Training Population Optimization for Genomic Selection in Miscanthus

Authors

Marcus O. Olatoye, University of Illinois, Urbana
Lindsay V. Clark, University of Illinois, Urbana
Nicholas R. Labonte, University of Illinois, Urbana
Hongxu Dong, University of Georgia
Maria S. Dwiyanti, Applied Plant Genome Laboratory, Research Faculty of Agriculture
Kossonou G. Anzoua, Applied Plant Genome Laboratory, Research Faculty of Agriculture
Joe E. Brummer, Colorado State University, Fort Collins
Bimal K. Ghimire, Konkuk University, Seoul
Elena Dzyubenko, Vavilov All-Russian Institute of Plant Genetic Resources
Nikolay Dzyubenko, Vavilov All-Russian Institute of Plant Genetic Resources
Larisa Bagmet, Vavilov All-Russian Institute of Plant Genetic Resources
Andrey Sabitov, Vavilov All-Russian Institute of Plant Genetic Resources
Pavel Chebukin, Vavilov All-Russian Institute of Plant Genetic Resources
Katarzyna Głowacka, University of Nebraska-Lincoln
Kweon Heo, Kangwon National University, Chuncheon
Xiaoli Jin, Zhejiang University, Hangzhou
Hironori Nagano, Applied Plant Genome Laboratory, Research Faculty of Agriculture
Junhua Peng, China National Seed Group Co. Ltd
Chang Y. Yu, Kangwon National University, Chuncheon
Ji H. Yoo, Kangwon National University, Chuncheon
Hua Zhao, Huazhong Agricultural University, Wuhan
Stephen P. Long, University of Illinois, UrbanaFollow
Toshihiko Yamada, Applied Plant Genome Laboratory, Research Faculty of Agriculture
Erik J. Sacks, University of Illinois, Urbana
Alexander E. Lipka, University of Illinois, UrbanaFollow

ORCID IDs

0000-0001-7082-9752

0000-0002-3881-9252

0000-0003-4829-176X

0000-0002-0594-8488

0000-0003-1571-8528

Date of this Version

2020

Citation

M. O. Olatoye et al. 2465-2476 https://doi.org/10.1534/g3.120.401402

Comments

2020 Olatoye et al.

Abstract

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 of predominately Msi individuals and a 598-member diversity panels of Msa individuals. We then assessed the ability of these two diversity panels to train GS models that predict breeding values in an interspecific diploid 216-member M·g F2 panel. Low and negative prediction accuracies were observed when various subsets of the two diversity panels were used to train theseGSmodels. To overcome the drawback of having only one interspecific M·g F2 panel available, we also evaluated prediction accuracies for traits simulated in 50 simulated interspecific M·g F2 panels derived from different sets of Msi and diploidMsa parents. The results revealed that genetic architectures with common causalmutations across Msi and Msa yielded the highest prediction accuracies. Ultimately, these results suggest that the ideal training set should contain the same causal mutations segregating within interspecific M·g populations, and thus efforts should be undertaken to ensure that individuals in the training and validation sets are as closely related as possible.