Trevor H. Yeats

The paper derives formulas to predict background noise in trait introgression programs and uses them to optimize breeding schemes for five generations.

This study evaluates multi-omics prediction models for oat traits across environments and populations, showing improved accuracy over genomic prediction with SNP data.

This study uses metabolomics and genomic approaches to improve prediction of healthful compounds in oat seeds by identifying latent factors associated with lipid metabolism.

This study identifies heritable temporal gene expression patterns in oat seeds and links them to metabolomic seed content, providing insights for breeding improved oat varieties.

The paper identifies genetic loci associated with seed fatty acid composition in oat using multivariate GWAS approaches, finding more significant SNPs than univariate analyses.