James C. Schnable

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 estimates protein sequence evolution rates in the Paniceae tribe to infer the evolutionary pathway of C4 photosynthesis, suggesting a C2 intermediate.

The paper shows synthetic maize leaf images improve leaf counting accuracy when real-world data are limited, outperforming real data in some cases.

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.

This paper introduces tGBS(R), a genome reduction method that improves SNP calling accuracy and reduces missing data for heterozygous loci compared to conventional GBS.

The paper identifies evolutionarily conserved alternative splicing events across nine plant species and finds they are enriched in signaling pathway-related genes.

The paper compares differential gene expression and differentially regulated orthologs (DROs) in maize and sorghum to analyze cold-responsive transcriptional regulation and its conservation.

STAG-CNS identifies short conserved non-coding sequences across multiple species by integrating promoter data, revealing their association with transcriptional regulation and expression context.