Quantitative trait loci for cell wall composition traits measured using near-infrared spectroscopy in the model C4 perennial grass Panicum hallii

Abstract

BackgroundBiofuels derived from lignocellulosic plant material are an important component of current renewable energy strategies. Improvement efforts in biofuel feedstock crops have been primarily focused on increasing biomass yield with less consideration for tissue quality or composition. Four primary components found in the plant cell wall contribute to the overall quality of plant tissue and conversion characteristics, cellulose and hemicellulose polysaccharides are the primary targets for fuel conversion, while lignin and ash provide structure and defense. We explore the genetic architecture of tissue characteristics using a quantitative trait loci (QTL) mapping approach in Panicum hallii, a model lignocellulosic grass system. Diversity in the mapping population was generated by crossing xeric and mesic varietals, comparative to northern upland and southern lowland ecotypes in switchgrass. We use near-infrared spectroscopy with a primary analytical method to create a P. hallii specific calibration model to quickly quantify cell wall components.ResultsAsh, lignin, glucan, and xylan comprise 68% of total dry biomass in P. hallii: comparable to other feedstocks. We identified 14 QTL and one epistatic interaction across these four cell wall traits and found almost half of the QTL to localize to a single linkage group.ConclusionsPanicum hallii serves as the genomic model for its close relative and emerging biofuel crop, switchgrass (P. virgatum). We used high throughput phenotyping to map genomic regions that impact natural variation in leaf tissue composition. Understanding the genetic architecture of tissue traits in a tractable model grass system will lead to a better understanding of cell wall structure as well as provide genomic resources for bioenergy crop breeding programs.Electronic supplementary materialThe online version of this article (10.1186/s13068-018-1033-z) contains supplementary material, which is available to authorized users.

Evolution analysis of FRIZZY PANICLE (FZP) orthologs explored the mutations in DNA coding sequences in the grass family (Poaceae)

“…Group II was comprised entirely of species of the genus Oryza and had a bootstrap value of 100%, which indicates the extremely high reliability of the group. Except for the C3 grass Dichanthelium oligosanthes (OEL14197.1) ( Studer et al, 2016 ), all other species in Group III were C4 plants, which included three species of the genus Panicum ( P. virgatum ( Flint et al, 2019 ), P. hallii ( Milano et al, 2018 ), and P. miliaceum ( Son & Sugiyama, 1992 )) and two species of the genus Setaria ( S. italica ( Heimann et al, 2013 ) and S. viridis ( Brutnell et al, 2010 )). The group also consisted of two genes from Z. mays ( BD1 and BD1B ), one gene from S. bicolor ( Paterson et al, 2009 ; Tazoe et al, 2016 ), and two genes from Saccharum spontaneum ( Zhang et al, 2018 ).…”

High-resolution Linkage Map with Allele Dosage Allows the Identification of an Apomixis Region and Complex Traits in Guinea Grass (Megathyrsus maximus)

“…Therefore, our results can contribute to the search for important genomic regions in other forage species. In contrast, in forage grasses of temperate climates, such as Panicum hallii (Milano et al, 2018) and Lolium perenne (Cogan et al, 2005), QTLs for nutritional and agronomic traits have been identified, even by using only single-dose markers.…”

Integrated NIRS and QTL assays reveal minor mannose and galactose as contrast lignocellulose factors for biomass enzymatic saccharification in rice

“…Near-infrared spectroscopy (NIRS) paired with multivariate analysis offers a fast and non-invasive method to determine these traits [ 23 ]. Importantly, NIRS models have been established for determining major wall polymers of rice [ 24 ], bamboo [ 25 ], Miscanthus [ 26 ], and sweet sorghum [ 27 ], but it is rarely applied to assist QTL mapping for lignocellulose recalcitrant traits [ 28 ]. Thereby, it remains interesting to combine NIRS modeling with QTL mapping for investigating minor monosaccharide roles in wall network construction and lignocellulose enzymatic saccharification.…”