Optimal harvest timing for brown midrib forage sorghum yield, nutritive value, and ration performance.

Forage sorghum [Sorghum bicolor (L.) Moench] is a viable alternative to corn silage (Zea mays L.) in double cropping rotations with forage winter cereals in New York due to a later planting date and potentially earlier harvest date of forage sorghum than is typical for corn silage. Our objective was to determine whether harvest of brachytic dwarf brown midrib forage sorghum can take place before the currently recommended soft dough harvest time while maintaining dry matter (DM) yield, forage nutritive value, and total mixed ration performance. Seven trials were conducted on 2 research farms in central New York from 2014 to 2017. Forage sorghum received 1 of 2 fertilizer N rates at planting (112 and 224 kg of N/ha). Stands were harvested at boot, flower, milk, and soft dough stages. Forage samples were analyzed for nutritive value and substituted for corn silage in a typical dairy total mixed ration at varying amounts using the Cornell Net Carbohydrate and Protein System. Timing of harvest affected yield and forage nutritive value for each individual trial and across trials, and the effects were independent of N fertilizer application rate. Averaged across trials, yield ranged from 10.7 Mg of DM/ha for the boot stage to 13.5, 15.2, and 15.8 Mg of DM/ha for the flower, milk, and soft dough stages, respectively. For individual trials, yield either remained constant with harvest beyond the flower stage (4 trials), or beyond the milk stage (1 trial), whereas for 2 trials yield increased up to the soft dough stage. At the later harvest stages, DM, starch, and nonfiber carbohydrates were increased, whereas crude protein, neutral detergent fiber, and 30-h neutral detergent fiber digestibility were decreased. Without adjusting for DM intake, substitution of corn silage by forage sorghum harvested at the soft dough stage resulted in stable predicted metabolizable energy allowable milk, whereas the reduced starch content of earlier harvested sorghum resulted in less metabolizable energy allowable milk with greater substitution of corn silage for sorghum. Forage sorghum can be harvested as early as the flower or milk stage without losing DM yield, allowing for timely planting of forage winter cereal in a double cropping rotation. However, energy supplementation in the diet is needed to make up for reduced starch concentrations with harvest of sorghum at flower and milk growth stages.

Genome-wide association study based on multiple imputation with low-depth sequencing data: application to biofuel traits in reed canarygrass.

Genotyping by sequencing allows for large-scale genetic analyses in plant species with no reference genome, but sets the challenge of sound inference in presence of uncertain genotypes. We report an imputation-based genome-wide association study (GWAS) in reed canarygrass (Phalaris arundinacea L., Phalaris caesia Nees), a cool-season grass species with potential as a biofuel crop. Our study involved two linkage populations and an association panel of 590 reed canarygrass genotypes. Plants were assayed for up to 5228 single nucleotide polymorphism markers and 35 traits. The genotypic markers were derived from low-depth sequencing with 78% missing data on average. To soundly infer marker-trait associations, multiple imputation (MI) was used: several imputes of the marker data were generated to reflect imputation uncertainty and association tests were performed on marker effects across imputes. A total of nine significant markers were identified, three of which showed significant homology with the Brachypodium dystachion genome. Because no physical map of the reed canarygrass genome was available, imputation was conducted using classification trees. In general, MI showed good consistency with the complete-case analysis and adequate control over imputation uncertainty. A gain in significance of marker effects was achieved through MI, but only for rare cases when missing data were <45%. In addition to providing insight into the genetic basis of important traits in reed canarygrass, this study presents one of the first applications of MI to genome-wide analyses and provides useful guidelines for conducting GWAS based on genotyping-by-sequencing data.

Accelerating the switchgrass (Panicum virgatum L.) breeding cycle using genomic selection approaches.

Switchgrass (Panicum virgatum L.) is a perennial grass undergoing development as a biofuel feedstock. One of the most important factors hindering breeding efforts in this species is the need for accurate measurement of biomass yield on a per-hectare basis. Genomic selection on simple-to-measure traits that approximate biomass yield has the potential to significantly speed up the breeding cycle. Recent advances in switchgrass genomic and phenotypic resources are now making it possible to evaluate the potential of genomic selection of such traits. We leveraged these resources to study the ability of three widely-used genomic selection models to predict phenotypic values of morphological and biomass quality traits in an association panel consisting of predominantly northern adapted upland germplasm. High prediction accuracies were obtained for most of the traits, with standability having the highest ten-fold cross validation prediction accuracy (0.52). Moreover, the morphological traits generally had higher prediction accuracies than the biomass quality traits. Nevertheless, our results suggest that the quality of current genomic and phenotypic resources available for switchgrass is sufficiently high for genomic selection to significantly impact breeding efforts for biomass yield.

Switchgrass genomic diversity, ploidy, and evolution: novel insights from a network-based SNP discovery protocol.

Switchgrass (Panicum virgatum L.) is a perennial grass that has been designated as an herbaceous model biofuel crop for the United States of America. To facilitate accelerated breeding programs of switchgrass, we developed both an association panel and linkage populations for genome-wide association study (GWAS) and genomic selection (GS). All of the 840 individuals were then genotyped using genotyping by sequencing (GBS), generating 350 GB of sequence in total. As a highly heterozygous polyploid (tetraploid and octoploid) species lacking a reference genome, switchgrass is highly intractable with earlier methodologies of single nucleotide polymorphism (SNP) discovery. To access the genetic diversity of species like switchgrass, we developed a SNP discovery pipeline based on a network approach called the Universal Network-Enabled Analysis Kit (UNEAK). Complexities that hinder single nucleotide polymorphism discovery, such as repeats, paralogs, and sequencing errors, are easily resolved with UNEAK. Here, 1.2 million putative SNPs were discovered in a diverse collection of primarily upland, northern-adapted switchgrass populations. Further analysis of this data set revealed the fundamentally diploid nature of tetraploid switchgrass. Taking advantage of the high conservation of genome structure between switchgrass and foxtail millet (Setaria italica (L.) P. Beauv.), two parent-specific, synteny-based, ultra high-density linkage maps containing a total of 88,217 SNPs were constructed. Also, our results showed clear patterns of isolation-by-distance and isolation-by-ploidy in natural populations of switchgrass. Phylogenetic analysis supported a general south-to-north migration path of switchgrass. In addition, this analysis suggested that upland tetraploid arose from upland octoploid. All together, this study provides unparalleled insights into the diversity, genomic complexity, population structure, phylogeny, phylogeography, ploidy, and evolutionary dynamics of switchgrass.