Search

Abstract Accelerating biomass improvement is a major goal of Miscanthus breeding. The development and implementation of genomic‐enabled breeding tools, like marker‐assisted selection (MAS) and genomic selection, has the potential to improve the efficiency of Miscanthus breeding. The present study conducted genome‐wide association (GWA) and genomic prediction of biomass yield and 14 yield‐components traits in Miscanthus sacchariflorus. We evaluated a diversity panel with 590 accessions of M. sacchariflorus grown across 4 years in one subtropical and three temperate locations and genotyped with 268,109 single‐nucleotide polymorphisms (SNPs). The GWA study identified a total of 835 significant SNPs and 674 candidate genes across all traits and locations. Of the significant SNPs identified, 280 were localized in mapped quantitative trait loci intervals and proximal to SNPs identified for similar traits in previously reported Miscanthus studies, providing additional support for the importance of these genomic regions for biomass yield. Our study gave insights into the genetic basis for yield‐component traits in M. sacchariflorus that may facilitate marker‐assisted breeding for biomass yield. Genomic prediction accuracy for the yield‐related traits ranged from 0.15 to 0.52 across all locations and genetic groups. Prediction accuracies within the six genetic groupings of M. sacchariflorus were limited due to low sample sizes. Nevertheless, the Korea/NE China/Russia (N = 237) genetic group had the highest prediction accuracy of all genetic groups (ranging 0.26–0.71), suggesting that with adequate sample sizes, there is strong potential for genomic selection within the genetic groupings of M. sacchariflorus. This study indicated that MAS and genomic prediction will likely be beneficial for conducting population‐improvement of M. sacchariflorus.

Abstract Miscanthus is a genus of perennial grasses native to East Asia that shows promise as a biofuel energy source. Breeding efforts for increasing biofuel capability in this genus have focused on two species, namely M. sinensis (Msi) and M. sacchariflorus (Msa). For these efforts to succeed, it is critical that both Msi and Msa, as well as their interspecific crosses, can be grown at a wide range of latitudes. Therefore, the purpose of this study was to investigate how well existing data from Msi and Msa trials grown at locations throughout the northern hemisphere can train state‐of‐the‐art genomic selection (GS) models to predict genomic estimated breeding values (GEBVs) of dry yield for untested Msi and Msa accessions in untested environments. We found that accounting for genotype by environment interaction in the GS model did not notably improve predictive ability. Additionally, we observed that locations at lower latitudes showed higher predictive ability relative to locations at higher latitudes. These results suggest that it is crucial to increase the number of trial locations at higher latitude locations to investigate the source of this correlation. This will make it possible to train GS models using data from environments that are similar to growing conditions at the locations targeted by Msi and Msa breeders. Such an increase of trial locations in target environments could pave the way toward advancing breeding efforts for overwintering ability in Msi and Msa, and ultimately support the potential of Miscanthus as a biofuel crop.

Abstract Discovery and analysis of genetic variants underlying agriculturally important traits are key to molecular breeding of crops. Reduced representation approaches have provided cost‐efficient genotyping using next‐generation sequencing. However, accurate genotype calling from next‐generation sequencing data is challenging, particularly in polyploid species due to their genome complexity. Recently developed Bayesian statistical methods implemented in available software packages, polyRAD, EBG, and updog, incorporate error rates and population parameters to accurately estimate allelic dosage across any ploidy. We used empirical and simulated data to evaluate the three Bayesian algorithms and demonstrated their impact on the power of genome‐wide association study (GWAS) analysis and the accuracy of genomic prediction. We further incorporated uncertainty in allelic dosage estimation by testing continuous genotype calls and comparing their performance to discrete genotypes in GWAS and genomic prediction. We tested the genotype‐calling methods using data from two autotetraploid species, Miscanthus sacchariflorus and Vaccinium corymbosum, and performed GWAS and genomic prediction. In the empirical study, the tested Bayesian genotype‐calling algorithms differed in their downstream effects on GWAS and genomic prediction, with some showing advantages over others. Through subsequent simulation studies, we observed that at low read depth, polyRAD was advantageous in its effect on GWAS power and limit of false positives. Additionally, we found that continuous genotypes increased the accuracy of genomic prediction, by reducing genotyping error, particularly at low sequencing depth. Our results indicate that by using the Bayesian algorithm implemented in polyRAD and continuous genotypes, we can accurately and cost‐efficiently implement GWAS and genomic prediction in polyploid crops.

