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QTLs for Biomass and Developmental Traits in Switchgrass (Panicum virgatum)

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Abstract

Genetic and genomic resources have recently been developed for the bioenergy crop switchgrass (Panicum virgatum). Despite these advances, little research has been focused on identifying genetic loci involved in natural variation of important bioenergy traits, including biomass. Quantitative trait locus (QTL) mapping is typically used to discover loci that contribute to trait variation. Once identified, QTLs can be used to improve agronomically important traits through marker-assisted selection. In this study, we conducted QTL mapping in Austin, TX, USA, with a full-sib mapping population derived from a cross between tetraploid clones of two major switchgrass cultivars (Alamo-A4 and Kanlow-K5). We observed significant among-genotype variation for the vast majority of growth, morphological, and phenological traits measured on the mapping population. Overall, we discovered 27 significant QTLs across 23 traits. QTLs for biomass production colocalized on linkage group 9b across years, as well as with a major biomass QTL discovered in another recent switchgrass QTL study. The experiment was conducted under a rainout shelter, which allowed us to examine the effects of differential irrigation on trait values. We found very minimal effects of the reduced watering treatment on traits, with no significant effect on biomass production. Overall, the results of our study set the stage for future crop improvement through marker-assisted selection breeding.

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Acknowledgments

We would like to especially thank the many volunteers and students that helped to conduct the fieldwork for this project. We also are grateful to John Crutchfield, who made the experiment possible. Charles Swanson (Texas A&M) designed our irrigation system and along with Guy Fipps (Texas A&M) developed our differential irrigation treatment plan. The University of Texas Freshman Research Initiative provided funding for training of undergraduate students that worked on this project. The National Science Foundation provided funding through a Plant Genome Research Program Award (IOS-0922457) to TJ. A US Department of Agriculture National Institute of Food and Agriculture—Agriculture and Food Research Initiative Postdoctoral Fellowship (2011-67012-30696) supported DL during the time that the experiment was conducted. California State University, Monterey Bay, and Michigan State University provided financial support to DL during the period of data analysis and writing of the manuscript.

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Authors and Affiliations

  1. Department of Plant Biology, Michigan State University, East Lansing, MI, 48824, USA

    David B. Lowry

  2. Biology Department, Bowdoin College, Brunswick, ME, 04011, USA

    Samuel H. Taylor

  3. Department of Integrative Biology and Institute for Cellular and Molecular Biology, University of Texas at Austin, 1 University Station C0990, Austin, TX, 78712, USA

    Jason Bonnette, Tim H. Keitt & Thomas E. Juenger

  4. Hawkesbury Institute for the Environment, University of Western Sydney, Penrith, NSW, 2751, Australia

    Michael J. Aspinwall

  5. Department of Biology, University of Texas at Arlington, Arlington, TX, 76019, USA

    Ashley L. Asmus

  6. United States Department of Agriculture–Agricultural Research Service, Western Regional Research Center, Albany, CA, 94710, USA

    Christian M. Tobias

Authors
  1. David B. Lowry
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  2. Samuel H. Taylor
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  4. Michael J. Aspinwall
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  7. Christian M. Tobias
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  8. Thomas E. Juenger
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Corresponding author

Correspondence to David B. Lowry.

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Figure S1

Irrigation of plants in 2012 was the same across the field through May. Starting in June, odd rows (blue) received more water than even rows (orange). (PDF 235 kb)

Figure S2

Plot of recombination fractions across the 18 linkage groups of the combined outbred map. (PDF 2415 kb)

Figure S3

An example of the removal of spatial structure for data on biomass collected from the Albany mapping population. A) The distribution of raw biomass trait values across the field. B) The predicted distribution of biomass based on spatial effects. C) The residual biomass used for QTL mapping follow the removal of spatial structure. (PDF 2127 kb)

Table S1

R/qtl input files used in this study for QTL mapping. (xlxs 1.02 MB)

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Lowry, D.B., Taylor, S.H., Bonnette, J. et al. QTLs for Biomass and Developmental Traits in Switchgrass (Panicum virgatum). Bioenerg. Res. 8, 1856–1867 (2015). https://doi.org/10.1007/s12155-015-9629-7

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  • DOI: https://doi.org/10.1007/s12155-015-9629-7

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