Brian P. Dilkes

The paper identifies multiple A1xA2 hybrid strains in Prymnesium parvum s.l. and discovers a new clade A3, suggesting hybridization drives genetic diversity and adaptive potential in these toxic algae.

The paper introduces a panel of near-isogenic lines from Arabidopsis thaliana to study additive and non-additive genetic effects on ecologically important traits, identifying FLC's role and novel epistatic interactions in flowering time.

The paper develops a method to identify conserved causative genes across species using multi-species GWAS data, finding that conserved genes are more frequently associated with trait variation than expected by chance.

The paper presents an economical ultra-low pass sequencing method for genotyping, mapping, and pedigree analysis in maize using skim sequencing.

The paper examines how natural variation in tetrapyrrole biosynthetic enzymes and their regulation affect chlorophyll levels in Oy1-N1989/+ mutants, revealing transcriptional feedback mechanisms and trans-regulatory hotspots.

This study identifies the HER7 gene, a brassinosteroid receptor kinase, as essential for antheridiogen-mediated sex determination in the fern Ceratopteris richardii using BSR-Seq.

This paper develops deep learning models to detect and quantify sorghum charcoal rot in field images, finding that EfficientNet-B3 and FCN achieve high accuracy and efficiency.

This study shows that a mutation in the maize aladin1 gene disrupts asymmetric cell division and affects plant growth and structure.

The paper identifies a modifier locus in maize that reveals cis-regulatory variation affecting chlorophyll biosynthesis through a mutant reporter allele.

The paper develops Camoco, a method integrating GWAS loci with co-expression networks to prioritize causal genes in maize, showing improved performance with tissue-specific expression data.

The paper improves whole genome sequence accuracy by using replication to detect false positives in mutagenized sorghum, identifying 1.2 million high-quality EMS-induced SNPs.

The paper uses multivariate analysis to identify genetic and environmental factors influencing element covariation in maize grain ionomes, revealing new loci and gene-environment interactions.