Abstract Miscanthus is a high‐yielding bioenergy crop that is broadly adapted to temperate and tropical environments. Commercial cultivation of Miscanthus is predominantly limited to a single sterile triploid clone of Miscanthus × giganteus, a hybrid between Miscanthus sacchariflorus and M. sinensis. To expand the genetic base of M. × giganteus, the substantial diversity within its progenitor species should be used for cultivar improvement and diversification. Here, we phenotyped a diversity panel of 605 M. sacchariflorus from six previously described genetic groups and 27 M. × giganteus genotypes for dry biomass yield and 16 yield‐component traits, in field trials grown over 3 years at one subtropical location (Zhuji, China) and four temperate locations (Foulum, Denmark; Sapporo, Japan; Urbana, Illinois; and Chuncheon, South Korea). There was considerable diversity in yield and yield‐component traits among and within genetic groups of M. sacchariflorus, and across the five locations. Biomass yield of M. sacchariflorus ranged from 0.003 to 34.0 Mg ha−1 in year 3. Variation among the genetic groups was typically greater than within, so selection of genetic group should be an important first step for breeding with M. sacchariflorus. The Yangtze 2x genetic group (=ssp. lutarioriparius) of M. sacchariflorus had the tallest and thickest culms at all locations tested. Notably, the Yangtze 2x genetic group's exceptional culm length and yield potential were driven primarily by a large number of nodes (>29 nodes culm−1 average over all locations), which was consistent with the especially late flowering of this group. The S Japan 4x, the N China/Korea/Russia 4x, and the N China 2x genetic groups were also promising genetic resources for biomass yield, culm length, and culm thickness, especially for temperate environments. Culm length was the best indicator of yield potential in M. sacchariflorus. These results will inform breeders' selection of M. sacchariflorus genotypes for population improvement and adaptation to target production environments.

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.

Abstract To breed improved biomass cultivars of Miscanthus ×giganteus, it will be necessary to select the highest‐yielding and best‐adapted genotypes of its parental species, Miscanthus sinensis and Miscanthus sacchariflorus. We phenotyped a diverse clonally propagated panel of 569 M. sinensis and nine natural diploid M. ×giganteus at one subtropical (Zhuji, China) and five temperate locations (Sapporo, Japan; Leamington, Ontario, Canada; Fort Collins, CO; Urbana, IL; and Chuncheon, Korea) for dry biomass yield and 14 yield‐component traits, in trials grown for 3 years. Notably, dry biomass yield of four Miscanthus accessions exceeded 80 Mg/ha in Zhuji, China, approaching the highest observed for any land plant. Additionally, six M. sinensis in Sapporo, Japan and one in Leamington, Canada also yielded more than the triploid M. ×giganteus ‘1993‐1780’ control, with values exceeding 20 Mg/ha. Diploid M. ×giganteus was the best‐yielding group at the northern sites. Genotype‐by‐environment interactions were modest among the five northern trial sites but large between Zhuji, and the northern sites. M. sinensis accessions typically yielded best at trial sites with latitudes similar to collection sites, although broad adaptation was observed for accessions from southern Japan. Genotypic heritabilities for third year yields ranged from 0.71 to 0.88 within locations. Compressed circumference was the best predictor of yield. These results establish a baseline of data for initiating selection to improve biomass yield of M. sinensis and M. ×giganteus in a diverse set of relevant geographies.

Abstract 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 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.