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Biotechnol Biofuels. 2018 Feb 3;11:25. doi: 10.1186/s13068-018-1033-z. eCollection 2018.

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

Author information

1
1Department of Integrative Biology, The University of Texas at Austin, Austin, TX 78712 USA.
2
2National Bioenergy Center, National Renewable Energy Laboratory, Golden, CO 80401 USA.
3
3Department of Energy Joint Genome Institute, Walnut Creek, CA 94598 USA.
4
4HudsonAlpha Institute for Biotechnology, Huntsville, AL 35806 USA.

Abstract

Background:

Biofuels 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.

Results:

Ash, 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.

Conclusions:

Panicum 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.

KEYWORDS:

Bioenergy feedstock; Cell wall composition; Lignocellulosic biomass; NIRS; Panicum hallii; QTL

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