Multivariate Genome-Wide Association Analyses Reveal the Genetic Basis of Seed Fatty Acid Composition in Oat (<i>Avena sativa</i>L.)

Carlson, Maryn O.; Montilla-Bascón, Gracia; Hoekenga, Owen A.; Tinker, Nicholas A.; Poland, Jesse; Baseggio, Matheus; Sorrells, Mark E.; Jannink, Jean‐Luc; Gore, Michael A.; Yeats, Trevor H.

doi:10.1534/g3.119.400228

Cited by 50 publications

(61 citation statements)

References 63 publications

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“…Researchers have identified four loci contributing to changes in the fatty acid composition and content in oat grain. However, genome regions conducive to changing the content of proteins, oils, saccharic and uronic acids, which, in their turn, produce a direct effect on grain quality, remain unexplored [74]. Furthermore, positive correlations were demonstrated in barley between 1000 grain weight and tocol concentration, between dietary fiber content and phenolic compounds, and between husk weight and total antioxidants in hulled barley [38,50].…”

Section: β-Glucansmentioning

confidence: 99%

Wheat, Barley, and Oat Breeding for Health Benefit Components in Grain

Лоскутов

Khlestkina

2021

Plants

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Cereal grains provide half of the calories consumed by humans. In addition, they contain important compounds beneficial for health. During the last years, a broad spectrum of new cereal grain-derived products for dietary purposes emerged on the global food market. Special breeding programs aimed at cultivars utilizable for these new products have been launched for both the main sources of staple foods (such as rice, wheat, and maize) and other cereal crops (oat, barley, sorghum, millet, etc.). The breeding paradigm has been switched from traditional grain quality indicators (for example, high breadmaking quality and protein content for common wheat or content of protein, lysine, and starch for barley and oat) to more specialized ones (high content of bioactive compounds, vitamins, dietary fibers, and oils, etc.). To enrich cereal grain with functional components while growing plants in contrast to the post-harvesting improvement of staple foods with natural and synthetic additives, the new breeding programs need a source of genes for the improvement of the content of health benefit components in grain. The current review aims to consider current trends and achievements in wheat, barley, and oat breeding for health-benefiting components. The sources of these valuable genes are plant genetic resources deposited in genebanks: landraces, rare crop species, or even wild relatives of cultivated plants. Traditional plant breeding approaches supplemented with marker-assisted selection and genetic editing, as well as high-throughput chemotyping techniques, are exploited to speed up the breeding for the desired genotуpes. Biochemical and genetic bases for the enrichment of the grain of modern cereal crop cultivars with micronutrients, oils, phenolics, and other compounds are discussed, and certain cases of contributions to special health-improving diets are summarized. Correlations between the content of certain bioactive compounds and the resistance to diseases or tolerance to certain abiotic stressors suggest that breeding programs aimed at raising the levels of health-benefiting components in cereal grain might at the same time match the task of developing cultivars adapted to unfavorable environmental conditions.

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Section: β-Glucansmentioning

confidence: 99%

Wheat, Barley, and Oat Breeding for Health Benefit Components in Grain

Лоскутов

Khlestkina

2021

Plants

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“…Thus, the genetic signal that is associated with these latent factors is relevant to both studies and phenotyping approaches (i.e., targeted and untargeted metabolomics). A comparison of the GWAS hits in (Carlson et al 2019) and those in our study showed little overlap, with two common associations identified for factor 13 and the tenth PC of fatty acid phenotypes in (Carlson et al 2019), and factor 17 and 14:0 in (Carlson et al 2019). Of these two factors, only factor 17 showed enrichment for “lipid and lipid like molecules” at only the super-class level.…”

Section: Discussionmentioning

confidence: 43%

“…The fatty acid dataset can be used to test whether the information learned by latent factors is reproducible, while the NIRS dataset provides a means to test whether this information is transmissible to related traits in new populations. We distinguish between these two because: (1) the majority of accessions included in the fatty acid dataset are accessions that were used for the factor analysis metabolome study, while less than 6% of accessions are common between the factor analysis and the NIRS studies; (2) the fatty acid data was generated using targeted metabolomics, meaning there should be a high correspondence between the metabolites measured in the fatty acids study and those that were assayed for the factor analysis metabolome study (Carlson et al 2019).…”

Section: Discussionmentioning

confidence: 99%

Translating insights from the seed metabolome into improved prediction for healthful compounds in oat (Avena sativa L.)

Campbell

Yeats

et al. 2020

Preprint

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AbstractOat (Avena sativa L.) seed is a rich resource of beneficial lipids, soluble fiber, protein, and antioxidants, and is considered a healthful food for humans. Despite these characteristics, little is known regarding the genetic controllers of variation for these compounds in oat seed. We sought to characterize natural variation in the mature seed metabolome using untargeted metabolomics on 367 diverse lines and leverage 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.

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“…After harvest, mature seeds were dehulled and analysed with gas chromatography–mass spectrometry (GC‐MS) and liquid chromatography–mass spectrometry (LC‐MS) at the Proteomics and Metabolomics Facility at Colorado State University following Carlson et al ().…”

Section: Methodsmentioning

confidence: 99%

Heritable temporal gene expression patterns correlate with metabolomic seed content in developing hexaploid oat seed

Gutiérrez-González

Liu

et al. 2020

Plant Biotechnology Journal

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Summary Oat ranks sixth in world cereal production and has a higher content of health‐promoting compounds compared with other cereals. However, there is neither a robust oat reference genome nor transcriptome. Using deeply sequenced full‐length mRNA libraries of oat cultivar Ogle‐C, a de novo high‐quality and comprehensive oat seed transcriptome was assembled. With this reference transcriptome and QuantSeq 3′ mRNA sequencing, gene expression was quantified during seed development from 22 diverse lines across six time points. Transcript expression showed higher correlations between adjacent time points. Based on differentially expressed genes, we identified 22 major temporal co‐expression (TCoE) patterns of gene expression and revealed enriched gene ontology biological processes. Within each TCoE set, highly correlated transcripts, putatively commonly affected by genetic background, were clustered and termed genetic co‐expression (GCoE) sets. Seventeen of the 22 TCoE sets had GCoE sets with median heritabilities higher than 0.50, and these heritability estimates were much higher than that estimated from permutation analysis, with no divergence observed in cluster sizes between permutation and non‐permutation analyses. Linear regression between 634 metabolites from mature seeds and the PC1 score of each of the GCoE sets showed significantly lower p‐values than permutation analysis. Temporal expression patterns of oat avenanthramides and lipid biosynthetic genes were concordant with previous studies of avenanthramide biosynthetic enzyme activity and lipid accumulation. This study expands our understanding of physiological processes that occur during oat seed maturation and provides plant breeders the means to change oat seed composition through targeted manipulation of key pathways.

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Multivariate Genome-Wide Association Analyses Reveal the Genetic Basis of Seed Fatty Acid Composition in Oat (Avena sativaL.)

Cited by 50 publications

References 63 publications

Wheat, Barley, and Oat Breeding for Health Benefit Components in Grain

Wheat, Barley, and Oat Breeding for Health Benefit Components in Grain

Translating insights from the seed metabolome into improved prediction for healthful compounds in oat (Avena sativa L.)

Heritable temporal gene expression patterns correlate with metabolomic seed content in developing hexaploid oat seed

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