Life Sciences Commons^™

Training Population Optimization For Genomic Selection In Miscanthus, Marcus O. Olatoye, Lindsay V. Clark, Nicholas R. Labonte, Hongxu Dong, Maria S. Dwiyanti, Kossonou G. Anzoua, Joe E. Brummer, Bimal K. Ghimire, Elena Dzyubenko, Nikolay Dzyubenko, Larisa Bagmet, Andrey Sabitov, Pavel Chebukin, Katarzyna Głowacka, Kweon Heo, Xiaoli Jin, Hironori Nagano, Junhua Peng, Chang Y. Yu, Ji H. Yoo, Hua Zhao, Stephen P. Long, Toshihiko Yamada, Erik J. Sacks, Alexander E. Lipka

Department of Biochemistry: Faculty Publications

Miscanthus is a perennial grass with potential for lignocellulosic ethanol production. To ensure its utility for this purpose, breeding efforts should focus on increasing genetic diversity of the nothospecies Miscanthus x giganteus (M·g) beyond the single clone used in many programs. Germplasm from the corresponding parental speciesM. sinensis (Msi) and M. sacchariflorus (Msa) could theoretically be used as training sets for genomic prediction of M·g clones with optimal genomic estimated breeding values for biofuel traits. To this end, we first showed that subpopulation structure makes a substantial contribution to the genomic selection (GS) prediction accuracies within a 538-member diversity panel …