James C. Schnable

This study identifies cis-regulatory variants in maize using RNA-seq and genome-wide data, showing that haplotype-rich variants explain most expression diversity and that single-variant summaries are incomplete.

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

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.

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 develops a sequence-based classifier to identify phenotype-associated genes in plants, validated across maize, rice, and arabidopsis.

This study examines how domestication and improvement in sorghum have affected genetic load and nitrogen-response variation by integrating genomic and phenotypic data.

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

The paper investigates how host genotype and environment interact to shape bacterial endophyte communities in maize stalks, finding that gene-by-environment interactions significantly influence microbial communities.

The paper identifies genetic variants at transcription factor binding sites that explain a large portion of phenotypic heritability in maize under different water conditions.

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 identifies genetic variants associated with photosynthetic traits in maize by controlling environmental and spatial confounders, revealing high-confidence GWAS signals and validating candidate genes through insertion alleles.

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 3D reconstruction from 2D images to measure phyllotaxy in sorghum, showing its feasibility for genetic analysis and breeding.

The paper uses image analysis models to quantify seed color in sorghum, finding they outperform human assessment and identify more genomic signals linked to seed color genes.

This study identifies genetic loci and haplotypes associated with plant architectural and biomass yield traits in sorghum using genome-wide association studies and network analysis.

This study uses CRISPR-Cas9 to generate sorghum mutants and analyzes their nitrogen response and growth trajectory using high-throughput phenotyping.

The paper creates the Pan-Grass Syntenic Gene Set (PGSGS), a database of orthologous and homeologous gene relationships across 17 grass species, to study genome evolution and improve functional genetic integration.

This study applies transcriptome-wide association study (TWAS) to soybean to identify trait-associated genes, finding novel genes and causal variations related to flowering time.

The paper uses a transformer model to predict gene expression patterns across plant organs and identifies regulatory regions in maize and sorghum genomes.

The paper predicts grain yield using non-yield traits and shows that a trait-based model performs comparably to genomic prediction models in cross-environment settings.

This study identifies genetic loci in sorghum that influence human gut microbiome composition through in vitro fermentation of grain.

This study shows that expression of a limited number of genes can predict flowering time in maize more accurately than genome-wide SNPs.

This study uses GWAS to identify genetic loci associated with sunflower head diameter, finding 106 and 8 significant SNPs using MLM and farmCPU methods, respectively.

The paper identifies a historically balanced locus under recent directional selection in maize linked to nitrogen response traits, revealing genetic changes associated with modern breeding practices.

This study investigates how heritable transcriptomic variation contributes to maize adaptation to temperate environments through eQTLs, sQTLs, and gene expression patterns.

This paper evaluates machine learning algorithms for predicting gene functions without relying on homology, finding random forest provides the most accurate results, particularly in Biological Process GO terms.

The paper identifies a distinct set of genes linked to phenome-wide variation in maize using a novel approach, finding these genes show stronger selection, conservation, and functional enrichment compared to conventional GWAS results.

The paper identifies seven genetic modifiers of a reproductive barrier in maize, revealing complexity beyond the known ga1 locus.

This study evaluates statistical methods for GWAS in crops with varying genetic architectures, finding that different approaches are optimal for different trait complexities.