Steven Andrew Yates

This study shows that combining genetic and agronomic biofortification strategies significantly increases wheat grain zinc while varying cadmium levels.

The paper characterizes a common buckwheat germplasm collection to identify agronomic traits and candidate genes for breeding improved varieties.

The paper analyzes SI genes in grasses, finding high allelic diversity, trans-generic polymorphism, and co-evolution of recognition genes.

This paper establishes the first NAM population in Italian ryegrass to identify candidate genes for seed shattering and related traits using ddRAD and whole genome sequencing.

The paper reports on a competition to predict maize yield using genetic and environmental data, showing that diverse modeling approaches can achieve good results.

This study applies statistical and deep learning models to multi-environmental genomic prediction in apple, showing improved predictive ability for certain traits.

ChromaX simulates genetic recombination and breeding value calculations using GPU acceleration, offering faster performance than existing tools.

GBSmode is a pipeline for analyzing GBS data that improves accuracy by incorporating biological features, reducing genotyping errors compared to SAMtools.

The paper presents a high-quality diploid genome assembly of Italian ryegrass, revealing extensive intergenic sequence variation due to its high heterozygosity.

This study de novo assembles a diploid and heterozygous genome of the apple cultivar 'Gala Galaxy' using long- and short-read sequencing and optical mapping, providing a high-quality reference genome for apple breeding.

This study identifies genetic loci associated with stomatal density in winter wheat, providing targets for improving water use efficiency through marker-assisted selection.