|RAMSTEIN, GUILLAUME - University Of Wisconsin|
|EVANS, JOSEPH - Michigan State University|
|KAEPPLER, SHAWN - University Of Wisconsin|
|Mitchell, Robert - Rob|
|BUELL, C. ROBIN - Michigan State University|
Submitted to: Genes, Genomes, Genetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/8/2016
Publication Date: 4/1/2016
Citation: Ramstein, G.P., Casler, M.D., Evans, J., Kaeppler, S., Mitchell, R., Vogel, K.P., Buell, C. 2016. Accuracy of genomic prediction in switchgrass (Panicum virgatum L.) improved by accounting for linkage disequilibrium. Genes, Genomes, Genetics 6:1049-1062.
Interpretive Summary: 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. Scientists from ARS, the University of Wisconsin, and Michigan State University developed a method of using DNA markers to predict the breeding value of switchgrass parental plants; in other words, to use DNA markers to identify those plants that will provide the best progeny. The accuracy of genomic prediction was as high as 0.62. This value will allow switchgrass breeders to employ genomic prediction in breeding programs, which is expected to triple the rate of genetic gain for biomass yield. If these results are accurate, it will allow us to achieve the 10 ton/acre goal by 2030. Experiments are already in place to employ genomic prediction in the USDA switchgrass breeding programs and to test the accuracy of these results.
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.