Discussion
There has been considerable interest in the molecular mechanisms of G x
E across a diversity of phenotypes, species, and environments. G x E is
common and is often driven by differential sensitivity of alleles and
may play an important role in adaptive plasticity and local adaptation
(Des Marais et al., 2013). With its large scale, our study evaluated the
genetic basis and examined the QTL x E of panicle morphological traits
in switchgrass grown at 10 field sites in the central United States
(Figure 1). Overall, we detected moderate heritability (except for the
field site Stillwater, OK) for panicle traits (Table 2) and positive
phenotypic and genetic correlations between traits at each site and
across sites (Table 3). These data suggest considerable standing genetic
variation in inflorescence characteristics available for natural or
artificial selection to act upon. We identified several QTL with
significant QTL x E effects and the potential environmental factors
underlying the QTL x E, indicating that panicle traits in switchgrass
result from the combination of QTL and environment. We also detected
pleiotropic effects between panicle traits and flowering time as well as
tiller count and biomass, suggesting a possible shared genetic basis
between different traits.
Our study identified genomic regions (QTL) that contribute to panicle
trait variation across a broad latitudinal gradient. These QTL exhibited
constant effects (i.e., no QTL x E), antagonistic pleiotropy, or
condition-specific effects across the studied environmental gradients.
QTLs with condition-specific effects are relatively easy to incorporate
into breeding programs because the selected favorable alleles will
confer an advantage in some environments, without a negative effect in
other environments (El-Soda et al., 2014). Antagonistic pleiotropy is a
genetic trade-off at an individual locus or QTL, that results in
opposite effects (i.e., sign change) on a trait in different
environments (Wadgymar et al., 2017). QTL with antagonistic pleiotropy
can result in tradeoffs and challenges in breeding if the preferred
allele depends strongly on the environment (El-Soda et al., 2014; Lowry
et al., 2019). Studying the molecular genetic basis of specific QTL
should greatly contribute to the mechanistic understanding of such QTL x
E. In our study, most of the QTL are conditionally neutral. This is
consistent with a recent meta-analyses which found that asymmetry of QTL
effect is more often caused by conditional neutrality than it is by
trades-offs (Wadgymar et al., 2017). Overall, our results show that
panicle traits are controlled by a combination of QTL and the
environment and, in a number of cases, their complex interaction with
the environment.
Inflorescence architecture is influenced by the
vegetative-to-reproductive phase transition, which also largely
determines patterns of vegetative growth and resource allocation. In our
study, 11 of 18 inflorescence QTLs co-localized with flowering time or
vegetative growth genomic intervals, which supports the hypothesis that
pleiotropy impacts the phenotypic integration of these vegetative and
reproductive structures. An exciting opportunity lies in the search for
the candidate genes that may underlie this integration. Fortunately,
extensive genetic mapping efforts in crops and model systems have
identified a number of candidate genes and a basic understanding of
their role in the development of the inflorescence. For example, a locus
on chromosome 9N (at 38.02 cM) was associated with the whole process of
vegetative-to-reproductive transition (PL, PBN, SBN, FL50, TC and BIO).
This QTL cluster is in the vicinity of homologs ofOsCOL10 and OsTB1 , which are known as the
key regulators in flowering and branch development (Tan et al., 2016;
Takeda et al., 2003). Specifically, OsCOL10functions as a flowering time repressor downstream of Ghd7 and
the OsTB1 gene negatively regulates lateral branching in rice.
Moreover, the locus on chromosome 3K (at 38 cM) was clustered with QTLs
for PBN, FL50 and TC. Significantly, this QTL clustering region had
large effects for PBN, suggesting a major QTL that coordinates vegetative
and reproductive processes. We identified a homolog of GA2ox3 in this
region, which is considered as a key factor in gibberellin catabolism
and plays a central role in plant development (Sakamoto et al., 2004).
These results imply that there may be a shared genetic basis between
vegetative and reproductive divergence within switchgrass populations.
The low to moderate prediction accuracy (0.34-0.62) of the
multienvironment mixed model (Eq. 1) is likely due to two factors. Our
model only accounts for significant QTL, while there are likely many
smaller QTL, that were below the threshold for detection, which
contribute to variation in these traits. Unfortunately, our power to
detect these small effects is likely low due to our modest sample sizes
(380 progeny). Additionally, the QTL model does not consider epistatic
effects or dominance effects between QTL. Epistasis is known to be an
important factor that affects genetic variation and phenotypic
expression in populations, especially for developmentally regulated
traits like inflorescence architecture. Epistatic effects on panicle
related traits have been identified in several studies (Leng et
al. , 2017; Ye et al ., 2009). Further inclusion of epistasis into
the multi-environment QTL model may help improve model prediction.
However, our approach provides a way of predicting the performance of
new genotypes under environments similar to the tested environments, and
can potentially help with suitable genotype selection for traits of
interest under a specific environment.
Temperature and photoperiod were the most significant predictors of QTL
x E interactions. This is consistent with the pattern of additive
effects of most of the QTL (Table 3), where QTL displayed conditional
neutrality with effects either in the northern or the southern sites
(Figure 5). Previous study also showed that temperature-based growing
degree days and photoperiod affected switchgrass morphology (Mitchell et
al., 1997). Solar radiation was also a significant driver of QTL x E for
secondary branching number (SBN) QTL. This is consistent with a rice
study in which SBN was more plastic in response to different light
resources (Adriani et al., 2016). No environmental factors were detected
for some of the QTL x E interactions, possibly because the appropriate
environmental factors (i.e., growing degree days, soil moisture etc.)
were not explored or were obscured by complex interactions between
environmental factors. The relative low heritability for panicle traits
at the field site Stillwater, OK (STIL, Table 2) also suggested that
there may be other environmental factor affecting panicle traits but not
being accounted for, such as effective soil moisture. As noted in Table
1, Stillwater, OK, Overton, TX (OVTN), and Manhattan, KS (MNHT) all have
sandy loam soil which may impact water status and subsequently plant
growth and organ expansion. In our study year, OVTN received ample rain
(~1400mm), MNHT had slightly cooler temperature and
received approximately 1000mm rain, while STIL only received around
700mm rain (Figure 1). This study could be expanded in the future to
include more field sites, multiple years, and more environmental data
collection such as soil composition and nutrient availability to better
capture the environmental drivers underlying the QTL x E interactions
and trait plasticity across large geographic regions and across multiple
years.
In summary, our results suggest that variation of panicle traits in
switchgrass is due to a combination of QTL and the environment, with QTL
displaying different effects across geographic regions. Future work
focusing on identifying the driver of QTL by environment interactions
and understanding the mechanisms underlying them will facilitate the
selection of suitable genotypes for specific environments in switchgrass
breeding programs.