title: Accuracy of Genomic Prediction in Switchgrass (Panicum virgatum L.) Improved by Accounting for Linkage Disequilibrium.
abstract: Switchgrass is a relatively high-yielding and environmentally sustainable biomass crop, but further genetic gains in biomass yield must be achieved to make it an economically viable bioenergy feedstock. Genomic selection (GS) is an attractive technology to generate rapid genetic gains in switchgrass, and meet the goals of a substantial displacement of petroleum use with biofuels in the near future. In this study, we empirically assessed prediction procedures for genomic selection in two different populations, consisting of 137 and 110 half-sib families of switchgrass, tested in two locations in the United States for three agronomic traits: dry matter yield, plant height, and heading date. Marker data were produced for the families' parents by exome capture sequencing, generating up to 141,030 polymorphic markers with available genomic-location and annotation information. We evaluated prediction procedures that varied not only by learning schemes and prediction models, but also by the way the data were preprocessed to account for redundancy in marker information. More complex genomic prediction procedures were generally not significantly more accurate than the simplest procedure, likely due to limited population sizes. Nevertheless, a highly significant gain in prediction accuracy was achieved by transforming the marker data through a marker correlation matrix. Our results suggest that marker-data transformations and, more generally, the account of linkage disequilibrium among markers, offer valuable opportunities for improving prediction procedures in GS. Some of the achieved prediction accuracies should motivate implementation of GS in switchgrass breeding programs.
authors: Department of Agronomy, University of Wisconsin-Madison, WI 53706 ramstein@wisc.edu.
authors: Guillaume P
authors: GP
authors: Ramstein
authors: Department of Energy Great Lakes Bioenergy Research Center, Michigan State University, East Lansing, MI 48824 Department of Plant Biology, Michigan State University, East Lansing, MI 48824.
authors: Joseph
authors: J
authors: Evans
authors: Department of Agronomy, University of Wisconsin-Madison, WI 53706 Department of Energy Great Lakes Bioenergy Research Center, University of Wisconsin-Madison, WI 53706.
authors: Shawn M
authors: SM
authors: Kaeppler
authors: Grain, Forage, and Bioenergy Research Unit, Agricultural Research Service, United States Department of Agriculture, University of Nebraska, Lincoln, NE 68583-0937.
authors: Robert B
authors: RB
authors: Mitchell
authors: Grain, Forage, and Bioenergy Research Unit, Agricultural Research Service, United States Department of Agriculture, University of Nebraska, Lincoln, NE 68583-0937.
authors: Kenneth P
authors: KP
authors: Vogel
authors: Department of Energy Great Lakes Bioenergy Research Center, Michigan State University, East Lansing, MI 48824 Department of Plant Biology, Michigan State University, East Lansing, MI 48824.
authors: C Robin
authors: CR
authors: Buell
authors: Department of Agronomy, University of Wisconsin-Madison, WI 53706 Agricultural Research Service, United States Department of Agriculture, Madison, WI 53706.
authors: Michael D
authors: MD
authors: Casler
mesh_terms: D000465:Algorithms; D000483:Alleles; D005787:Gene Frequency; D008040:Genetic Linkage; D014644:Genetic Variation; D018745:Genome, Plant; D023281:Genomics / Q000379:methods; D015810:Linkage Disequilibrium; D008957:Models, Genetic; D008897:Panicum / Q000235:genetics*; D010641:Phenotype; D019655:Quantitative Trait, Heritable; D015203:Reproducibility of Results
keywords: GenPred; Panicum virgatum L; Shared data resource; bioenergy; exome capture; genomic selection; linkage disequilibrium