Because of its wind facilitated pollination behavior, switchgrass is typically characterized as an obligate outcrosser (Martinez-Reyna and Vogel 2002). Although homozygous inbred lines of switchgrass had not been reported yet, some studies indicated that the ratio of self-pollination was high in some specific genotypes (Casler et al. 2011; Liu and Wu 2012a). Through continuous selfing, development of inbred lines is possible, which will enable to produce single cross hybrid cultivars in switchgrass to increase biomass yield (Casler et al. 2011; Aguirre et al. 2012; Liu and Wu 2012a; Liu et al. 2013a). Morphological traits, such as pubescence on the adaxial surface of the leaf blade, foliage color, and seed size, were used to identify selfed and crossed progeny in previous experiments (Martinez-Reyna et al. 2001). However, they are not only genotype-dependent but also may be environmentally sensitive. In contrast, molecular markers have many advantages and have been used for identification of selfed progeny. Liu and Wu (2012a) adopted 12 SSR markers to test the selfed progeny of a genotype, and found the percentage of selfing ratio reached 61.2. Todd et al. (2011a) used six SSR markers and reported the confirmation of selfed progeny in switchgrass. Recently a genome-wide multiple duplex-SSR protocol was established, which was used for the identification of switchgrass selfed progeny in different growth conditions (Liu and Wu 2012b). These results indicate molecular markers are powerful in switchgrass breeding.

Perspectives

Although vast genetic variations of switchgrass have been revealed by molecular markers, the species is still an undomesticated organism with great potential for improvement of agronomic and biofuel traits (Casler

2012) . Genetic dissection of bioenergy-related traits is a critical step in breeding switchgrass. It is expected a large amount of data on associations between markers and sequences with economically important traits will be published in the near future from the current ongoing projects. However, little work is conducted to identify genes related to molecular mechanism of biomass development in switchgrass. Map-based-cloning of QTL and associate mapping can be utilized to isolate target genes and elucidate basic molecular mechanisms in the future. At the same time, the identification of easily-used molecular markers that are tightly linked or co-segregating with bioenergy-related traits will greatly accelerate breeding progress for enhancing biomass production and processing traits via marker assisted selection and advanced breeding procedures.

Acknowledgements

The writing of this chapter is partially supported by the National Science Foundation award EPS 0814361.

The lignin polymer can be composed of different ratios of S, G, and H, and can tolerate incorporation of other phenolic components. For example, in interfascicular fibers of Arabidopsis stems, lignin has a high proportion of G monolignols; however, in vascular bundles of Arabidopsis stems, lignin is primarily composed of S monolignols (Chapple et al. 1992). It has also been shown that lignin can comprise about 90 percent of the benzodioxane units in transgenic Arabidopsis with up-regulated Ferulate 5-Hydroxylase (F5H) and downregulated COMT (Vanholme et al. 2010; Weng et al. 2010). Notably, transgenic Arabidopsis has a dwarf stature but still produces viable seeds (Vanholme et al. 2010; Weng et al. 2010), echoing the flexibility of lignin polymers. Lignin polymers with different compositions often have different strengths of chemical bonds, which impacts lignin digestion and degradation. The frequency of resistant bonds (or condensed bonds) in lignin can be detected by the monolignol yield in thioacidolysis, such that a higher frequency of resistant bonds results in a lower thioacidolysis yield (Berthet et al. 2011). S-enriched lignin is thought to have fewer crosslinked bonds than G-enriched lignin, and thereby an increase in the S/G ratio could lead to easier lignin digestion and degradation (Abramson et al.

2010) . For example, transgenic Arabidopsis with S-enriched lignin has a higher enzymatic hydrolysis efficiency than wild type plants after hot-water pretreatment (Li et al. 2010). Although the correlation between the S/G ratio and the enzymatic hydrolysis efficiency has not yet been universally recognized (Chen and Dixon 2007), the hypothesis and current experimental results suggest that altering lignin composition may decrease the strength of lignin bonds, which would facilitate enzymatic hydrolysis of plant cell walls.

