QTL mapping of winter dormancy and associated traits in two switchgrass pseudo-F1 populations: lowland x lowland and lowland x upland

doi:10.21203/rs.3.rs-18410/v1

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Research article

QTL mapping of winter dormancy and associated traits in two switchgrass pseudo-F1 populations: lowland x lowland and lowland x upland

https://doi.org/10.21203/rs.3.rs-18410/v1

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published 30 Nov, 2020

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Background

Switchgrass (Panicum virgatum) undergoes seasonal growth changes based on the perception of photoperiod and temperature. It transitions to winter dormancy in early fall, breaks dormancy in the spring, and resume the cycle starting from flowering which is the cue for senescence. The length of growing season can impact the biomass accumulation and yield. In this study, we conducted QTL mapping of winter dormancy measured by fall regrowth height (FRH) and normalized difference vegetation index (NDVI), spring emergence (SE), and flowering date (FD) in two bi-parental pseudo-F1 populations derived from crosses between the lowland AP13 with the lowland B6 (AB) with 285 progenies, and the lowland B6 with the upland VS16 (BV) with 227 progenies.

Methods

Pearson correlation coefficient between phenotypic traits was calculated to determine if some traits can be as surrogates for other traits. Broad-sense heritabilities were calculated to determine the inheritance, and QTL mapping was conducted for two-years phenotypic data to identify QTLs associated with the trais.

Results

We identified 18 QTLs for FRH, 18 QTLs for NDVI, 21 QTLs for SE, and 30 QTLs for FD. The ranges of percent variance explained by these QTLs varied between 4.21–23.27% for FRH, 4.47–24.06% for NDVI, 4.35–32.77% for SE, and 4.61–29.74% for FD. A higher number of QTL was discovered in the BV population, suggesting more variants in the lowland x upland population contributing to the expression of seasonal dormancy underlying traits. We identified 9 regions of colocalized QTL with possible pleiotropic gene action. The positive correlation between FRH or NDVI with dry biomass weight suggests that winter dormancy level could affect switchgrass biomass yield. The medium to high heritability levels of FRH (0.55–0.64 H²) and NDVI (0.30–0.61 H²) support the possibility of using the traits for indirect selection for biomass yield.

Conclusion

Markers found within the significant QTL interval can serve as genomic resource for breeding non-dormant and semi-dormant switchgrass cultivars for the southern regions, where growers can benefit from the longer production season.

Plant Physiology and Morphology

QTL mapping

Switchgrass

Winter dormancy

Spring emergence

Flowering date

Switchgrass, Panicum virgatum L. is a C4 warm season grass native to most of North America, ranging from southern Canada to northern Mexico [1]. Switchgrass uses include animal grazing, soil conservation, landscape ornament, and more recently as biofuel feedstock [1]. Switchgrass has been selected as a herbaceous model species by the U.S. Department of Energy (DOE) Biofuel Feedstock Development Program (BFDP) in 1991 for biofuel production [2]. Its suitability for planting in marginal land unsuitable for row crops makes it the right species for biofuel [3].

Switchgrass germplasm is divided taxonomically into two major ecotypes: upland and lowland based on their phenotypic divergence caused by the difference in latitudinal adaptation [1]. The phenotypic differences are the results of the prevalent photoperiods and temperatures along the latitudes of their adaptive environments [1, 4–8]. Upland ecotypes are mostly found at the northern latitudes where they evolved adaptation traits for a short growing season and tolerance to cold winter temperatures [9]. Lowland ecotypes are found at the southern latitudes and are adapted to a longer growing season but are sensitive to cold temperatures. Lowland ecotypes differ from the upland ecotypes in that they are taller, have fewer and larger tillers, longer and wider leaf blades, and thicker stems. Upland ecotypes may flower as early as late June or early July while lowland ecotypes flower as late as mid-October [10].

Casler et al. [4] measured the ground cover after spring emergence and in fall harvest and found lower values for both ecotypes planted at their non-adapted locations, suggesting planting at different latitudes only favors the adapted ecotypes [4]. Because of the different adaptability to the photoperiod and temperature, upland ecotypes are naturally found in the northern hardiness zones of 2–7 while lowland ecotypes can be found in the southern zones of 6–10 [9]. Casler [1] reported eight regional gene pools or cultivar deployment zones based on the adaptation of accessions to the regional photoperiod and temperature levels. These two factors largely affect switchgrass spring emergence, flowering time, cold and heat tolerance, and the onset of winter dormancy [1, 4, 11].

Switchgrass like other warm-season perennial grasses responds to photoperiod changes and this sensitivity is genetically controlled. They depend on the photoperiod cues to initiate growth in favorable conditions while going dormant when conditions are unfavorable [9]. Under longer photoperiod, the growth of switchgrass is much greater and flowering is delayed [5, 6]. Van Esbroeck et al. [6] found a delay in panicle emergence and a longer duration of panicle exertion in the northern switchgrass cultivar, Cave-in-Rock, under longer photoperiod. The delay was thought to be associated with an increase in the phyllocron, the intervening period between the sequential emergences of leaves on the main stem of a plant. The study confirmed that switchgrass has a facultative short-day response, flowering earlier under short days. Northern populations of switchgrass that are adapted to longer daily photoperiod in summer tend to flower early and mature more rapidly when planted at southern latitudes [12]. The opposite happened when southern populations were planted at northern latitudes; they flowered later and remained vegetative longer [12].

Temperature is another factor controlling switchgrass growth. Metabolic pathways such as photosynthesis, respiration, and growth processes are catalyzed by enzymes which activities are affected by temperature. An optimum switchgrass growth was determined at a moderately high temperature between 25 to 30 °C but decreased at a higher temperature of 37.6 °C [13] or a cooler temperature of 14 °C [14]. Freezing temperatures could also result in plant death. Evaluation of both switchgrass ecotypes in northern locations showed near-complete survival of upland ecotypes and near complete mortality of the lowland ecotypes due to the inadaptability to extreme cold temperatures [15].

Dormancy means lack of visible growth [16, 17], an adaptive mechanism of plant species for survival in threatening environments [18]. An example of harsh environment for plant growth is the cold weather in winter. Perennial plant undergoes cyclical growth responds to winter by terminating meristem growth and being insensitive to growth-promoting signals [19]. Failing to become dormant will expose the plant to the adverse effects of cold temperatures on the plant physiological functions such as photosynthesis, cellular transport, and the ability to deal with reactive oxygen species [20]. Freezing temperatures could also lead to ice formation intracellularly which could damage the cell structures and extracellular ice formation which can reduce the availability of water for absorption leading to cell dehydration [21].

The classification of dormancy is based on the primary physiological reactions causing dormancy [22] and the external trigger [23]. The terms ecodormancy, paradormancy, and endodormancy were suggested to describe three types of dormancy; ecodormancy is growth inhibition by temporary unfavorable environmental conditions; paradormancy is inhibition of growth by signals from distal organs; endodormancy is growth repression by internal signals [22, 24, 25]. Rohde and Bhalerao [19] defined endodormancy as “the inability to initiate growth from meristems under favorable conditions”. Preston and Sandve [21] defined endodormancy as physiological reactions that are sensitive to outside factors but do not necessarily require them for dormancy induction, in other words becoming dormant even when outside conditions are favorable for growth.

In switchgrass, endodormancy happens when the plant senses the changes in photoperiod and temperature before winter and consequently undergo senescence. During senescence, the leaf will endure systematic changes from cellular metabolism to degradation of cellular structures like chloroplast, mitochondria, and nucleus [26]. The degradation of macromolecules enables the relocation of nutrients to the below-ground stems and storage organs [26]. The reserves stored in the switchgrass rhizomes and crowns will drive new tiller growth in the spring when the conditions are favorable for growth [21, 27, 28]. After spring emergence, warm-season grasses undergo vegetative phase (leaf growth) followed by an elongation phase (internode length growth). The flowering phase will start in switchgrass when there are appropriate flowering signals perceived, switching the vegetative tiller meristems into reproductive tillers [27]. Flowering and seed developments are cues for tiller senescence in C4 perennial grasses and are possible factors in the induction of dormancy in the rhizomes [27].

Manipulating the onset and duration of winter dormancy is a potential strategy to increase biomass yield and distribution of switchgrass in the Southern USA where winters are mild. Currently, the selection of non-dormant plants is challenging because there is no dormancy rating system in place and phenotyping dormancy in segregating populations would require several seasons and multiple locations to have an accurate characterization. Understanding the genetic basis of seasonal dormancy in switchgrass and identifying quantitative trait loci (QTL) associated with the trait will enable the development of genomic resources for marker-assisted selection. In this study, QTL mapping of winter dormancy, spring emergence, and flowering date was conducted in two bi-parental pseudotestcross populations derived from the crosses between a non-dormant lowland tetraploid genotype, B6 and a dormant lowland tetraploid genotype, AP13 (AB population) and between B6 and a dormant upland tetraploid genotype, VS16 (BV population).

