Cereal Chem. 87(6):574-580Because of the large number of cultivars that require examination in the development of spring wheat (Triticum aestivum L.) cultivars, breeding programs use predictive methods to test end use quality. The Mixograph is a widely used predictive test with which end use quality of many genotypes can be assessed in a short time. By comparison, the Mixolab is a relatively new device with additional capability that might be used for the same purpose. Our objective was to document variability of, and relationships among, 20 parameters obtained from Mixolab, Mixograph, and bake tests. Tests were performed on flour from 18 genotypes grown in 20 environments. Both genotype and environment had significant effects on quality parameter values. Several Mixograph and Mixolab parameters were highly significantly correlated, particularly when genotype mean values over environments were considered. Correlations between loaf volume and Mixolab parameters within environments were inconsistent and suggest that average genotype values over environments will be most useful. For example, the correlation between Mixolab stability and loaf volume (r = 0.25, P < 0.001) was much higher when genotype averages (r = 0.70, P < 0.001) were considered. Our results show that selection for Mixolab stability and water absorption should help delineate and improve the selection of genotypes with greater loaf volume.Hard red spring (HRS) wheat (Triticum aestivum L.) is characterized by grain with high protein content and excellent milling and baking performance (Carson and Edwards 2009). To maintain this distinction, breeders must create and select experimental breeding lines that have superior and consistent end use quality characteristics. One of the most important breadmaking quality characteristics is loaf volume (Chung 2003). Loaf volume is affected by environmental conditions when grain is produced and by the genetic potential of a cultivar. Within a population of 105 recombinant inbred lines derived from a cross between good (HI977) and poor (HD2329) breadmaking quality genotypes, Elangovan et al (2008) reported that 75% of observed loaf volume variation was attributable to genotype.Although actually baking a loaf of bread provides the most direct and reliable assessment of breadmaking quality, it is time-consuming, labor intensive, and requires costly infrastructure as well as experienced personnel. Therefore, baking tests are typically performed only in the final stages of experimental line evaluation. Before this stage, however, breeding line selection is performed almost entirely with predictive methods. Several end use quality prediction methods are based on dough rheological properties. The Mixograph, which is likely the most extensively used predictive method within U.S. wheat breeding programs, is a recording dough mixer that measures flour mixing requirements and tolerance to overmixing (Finney and Shogren 1972). These characteristics are indicative of dough strength and protein quality. Unfortunately, Mixograph...

Oat (Avena sativa L.) seed is a rich resource of beneficial lipids, soluble fiber, protein, and antioxidants, and is considered a healthful food for humans. Little is known regarding the genetic controllers of variation for these compounds in oat seed. We characterized natural variation in the mature seed metabolome using untargeted metabolomics on 367 diverse lines and leveraged this information to improve prediction for seed quality traits. We used a latent factor approach to define unobserved variables that may drive covariance among metabolites. One hundred latent factors were identified, of which 21% were enriched for compounds associated with lipid metabolism. Through a combination of whole-genome regression and association mapping, we show that latent factors that generate covariance for many metabolites tend to have a complex genetic architecture. Nonetheless, we recovered significant associations for 23% of the latent factors. These associations were used to inform a multi-kernel genomic prediction model, which was used to predict seed lipid and protein traits in two independent studies. Predictions for eight of the 12 traits were significantly improved compared to genomic best linear unbiased prediction when this prediction model was informed using associations from lipid-enriched factors. This study provides new insights into variation in the oat seed metabolome and provides genomic resources for breeders to improve selection for health-promoting seed quality traits. More broadly, we outline an approach to distill high-dimensional ‘omics’ data to a set of biologically-meaningful variables and translate inferences on these data into improved breeding decisions.

