Skip to main content
ARS Home » Midwest Area » Madison, Wisconsin » U.S. Dairy Forage Research Center » Dairy Forage Research » Research » Publications at this Location » Publication #324983

Title: Accuracy of genomic prediction in switchgrass (Panicum virgatum L.) improved by accounting for linkage disequilibrium

item RAMSTEIN, GUILLAUME - University Of Wisconsin
item Casler, Michael

Submitted to: Plant and Animal Genome
Publication Type: Abstract Only
Publication Acceptance Date: 11/15/2015
Publication Date: 1/8/2016
Citation: Ramstein, G., Casler, M.D. 2016. Accuracy of genomic prediction in switchgrass (Panicum virgatum L.) improved by accounting for linkage disequilibrium [abstract]. Plant and Animal Genome XXIV. Paper No. W088.

Interpretive Summary:

Technical 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 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 was produced for the families’ parents by exome capture and sequencing, generating up to 108,077 polymorphic markers with available genomic location and annotation information. Prediction procedures varied not only by learning schemes and prediction models, but also by the way the data was preprocessed to account for redundancy in marker information. Probably because of the small sample sizes, the more complex genomic prediction procedures were generally not significantly more accurate than the simplest procedure. Nevertheless, highly significant gain in prediction accuracy could be achieved in one case by transforming the marker data through a 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 genomic selection. Some of the achieved prediction accuracies should motivate the implementation of genomic selection in switchgrass breeding programs.