Jonathan Turkus

The paper uses standardized leaf imaging and vision encoders to capture disease response variation missed by human scoring, identifying genomic hotspots linked to disease symptoms.

The paper demonstrates that integrating transcriptomic and phenomic data with genotypes improves trait prediction in maize across environments.

This study identifies genetic determinants of flowering time variation in sorghum using genome and transcriptome-wide association studies, revealing conserved gene functions but non-orthologous gene involvement compared to related species.

This study assesses how yield plasticity affects hybrid performance in maize across diverse environments and finds no clear tradeoff between plasticity and mean performance.

The paper identifies genomic intervals and transcripts associated with metabolite abundance variation in maize under field conditions using GWAS and transcriptome-wide association studies.

This study uses transcriptome-wide association studies in maize to identify flowering time genes, finding TWAS detects more genes than GWAS and links gene expression to function across tissues and environments.

The paper develops a wheat haplotype graph database for efficient genotype storage and imputation, showing improved accuracy over existing methods.