Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations.

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. 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.

Improving Genomic Prediction for Seed Quality Traits in Oat (Avena sativa L.) Using Trait-Specific Relationship Matrices.

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.

Translating insights from the seed metabolome into improved prediction for lipid-composition traits in oat (Avena sativa L.).

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 8 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.

Winter Wheat Grain Yield Response to Fungicide Application is Influenced by Cultivar and Rainfall.

Winter wheat is susceptible to several fungal pathogens throughout the growing season and foliar fungicide application is one of the strategies used in the management of fungal diseases in winter wheat. However, for fungicides to be profitable, weather conditions conducive to fungal disease development should be present. To determine if winter wheat yield response to fungicide application at the flowering growth stage (Feekes 10.5.1) was related to the growing season precipitation, grain yield from fungicide treated plots was compared to non-treated plots for 19 to 30 hard red winter wheat cultivars planted at 8 site years from 2011 through 2015. At all locations, Prothioconazole + Tebuconazole or Tebuconazole alone was applied at flowering timing for the fungicide treated plots. Grain yield response (difference between treated and non-treated) ranged from 66-696 kg/ha across years and locations. Grain yield response had a positive and significant linear relationship with cumulative rainfall in May through June for the mid and top grain yield ranked cultivars (R2=54%, 78%, respectively) indicating that a higher amount of accumulated rainfall in this period increased chances of getting a higher yield response from fungicide application. Cultivars treated with a fungicide had slightly higher protein content (up to 0.5%) compared to non-treated. These results indicate that application of fungicides when there is sufficient moisture in May and June may increase chances of profitability from fungicide application.

Incidence of Penicillium verrucosum in Grain Samples from Oat Varieties Commonly Grown in South Dakota.

Ochratoxin A (OTA) can cause toxicogenic effects in humans and animals when contaminated food products are consumed. Oat ( Avena sativa), like any other cereal grain, can be contaminated with OTA when storage conditions are favorable for fungal growth and toxin production. South Dakota is among the leading oat-producing states in the United States. It is therefore important to determine the frequency of occurrence of the primary OTA-producing fungal species on oat grains produced in the state. In this study, we evaluated oat grain samples from South Dakota for the incidence of Penicillium verrucosum, the major ochratoxigenic fungus in temperate regions. Kernels from 12 oat cultivars grown at multiple locations in South Dakota from 2014 to 2016 (15 location-year combinations) were plated on dichloran yeast extract sucrose glycerol agar medium. P. verrucosum was detected on 0.30, 0.19, and 0.05% of the kernels tested in 2014, 2015, and 2016, respectively. Overall, 22 of the 360 evaluated samples had kernels contaminated with P. verrucosum. The fungal incidence of the contaminated samples ranged from 1 to 16%, and the majority of those samples originated from one location. All samples from 2014 and 2015 ( n = 240), except two, had no detectable levels of OTA. The concentration of OTA was well under the maximum limit recommended by the European Union for the two samples with detectable levels of OTA.