Accuracy of Genomic Prediction in Switchgrass (Panicum virgatum L.) Improved by Accounting for Linkage Disequilibrium

Authors

Guillaume P. Ramstein, University of Wisconsin- MadisonFollow
Joseph Evans, Michigan State University
Shawn M. Kaeppler, University of Wisconsin-MadisonFollow
Robert B. Mitchell, University of Nebraska-LincolnFollow
Kenneth P. Vogel, University of Nebraska-LincolnFollow
C. Robin Buell, Michigan State University
Michael D. Casler, University of Wisconsin-MadisonFollow

Document Type

Article

Date of this Version

2016

Citation

G3, Volume 6 April 2016, pp. 1049

Comments

Copyright © 2016 Ramstein et al.

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.