University of Nebraska, Lincoln, NE, (United States).

of Energy Great Lakes Bioenergy Research Center

University of Wisconsin, Madison, WI, (United States).

Department of Plant Biology Department of Energy Great Lakes Bioenergy Research Center

Michigan State University, East Lansing, MI, (United States).Furthermore, some of the achieved prediction accuracies should motivate implementation of GS in switchgrass breeding programs. Our results more » suggest that marker-data transformations and, more generally, the account of linkage disequilibrium among markers, offer valuable opportunities for improving prediction procedures in GS. Nevertheless, a highly significant gain in prediction accuracy was achieved by transforming the marker data through a marker correlation matrix. More complex genomic prediction procedures were generally not significantly more accurate than the simplest procedure, likely due to limited population sizes. 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. 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. 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. 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.

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.

Predictions asreml