Development of mapping populations

Two F1 populations consisting of 285 and 227 progenies derived from two crosses, AP13 x B6 (AB) and B6 x VS16 (BV), respectively, were produced in the greenhouse in 2015 and 2016. A non-dormant lowland tetraploid genotype B6 was crossed to a dormant lowland tetraploid genotype AP13 and to a dormant upland tetraploid genotype VS16. B6, AP13, and VS16 were selected from PI422001, “Alamo”, and “Summer” accessions, respectively. PI422001 accession was obtained from the Germplasm Resources Information Network (GRIN), while AP13 and VS16 were the parents of the mapping population AP13 x VS16 [29].

Both crosses were made in isolation in separate greenhouse sections to prevent cross-pollination from unidentified sources of switchgrass pollen. The plants were cross-pollinated by placing every two parents in close proximity. The seed produced from cross-pollinated plants were collected at maturity and dried at room temperature before undergoing pre-chilling treatment to break seed dormancy. In pre-chilling treatment, seeds were placed on a wet filter paper in a petri dish. Petri dish was closed and sealed with parafilm then placed in a 4°C refrigerator for two weeks. After that, the seeds were planted in flats for germination.

Seedlings were first genotyped using one polymorphic SSR marker before transferring them into bigger pots to ensure they are hybrids. The plants were later divided into three clones for replication. The parents used in the crosses were also divided into 3 clones and included in the field experiment. All 285 progenies and both parents from the AB population were transplanted to the field on April 2017. The BV population was planted in two steps. The first subset of the BV population comprising 66 progenies and both parents were transplanted in April 2017 (Planting date 1), while the second subset of 161 progenies was transplanted in May 2018 (Planting date 2). Both fields were located at the University of Georgia Iron horse plant sciences farm in Greene County, GA (33.73° N, -83.30° W). The experimental design was a randomized complete block design with three replications. Plants were spaced apart by 3 feet (91.4 cm). The soil type is Cecil gravelly sandy loam. Field management included preemergence and postemergence (Pendimethalin and Atarazine) herbicides application before planting and irrigation of the field after planting. Herbicide application was repeated in the fall after harvest and in the spring before the emergence of switchgrass to prevent the germination of seeds shattered from the previous season.

Phenotypic data collection

Phenotypic data were collected in two growing seasons and comprised 1) Level of senescence and plant regrowth height after clipping in early fall, 2) Date of spring emergence, and 3) Date of flowering. The amount of senescence and plant regrowth after clipping in early fall were measured as indicators of dormancy initiation. Plants that enter dormancy early will have very slow growth and start senescence as soon as the photoperiod is shortened and temperature drops, which is expected to take place from late summer till early fall. To measure the level of regrowth in the fall, we clipped the plants on 31^st August 2017 and on 24^th September 2018, at the height of 10 cm above the ground and let the plants grow for 4 weeks. The height of regrowth tillers was measured and termed as fall regrowth height (FRH). Senescence was estimated using a normalized difference vegetation index (NDVI), whereby a high NDVI means low senescence level [30]. NDVI has been used in many studies to measure the plant ‘greenness’ and vegetation cover [31-36]. NDVI scanning was conducted using a GreenSeeker handheld crop sensor (http://www.trimble.com/Agriculture/gs-handheld.aspx), by scanning the plants at a fixed height of 80 cm above the ground.

To measure dormancy exit, the date of spring emergence was recorded starting from the first week of February until the end of April at the frequency of one observation every three days. Date of spring emergence was converted to Julian calendar days, where the earliest regrowth will have the smallest Julian date. For the flowering date, the date when panicle emerges on at least one stem was recorded and converted to Julian calendar days. Flowering date observations took place from early June until the end of July and were done every three days.

In addition to these four traits, we harvested and measured plant dry biomass weight to calculate the correlation between the four traits with biomass yield. First and second-year harvests were done on 24^th September 2018 and 29^th September 2019 using a Swift Machine forage plot harvester (Swift Machine & Welding Limited, Canada) during the 2018 harvest and a Wintersteiger Cibus F/S harvester (Wintersteiger Seedmech, Austria) during the 2019 harvest. Fresh biomass weight from the whole plant was measured after clipping. A sample from each plant was weighed and dried in a convection oven (60°C temperature) for two days and then weighed again to estimate dry matter content. Whole-plant dry weight was calculated using each plant’s dry matter content multiplied by the fresh biomass weight.

QTL mapping

Three data files were used for QTL mapping: the genetic linkage map (linkage groups with ordered markers), the phenotypic data, and the marker profiles for the breeding population. SNPs marker generation and construction of linkage maps were described in a separate study. In brief, SNP markers were developed using genotyping-by-sequencing methodology using two methylation-sensitive restriction enzymes: PstI and MspI. Two genetic maps were constructed for maternal and paternal recombination events in each population. Single-dose alleles (heterozygous in one parent and homozygous in the other parent) were used in marker grouping and ordering by JoinMap 5.0 [37].

Least square means for all phenotypic data were calculated separately for each year to avoid the effect of environment and genotype by environment interaction in QTL detection. For the BV population that was planted over two planting dates, we calculated the LS means separately for each planting date in each year. A mixed linear model was used to estimate the LS means where genotype was set as a fixed effect and replication as a random effect. We also pooled the data across years for AB and across years and planting dates for BV using Best Linear Unbiased Predictor (BLUP). BLUP shrinks the variance resulting from testing in different replicates and environments and subsequently generates the best predictive value for each genotype [38]. We used a mixed model to calculate BLUP where the random effect was set for genotype and a fixed effect for replication and year. For the BV population, the planting date was included as a fixed effect too. Piepho and Eckl [39] demonstrated how ryegrass varieties with different establishment dates within the same location were analyzed in a single mixed model; the trial date or planting date is treated as a blocking factor. Both BLUP and LS means were calculated using PROC MIXED in SAS 9.4 (SAS Institute Inc., Cary, NC, USA).

QTL mapping was carried out for four traits: FRH, NDVI, SE, and FD. We used both the LS means in each year and BLUP values to carry out the mapping using the Composite interval mapping (CIM) program in WinQTL Cartographer 2.5 [40]. Since this software is designed for inbred species with known linkage phase, the genetic marker of an outcrosser species needs to be adjusted to properly define the recombination interval. For this step, the progenies' genotypes at a particular marker locus were inverted according to the linkage phase information given by the JoinMap data output. All markers’ linkage phase (in each LG) were standardized to one phase, i.e. all {1-} or {0-}, and the genotypes at the marker locus with different phase were inverted accordingly; heterozygous to homozygous and vice versa. QTL mapping was then carried out using this newly formed dataset that has only one linkage phase for every LG. For the QTL mapping procedure, the settings for the CIM program were forward and backward stepwise regression, a window size of 10, 2.0 cM walking speed, and a p-value < 0.05 after 1000 permutations. QTL interval was defined for the chromosomal region having a LOD score above the permutation threshold.

We searched for regions of colocalized QTLs which include all QTLs mapped for FRH, NDVI, SE, and FD using LS means and BLUP. This was done to search for chromosomal regions containing putative pleiotropic genes controlling multiple traits. Colocalized QTLs are those with the overlapped QTL intervals.

Statistical analyses

Test for normality of phenotypic data was first conducted using PROC UNIVARIATE of SAS (SAS 9.4, SAS Institute Inc., Cary, NC, USA) where normality was analyzed using a Q-Q plot of residuals. Analysis of variance (ANOVA) was conducted to test the effects of genotype, year, and genotype by year interaction on the measured traits, using PROC MIXED of SAS (SAS 9.4, SAS Institute Inc., Cary, NC, USA). Genotype, year, and genotype by year interaction were treated as fixed effects, while replications within years were treated as a random effect. Broad sense heritability for each trait was calculated using the formula:

[Please see the supplementary files section to view the equation.]

where V_g = Genotypic variance, V_gy = Genotype by year variance, V_e = Error variance, r = number of replication, and y = number of year. Variance components were calculated using PROC VARCOMP of SAS (SAS 9.4, SAS Institute Inc., Cary, NC, USA) using the restricted maximum likelihood (REML) method. Correlation among traits and with biomass weight was estimated by Pearson’s correlation coefficient using PROC CORR of SAS (SAS 9.4, SAS Institute Inc., Cary, NC, USA). BLUP value of individual genotypes was used in the calculation of correlation.