Some transcriptional factors directly regulating monolignol biosynthetic genes have been identified (Zhou et al. 2009; Zhao et al. 2010a, b; Ambavaram et al. 2011). In Arabidopsis, MYB Domain Protein 58 (MYB58) directly regulates expression of genes involved in monolignol biosynthesis, except for F5H; and the expression of MYB58 is regulated by a "master regulator of genes" for secondary cell wall formation — NST1 /NST2 / MYB46/VND6/VND7 (NST stands for NAC Secondary Wall Thickening Promoting Factor, VND for Vascular-related NAC-domain Protein) (Zhou et al. 2009). Interestingly, F5H is directly regulated by NST1 and Secondary Wall-associated NAC Domain Protein 1 (SND1) (Zhao et al. 2010a). On the other hand, since secondary cell wall structure is hallmarked by not only lignification but also higher cellulose and hemicellulose contents, other transcriptional factors downstream of NST1 /SND1 /VND6/VND7 should be involved in the activation of cellulose and hemicellulose biosynthesis. Recently, an Arabidopsis gene, SHN, when overexpressed in rice, caused increased cellulose but decreased lignin content by directly binding to promoters of rice MYB58/63, NST1 /2, SND1, VND4/5/6, and MYB20/43 to downregulate genes involved in monolignol biosynthesis and to upregulate genes in cellulose biosynthesis (Ambavaram et al. 2011).

Most monolignol biosynthetic genes (except F5H) have AC elements (ACCT/AAC/AC) in their promoter regions (Raes et al. 2003). The AC cis-element can be bound by some MYB proteins, such as transactivators MYB58/63/85, and transrepressors MYB4/32 (Goicoechea et al. 2005; Zhou et al. 2009; Zhao and Dixon 2011). Many MYB transcription factors are regulated by environmental cues and by plant hormones, which at least partially explains why cell wall lignification is largely influenced by plant growth conditions (see review by Zhao and Dixon 2011). Understanding the functions of these transcriptional factors may assist in engineering low lignin content switchgrass independent of field conditions.

Monolignols are synthesized inside the cytoplasm and then transported across the cell membrane to the cell wall where they are oxidized and polymerized into lignin polymers (Miao and Liu 2010). Monolignol transportation is mediated by ATP-dependent ATP-binding cassette-like transporters. The transporters in the plasma membrane preferentially transport monolignol aglycones, whereas transporters in the vacuolar membrane prefer gluco-conjugated monolignols for vacuole storage (Miao and Liu 2010). Genes encoding proteins for these transporters have not yet been identified and it is unclear whether different transporters have preferences for different monolignols. Nonetheless, it is possible to reduce lignin content or alter lignin composition by engineering these transporters in the future.

Laccases and guaiacol peroxidases (class III peroxidases) have been proposed to oxidize monolignols to form lignin polymers. In Arabidopsis, 73 peroxidases and 17 laccase-like genes have been identified (Berthet et al.

2011) . The high number of guaiacol peroxidase genes potentially involved in the oxidization of lignin polymers makes it difficult to assign a specific function to each gene (Mathe et al. 2010). Certain laccase genes are expressed exclusively in lignifying cells (Boerjan et al. 2003). Recently, a study showed that Laccase 4 (LAC4) and LAC17 are involved in the lignification of stems because the lac17 single mutant has a reduced G lignin deposition and the lac4/17 double mutant has approximately 40 percent less overall lignin content (Berthet et al. 2011). Another laccase gene, LAC15, is specifically involved in oxidative polymerization of flavonoids and monolignols in Arabidopsis seed coats (Liang et al. 2006). Notably, MYB58 and MYB63 can directly transactivate the expression of the LAC4 gene (Zhou et al. 2009). Interestingly, the double mutant lac4/17 has semi-dwarfed peduncles under long-day conditions but retains normal plant size under continuous light; however, lignin content is consistently reduced under both conditions (Berthet et al. 2011). This result further suggests that many genes involved in lignin synthesis are affected by environmental cues; however, reducing lignin content is not necessarily associated with reduced biomass yield.

