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Abstract
AbstractTo 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 multilocation phenotypic data. A panel of 568 Miscanthus sinensis accessions was genotyped with 46,177 single nucleotide polymorphisms (SNPs) and evaluated at one subtropical and five temperate locations over 3 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 across 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 0.31 to 0.35 across five northern sites and from 0.13 to 0.18 for the subtropical location, depending on the estimation method. Genomic prediction accuracies of all traits were similar for single‐location and multilocation 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 Miscanthus × giganteus, our results will accelerate the breeding of these species for biomass in diverse environments.
Highlights
Miscanthus is a promising crop for lignocellulosic bioenergy and bioproducts, but it is in the very early stages of domestication and breeding (Clifton‐Brown, Chiang, & Hodkinson, 2008; Clifton‐Brown et al, 2019; Dwiyanti, Stewart, & Yamada, 2013; Sacks, Juvik, Lin, Stewart, & Yamada, 2013)
Our results indicate that genome‐wide association (GWA) and genomic selection are likely to accelerate the breeding of M. sinensis, even with relatively low coverage of the genome by RAD‐seq single nucleotide polymorphism (SNP)
Because we have previously identified genetic groups within M. sinensis (Clark et al, 2014, 2015) and evaluated phenotypic differences among these groups in this set of field trials (Clark et al, 2019), we corrected for population structure by removing variance attributable to genetic groups by (a) performing genomic prediction within individual groups (Tables 4 and 5), (b) estimating Best linear unbiased predictor (BLUP) for genotype‐within‐genetic‐group from Equations 3 and 4 (G(D); Table 3), or (c) analyzing residuals of genotype BLUPs fitted to genetic group (R; Equation 5)
Summary
Miscanthus is a promising crop for lignocellulosic bioenergy and bioproducts, but it is in the very early stages of domestication and breeding (Clifton‐Brown, Chiang, & Hodkinson, 2008; Clifton‐Brown et al, 2019; Dwiyanti, Stewart, & Yamada, 2013; Sacks, Juvik, Lin, Stewart, & Yamada, 2013). Both M. sinensis and M. sacchariflorus are native to a broad range of environments across East Asia and should provide a wealth of breeding material for developing new biomass cultivars (Clifton‐Brown et al, 2008) Such breeding can be accelerated by genomic selection, especially because Miscanthus is a long‐lived perennial that requires 3 years of field testing to obtain high‐quality yield data. Zhao et al (2013) studied 300 M. sinensis genotypes collected from a broad geographic range across China in a field trial at Wuhan, China, using 115 alleles from 23 microsatellite markers They identified nine significant associations with heading date, plant height, and yield, a relatedness matrix was not included in the GWA model, increasing the chance of false positives. | 990 results across years, locations, and correlated traits; and (c) estimate genomic prediction accuracy in order to determine the feasibility of genomic selection
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