Cereal Chem. 88(2):201-208Dough extensibility affects processing ease, gas retention, and loaf volume of finished products. The Kieffer dough extensibility test was developed to assess extensibility of small dough samples and is therefore adapted for use in breeding programs. Information is lacking on relationships between wheat growing environments and dough properties measured by the Kieffer dough extensibility test. This study documents the variability of dough extensibility (Ext), maximum resistance to extension (Rmax), and area under the extensibility curve (Area) in relation to breadmaking quality, and the effect of wheat growing environments. Mixograph, Kieffer dough extensibility, and bake tests were performed on flour milled from 19 hard red spring wheat (Triticum aestivum L.) genotypes grown during three growing seasons (2007)(2008)(2009)) at six South Dakota locations. Although both genotype and environment had significant effects on Kieffer dough extensibility variables, environment represented the largest source of variation. Among genotype means, Area was most correlated (r = 0.63) with loaf volume, suggesting that by selecting lines with increased Area, loaf volume should improve. Rmax was positively correlated (r = 0.58) with loaf volume among genotype means but negatively correlated (r = -0.80) among environmental means. Ext was positively correlated (r = 0.90) with loaf volume among environmental means. Weather variables were correlated with Rmax, Ext and loaf volume and therefore could help predict end-use quality.

Key message Integration of multi-omics data improved prediction accuracies of oat agronomic and seed nutritional traits in multi-environment trials and distantly related populations in addition to the single-environment prediction. Abstract Multi-omics prediction has been shown to be superior to genomic prediction with genome-wide DNA-based genetic markers (G) for predicting phenotypes. However, most of the existing studies were based on historical datasets from one environment; therefore, they were unable to evaluate the efficiency of multi-omics prediction in multi-environment trials and distantly related populations. To fill those gaps, we designed a systematic experiment to collect omics data and evaluate 17 traits in two oat breeding populations planted in single and multiple environments. In the single-environment trial, transcriptomic BLUP (T), metabolomic BLUP (M), G + T, G + M, and G + T + M models showed greater prediction accuracy than GBLUP for 5, 10, 11, 17, and 17 traits, respectively, and metabolites generally performed better than transcripts when combined with SNPs. In the multi-environment trial, multi-trait models with omics data outperformed both counterpart multi-trait GBLUP models and single-environment omics models, and the highest prediction accuracy was achieved when modeling genetic covariance as an unstructured covariance model. We also demonstrated that omics data can be used to prioritize loci from one population with omics data to improve genomic prediction in a distantly related population using a two-kernel linear model that accommodated both likely casual loci with large-effect and loci that explain little or no phenotypic variance. We propose that the two-kernel linear model is superior to most genomic prediction models that assume each variant is equally likely to affect the trait and can be used to improve prediction accuracy for any trait with prior knowledge of genetic architecture.

The observable phenotype is the manifestation of information that is passed along different organization levels (transcriptional, translational, and metabolic) of a biological system. The widespread use of various omic technologies (RNA-sequencing, metabolomics, etc.) has provided plant genetics and breeders with a wealth of information on pertinent intermediate molecular processes that may help explain variation in conventional traits such as yield, seed quality, and fitness, among others. A major challenge is effectively using these data to help predict the genetic merit of new, unobserved individuals for conventional agronomic traits. Trait-specific genomic relationship matrices (TGRMs) model the relationships between individuals using genome-wide markers (SNPs) and place greater emphasis on markers that most relevant to the trait compared to conventional genomic relationship matrices. Given that these approaches define relationships based on putative causal loci, it is expected that these approaches should improve predictions for related traits. In this study we evaluated the use of TGRMs to accommodate information on intermediate molecular phenotypes (referred to as endophenotypes) and to predict an agronomic trait, total lipid content, in oat seed. Nine fatty acids were quantified in a panel of 336 oat lines. Marker effects were estimated for each endophenotype, and were used to construct TGRMs. A multikernel TRGM model (MK-TRGM-BLUP) was used to predict total seed lipid content in an independent panel of 210 oat lines. The MK-TRGM-BLUP approach significantly improved predictions for total lipid content when compared to a conventional genomic BLUP (gBLUP) approach. Given that the MK-TGRM-BLUP approach leverages information on the nine fatty acids to predict genetic values for total lipid content in unobserved individuals, we compared the MK-TGRM-BLUP approach to a multi-trait gBLUP (MT-gBLUP) approach that jointly fits phenotypes for fatty acids and total lipid content. The MK-TGRM-BLUP approach significantly outperformed MT-gBLUP. Collectively, these results highlight the utility of using TGRM to accommodate information on endophenotypes and improve genomic prediction for a conventional agronomic trait.