Strategies for Promoting Switchgrass Vegetative Growth

Research on pest and disease control on switchgrass grown for bioenergy has been limited. A few diseases have been described in switchgrass, and some may have potential for significantly affecting production (Sanderson et al. 2012). When or if switchgrass begins to be used in large-scale monocultures, more disease pressures may emerge (Parrish and Fike 2005).

Pathogens and Their Treatments

Rust (Puccinia spp.) have been reported on switchgrass cultivars (Zale et al. 2008; Hirsch et al. 2010) and cultivars of northern origin appear more susceptible (Cassida et al. 2005b). However, heritability exists for rust resistance (Gustafson et al. 2003), and differences in resistance among accessions were noted and characterized early on by Cornelius and Johnston (1941). This variation provides breeders an opportunity to make improvements in yield, but we note that the level of resistance displayed by an accession or cultivar may be affected by growing conditions—and thus by their location of planting. E. g., in on-going research in Virginia—which typically has humid conditions—"rust-resistant" southern lowlands have sometimes displayed greater rust infestation during exceptionally dry periods, while their "rust-susceptible" counterparts displayed few such symptoms (B. Zhao, personal communication).

Switchgrass is a host for numerous fungal species in the United States; 65 were catalogued by Farr et al. in 2004. Wide-spread interest in and plantings of the species likely are drivers in the more than 150 species that have been catalogued at the time of this writing (Farr et al. 2012). Most reports provide no evidence of fungal pathogenicity, although such reports, too, are increasing (as, e. g., Vanky 2004; Crouch et al. 2009; Waxman and Bergstrom 2011b).

Several Bipolaris (Cochliobolus) species have been reported on switchgrass in recent years (Krupinsky et al. 2004; Tomaso-Peterson and Balbalian 2010; Vu et al. 2011a, b; Waxman and Bergstrom 2011a). Previous reports of B. sorokiniana (Sacc.) Shoemaker indicate this fungus is widespread and attacks a wide range of grasses (Braverman 1986; Sivanesan 1987; Roane and Roane 1997; Gravert et al. 2000; Farr et al. 2004). The recent reports of spot blotch attacks by B. sorkiniana on switchgrass come from diverse regions of North America including Mississippi, New York, and Tennessee (Tomaso — Peterson and Balbalian 2010; Waxman and Bergstrom 2011a; Vu et al. 2011b) and studies by Vu et al. (2011b) suggest the disease is seed borne. Whether these recent, geographically dispersed accounts of B. sorkiniana infestations reflect the natural or anthropogenic spread of this disease—or simply a greater interest given the relevance to expanded plantings—is unclear.

Leaf spot caused by B. oryzae has also been reported for switchgrass grown in North Dakota (Krupinsky et al. 2004) and the fungus was suggested as the causative agent for disease observed in West Virginia (Belesky and Fedders 1995), again demonstrating the widespread nature of these diseases. Spot blotches from Bipolaris spp. generally cause moderate to severe leaf tissue damage and Zeiders (1984) suggested these have potential to be the most important switchgrass diseases in future. Although fertility (low soil phosphorus) was empirically associated with the B. oryzae outbreak observed by Zeiders (1984), genetic variability for resistance was also evident among switchgrasses. Such resistance may be of increasing importance with further domestication of switchgrass and expansion of plantings.

Disease-related yield declines have been reported in extensive, long­term plantings in southern Iowa, USA (Gravert et al. 2000). A smut caused by Tilletia maclagani (Berk.) G. P. Clinton was found in 15 of 17 fields surveyed and considered the likely cause of yield reductions. Fifty to 82% of the area in production was infested with the fungus and the relationship between percent of smut-infected tillers and overall yield reduction was very close. Subsequent yield loss estimates due to T. maclaganii in 10 Iowa fields ranged from 1.7 to 40.1%, with 38 to 82% reductions in tiller mass (Thomsen et al.

