Jacob D. Washburn

This study evaluates genetic variation in maize's response to chilling stress and recovery, identifying heritable traits linked to cold resilience.

This paper introduces a regression-based framework to predict flowering time from sparse UAV observations, reducing the need for repeated data collection.

Drone imagery from 356 flights in 19 Genomes to Fields maize environments; vegetation-index functional principal components complement genomic kernels, predicting yield of held-out hybrids at r up to 0.501.

The paper uses drone-based multispectral imaging to study flood tolerance in maize hybrids, identifying spatial and genetic factors associated with post-flood survival.

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 constructs a nuclear phylogeny of grasses using genome-scale data, showing agreement with plastome phylogeny and evidence for repeated reticulation events.

This study uses k-mer GWAS to identify genetic associations with maize traits and shows comparable prediction accuracy to SNP-based methods.

The paper confirms a two-step hybridization model for the formation of hexaploidy in Brassiceae and shows subgenomes have distinct loss rates but functionally compatible genes.

The paper develops deep learning methods that incorporate evolutionary relationships to predict transcript abundance from DNA sequence, achieving auROC values between 0.72 and 0.94.

The paper examines subgenome dominance in polyploids, showing rapid expression dominance and methylation changes across generations in hybrids and allopolyploids.