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2019, DOI: 10.1111/gcbb.12620

Genome-wide association and genomic prediction for biomass yield in a genetically diverse Miscanthus sinensis germplasm panel phenotyped at five locations in Asia and North America

GCB Bioenergy

Lindsay V. Clark, Maria S. Dwiyanti, Kossonou G. Anzoua, Joe E. Brummer, Bimal Kumar Ghimire Katarzyna Głowacka, Megan Hall, Kweon Heo, Xiaoli Jin, Alexander E. Lipka, Junhua Peng, Toshihiko Yamada, Ji Hye Yoo, Chang Yeon Yu, Hua Zhao, Stephen P. Long, and Erik J. Sacks


To improve the efficiency of breeding of Miscanthus for biomass yield, there is a need to develop genomics -assisted selection for this long -lived perennial crop by relating genotype to phenotype and breeding value across a broad range of environments. We present the first genome -wide association (GWA) and genomic prediction study of Miscanthus that utilizes multi -location phenotypic data. A panel of 568 M. sinensis accessions was genotyped with 46,177 SNP s and evaluated at one subtropical and five temperate locations over three years for biomass yield and 14 yield -component traits. GWA and genomic prediction were performed separately for different years of data in order to assess reproducibility. The analyses were also performed for individual field trial locations, as well as combined phenotypic data across groups of locations. GWA analyses identified 27 significant SNPs for yield, and a total of 504 associations across 298 unique SNPs over all traits, sites, and years. For yield, the greatest number of significant SNPs was identified by combining phenotypic data across all six locations. For some of the other yield -component traits, greater numbers of significant SNPs were obtained from single site data, although the number of significant SNPs varied greatly from site to site. Candidate genes were identified. Accounting for population structure, genomic prediction accuracies for biomass yield ranged from 0.32 -0.36 over five northern sites and 0.15 -0.20 for the subtropical location, depending on estimation method. Genomic prediction accuracies of all traits were similar for single -location and multi -location data, suggesting that genomic selection will be useful for breeding broadly -adapted M. sinensis as well as M. sinensis optimized for specific climates. All of our data, including DNA sequences flanking each SNP, are publicly available. By facilitating genomic selection in M. sinensis and M. ×giganteus, our results will accelerate the breeding of these species for biomass in diverse environments.

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The LongLab is supported by many public and private partnerships, including the Bill & Melinda Gates Foundation, the Foundation for Food and Agriculture Research, the UK Government's Department for International Development, the U.S. Department of Energy, and the Advanced Research Projects Agency-Energy.

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