Introduction
As the bearers of grain, the grass panicle (or inflorescence) has been
the target of selection for thousands of years (Doust, 2007). There is
enormous diversity in panicle architecture within and among grass
species (Coen & Nugent, 1994). Panicle architectures are critical
determinant of interspecies differences in plants morphology and life
history, and are often measured as variation in panicle length,
branching (number, length, and pattern), and flower number and size
borne on each branch type. Simple panicles may have only primary
branches, while complex panicles can possess many secondary and tertiary
branches (Bommert & Whipple, 2018; Glemin & Bataillon, 2009). In wild
grasses, branching pattern plays an important role in wind pollination
and affects the number and size of seeds, which ultimately influences
seed yield and plant fitness (Friedman & Harder, 2004). In domesticated
species, there is a direct association between panicle architecture and
seed productivity (Brown et al., 2006; Crowell et al., 2016; Wang & Li,
2005). Analysis of the phylogenetic distribution of panicle variation in
the grasses suggests that different panicle architecture have arisen
independently many times, and homoplasy across the grass phylogeny has
obscured the mechanisms of panicle diversity (Doust & Kellogg, 2002;
Kellogg, 2000). These traits likely evolve in response to natural
selection mediated by pollinator (Friedman & Harder, 2004) and
environmental variation such as light (Vogler et al., 1999), drought
(Mal & Doust, 2005), nutrient availability (Dorken & Barrett, 2004),
soil water availability (Caruso, 2006), and intraspecific competition
(Wolfe & Mazer, 2005). Given the importance of inflorescence
architecture to the fitness and productivity of both wild and
domesticated species, it is of great interest to understand genetic
variation in panicle architecture.
Organisms often respond to changing resource availability and
environmental signals through phenotypically plastic changes (Sultan,
2000). Panicle traits often display plasticity in response to different
environmental cues, as panicle development involves complex regulatory
mechanisms and these mechanisms interact with environmental signals
(Adriani et al., 2016; Bai et al., 2016; Tu et al., 2019). For example,
Adriani et al. (2016) found that secondary branch number was the most
variable (or plastic) trait of panicle architecture in response to light
resources in rice. Secondary branching was also found to be influenced
by water deficit more than primary branching in a rice recombinant
inbred family (Liu et al., 2010). Abiotic components such as heat,
drought, and light affect panicle development and ultimately panicle
architecture (Adriani et al., 2016; Wu et al., 2017; Wu et al., 2016;
Mitchell et al, 1997). In addition to environmental effects on variation
in panicle traits, standing genetic variation within species in panicle
traits is also common (Jamal et al., 2009; Brown et al., 2006; Hong &
Yan, 2004; Ungerer et al., 2002).
Genetic variation in phenotypic plasticity in response to the
environment is better known as genotype-by-environment interactions (G x
E) (Des Marias et al., 2013). Quantitative studies of G x E in many
crops (e.g., maize, rice) have identified important quantitative trait
loci (QTL) impacting many panicle traits (Kovi et al., 2011; Leng et
al., 2017; Zhao et al., 2017). For example, Leng et al. (2017)
identified 17 QTL for five panicle related traits in a double haploid
population in rice. Among these QTL, six QTL showed QTL-by-environment
interactions, indicating that panicle related traits are susceptible to
environmental influence. Zhao et al. (2017) found that 11 out of 19 QTL
were involved in QTL-by-environment interactions for tassel primary
branching number in maize under different watering environments. G x E
is common in QTL studies and identifying G x E and the pattern of
interactions is of great interest to understand the genetic architecture
underlying phenotypic traits.
Pleiotropy is the phenomenon of a single gene affecting multiple
distinct traits (Williams, 1957) and is an important driver of trait
integration and modularity (Armbruster et al., 2014; Klingenberg, 2008).
Pleiotropy contributes to the genetic correlation among traits and
therefore has broad implications in genetics, development and adaptive
evolution (Armbruster et al., 2014; Auge et al. 2019; Pigliucci and
Preston 2004;). In plants, the formation of all aboveground organs, such
as leaves, tiller, internodes, and inflorescences is mainly dictated by
the activity and determinacy of the shoot apical and axillary meristems.