Frequency distribution of the phenotypic traits

FRH and NDVI were the first data collected in the first year of planting. Both populations had a similar range of distribution for FRH and NDVI (Figure 1). B6 had higher FRH and NDVI than AP13 and VS16. For FRH and NDVI collected in fall 2018, both population distributions were skewed to the right, indicating lower population FRH and NDVI values. The reason for the shift in values was because we clipped the plants on 31^st August in 2017 and on September 21^st in 2018. Thus, most of the plants were already dormant or progressing toward dormancy in fall 2018, and hence the lower FRH and NDVI values. There was no B6 value in AB plot for 2018 because the plant was growing poorly in spring 2018 and eventually died in fall 2018. In the BV plot in the fall 2018 B6 value was similar to VS16. B6 had earlier emergence than AP13 but later emergence than VS16 in spring 2018. In spring 2019, B6 had earlier emergence than VS16. We think the reason for B6 year to year variation is most likely due to its intolerance to cold temperature. B6 grows throughout the winter in the greenhouse where the temperature is warmer while AP13 and VS16 go dormant (Figure 2a). Both populations had earlier emergence in spring 2019 than 2018, indicating that the environment in early spring 2019 was more conducible for growth. For the flowering date, both populations have about the same range of distribution in 2018 and 2019. B6 flowered later than AP13 and VS16, which suggests that B6 completed its growth later than the other two parents.

Analysis of variance

There was a significant genotype effect on all traits in AB, BV planting date 1, and BV planting 2, and a significant year effect for FRH, NDVI, and SE in AB; FRH, NDVI, SE, and FD for BV (Table 1). Significant genotype by year effect was observed for NDVI in AB, FRH, NDVI, SE, and FD for BV planting date 1. Because of this significant interaction, QTL mapping was done using LS means for each year and BLUP for trait values across years.

Broad-sense heritability (H²)

There were medium ranges of H² for FRH (0.54 – 0.64) and NDVI (0.30 – 0.61), a small to medium range of H² for SE (0.13 – 0.56), and a high range of H² for FD (0.61 – 0.88) across AB, BV planting date 1, and BV planting date 2 (Table 2). In most cases, the highest H² was observed for FD, followed by FRH, NDVI, and SE.

Correlation between traits

A similar trend of correlation was observed for AB and BV populations (Table 3). Biomass weight was positively correlated with FRH, NDVI, and FD, while negatively correlated with SE, which means higher biomass weight is correlated with lower dormancy level, earlier spring emergence, and later flowering. FD had the lowest correlation with other traits; it was not correlated with biomass in BV, and with SE in AB and BV.

Significant QTLs

Using 2,772 and 3,766 SNP markers for AB and BV population, respectively, for QTL mapping, we identified 16, 14, 12, and 20 QTLs for FRH, NDVI, SE, and FD, respectively, mapped in both populations and years using LS means (Supplementary table 1 - 4). For FRH QTLs, 3 were mapped in AB and 13 in BV. For NDVI QTLs, 4 were mapped in AB and 10 from BV. For SE QTLs, 7 were mapped in AB and 5 in BV. For FD QTLs, 11 were mapped in AB and 9 in BV. A higher total number of QTLs were mapped in the BV population; these could be due to the separate mapping done for two subsets of BV population. Another reason could be due to a higher genetic variance between B6 and VS16, leading to more QTLs contributing to phenotypic expression in F1 progenies. To have a meaningful comparison, QTL numbers are compared using BLUP QTLs. We discovered 9, 6, 11, and 14 significant BLUP QTLs for FRH, NDVI, SE, and FD, respectively (Supplementary table 1 – 4). A higher number of BLUP QTLs were observed in the BV population for FRH (6 QTLs) and NDVI (4), while a higher BLUP QTLs were found for SE in AB population (6 QTLs), and an equal amount of QTLs found in both populations (7 QTLs each).

Some of the QTLs mapped with BLUP values overlapped with those mapped using LS means. We identified 7, 2, 2, and 4 BLUP QTLs for FRH, NDVI, SE, and FD, that are redundant to LS means QTLs (Supplementary table 5). Adding all QTLs mapped with LS means and unique BLUP QTLs, we have a total of 18 QTLs for FRH, 18 QTLs for NDVI, 21 QTLs for SE, and 30 QTLs for FD (Figure 3 - 6). For FRH, the range of percentage of variance explained (PVE) by each QTL is 4.21 – 23.27%, for NDVI this is 4.47 – 24.06%, for SE the range is 4.35 – 32.77%, and for FD the range is 4.61 – 29.74%. FRH QTLs were mapped in LG 5N and 4K in the AB population; LG 1N, 5K, 5N, 6K, 9K, and 9N in the BV population. NDVI QTLs were found in LG 2K, 3K, 5N, and 6N in the AB population; LG 1N, 5N, 9K, and 9N in the BV population. SE QTLs were mapped in LG 1K, 1N, 2K, 5N, 7K, 9K, 9N in the AB population; LG 1N, 2N, 5K, 5N, and 9K in the BV population. FD QTLs were found in LG 3K, 4K, 5N, and 9K in the AB population; LG 1K, 1N, 2N, 3N, 5N, 6N, 7K, and 9K in the BV population.

QTL colocalization

Combining all QTLs mapped using LS means and BLUP (redundant BLUP QTLs were not included), we observed a total of 16 QTL colocalization regions within all parental maps in both populations (Figure 3 - 6, Supplementary table 6). There are three chromosomal regions with colocalization of 3 QTLs. The first region is in LG 5N of B6.AB map; the three QTLs found here are mapped for SE2019 (marker AB6919), NDVI2018 (marker AB6919), and FD2018 (marker AB8070). The NDVI and FD QTLs in this region have positive additive effects (α) while SE QTL has a negative α. This is a possible indication that the transmission of this chromosomal region can reduce the dormancy level by having a higher magnitude of plant greenness in the fall, earlier dormancy break as indicated by early spring emergence, and later flowering/maturity.

The second region with 3 colocalized QTLs is in LG 5N of the B6.BV map. The QTLs found in this region were mapped for FRH2018 (marker BV17469), NDVI2018 (marker BV17469), and SE2019 (marker BV17309). The FRH and NDVI QTLs have negative α while the SE QTL has a positive α. Since this region is associated with a higher dormancy level (positive α for FRH and NDVI) and later dormancy break (negative α), we can potentially use the opposite marker genotype to screen the plants with the opposite trait direction, i.e. lower dormancy and early emergence. A similar condition is observed in the third colocalization region with QTLs mapped for FRH2018 (marker BV7842), SE2019 (marker BV7842), and FD2018 (marker BV7842); the FRH and FD QTLs have negative α while the SE QTL has a positive α.

FRH and NDVI are two traits that shared the most number of colocalized QTLs regions with 8 regions in total. The second highest is NDVI and SE with 4 regions. NDVI has the most number of overlapped QTL regions with other traits with 14 regions in total, while FD had the least number of colocalized regions with other traits with 5 colocalized regions. In contrast, FD has the most number of the same QTL mapped across years with 3 QTLs in total.

Initiation of winter dormancy and its release, vegetative growth, and development of reproductive organs occur sequentially following plant perception of environmental stimuli like changes in day length and temperature. These stimuli trigger the expressions of genes that regulate the growth of different plant structures such as shoots, stems, flowers, rhizomes, and roots. The identification of the genomic regions associated with seasonal growth changes offers the possibility of tapping the genetic potential of switchgrass to produce higher biomass yield through the extension of the growth period.

Through our observation of most switchgrass growth in the field, it peaks during the long days and warm temperature of the late spring (May-June), starts flowering when sensing the gradual decrease in daylengths in mid-summer (July to early August), and finally undergoes senescence (September -October) and becomes dormant (November to January) when days are short and cold in the winter. Identifying the QTLs associated with these growth changes, would enable using them as markers for selecting progenies with delayed dormancy and flowering, and with early spring emergence. In our study, we used FRH and NDVI as indicators of dormancy status, SE for dormancy exit, and FD as an indication for the plant reaching the end of its growth cycle.

It is important to understand the mechanism underlying stand persistence of switchgrass so that the changes made to the growth cycle will not negatively affect its survival. Switchgrass persistence is particularly impacted by C and N translocation from the shoots to crowns, rhizomes, and roots where lower mobilization can result in plant death [27]. This can happen in the case of low tillering and disruption of senescence [27]. Sarath et al. [27] suggested a longer growth period of the southern germplasms compared to the northern germplasms when planted in a northern environment. However the plants do not have a defined period of senescence prior to winter dormancy, and hence do not fully cycle C and N. As a consequence, the non-adapted germplasm suffers from the loss of structures crucial to perenniality [27]. In southern locations where winter usually starts at a later date and generally less cold than northern locations, planting non-dormant or semi-dormant cultivars can potentially increase biomass yield, as long as the plant is able to properly translocate nutrients to the belowground storage organs before winter starts. In addition to death caused by poor C and N translocation, switchgrass deaths can also be caused by severe frost, suggesting a non-adaptation of cellular mechanisms to freezing and lack of cold acclimation [27].

