopy height with these composition traits (respectively 0.44 and − 0.55) tend to validate the existence of this cluster. However, further analysis according to the genes underlying this cluster is necessary to confirm this assumption.
The QTL clusters identified for biomass production and composition traits could be explained by different genetic factors, such as the pleiotropic effects of the genes underlying these QTL or linked genes. Sometimes, these clusters can originate from genomic regions with segregation distortion, but this may not be possible in our study as we carefully filtered the distorted markers for the construction of our integrated genetic map. The staggered-start design led to the identification of QTL clusters located in LG4, LG5, LG7, LG13, LG15, LG16, LG18 for a range of climatic conditions and ages that consider biomass production and composition traits together, for the first time in miscanthus.
Orthologous cell-wall-related genes previously known in sorghum and maize made possible the identification of putative cell-wall-related genes in M. sinensis
Some of the 62 M. sinensis genes that were identified in the QTL clusters based on the orthologous cell-wall-related genes known in sorghum and maize, belong to interesting gene families. Twelve genes among the 62 genes belong to families that were previously found to contain genes involved in the secondary cell-wall (SCW) biosynthesis of miscanthus [78, 79] and are highlighted in bold in Table 5b. It can be noted that some genes are repeated in Table 5b as they belong to different QTL clusters. Hu et al.  carried out a transcriptome analysis of genes involved in secondary cell-wall biosynthesis in developing internodes of M. lutarioriparius: they highlighted different gene members in specific gene families. These gene families were 4CL, CCR and LAC that contain genes involved in the lignin biosynthesis based on the phenylpropanoid pathway . They also identified the CSL and GT gene families that are involved in the biosynthesis of cellulose and hemicellulose components in plants. Finally, three other gene families in common with Hu et al.  were identified: the FLA gene family, for which genes are involved in cell-wall modification and assembly; the TFNAC and TFWRKY families that contain transcriptional factors for the regulation of secondary cell-wall development. In addition, Zeng et al.  identified major genes involved in the monolignol biosynthesis pathway in M. × giganteus based on genetic and transcriptional analyses. Just like the phenylpropanoid pathway, this monolignol pathway leads to the synthesis of monolignols that are involved in lignin biosynthesis . Some of the major genes identified by Zeng et al.  belong to gene families in which we also found genes: the 4CL and CCR families that were also reported by Hu et al. , as well as the HCT family.
Based on these different comparisons, we can hypothesize that twelve M. sinensis genes out of the 62 genes previously identified are involved in secondary cell-wall development. This hypothesis can be supported by the fact that these genes were mainly found in the QTL clusters composed of M. sinensis biomass composition traits, especially for the clusters located in LG4, LG13 and LG15. Cluster 2 in LG4 and clusters 1 and 3 in LG15 were particularly interesting, as the R2 of the related QTL mainly ranged from 11,3 to 29.4% (Table 5a).