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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.

Miscanthus is a close relative of Saccharum and a potentially valuable genetic resource for improving sugarcane. Differences in flowering time within and between Miscanthus and Saccharum hinders intra- and interspecific hybridizations. A series of greenhouse experiments were conducted over three years to determine how to synchronize flowering time of Saccharum and Miscanthus genotypes. We found that day length was an important factor influencing when Miscanthus and Saccharum flowered. Sugarcane could be induced to flower in a central Illinois greenhouse using supplemental lighting to reduce the rate at which days shortened during the autumn and winter to 1 min d-1, which allowed us to synchronize the flowering of some sugarcane genotypes with Miscanthus genotypes primarily from low latitudes. In a complementary growth chamber experiment, we evaluated 33 Miscanthus genotypes, including 28 M. sinensis, 2 M. floridulus, and 3 M. ×giganteus collected from 20.9° S to 44.9° N for response to three day lengths (10 h, 12.5 h, and 15 h). High latitude-adapted M. sinensis flowered mainly under 15 h days, but unexpectedly, short days resulted in short, stocky plants that did not flower; in some cases, flag leaves developed under short days but heading did not occur. In contrast, for M. sinensis and M. floridulus from low latitudes, shorter day lengths typically resulted in earlier flowering, and for some low latitude genotypes, 15 h days resulted in no flowering. However, the highest ratio of reproductive shoots to total number of culms was typically observed for 12.5 h or 15 h days. Latitude of origin was significantly associated with culm length, and the shorter the days, the stronger the relationship. Nearly all entries achieved maximal culm length under the 15 h treatment, but the nearer to the equator an accession originated, the less of a difference in culm length between the short-day treatments and the 15 h day treatment. Under short days, short culms for high-latitude accessions was achieved by different physiological mechanisms for M. sinensis genetic groups from the mainland in comparison to those from Japan; for mainland accessions, the mechanism was reduced internode length, whereas for Japanese accessions the phyllochron under short days was greater than under long days. Thus, for M. sinensis, short days typically hastened floral induction, consistent with the expectations for a facultative short-day plant. However, for high latitude accessions of M. sinensis, days less than 12.5 h also signaled that plants should prepare for winter by producing many short culms with limited elongation and development; moreover, this response was also epistatic to flowering. Thus, to flower M. sinensis that originates from high latitudes synchronously with sugarcane, the former needs day lengths >12.5 h (perhaps as high as 15 h), whereas that the latter needs day lengths