We did not phenotype cold tolerance in this study but recognize the importance of this trait for warm-season plant survival under low temperatures. We observed some mortality of the B6 parent that might be a result of intolerance to freezing temperature as it originated from a Florida collection. The B6 parent has been observed to persist and grow very well through winter at the more southern location Tifton, where winter is milder than the location where we carried out this study (Fig. 2b). It has also been observed to grow during winter in the greenhouse where other parents (AP13 and VS16) were gone completely dormant, typical of a nondormant genotype(Fig. 2a). AP13 is a lowland genotype that is known to have a high biomass yield while VS16 is an upland genotype with lower yield but better tolerance to cold. Both populations exhibited a continuous bell-shaped distribution for all four phenotypes, suggesting they are quantitative traits controlled by many genes. This explains the large number of QTL that were successfully mapped for all traits.

Heritability was calculated to see if traits can be passed on to progenies without being affected by the environmental changes. Although we did not test the progenies in different locations, the different years bring enough environmental differences because of the variability in rainfall, temperature, nutrient status, etc. We also clipped the plants at a later date in 2018, thus the FRH and NDVI values were smaller in 2018 compared to 2017 for both populations. The difference in the year impact was shown by the significant year and genotype by year interaction for the majority of the traits. In our study we found FD to have the highest heritability, followed by FRH, NDVI, and SE.

Correlation of traits with dry biomass yield was calculated to test the hypothesis that an extended growth period leads to more biomass accumulation in these two F1 populations. We have previously found a positive correlation in a switchgrass diversity panel consisting of 17 lowland, 5 intermediate, and 14 upland accessions [30]. In the current study biomass weight was confirmed again to be positively correlated with FRH and NDVI, while negatively correlated with SE, for both populations. The positive correlation between biomass weight and FD was weak for the AB population and non-significant for the BV population. This means biomass weight increases with the increase in growth and plant greenness in the fall, and earlier plant emergence. Later flowering date increases the biomass yield only to a smaller magnitude in AB, which indicates the disadvantage of using the trait for indirect selection of high biomass yield, particularly in the southern region with longer growing season and switchgrass is observed to flower in early summer. We found a high correlation between FRH and NDVI (0.78 r in AB and 0.63 r in BV), intermediate correlations between FRH and SE (-0.38 r in AB and − 0.44 r in BV) and between NDVI and SE (-0.40 r in AB and − 0.37 r in BV), and low or non-significant correlations between FD and other traits. For highly correlated traits, we think a similar gene pathway or gene action is involved in controlling the expression of the traits.

We have successfully mapped 18 QTLs for FRH, 18 QTLs for NDVI, 21 QTLs for SE, and 30 QTLs for FD. We found a higher number of QTL in the BV population, specifically in the VS16 map. This can be explained by the higher genetic divergence of the parents used for the cross. We have shown in a previous study that the BV population contained more polymorphic markers and that the parents have the largest genetic distance. Since the parents were originally adapted to different latitudes they should have more variants contributing to traits segregation in the progenies. In the case of linkage mapping in a pseudo-testcross population (with separate parental genetic maps), the variation within the parental genome is captured through the trait-marker association. On the other hand for the AB population, since both parents originally adapted to southern latitudes, they theoretically have less genetic diversity, thus fewer variants contributing to trait segregation. Another reason might be the high rates of segregation distortion of alleles in the progenies of the AB population, which may have resulted in a lower percentage of mappable markers and possible dropout of alleles associated with the traits understudied [41, 42].

We found some QTLs reoccurring in the second year of evaluation (using LS means); the reoccurrence of these QTLs suggests the high heritability of the genes linked to those QTLs. There are 3 common FD QTLs and 1 common FRH QTLs mapped for both years. As both FD (0.62–0.88 H²) and FRH (0.55–0.64 H²) have high trait heritability, this explains why common QTLs were found for these two traits. The markers located near the QTL positions can potentially be used for progeny screening as they give more confidence in the trait expression.

QTLs colocalization indicates either pleiotropic gene action or different genes that are closely linked. For the latter, the association between different traits can be broken after a few cycles of recombination. For pleiotropic gene action, we can utilize the QTL to simultaneously select for multiple favorable traits such as lower dormancy level, early emergence, and later flowering/maturity. Colocalization of QTLs could also explain why certain traits are highly correlated with each other. We found 8 colocalized regions with FRH and NDVI QTLs, and these two traits were highly correlated with each other (0.78 r in AB and 0.63 r in BV). FD has the lowest number of colocalized regions with other traits, as also shown by its low correlation with other traits.

There are several QTL mapping studies done in switchgrass, these include mapping of QTL for spring green-up, flowering time, developmental traits, and biomass QTLs [43–50]. To date, there are no published QTL mapping studies on winter dormancy besides the few transcriptomic studies aiming to identify the gene pathways and processes involved during senescence and dormancy [51–53]. Our study is the first to describe the genomic regions that are associated with winter dormancy. The markers found within the QTL interval are potential genomic resources that can be used in a marker-assisted breeding programs. These markers warrant more investigations in the future work to verify the status and level of association with the traits in a diverse population with different genetic backgrounds.

Identifying the genomic regions associated with switchgrass seasonal growth changes is important for the genetic manipulation and extension of switchgrass growing season. Planting non-dormant or semi-dormant switchgrass in the southern location with mild winters is a potential strategy for the accumulation of more biomass. We have successfully identified 18 QTLs for FRH, 18 QTLs for NDVI, 21 QTLs for SE, and 30 QTLs for FD, with a higher number of QTLs mapped in the BV (lowland x upland) population. Breeding superior cultivars can be done through the incorporation of alleles that are associated with improved traits. Using markers linked to these traits enabled the screening of progenies in the early growth stage.

FRH

Fall regrowth height

NDVI

Normalized difference vegetation index

Spring emergence

Flowering date

AP13 x B6 population

B6 x VS16 population

QTL

Quantitative trait loci

LS means

Least square means

BLUP

Best Linear Unbiased Predictor

Linkage group

H²

Broad-sense heritability

Correlation coefficient

LOD

Logarithm of odds

centiMorgan

PVE

Percentage of trait variance

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable

Availability of data and materials

All data generated or analyzed during this study are included in this published article (and its Supplementary Information files).

Competing interests

The authors declare no competing interests.

Funding

The Malaysian Rubber Board funded RR’s bench fees.

Partial Funding was provided by The Center for Bioenergy Innovation a US Department of Energy Research Center supported by the Office of Biological and Environmental Research in the DOE Office of Science, for biomass harvesting equipment and technical staff.

Authors contribution

RR conducted the experiment and wrote the manuscript, AM conceptualized and supervised the study, and edited the manuscript. The authors read and approved the final manuscript.

Acknowledgement

We thank Jonathan Markham for assisting in greenhouse and field works.

