Genomic prediction of seed nutritional traits in biparental families of oat (Avena sativa).

Selection for more nutritious crop plants is an important goal of plant breeding to improve food quality and contribute to human health outcomes. While there are efforts to integrate genomic prediction to accelerate breeding progress, an ongoing challenge is identifying strategies to improve accuracy when predicting within biparental populations in breeding programs. We tested multiple genomic prediction methods for 12 seed fatty acid content traits in oat (Avena sativa L.), as unsaturated fatty acids are a key nutritional trait in oat. Using two well-characterized oat germplasm panels and other biparental families as training populations, we predicted family mean and individual values within families. Genomic prediction of family mean exceeded a mean accuracy of 0.40 and 0.80 using an unrelated and related germplasm panel, respectively, where the related germplasm panel outperformed prediction based on phenotypic means (0.54). Within family prediction accuracy was more variable: training on the related germplasm had higher accuracy than the unrelated panel (0.14-0.16 and 0.05-0.07, respectively), but variability between families was not easily predicted by parent relatedness, segregation of a locus detected by a genome-wide association study in the panel, or other characteristics. When using other families as training populations, prediction accuracies were comparable to the related germplasm panel (0.11-0.23), and families that had half-sib families in the training set had higher prediction accuracy than those that did not. Overall, this work provides an example of genomic prediction of family means and within biparental families for an important nutritional trait and suggests that using related germplasm panels as training populations can be effective.

Generalizable approaches for genomic prediction of metabolites in plants.

Plant metabolites are important traits for plant breeders seeking to improve nutrition and agronomic performance yet integrating selection for metabolomic traits can be limited by phenotyping expense and degree of genetic characterization, especially of uncommon metabolites. As such, developing generalizable genomic selection methods based on biochemical pathway biology for metabolites that are transferable across plant populations would benefit plant breeding programs. We tested genomic prediction accuracy for >600 metabolites measured by gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) in oat (Avena sativa L.) seed. Using a discovery germplasm panel, we conducted metabolite genome-wide association study (mGWAS) and selected loci to use in multikernel models that encompassed metabolome-wide mGWAS results or mGWAS from specific metabolite structures or biosynthetic pathways. Metabolite kernels developed from LC-MS metabolites in the discovery panel improved prediction accuracy of LC-MS metabolite traits in the validation panel consisting of more advanced breeding lines. No approach, however, improved prediction accuracy for GC-MS metabolites. We ranked model performance by metabolite and found that metabolites with similar polarity had consistent rankings of models. Overall, testing biological rationales for developing kernels for genomic prediction across populations contributes to developing frameworks for plant breeding for metabolite traits.

Selection for seed size has uneven effects on specialized metabolite abundance in oat (Avena sativa L.).

Plant breeding strategies to optimize metabolite profiles are necessary to develop health-promoting food crops. In oats (Avena sativa L.), seed metabolites are of interest for their antioxidant properties, yet have not been a direct target of selection in breeding. In a diverse oat germplasm panel spanning a century of breeding, we investigated the degree of variation of these specialized metabolites and how it has been molded by selection for other traits, like yield components. We also ask if these patterns of variation persist in modern breeding pools. Integrating genomic, transcriptomic, metabolomic, and phenotypic analyses for three types of seed specialized metabolites-avenanthramides, avenacins, and avenacosides-we found reduced heritable genetic variation in modern germplasm compared with diverse germplasm, in part due to increased seed size associated with more intensive breeding. Specifically, we found that abundance of avenanthramides increases with seed size, but additional variation is attributable to expression of biosynthetic enzymes. In contrast, avenacoside abundance decreases with seed size and plant breeding intensity. In addition, these different specialized metabolites do not share large-effect loci. Overall, we show that increased seed size associated with intensive plant breeding has uneven effects on the oat seed metabolome, but variation also exists independently of seed size to use in plant breeding. This work broadly contributes to our understanding of how plant breeding has influenced plant traits and tradeoffs between traits (like growth and defense) and the genetic bases of these shifts.

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.

Divergence of defensive cucurbitacins in independent Cucurbita pepo domestication events leads to differences in specialist herbivore preference.

Crop domestication and improvement often concurrently affect plant resistance to pests and production of secondary metabolites, creating challenges for isolating the ecological implications of selection for specific metabolites. Cucurbitacins are bitter triterpenoids with extreme phenotypic differences between Cucurbitaceae lineages, yet we lack integrated models of herbivore preference, cucurbitacin accumulation, and underlying genetic mechanisms. In Cucurbita pepo, we dissected the effect of cotyledon cucurbitacins on preference of a specialist insect pest (Acalymma vittatum) for multiple tissues, assessed genetic loci underlying cucurbitacin accumulation in diverse germplasm and a biparental F2 population (from a cross between two independent domesticates), and characterized quantitative associations between gene expression and metabolites during seedling development. Acalymma vittatum affinity for cotyledons is mediated by cucurbitacins, but other traits contribute to whole-plant resistance. Cotyledon cucurbitacin accumulation was associated with population structure, and our genetic mapping identified a single locus, Bi-4, containing genes relevant to transport and regulation - not biosynthesis - that diverged between lineages. These candidate genes were expressed during seedling development, most prominently a putative secondary metabolite transporter. Taken together, these findings support the testable hypothesis that breeding for plant resistance to insects involves targeting genes for regulation and transport of defensive metabolites, in addition to core biosynthesis genes.

Mechanisms of Resistance to Insect Herbivores in Isolated Breeding Lineages of Cucurbita pepo.

Although crop wild ancestors are often reservoirs of resistance traits lost during domestication, examining diverse cultivated germplasm may also reveal novel resistance traits due to distinct breeding histories. Using ten cultivars from two independent domestication events of Cucurbita pepo (ssp. pepo and texana), we identified divergences in constitutive and induced resistance measured by growth of generalist caterpillars and leaf traits. C. p. texana cultivars were consistently more resistant to Trichoplusia ni and Spodoptera exigua, and this was not due to expected mechanisms including cucurbitacins, nitrogen, sticky phloem sap, or toxicity. Although more susceptible on average, C. p. pepo cultivars showed stronger induced resistance, suggesting a trade-off between constitutive and induced resistance. To test the hypothesis that leaf volatiles accounted for differences in resistance to caterpillars, we devised a novel method to evaluate resistance on artificial diet while larvae are exposed to leaf volatiles. In both subspecies, cultivar-specific induced volatiles that reduced T. ni growth were present in highly inducible cultivars, but absent in those that showed no induction. These results have important agricultural implications as cultivar-specific resistance to caterpillars mirrored that of specialist beetles from field trials. Overall, the eponymous cucurbitacin defenses of the Cucurbitaceae are not the mechanistic basis of differences in constitutive or induced resistance between C. pepo subspecies or cultivars. Instead, deterrent cultivar-specific volatiles appear to provide general resistance to insect herbivores. Divergence during breeding history within and between subspecies revealed this pattern and novel resistance mechanism, defining new targets for plant breeding.