2008) . T. maclaganii outbreaks have also occurred in New York and Texas (Carris et al. 2008; Layton and Bergstrom 2011).

Switchgrass seeds coated with fungicides have been used in humid climates to increase seedling emergence. It is unclear, however, if fungicide application limits the symbiotic relationship between switchgrass and arbuscular mycorrhizal fungi (Parrish and Fike 2005). Interrante et al. (2011) reported that in Oklahoma and Georgia USA, Proceed [prothioconazole + tebuconazole + metalaxyl] fungicide mixture applied alone did not improve switchgrass seedling counts or establishment.

Reports of viral infections of switchgrass are more limited than for fungal diseases. Panicum mosaic virus (PMV) was first reported in switchgrass in 1957 (Sill and Pickett 1957) but nearly all subsequent research on PMV disease effects have studied the virus as a disease agent in other species. Switchgrass has susceptibility to some strains of barley yellow dwarf virus (Garrett et al. 2004), which are transmitted by aphids. A range of switchgrass populations (near wild-type to highly selected cultivars) was tested to determine the factors most associated with infection. Interestingly, plant growth rate was a stronger predictor of aphid-transmitted virus disease susceptibility than foliar digestibility (Schrotenboer et al. 2011). The authors suggested that selection for biofuel crops should account for increasing virus susceptibility to avoid potential disease vulnerability. In a similar vein, Thomsen et al. (2008) noted that research on management approaches to deal with such issues are of critical importance for the long­term success of switchgrass for biomass production systems.

Surface-sterilized samples containing endophytic bacteria are ground with sterile water, and serial dilutions are prepared and plated on Luria-Bertani (LB) medium or other bacterial media and grown at 28-37°C for a few days. It is advantageous to add a fungicide such as Benomyl (DuPont) at 50 gg/ ml to the bacterial culture media to prevent fungal growth, particularly if there are fungal contaminations (Coombs and Franco 2003). Observations should be taken every day in order to isolate individual colonies for further identification. Individual colonies are streaked and re-grown two additional times to obtain pure bacterial strains.

Yanqi Wu

Introduction

Switchgrass (Panicum virgatum L.) is an economically important, warm- season, erect-growing, perennial and widely adapted species in Panicum s. s., which is a taxonomically recognized genus of about 100 specific taxa on the basis of data collected in recent molecular phylogenetic investigations and morphological characteristics (Aliscioni et al. 2003). The North American species is an important component of tallgrass prairies and a common resident in many other habitats on the continent. It is a natural forage species of many herbivorous animals evolved and raised in North America. Since the 1920s, switchgrass has been used to establish plantings for soil conservation, grazing and forage production in mono stands or mixtures with other compatible species (Vogel 2004). For the past two decades, switchgrass has received increasingly more attention in research due to its potential as a bioenergy feedstock crop.

The US Department of Energy sponsored a program, "Herbaceous Crops Research" (HECP) in the late 1980s to perform herbaceous crop screening trials (Wright and Turhollow 2010). The trials of 34 herbaceous species including switchgrass, sorghum and other plants were conducted at multiple sites of different soils and climates in Alabama, Virginia, Indiana,

Department of Plant and Soil Sciences, Oklahoma State University, Stillwater, OK 74078. Email: yanqi. wu@okstate. edu

New York, Ohio, Iowa and North Dakota. Switchgrass along with several other species was recommended for further research as an herbaceous crop, due to its reliable biomass yield across the tested locations, low cultural requirements of water and nitrogen, productivity potential on marginal lands, and favorable characteristics to the environment. In 1991, with a comprehensive and prudent consideration of scientific merits, environmental attributes and DOE funding situations, switchgrass was selected as a sole model herbaceous bioenergy crop (Wright and Turhollow 2010).