Casler MD. Switchgrass breeding, genetics, and genomics. In: Monti A, editor. Switchgrass. Green energy and technology. London: Springer; 2012. pp. 29–53. .
Kszos LA, Downing ME, Wright LL, Cushman JH, McLaughlin SB, Tolbert VR, Tuskan GA, Walsh ME. Bioenergy Feedstock Development Program Status Report. Oak Ridge National Laboratory; 2000. . Accessed 21 December 2015.
Parrish DJ, Fike JH. The biology and agronomy of switchgrass for biofuels. Crit Rev Plant Sci. 2005;24:423–59. .
Casler MD, Vogel KP, Taliaferro CM, Ehlke NJ, Berdahl JD, Brummer EC, Kallenbach RL, West CP, Mitchell RB. Latitudinal and longitudinal adaptation of switchgrass populations. Crop Sci. 2007;47:2249–60. .
Benedict HM. Effect of day length and temperature on flowering and growth of four species of grasses. J Agric Res. 1940;61:661–71.
Van Esbroeck GA, Hussey MA, Sanderson MA. Variation between Alamo and Cave-in-rock switchgrass in response to photoperiod extension. Crop Sci. 2003;43:639–43. .
Casler MD, Vogel KP, Taliaferro CM, Wynia RL. Latitudinal adaptation of switchgrass populations. Crop Sci. 2004;44:293–303. .
McMillan C, Weiler J. Cytogeography of Panicum virgatum in Central North America. Am J Bot. 1959;46:590–3. .
Lowry DB, Behrman KD, Grabowski P, Morris GP, Kiniry JR, Juenger TE. Adaptations between ecotypes and along environmental gradients in Panicum virgatum. Am Nat. 2014;183:682–92. .
Casler MD, Tobias CM, Kaeppler SM, Buell CR, Wang Z-Y, Cao P, Schmutz J, Ronald P. The switchgrass genome: Tools and strategies. Plant Genome. 2011;4:273–82. .
Aspinwall MJ, Lowry DB, Taylor SH, Juenger TE, Hawkes CV, Johnson MVV, Kiniry JR, Fay PA. Genotypic variation in traits linked to climate and aboveground productivity in a widespread C4 grass: evidence for a functional trait syndrome. New Phytol. 2013;199:966–80. .
Newell LC. Effects of strain source and management practice on forage yields of two warm-season prairie grasses. Crop Sci. 1968;8:205–10. .
Kandel TP, Wu Y, Kakani VG. Growth and yield responses of switchgrass ecotypes to temperature. Am J Plant Sci. 2013;4:1173–80. .
Mitchell JLB, Halter M, Neal Stewart C Jr, Nilsen ET. Cool temperature effects on photosynthetic parameters of two biomass fuel feedstocks in a low light intensity environment. Biofuels. 2014;5:533–44. .
Nielsen EL. Analysis of variation in Panicum virgatum. J Agric Res. 1944;69:327–53.
Romberger JA. Meristems, growth and development in woody plants: an analytical review of anatomical, physiological, and morphogenic aspects. Technical Bulletins No. 1293. United States Department of Agriculture; 1963.
Schoot CVD. Dormancy and symplasmic networking at the shoot apical meristem. In: Lang GA, editor. Plant dormancy: physiology, biochemistry and molecular biology. Wallingford: CAB International; 1996. pp. 59–81.
Vegis A. Dormancy in higher plants. Annu Rev Plant Physiol. 1964;15:185–224. .
Rohde A, Bhalerao RP. Plant dormancy in the perennial context. Trends Plant Sci. 2007;12:217–23. .
Thomashow MF. Plant cold acclimation: freezing tolerance genes and regulatory mechanisms. Annu Rev Plant Physiol Plant Mol Biol. 1999;50:571–99. .
Preston JC, Sandve SR. Adaptation to seasonality and the winter freeze. Front Plant Sci. 2013;4:1–18. .
Lang GA. Dormancy: a new universal terminology. HortSci. 1987;22:817–20.
Junttila O. To be or not to be dormant: some comments on the new dormancy nomenclature. HortSci. 1988;23:805–6.
Lang GA, Early JD, Martin GC, Darnell RL. Endo-, para-, and ecodormancy: physiological terminology and classification for dormancy research. HortSci. 1987;22:371–7.
Lang GA, Martin GC, Stutte GW, Darnell RL, Early JD, Arroyave NJ. Dormancy: toward a reduced, universal terminology. HortSci. 1985;20:809–11.
Woo HR, Kim HJ, Nam HG, Lim PO. Plant leaf senescence and death - regulation by multiple layers of control and implications for aging in general. J Cell Sci. 2013;126:4823–33. .
Sarath G, Baird LM, Mitchell RB. Senescence, dormancy and tillering in perennial C4 grasses. Plant Sci. 2014;217/218:140–51. .
Palmer NA, Saathoff AJ, Tobias CM, Twigg P, Xia Y, Vogel KP, Madhavan S, Sattler SE, Sarath G. Contrasting metabolism in perenniating structures of upland and lowland switchgrass plants late in the growing season. PLoS One. 2014;9:e105138. .
Missaoui AM, Paterson AH, Bouton JH. Investigation of genomic organization in switchgrass (Panicum virgatum L.) using DNA markers. Theor Appl Genet. 2005;110:1372–83. .
Razar R, Missaoui AM. Phenotyping winter dormancy in switchgrass to extend the growing season and improve biomass yield. J Sustain Bioenergy Syst. 2018;8:1–22. .
Carlson TN, Ripley DA. On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens Environ. 1997;62:241–52. .
Di Bella CM, Paruelo JM, Becerra JE, Bacour C, Baret F. Effect of senescent leaves on NDVI-based estimates of fAPAR: experimental and modelling evidences. Int J Remote Sens. 2004;25:5415–27. .
Calera A, Martinez C, Melia J. A procedure for obtaining green plant cover: relation to NDVI in a case study for barley. Int J Remote Sens. 2001;22:3357–62. .
Laidler GJ, Treitz PM, Atkinson DM. Remote sensing of arctic vegetation: relations between the NDVI, spatial resolution and vegetation cover on Boothia Peninsula, Nunavut. Arctic. 2008;61:1–13.
Galvao LS, Vitorello I, Pizarro MA. An adequate band positioning to enhance NDVI contrasts among green vegetation, senescent biomass, and tropical soils. Int J Remote Sens. 2000;21:1953–60. .
Wittich KP. Some simple relationships between land-surface emissivity, greenness and the plant cover fraction for use in satellite remote sensing. Int J Biometeorol. 1997;41:58–64. .
Van Ooijen JW. Multipoint maximum likelihood mapping in a full-sib family of an outbreeding species. Genet Res. 2011;93:343–9. .
Piepho HP, Möhring J, Melchinger AE, Büchse A. BLUP for phenotypic selection in plant breeding and variety testing. Euphytica. 2008;161:209–28. .
Piepho HP, Eckl T. Analysis of series of variety trials with perennial crops. Grass Forage Sci. 2013;69:431–40. .
Wang S, Basten CJ, Zeng Z-B. Windows. QTL Cartographer 2.5. Raleigh. North Carolina: Department of Statistic, North Carolina State University; 2012.
Zhang L, Wang S, Li H, Deng Q, Zheng A, Li S, Li P, Li Z, Wang J. Effects of missing marker and segregation distortion on QTL mapping in F2 populations. Theor Appl Genet. 2010;121:1071–82. .
Xu S. Quantitative trait locus mapping can benefit from segregation distortion. Genetics. 2008;180:2201–8. .
Ali S, Serba DD, Jenkins J, Kwon S, Schmutz J, Saha MC. High-density linkage map reveals QTL underlying growth traits in AP13 × VS16 biparental population of switchgrass. GCB Bioenergy. 2019;11:672–90. .
Tornqvist CE, Taylor M, Jiang Y, Evans J, Buell CR, Kaeppler SM, Casler MD. Quantitative trait locus mapping for flowering time in a lowland × upland switchgrass pseudo-F2 population. Plant Genome. 2018;11:1–9. .
Taylor M, Tornqvist C-E, Zhao X, Doerge RW, Casler MD, Jiang Y. Identification of quantitative trait loci for plant height, crown diameter, and plant biomass in a pseudo-F2 population of switchgrass. BioEnerg Res. 2019;12:267–74. .
Makaju SO, Wu Y, Anderson MP, Kakani VG, Smith MW, Liu L, Dong H, Chang D. Yield-height correlation and QTL localization for plant height in two lowland switchgrass populations. Front Agr Sci Eng. 2018;5:118–28. .
Chang D, Wu Y, Liu L, Lu-Thames S, Dong H, Goad C, Bai S, Makaju S, Fang T. Quantitative trait loci mapping for tillering-related traits in two switchgrass populations. Plant Genome. 2016;9:1–12. .
Lowry DB, Taylor SH, Bonnette J, Aspinwall MJ, Asmus AL, Keitt TH, Tobias CM, Juenger TE. QTLs for biomass and developmental traits in switchgrass (Panicum virgatum). BioEnerg Res. 2015;8:1856–67. .
Dong H, Thames S, Liu L, Smith M, Yan L, Wu Y. QTL mapping for reproductive maturity in lowland switchgrass populations. Bioenerg Res. 2015;8:1925–37. .
Serba DD, Daverdin G, Bouton JH, Devos KM, Brummer EC, Saha MC. Quantitative trait loci (QTL) underlying biomass yield and plant height in switchgrass. BioEnerg Res. 2015;8:307–24. .
Yang J, Worley E, Ma Q, Li J, Torres-Jerez I, Li G, Zhao PX, Xu Y, Tang Y, Udvardi M. Nitrogen remobilization and conservation, and underlying senescence-associated gene expression in the perennial switchgrass Panicum virgatum. New Phytol. 2016;211:75–89. .
Palmer NA, Donze-Reiner T, Horvath D, Heng-Moss T, Waters B, Tobias C, Sarath G. Switchgrass (Panicum virgatum L) flag leaf transcriptomes reveal molecular signatures of leaf development, senescence, and mineral dynamics. Funct Integr Genomics. 2015;15:1–16. .
Palmer NA, Saathoff AJ, Scully ED, Tobias CM, Twigg P, Madhavan S, Schmer M, Cahoon R, Sattler SE, Edmé SJ, Mitchell RB, Sarath G. Seasonal below-ground metabolism in switchgrass. Plant J. 2017;6:1059–75. .

