Biology subjects

Yeats, T.

Publications and source records attributed to Yeats, T..

3 recordsLinked to original sources

bioRxiv2024

Deterministic Formulas and Procedures for Stochastic Trait Introgression Prediction

Key messageWe derive formulas for the background noise during trait introgression programs and use these formulas to quickly predict noise for up to five future generations without using simulation. Trait introgression is a common method for introducing valuable traits into breeding populations and inbred cultivars. The process involves recurrent backcrossing of a donor individual (and its descendants) with a desirable, inbred line that lacks the aforementioned traits. The process typically concludes with a final generation of selfing in order to recover lines with the traits of interest fixed in the homozygous state. The particular breeding scheme is usually designed to maximize the genetic similarity of the converted lines to the recurrent parent while minimizing a breeders cost and time to recovering the near isogenic lines. Thus, key variables include the number of generations, number of crosses, and how to apply genotyping and selection during the process. In this paper, we derive analytical formulas that characterize the stochastic nature of residual donor geneome (i.e., "background noise") during trait introgression. We use these formulas to predict the background noise in simulated trait introgression programs for five generations of progeny, as well as to construct a novel mathematical program to optimally allocate progeny to available parents. This provides a framework for the design of optimal breeding schemes for trait introgression involving one or more traits subject to the requirements of specific crops and breeding programs.

genomics↗

bioRxiv2020

Translating insights from the seed metabolome into improved prediction for healthful compounds in oat

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. 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.View Full Text

genomics↗

bioRxiv2019

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

Oat ranks sixth in world cereal production and has a higher content of health-promoting compounds compared to 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. 17 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.

genetics↗