During the vegetative-to-reproductive transition, many developmentally
related traits originate from the same meristem and complex
environmental signals may affect the different type of meristems by
similar regulatory networks (Wang & Li, 2008; Xue et al., 2020).
Therefore, the loci for vegetative and reproductive development-related
traits frequently show pleiotropy. For example, most flowering time
pathway genes show pleiotropic effects on tiller number and yield
potential in crops (Auge et al. , 2019). Genes involved in hormone
pathways frequently affect both vegetative growth and reproductive
development (Lee et al., 2019), in part through their developmental
impacts on meristems (Azizi et al., 2015). QTL mapping is one of the
many approaches that have been used to estimate genome-wide pleiotropy,
and fits squarely in the context of developmental pleiotropy (Paaby &
Rockman, 2013). Pleiotropic effects identified through overlapping QTL
locations have been observed in panicle development in sorghum and rice
(Brown et al., 2006; Endo-Higashi & Izawa, 2011; Komatsu et al., 2001;
Miura et al., 2010; Yu et al., 2017), but these patterns have not been
investigated in native perennial grasses.
Switchgrass (Panicum virgatum L.) has been championed as a
potential biofuel crop since it was selected by the US Department of
Energy (US DOE) as a model grass species for bioenergy in the early
1990s (Hohenstein & Wright, 1994; McLaughlin, 1993). Its potential for
high biomass production on marginal land, adaptation to a wide range of
environments, and ecosystem service such as carbon sequestration, water
flow management and erosion control, makes switchgrass an excellent
candidate for filling bioenergy needs (Mitchell, Vogel, & Uden, 2012;
Robertson et al., 2017). Switchgrass is a warm-season C4 perennial grass
native to the North America, with a range that extends from the eastern
seaboard west to the Rocky Mountains and from southern Canada south to
the Texas Coastal Plain and Northern Mexico (Casler, 2007; Hopkins,
1995). Two major distinctive populations have been classified in the
past based on morphology and habit preference, northern upland and
southern lowland ecotypes (Porter Jr, 1966). A very recent study based
on a resequenced switchgrass diversity panel was able to define a third
coastal ecotype, which is broadly sympatric with the lowland ecotype but
possesses upland leaf characters and lowland plant morphotype (Lovell et
al., 2020, in review).
Information on panicle morphology is limited in switchgrass, although
panicle length differences have been reported between switchgrass
ecotypes and cultivars (Porter Jr, 1966; Price, 2014; Van Esbroeck,
2003). We hypothesize that panicle evolution in switchgrass may be
related to selection on aspects of mating system and degree of
investment in vegetative versus sexual reproduction, especially in the
context of seedling establishment in differing habitats. For example,
lowland switchgrass has a restricted bunch grass growth form and occurs
primarily in patchy distributions along riparian areas. In contrast,
upland switchgrass has a rhizomatous spreading growth form that occurs
in many prairie habitats. Pattern of pollen dispersal across patches, or
aspects of seed establishment (e.g. seed size/number tradeoffs or
disturbance regimes) likely differ in these habitats and may have driven
divergence in panicle form.
Panicle morphology and its
relationship to seed quality may be important targets of selection and
breeding, as consistent seed production will be critical to meet the
demands for large-scale biofuel production (Das & Taliaferro, 2009;
Vogel, 2000).
In this study, we evaluated the genetic architecture of panicle traits
across ten field sites in the central US to investigate the importance
and nature of G x E for panicle traits. To accomplish this goal, we
planted clonal divisions of progeny from a four-way outbred mapping
population derived from upland and lowland germplasm, along with the
four grandparents and F1 hybrids, at ten field sites spanning a large
latitudinal gradient. Three panicle traits including panicle length
(PL), primary branching number (PBN) per panicle, and secondary
branching number (SBN) on the panicle, were assessed at each site to
investigate: (1) the genetic architecture underlying these three traits,
(2) the sensitivity of QTL and their effects across different
environments (i.e., QTL x E), (3) the extent of pleiotropy between
panicle and other traits, and (4) the environmental factors contributing
to QTL x E interactions. Finally, we identified candidate genes
potentially involved in regulating panicle architecture in switchgrass.