Table 1 Mean squares and significance of fall regrowth height (FRH), normalized difference vegetation index (NDVI), spring emergence (SE), and flowering date (FD) in two switchgrass F1 populations
Source of variation	FRH		NDVI		SE		FD
Source of variation	df	MS	df	MS	df	MS	df	MS
AP13 x B6
Genotype (G)	284	150.25**	284	0.021**	284	9**	284	21**
Year (Y)	1	589950.00**	1	63.400**	1	16372**	1	45^ns
G x Y	284	70.12^ns	284	0.013**	284	5^ns	284	4^ns
Rep(Year)	4	6800.37**	4	0.301**	4	13*	4	130**
Residuals	1117	68.08	1118	0.009	1114	5	1115	6

B6 x VS16 (Planting 1)^A
Genotype (G)	65	220.92**	65	0.020**	65	3*	65	30**
Year (Y)	1	159930.00**	1	14.779**	1	990**	1	1567*
G x Y	65	87.86**	65	0.013**	65	4**	65	6*
Rep(Year)	4	215.87**	4	0.016^ns	4	15**	4	76**
Residuals	237	51.30	236	0.008	236	2	230	4

B6 x VS16 (Planting 2)^B
Genotype	160	255.89**	160	0.002**	160	15**	160	19**
Rep	2	242.32^ns	2	0.004**	2	5^ns	2	76**
Residuals	315	93.83	314	0.001	302	8	302	8
^AThe first group of BV population that was planted in April 2017; ^BThe second group of BV population that was planted in May 2018; MS mean square *p < 0.01; p < 0.05; ^ns not significant

Table 2 Broad-sense heritability and variance component for fall regrowth height (FRH), normalized difference vegetation index (NDVI), spring emergence (SE), and flowering date (FD) in two switchgrass F1 populations
Variance component	FRH	NDVI	SE	FD
AP13 x B6
V_G	14.1634	0.0015	1.1137	3.1996
V_GY	0.9358	0.0016	0.0547	0.0000
V_E	68.0466	0.0088	5.1854	5.5072
H²	0.5453	0.3933	0.5554	0.7771

B6 x VS16 (Planting 1)^A
V_G	23.9085	0.0012	0.1453	7.1659
V_GY	17.5303	0.0029	1.0892	0.5953
V_E	51.4146	0.0078	2.4063	4.0671
H²	0.5797	0.2997	0.1332	0.8802

B6 x VS16 (Planting 2)^B
V_G	54.6427	0.0004	2.1842	4.0238
V_E	93.9269	0.0007	8.5569	7.6346
H²	0.6357	0.6068	0.4337	0.6126
V_G Genotypic variance; V_GYGenotype by year variance; V_E Error variance; H² Broad-sense heritability; ^AThe first portion of BV population that was planted in April 2017; ^BThe second portion of BV population that was planted in May 2018

Table 3 Pearson correlation coefficients between winter dormancy related traits and dry biomass weight for AB (upper diagonal) and BV (lower diagonal) populations
BV population		AB population
		FRH	NDVI	SE	FD	Biomass
	FRH		0.78**	-0.38**	0.15*	0.51**
	NDVI	0.63**		-0.40**	0.22**	0.66**
	SE	-0.44**	-0.37**		-0.06^ns	-0.25**
	FD	0.14*	0.20**	-0.06^ns		0.16*
	Biomass	0.35**	0.45**	-0.29**	0.08^ns
*p < 0.01; p < 0.05; ^ns not significant

Supplementary table 1 QTL found significantly associated with fall regrowth height (FRH) in two switchgrass F1 populations for two years of field evaluation and with BLUP values across years
Year	Map-Population	LG	Marker at LOD peak	Position (cM)	LOD	PVE (%)	Additive (cm)	Left flanking marker	Right flanking marker
2017	B6.AB	5N	AB7387	28.86	4.73	6.22	3.72	AB7387	AB7387
	B6.AB	5N	AB14427	101.44	4.64	6.08	3.71	AB7134	AB8135
	B6.BV.P1	5N	BV15640	14.24	3.18	12.28	-6.84	BV15640	BV15640
	B6.BV.P1	6K	BV17760	47.36	3.84	15.03	7.20	BV17760	BV30761
	B6.BV.P1	6K	BV17729	55.19	3.70	14.72	7.18	BV17700	BV17729
	VS16.BV.P1	5K	BV13227	22.89	3.55	13.77	-7.07	BV13227	BV13350
	VS16.BV.P1	5N	BV15983	40.68	5.56	23.27	-18.71	BV15974	BV16032
2018	B6.AB	5N	AB7387	28.86	6.80	9.39	3.36	AB8078	AB7373
	B6.BV.P1	5N	BV16525	45.68	3.96	16.77	-4.97	BV28750	BV16498
	B6.BV.P1	5N	BV16808	53.58	3.31	14.33	-4.61	BV16808	BV30676
	B6.BV.P1	6K	BV17912	73.02	4.58	21.48	5.89	BV18296	BV17912
	B6.BV.P1	9N	BV28082	47.17	3.13	14.88	4.45	BV28082	BV28082
	B6.BV.P2	5N	BV17469	58.05	5.07	11.45	-6.63	BV17590	BV17375
	VS16.BV.P2	1N	BV7842	16.19	4.85	12.19	-6.59	BV7842	BV2083
	VS16.BV.P2	9K	BV26161	63.51	4.39	9.85	-5.94	BV26161	BV27023
	VS16.BV.P2	9K	BV26581	75.92	4.90	9.96	-6.00	BV26632	BV26581
BLUP	AP13.AB	4K	AB5171	28.62	4.75	7.32	-1.91	AB5171	AB5205
	B6.AB	5N	AB7387	28.86	9.13	12.57	1.77	AB8078	AB7373
	B6.AB	5N	AB14426	101.38	3.39	4.21	1.05	AB14426	AB14427
	B6.BV	5N	BV16393	43.89	7.92	13.66	-4.27	BV15934	BV16843
	VS16.BV	1N	BV7842	16.19	6.64	11.40	-3.84	BV1894	BV2253
	VS16.BV	5N	BV15983	40.68	4.01	5.77	-2.77	BV15974	BV16098
	VS16.BV	5N	BV16488	48.52	3.70	5.43	-2.71	BV16488	BV16488
	VS16.BV	9K	BV27070	69.45	3.51	5.67	-2.72	BV27070	BV27023
	VS16.BV	9K	BV26581	75.92	4.32	6.23	-2.85	BV26581	BV26581
Map-population, AP13.AB AP13 map of the AB population, B6.AB B6 map of the AB population, B6.BV.P1 B6 map of the first subset of the BV population, B6.BV.P2 B6 map of the second subset of the BV population, VS16.BV.P1 VS16 map of the first subset of the BV population, VS16.BV.P2 VS16 map of the second subset of the BV population; LG linkage group; cM Centimorgan position; LOD logarithm of odds score; PVE percentage of trait variance explained by the QTL

Supplementary table 2 QTL found significantly associated with normalized difference vegetation index (NDVI) in two switchgrass F1 populations for two years of field evaluation and with BLUP values across years
Year	Map-Population	LG	Marker at LOD peak	Position (cM)	LOD	PVE (%)	Additive (cm)	Left flanking marker	Right flanking marker
2017	AP13.AB	6N	AB9342	87.18	4.55	6.45	-0.054	AB9281	AB9342
	B6.AB	3K	AB4513	94.11	3.14	4.47	-0.044	AB4513	AB4513
	B6.AB	5N	AB7387	28.86	5.95	8.27	0.063	AB7387	AB7369
	VS16.BV.P1	1N	BV2253	25.60	3.95	18.43	-0.092	BV2253	BV2253
2018	B6.AB	5N	AB6919	75.36	5.78	8.33	0.023	AB7732	AB4422
	B6.BV.P1	5N	BV16498	48.83	5.65	24.06	-0.024	BV28750	BV30676
	B6.BV.P1	5N	BV30682	55.37	4.90	20.65	-0.023	BV30682	BV30682
	B6.BV.P1	9N	BV28082	47.17	4.18	17.93	0.020	BV28067	BV28082
	B6.BV.P2	5N	BV17469	58.05	5.72	12.39	-0.019	BV17153	BV17375
	VS16.BV.P2	1N	BV2509	29.77	3.71	7.41	-0.014	BV24860	BV2403
	VS16.BV.P2	1N	BV2351	35.77	4.34	8.65	-0.015	BV2351	BV2351
	VS16.BV.P2	5N	BV16155	44.90	3.42	6.95	-0.013	BV16119	BV16154
	VS16.BV.P2	9K	BV27022	69.62	5.63	11.33	-0.017	BV27070	BV26833
	VS16.BV.P2	9K	BV26581	75.92	4.01	8.27	-0.015	BV26807	BV26581
BLUP	AP13.AB	6N	AB9304	80.16	3.59	5.52	-0.015	AB9281	AB9342
	B6.AB	2K	AB2582	86.90	3.58	5.12	-0.015	AB2582	AB2582
	B6.BV	5N	BV16393	43.89	6.44	10.96	-0.012	BV15934	BV16843
	VS16.BV	1N	BV2253	25.60	4.07	6.53	-0.009	BV2253	BV2459
	VS16.BV	1N	BV2504	33.36	3.35	5.40	-0.008	BV2657	BV2593
	VS16.BV	5N	BV15974	39.13	3.86	6.49	-0.009	BV15974	BV16098
Map-population, AP13.AB AP13 map of the AB population, B6.AB B6 map of the AB population, B6.BV.P1 B6 map of the first subset of the BV population, B6.BV.P2 B6 map of the second subset of the BV population, VS16.BV.P1 VS16 map of the first subset of the BV population, VS16.BV.P2 VS16 map of the second subset of the BV population; LG linkage group; cM Centimorgan position; LOD logarithm of odds score; PVE percentage of trait variance explained by the QTL