Genetic improvement and development of new cultivars through classic breeding, along with sustainable best management practices through agronomic research is the foundation for the development of switchgrass as a dedicated biomass crop (McLaughlin and Kszos 2005). This chapter is attempted to describe and discuss progress in switchgrass improvement using classic genetics and breeding, focusing on target bioenergy traits, basic information of inheritance and cytogenetics, germplasm pools and collections, and breeding and selection methods and the potential to develop hybrid cultivars in switchgrass.

In most cases, plant miRNA functions by suppressing expression of its target genes. MiRNAs recognize their mRNA targets based on sequence complementarity. Unlike the animal miRNAs, a specificity of plant miRNAs is that they share very high complementarity to their targets with few or no mismatches (Chuck et al. 2009; Poethig 2009; Voinnet 2009). Since plant miRNAs recognize their target mRNAs by near-perfect base pairing, identifying potential target by computational approaches is easier than in animals. Currently, a number of miRNA target prediction algorithms, programs and web servers have been developed (Zhang 2005; Kruger and Rehmsmeier 2006; Bonnet et al. 2010; Wu et al. 2012), and many plant miRNA-target genes have been predicted and experimentally validated (Addo-Quaye et al. 2008; Li et al. 2010; Zhou et al. 2010).

Many plant miRNAs are encoded by gene families. The mature miRNAs often have multiple targets with similar complementary sequence in their mRNAs (Axtell and Bowman 2008; Bartel 2009). In animals, approximately 60% of protein-coding genes appear to be regulated by miRNAs (Friedman et al. 2009). However, plant miRNAs target only a small number of mRNAs (Addo-Quaye et al. 2008; German et al. 2008; Li et al. 2010).

Plant miRNAs target many kinds of genes, suggesting that they play critical roles in a variety of developmental and physiological processes (Llave et al. 2002; Aukerman and Sakai 2003; Chen 2004). Interestingly, many of the target mRNAs are transcription factors. For example, miR156 has been reported to target SQUAMOSA promoter-binding-like (SPL) transcription factor genes (Xie et al. 2006; Schwarz et al. 2008; Yang et al. 2008; Yamaguchi et al. 2009); miR159 could target several members of MYB genes; miR166 could target some members of Class III HD-Zip and KANADI families; miR319 has been reported to target TEOSINTE BRANCHED/CYCLOIDEA/ PCF (TCP) genes (Palatnik et al. 2003).

Switchgrass is cross-pollinated by wind-dispersed pollen and has significant genetic variation within and among populations. Switchgrass is a polyploid with a basic chromosome number of x=9 (Gould 1975). Switchgrass has two genetically and morphologically distinct ecotypes, generally referred to as upland and lowland ecotypes due to the landscape position they historically occupied (Vogel et al. 2011). Upland ecotypes originated in upland areas that were not subject to flooding and often prone to drought, whereas lowland ecotypes occurred in flood plains and riparian zones subject to occasional flooding (Casler et al. 2012). Forage type switchgrass cultivars historically have been upland ecotypes with fine stems and reduced plant height, more stems per plant, and more decumbent leaves (Fig. 1). Lowland ecotypes

have upright leaves with a bluish tint and a later heading date than uplands (Casler et al. 2012). Lowland ecotypes are tetraploids (2n=4x=36), whereas the upland ecotypes can be either tetraploids or octoploids (2n=8x=72; Vogel et al. 2011). Upland and lowland tetraploids can be crossed (Martinez-Reyna et al. 2001) and can produce high-yielding F1 hybrids (Vogel and Mitchell

2008) . Upland x lowland hybrids averaged 30 to 38% high-parent heterosis for aboveground biomass (Vogel and Mitchell 2008). Chapters 5, 6, and 7 address switchgrass breeding and genetics in more detail.

