Kevin P. Smith

This study analyzes mutations induced by sodium azide in barley, finding specific patterns of SNVs and indels that differ from natural variation.

The paper sequenced wild barley to identify genetic markers linked to agronomic traits and found novel loci for stem rust resistance and days to heading.

The paper develops genomic prediction methods for plant metabolites using metabolite GWAS and kernel models, improving accuracy for LC-MS metabolites but not GC-MS.

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.

The paper shows that domestication of barley reduced thermal plasticity of circadian rhythms and identifies genetic loci that influence these changes and yield traits.

This study identifies genetic variation affecting tiller number and rate in barley, finding correlations with agronomic traits and QTL overlapping with days to heading and spike row-type.

The paper identifies and analyzes deleterious SNPs in a barley breeding population, showing they decrease in frequency over breeding cycles and are less common in high-yielding lines.

This study predicts genetic correlations in plant breeding crosses to improve multiple traits, finding that cross selection based on predicted correlations increases multi-trait genetic gain.

This study evaluates methods for updating training data in genomewide selection over breeding cycles, finding that updating with best predicted lines or both best and worst predicted lines yields high accuracy and genetic gain in the short-term, while smaller, more recent training populations provide a slight advantage long-term.