Supplementary table 3 QTL found significantly associated with spring emergence (SE) in two switchgrass F1 populations for two years of field evaluation and with BLUP values across years
Year	Map-Population	LG	Marker at LOD peak	Position (cM)	LOD	PVE (%)	Additive (cm)	Left flanking marker	Right flanking marker
2018	AP13.AB	1K	AB148	32.97	3.46	4.55	-0.77	AB148	AB148
	AP13.AB	1N	AB1512	91.66	4.20	5.52	0.82	AB1512	AB1512
	AP13.AB	7K	AB9971	87.44	3.48	4.62	-0.75	AB9971	AB9971
	B6.BV.P1	2N	BV6999	53.24	3.96	17.12	-1.79	BV6937	BV7179
	VS16.BV.P1	9K	BV25526	25.25	6.80	32.77	-3.08	BV25473	BV25526
2019	AP13.AB	9K	AB11882	29.92	2.95	4.59	-0.60	AB11882	AB11882
	B6.AB	2K	AB14332	14.59	3.71	5.32	-0.65	AB1810	AB1862
	B6.AB	2K	AB1921	22.91	3.60	5.21	-0.65	AB1921	AB1921
	B6.AB	5N	AB6919	75.36	3.05	4.35	-0.59	AB6919	AB6919
	B6.BV.P2	5N	BV17309	60.18	3.41	7.67	1.38	BV16821	BV17375
	VS16.BV.P1	5K	BV13381	27.16	3.46	15.55	0.43	BV13227	BV13480
	VS16.BV.P2	1N	BV7842	16.19	3.53	7.44	1.32	BV7842	BV2253
BLUP	AP13.AB	1K	AB148	32.97	3.42	4.61	-0.30	AB148	AB148
	AP13.AB	1K	AB217	43.95	3.33	4.53	-0.30	AB217	AB217
	AP13.AB	9N	AB12976	20.49	3.35	4.58	0.30	AB12976	AB12989
	B6.AB	2K	AB2223	68.66	3.65	5.32	0.37	AB2223	AB2221
	B6.AB	2K	AB3334	73.97	4.70	6.89	0.41	AB14404	AB6461
	B6.AB	2K	AB2582	86.90	5.57	8.64	0.44	AB2355	AB2582
	B6.BV	5N	BV17153	51.94	3.78	6.43	0.49	BV17153	BV17157
	B6.BV	5N	BV17345	60.35	3.36	5.74	0.46	BV16821	BV17345
	VS16.BV	1N	BV24860	29.21	4.19	6.74	0.49	BV2083	BV2470
	VS16.BV	5N	BV17586	100.07	3.73	5.96	0.46	BV17586	BV17598
	VS16.BV	5N	BV17612	105.11	3.30	5.30	0.43	BV17626	BV17624
Map-population, AP13.AB AP13 map of the AB population, B6.AB B6 map of the AB population, B6.BV.P1 B6 map of the first subset of the BV population, B6.BV.P2 B6 map of the second subset of the BV population, VS16.BV.P1 VS16 map of the first subset of the BV population, VS16.BV.P2 VS16 map of the second subset of the BV population; LG linkage group; cM Centimorgan position; LOD logarithm of odds score; PVE percentage of trait variance explained by the QTL

Supplementary table 4 QTL found significantly associated with flowering date (FD) in two switchgrass F1 populations for two years of field evaluation and with BLUP values across years
Year	Map-Population	LG	Marker at LOD peak	Position (cM)	LOD	PVE (%)	Additive (cm)	Left flanking marker	Right flanking marker
2018	AP13.AB	3K	AB4202	76.51	4.79	6.22	-0.77	AB4090	AB4107
	AP13.AB	4K	AB5480	81.41	4.31	5.33	0.67	AB5480	AB5940
	AP13.AB	5N	AB7559	70.63	6.72	8.63	-0.87	AB7575	AB7749
	B6.AB	3K	AB3868	18.48	3.52	4.61	0.63	AB3868	AB3868
	B6.AB	5N	AB7680	65.50	3.44	4.68	0.61	AB7680	AB7742
	B6.AB	5N	AB8070	77.04	5.57	7.50	0.78	AB8005	AB3733
	B6.BV.P1	2N	BV7292	59.77	3.54	11.26	-1.94	BV6885	BV7409
	B6.BV.P1	9K	BV26182	50.29	5.60	19.10	-2.58	BV26158	BV26512
	VS16.BV.P1	1K	BV258	10.92	3.59	13.40	2.06	BV240	BV200
	VS16.BV.P1	1N	BV7842	16.19	4.33	16.70	-2.36	BV7842	BV7842
	VS16.BV.P1	7K	BV21199	68.81	5.29	23.58	3.01	BV21136	BV21199
2019	AP13.AB	3K	AB13389	86.43	5.74	7.33	-1.48	AB4202	AB4399
	AP13.AB	4K	AB5379	69.72	4.72	5.89	1.27	AB5392	AB5422
	AP13.AB	4K	AB5460 - AB5480	76.682 - 81.411	3.45	4.75	1.14	AB5460	AB5480
	AP13.AB	5N	AB7749	80.17	7.13	9.05	-1.62	AB7561	AB7859
	B6.AB	9K	AB12144	56.03	3.32	4.78	-1.11	AB12144	AB12144
	B6.BV.P1	9K	BV26345	53.87	4.47	23.10	-2.64	BV26171	BV26398
	B6.BV.P2	6N	BV19770	52.80	3.34	7.22	1.44	BV19846	BV19770
	VS16.BV.P1	7K	BV21805	0.00	4.50	29.74	-2.71	BV21805	BV21805
	VS16.BV.P1	9K	BV25364	0.00	3.40	14.29	1.83	BV25364	BV25363
BLUP	AP13.AB	3K	AB4202	76.51	5.15	6.58	-0.81	AB4076	AB13389
	AP13.AB	4K	AB5365	67.69	3.98	4.93	0.66	AB5267	AB5365
	AP13.AB	4K	AB5460 - AB5480	76.682 - 81.411	4.25	5.57	0.70	AB5460	AB5480
	AP13.AB	5N	AB7733	77.62	6.80	8.78	-0.89	AB7561	AB7859
	B6.AB	3K	AB4634	7.29	3.53	4.73	0.65	AB4634	AB4634
	B6.AB	5N	AB8070	77.04	4.67	6.25	0.73	AB8071	AB7360
	B6.AB	9K	AB12002	32.15	3.73	4.94	-0.67	AB11837	AB12002
	B6.BV	5N	BV15694	20.10	4.19	7.31	-1.04	BV15694	BV15694
	B6.BV	5N	BV15847 - BV15934	30.781 - 35.579	6.22	11.04	-1.28	BV15847	BV16393
	B6.BV	9K	BV26380	54.79	6.06	9.71	-1.18	BV25922	BV26560
	VS16.BV	3N	BV24226	7.17	3.15	5.20	0.96	BV24226	BV24226
	VS16.BV	5N	BV16098	43.46	3.16	5.24	-0.87	BV16098	BV16098
	VS16.BV	7K	BV20802	54.38	3.64	6.11	0.94	BV20735	BV20873
	VS16.BV	7K	BV20829	57.51	4.17	6.96	1.00	BV20931	BV20780
Map-population, AP13.AB AP13 map of the AB population, B6.AB B6 map of the AB population, B6.BV.P1 B6 map of the first subset of the BV population, B6.BV.P2 B6 map of the second subset of the BV population, VS16.BV.P1 VS16 map of the first subset of the BV population, VS16.BV.P2 VS16 map of the second subset of the BV population; LG linkage group; cM Centimorgan position; LOD logarithm of odds score; PVE percentage of trait variance explained by the QTL