Canopy architectural traits such as morphologic development, phenology, tiller density, and leaf area index (LAI) are in a continual state of flux (Moore and Moser 1995; Redfearn et al. 1997, Mitchell and Moser

2000) . Switchgrass is photoperiod sensitive and requires shortening day length for floral induction, resulting in switchgrass morphology being strongly correlated to day of the year (DOY) and growing degree days (GDD) (Mitchell et al. 1997). Grassini et al. (2009) developed a model for predicting switchgrass growth and development. Switchgrass has a determinate growth habit where most vegetative growth terminates with inflorescence development (Mitchell et al. 1997). After flowering, tillers advance to the seed ripening stages, growth stops, and tiller senescence

occurs. In switchgrass swards in eastern Nebraska, there were no vegetative tillers present by DOY 196 and 100% of the tillers had elevated apical meristems (Mitchell et al. 1998). As phenology advanced, tiller density declined by an average of 9.4 tillers m-2 d1 and an average tiller density of 1525 tillers m-2 (Mitchell et al. 1998). In Texas, tiller density and mass increased as row width increased and tiller mass increased as N fertility increased (Muir et al. 2001). Switchgrass LAI increased as phenology advanced and varied across years with maximum LAI ranging from 4.9 to 7.7, with at least 95% of the variation in LAI explained by dOy (Mitchell et al. 1998). The predictability of switchgrass development in response to DOY and GDD indicates switchgrass management recommendations for adapted cultivars may be made based on DOY within a region (Mitchell et al. 1997). It is likely that bioenergy specific switchgrass strains will have similar responses. One concern with feedstock delivered to the biorefinery is product consistency (Schmer et al. 2012). Targeting harvest date based on phenologic stage is one mechanism by which feedstock uniformity could be managed. It is likely that harvesting after senescence will minimize the variability in feedstock composition and provide a more uniform product to the biorefinery.

Chuansheng Mei, u* Alejandra Lara-Chavez,[6] [7]
Scott Lowman[8] and Barry Flinn1

Introduction

Switchgrass (Panicum virgatum L.), a native warm-season perennial grass found throughout the US, characteristically produces high biomass yields annually with low inputs, and can grow on marginal land. Since the introduction of the Department of Energy’s Bioenergy Feedstock Development Program over 3 decades ago, switchgrass has been the subject of intensive study, yielding a plethora of data regarding plant growth and stress resistance. As a C4 species, switchgrass is efficient at converting the sun’s energy into carbohydrate compounds, and combined with being

perennial, the plant offers much promise for future biomass production on a large scale, helping offset the use of fossil fuels. In fact, switchgrass yielded 504% of the energy consumed in a large, multi-farm study in the Central Plains (Schmer et al. 2008), and stands can produce for more than a decade. Furthermore, compared with other bioenergy crops, switchgrass cultivation is relatively simple and requires no specialized equipment by the producer. While yields are high, much more could further be improved for bioenergy purposes. Beneficial plant-microbe interactions, a field of study generating much interest in the past two decades, offer new solutions to improve switchgrass biomass yields, stress tolerance, first-year establishment, and sustainability.

Both bacterial and fungal microorganisms form ancient and mutually beneficial symbiosis with plants, and mycorrhizal fungi in particular are associated with the initial colonization of land by plants (Wang and Qiu 2006; Ryan et al. 2008). A cultivated field of plants represents a complex community of microbes, interacting, competing, and often assisting with plant growth promotion and stress resistance. Generally, beneficial plant — microbe interactions provide plant growth promotion via production of plant hormones, such as auxin, aiding in stress resistance to abiotic stresses including drought and salinity, production of antimicrobial compounds against plant pathogens, and nutrient acquisition such as atmospheric nitrogen fixation and solubilization of phosphorus in soil. These interactions are intricate and multifaceted, often dependent on time of development, genotype, environmental conditions, and native soil communities. Although mycorrhizal fungi and switchgrass interactions have been intensively studied (Parrish and Fike 2005), only a few articles have been published focusing on endophytes in switchgrass and their influence on growth promotion (Ghimire et al. 2009; Kim et al. 2012). Together, beneficial microorganisms could have the potential to help in the development of a low input and sustainable switchgrass production system (Nowak et al. 2011) and offer a practical way to improve plant growth and disease resistance.

