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Training Population Optimization for Genomic Selection in Miscanthus

Authors
  • Olatoye, Marcus O.1
  • Clark, Lindsay V.1
  • Labonte, Nicholas R.1
  • Dong, Hongxu2
  • Dwiyanti, Maria S.3
  • Anzoua, Kossonou G.4
  • Brummer, Joe E.5
  • Ghimire, Bimal K.6
  • Dzyubenko, Elena7
  • Dzyubenko, Nikolay7
  • Bagmet, Larisa7
  • Sabitov, Andrey7
  • Chebukin, Pavel7
  • Głowacka, Katarzyna8
  • Heo, Kweon9
  • Jin, Xiaoli10
  • Nagano, Hironori4
  • Peng, Junhua11
  • Yu, Chang Y.12
  • Yoo, Ji H.12
  • And 5 more
  • 1 Dept. of Crop Sciences, University of Illinois, Urbana, IL
  • 2 Plant Genome Mapping Laboratory, University of Georgia, 111 Riverbend Road, Athens, GA 30605
  • 3 Applied Plant Genome Laboratory, Research Faculty of Agriculture, Hokkaido University, Japan
  • 4 Field Science Center for Northern Biosphere, Hokkaido University, Sapporo, Hokkaido 060-0810, Japan
  • 5 Department of Soil and Crop Sciences, Colorado State University, Fort Collins, CO 80523
  • 6 Department of Applied Bioscience, Konkuk University, Seoul 05029, South Korea
  • 7 Vavilov All-Russian Institute of Plant Genetic Resources, 42–44 Bolshaya Morskaya Street, 190000 St. Petersburg, Russia
  • 8 Department of Biochemistry, University of Nebraska-Lincoln, NE 68588
  • 9 Department of Applied Plant Science, Kangwon National University, Chuncheon 24341, South Korea
  • 10 Department of Agronomy, Zhejiang University, Hangzhou 310058, China
  • 11 China National Seed Group Co. Ltd, Wuhan, Hubei 430040, China
  • 12 Department of Applied Plant Sciences, Kangwon National University, Chuncheon, Gangwon 200-701, South Korea
  • 13 College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, China
Type
Published Article
Journal
G3 Genes|Genome|Genetics
Publisher
The Genetics Society of America
Publication Date
May 26, 2020
Volume
10
Issue
7
Pages
2465–2476
Identifiers
DOI: 10.1534/g3.120.401402
PMID: 32457095
PMCID: PMC7341128
Source
PubMed Central
Keywords
Disciplines
  • Genomic Prediction
License
Green

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 × giganteus (M×g) beyond the single clone used in many programs. Germplasm from the corresponding parental species M. 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 these GS models. 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 diploid Msa parents. The results revealed that genetic architectures with common causal mutations 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.

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