Supplementary table 5 The same QTLs mapped using LS means and BLUP values
No.	Trait	Map-Population	LG	Marker at LOD peak	Position (cM)	LOD	Additive	PVE (%)
1	SE_BLUP	AP13.AB	1K	AB148	32.97	3.42	-0.30	4.61
	SE2018	AP13.AB	1K	AB148	32.97	3.46	-0.77	4.55
2	FD2018	AP13.AB	3K	AB4202	76.51	4.79	-0.77	6.22
	FD_BLUP	AP13.AB	3K	AB4202	76.51	5.15	-0.81	6.58
	FD2019	AP13.AB	3K	AB13389	86.43	5.74	-1.48	7.33
3	FD2019	AP13.AB	4K	AB5460 - AB5480	76.68 - 81.41	3.45	1.14	4.75
	FD_BLUP	AP13.AB	4K	AB5460 - AB5480	76.68 - 81.41	4.25	0.70	5.57
	FD2018	AP13.AB	4K	AB5480	81.41	4.31	0.67	5.33
4	FRH_BLUP	B6.AB	5N	AB7387	28.86	9.13	1.77	12.57
	FRH2017	B6.AB	5N	AB7387	28.86	4.73	3.72	6.22
	FRH2018	B6.AB	5N	AB7387	28.86	6.80	3.36	9.39
5	FD_BLUP	B6.AB	5N	AB8070	77.04	4.67	0.73	6.25
	FD2018	B6.AB	5N	AB8070	77.04	5.57	0.78	7.50
6	FRH_BLUP	B6.AB	5N	AB14426	101.38	3.39	1.05	4.21
	FRH2017	B6.AB	5N	AB14427	101.44	4.64	3.71	6.08
7	FRH_BLUP	B6.BV	5N	BV16393	43.89	7.92	-4.27	13.66
	FRH2018	B6.BV.P1	5N	BV16525	45.68	3.96	-4.97	16.77
8	NDVI_BLUP	B6.BV	5N	BV16393	43.89	6.44	-0.01	10.96
	NDVI2018	B6.BV.P1	5N	BV16498	48.83	5.65	-0.02	24.06
9	SE2019	B6.BV.P2	5N	BV17309	60.18	3.41	1.38	7.67
	SE_BLUP	B6.BV	5N	BV17345	60.35	3.36	0.46	5.74
10	FD2018	B6.BV.P1	9K	BV26182	50.29	5.60	-2.58	19.10
	FD2019	B6.BV.P1	9K	BV26345	53.87	4.47	-2.64	23.10
	FD_BLUP	B6.BV	9K	BV26380	54.79	6.06	-1.18	9.71
11	FRH2018	VS16.BV.P2	1N	BV7842	16.19	4.85	-6.59	12.19
	FRH_BLUP	VS16.BV	1N	BV7842	16.19	6.64	-3.84	11.40
12	NDVI_BLUP	VS16.BV	1N	BV2253	25.60	4.07	-0.01	6.53
	NDVI2017	VS16.BV.P1	1N	BV2253	25.60	3.95	-0.09	18.43
13	FRH2017	VS16.BV.P1	5N	BV15983	40.68	5.56	-18.71	23.27
	FRH_BLUP	VS16.BV	5N	BV15983	40.68	4.01	-2.77	5.77
14	FRH2018	VS16.BV.P2	9K	BV26161	63.51	4.39	-5.94	9.85
	FRH_BLUP	VS16.BV	9K	BV27070	69.45	3.51	-2.72	5.67
15	FRH_BLUP	VS16.BV	9K	BV26581	75.92	4.32	-2.85	6.23
	FRH2018	VS16.BV.P2	9K	BV26581	75.92	4.90	-6.00	9.96
Map-population, AP13.AB AP13 map of the AB population, B6.AB B6 map of the AB population, B6.BV.P1 B6 map of the first subset of the BV population, B6.BV.P2 B6 map of the second subset of the BV population, VS16.BV.P1 VS16 map of the first subset of the BV population, VS16.BV.P2 VS16 map of the second subset of the BV population; LG linkage group; cM Centimorgan position; LOD logarithm of odds score; PVE percentage of trait variance explained by the QTL

Supplementary table 6 Colocalized QTLs within the same linkage map
No.	Trait	Map-Population	LG	Marker at LOD peak	cM	QTL interval (cM)	LOD	Additive	PVE (%)
1	FD2018	AP13.AB	3K	AB4202	76.51	68.79 - 83.76	4.79	-0.77	6.22
	FD2019	AP13.AB	3K	AB13389	86.43	74.19 - 97.93	5.74	-1.48	7.33
2	FD2019	AP13.AB	4K	AB5460 - AB5480	76.68 - 81.41	78.69 - 78.69	3.45	1.14	4.75
	FD2018	AP13.AB	4K	AB5480	81.41	78.69 - 90.67	4.31	0.67	5.33
3	NDVI_BLUP	B6.AB	2K	AB2582	86.90	86.61 - 88.91	3.58	-0.02	5.12
	SE_BLUP	B6.AB	2K	AB2582	86.90	77.9 - 91.91	5.57	0.44	8.64
4	FRH2017	B6.AB	5N	AB7387	28.86	28.14 - 32.86	4.73	3.72	6.22
	NDVI2017	B6.AB	5N	AB7387	28.86	26.14 - 35.16	5.95	0.06	8.27
	FRH2018	B6.AB	5N	AB7387	28.86	18.5 - 37.74	6.80	3.36	9.39
5	SE2019	B6.AB	5N	AB6919	75.36	75.37 - 75.37	3.05	-0.59	4.35
	NDVI2018	B6.AB	5N	AB6919	75.36	67.51 - 84.73	5.78	0.02	8.33
	FD2018	B6.AB	5N	AB8070	77.04	71.59 - 78.79	5.57	0.78	7.50
6	FRH2018	B6.BV.P1	5N	BV16525	45.68	38.28 - 48.84	3.96	-4.97	16.77
	NDVI2018	B6.BV.P1	5N	BV16498	48.83	40.28 - 54.2	5.65	-0.02	24.06
7	FRH2018	B6.BV.P2	5N	BV17469	58.05	22.11 - 62.65	5.07	-6.63	11.45
	NDVI2018	B6.BV.P2	5N	BV17469	58.05	50.84 - 62.65	5.72	-0.02	12.39
	SE2019	B6.BV.P2	5N	BV17309	60.18	57.05 - 62.65	3.41	1.38	7.67
8	FD2018	B6.BV.P1	9K	BV26182	50.29	48.55 - 59.32	5.60	-2.58	19.10
	FD2019	B6.BV.P1	9K	BV26345	53.87	49.83 - 55.61	4.47	-2.64	23.10
9	FRH2018	B6.BV.P1	9N	BV28082	47.17	49.18 - 49.18	3.13	4.45	14.88
	NDVI2018	B6.BV.P1	9N	BV28082	47.17	45.3 - 53.18	4.18	0.02	17.93
10	FRH2018	VS16.BV.P2	1N	BV7842	16.19	14.3 - 25.31	4.85	-6.59	12.19
	SE2019	VS16.BV.P2	1N	BV7842	16.19	16.2 - 25.61	3.53	1.32	7.44
	FD2018	VS16.BV.P1	1N	BV7842	16.19	14.3 - 18.2	4.33	-2.36	16.70
11	NDVI2017	VS16.BV.P1	1N	BV2253	25.60	23.31 - 25.61	3.95	-0.09	18.43
	SE_BLUP	VS16.BV	1N	BV24860	29.21	16.2 - 32.13	4.19	0.49	6.74
12	FRH2017	VS16.BV.P1	5K	BV13227	22.89	20.41 - 24.92	3.55	-7.07	13.77
	SE2019	VS16.BV.P1	5K	BV13381	27.16	20.41 - 30.21	3.46	0.43	15.55
13	NDVI_BLUP	VS16.BV	5N	BV15974	39.13	31.18 - 43.47	3.86	-0.01	6.49
	FRH2017	VS16.BV.P1	5N	BV15983	40.68	33.18 - 42.39	5.56	-18.71	23.27
14	FD_BLUP	VS16.BV	5N	BV16098	43.46	43.47 - 43.47	3.16	-0.87	5.24
	NDVI2018	VS16.BV.P2	5N	BV16155	44.90	43.47 - 45.54	3.42	-0.01	6.95
15	FRH2018	VS16.BV.P2	9K	BV26161	63.51	63.52 - 69.63	4.39	-5.94	9.85
	NDVI2018	VS16.BV.P2	9K	BV27022	69.62	65.52 - 72.72	5.63	-0.02	11.33
16	FRH2018	VS16.BV.P2	9K	BV26581	75.92	74.66 - 77.93	4.90	-6.00	9.96
	NDVI2018	VS16.BV.P2	9K	BV26581	75.92	74.06 - 77.93	4.01	-0.01	8.27
Map-population, AP13.AB AP13 map of the AB population, B6.AB B6 map of the AB population, B6.BV.P1 B6 map of the first subset of the BV population, B6.BV.P2 B6 map of the second subset of the BV population, VS16.BV.P1 VS16 map of the first subset of the BV population, VS16.BV.P2 VS16 map of the second subset of the BV population; LG linkage group; cM Centimorgan position; LOD logarithm of odds score; PVE percentage of trait variance explained by the QTL

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QTL mapping of winter dormancy and associated traits in two switchgrass pseudo-F1 populations: lowland x lowland and lowland x upland

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