Theoretical and actual biofuel yields from switchgrass rely on the composition of the starting material, limiting the amount of carbon available for conversion to fuel. Figure 1 summarizes the average lignicellulose content from a recent detailed analysis of genetically diverse, geographically dispersed, primarily mature switchgrass samples (Vogel et al. 2010). Dry biomass from switchgrass consists mostly of cell wall residue (69 ± 6%). On average, most of the remainder is water (9 ± 1%, even with active drying), silica and other minerals (8 ± 2%), proteins (6 ± 3%), and nonstructural sugars (5.5 ± 2.6%), mostly sucrose (Vogel et al. 2010). Sugar polymers make up 75% of the switchgrass cell wall material and on average are composed half of cellulose and half of matrix polysaccharides, which include pectins and hemicelluloses (Fig. 1). Cellulose is a polymer of the 6-carbon sugar glucose; whereas, about 85% of switchgrass matrix polysaccharide consists

of the 5-carbon sugars, xylose and arabinose. The remaining 25% of the mature switchgrass cell wall consists of lignin and other phenylpropanoid — derived constituents.

Actual biofuel yields are dependent not only on the crude content, but also the structure of cell walls. Current models envision that the walls of a growing plant are composed of a network of cellulose microfibrils that are crosslinked to each other by matrix polysaccharides (Cosgrove 1999). While cellulose structure appears to be largely conserved across higher plants, matrix polysaccharides and lignin differ between grasses and other relatively recently evolved Commelinid monocotyledonous species and dicotyledenous species, such as the reference Arabidopsis thaliana and the bioenergy tree, poplar. The similarities and differences between dicots and grasses are especially relevant when researchers consider rational strategies to engineer or select for switchgrass with improved cell wall quality. While functional information gleaned from genetic studies will likely be transferable from dicots in many cases, due to their different compositions we expect some differences in strategies for optimizing grass and dicot walls, as is elaborated below.

Plant cell wall content is intimately connected with the functions of the cells they surround and the age of the tissues in which they reside. Primary cell walls surround every vegetative cell, dictate shape, and control growth (Wolf et al. 2012). Upon differentiation and growth cessation, many cell types develop additional layers of thicker cell wall material. Developmental progression of grass leaves and stems proceeds both temporally and spatially away from the meristems, which are found at the base of each internode and each leaf. Over time, each internode and associated leaf develops and elongates in sequence from the base of the plant upward. Thus the lower leaves and stems are older and characterized by greater secondary growth compared to upper segments (Sarath et al. 2007). Vascular and schlerenchyma cells, in particular, are characterized by secondary wall formation (Sarath et al. 2007; Shen et al. 2009). Indeed, switchgrass cell wall quality (for forage traits) is well predicted by growing degree days (Mitchell et al. 2001), which is a good indicator of morphological development in perennial grasses (Mitchell et al. 1997).

A number of software tools for genetic map construction are available (Cheema and Dicks 2009). A broader list of software tools for genetic map development can be found at http://www. nslij-genetics. org/soft/. Here we just choose two software tools for discussion because they are popular amongst our colleagues in the switchgrass community.

MAPMAKER

The MAPMAKER software combines an Expectation-Maximization (EM) algorithm for recombination fraction estimation with a Hidden Markov Model (HMM) method for calculating the expected number of inter-marker recombination events, significantly lowering computation time for large datasets. Marker order estimation is conducted through a pre-processing step that involves a three-point analysis of all linked triples. Once a marker order is found it can be locally optimized through a "ripple" procedure, whereby the order of neighboring markers is reorganized, to check for improvements in the marker order (Lander et al. 1987). MAPMAKER was written for DOS and for UNIX operating systems. It includes an interactive command language that enables data exploration and has been very widely used and cited in the construction of linkage maps for many species including human and major crops. However, since the latest version (V3.0) was released in 1993, the updated version is not available. In switchgrass, an early linkage map was constructed with MAPMAKER (Missaoui et al. 2005b).