Edward S. Buckler

The paper trains deep learning models on ATAC-seq and DNA methylation data from 12 and 10 angiosperm species to predict chromatin accessibility and methylation across species, finding strong conservation of chromatin mechanisms.

The paper introduces the Practical Haplotype Graph (PHG) platform for storing and using pangenomes, demonstrating its effectiveness in imputing genomes from low-coverage data in crops like maize.

AnchorWave improves genome alignment for genomes with high diversity, structural polymorphism, and whole-genome duplication by using a duplication-informed algorithm and gap cost strategy.

The paper demonstrates the effectiveness of the Practical Haplotype Graph for genotype imputation in the highly heterozygous crop cassava, achieving higher accuracy than Beagle.

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

This paper introduces HARE, a method for imputing cis haplotype-associated RNA expression to improve genomic prediction of complex traits across maize populations.

The paper develops models to predict protein temperature adaptation across prokaryotes and eukaryotes, finding site-specific models best transfer across the divide.

The paper develops a multiple genome alignment workflow for plants, showing that k-mer masking and parameter tuning improve alignment of functional regions.

This study identifies genetic factors influencing zinc and cadmium concentrations in sweet corn kernels through genome-wide association analysis.

The paper shows that variations in upstream open reading frames (uORFs) affect protein abundance by repressing or derepressing translation of the main open reading frame (mORF), contributing to allelic diversity in protein levels.

The paper identifies eleven biosynthetic genes explaining most of the natural variation in carotenoid levels in maize grain through QTL analysis and expression correlation.

The paper constructs a maize pangenome haplotype graph (PHG) to improve genotype imputation accuracy across diverse germplasm using WGS and GBS data.

The paper develops a model using tRNA sequences to predict optimal growth temperature (OGT) for microbes, achieving high accuracy with minimal input data.

This paper introduces a sorghum Practical Haplotype Graph (PHG) that enables accurate SNP calling and imputation from low-coverage sequencing data, improving genomic prediction accuracy and reducing genotyping costs.

This study uses ChIP-seq to map TF binding in maize leaves, reconstructs a regulatory network covering 77% of expressed genes, and shows its conserved evolution across species.

This paper develops Bayesian networks for genomic prediction of developmental traits in biomass sorghum, showing improved accuracy for plant height and biomass yield using multi-time and multi-trait models.

This study compares deleterious mutation patterns between sorghum and maize, finding sorghum diverges from the domestication cost hypothesis and develops a gene deleterious index with accuracy over 0.5.

This study shows dominance and gene annotations improve genomic prediction of agronomic traits in maize hybrids.

The paper shows that combining transcriptome-wide association studies (TWAS) with genome-wide association studies (GWAS) improves the detection of genes linked to agronomic and seed quality traits in maize.

The paper identifies adaptive genetic loci in Mexican maize landraces by analyzing genotype-environment interactions across multiple environments, showing their potential for climate adaptation.

The paper uses metabolome-scale GWAS to identify genetic loci controlling maize specialized metabolites, revealing chemical diversity and gene-metabolite associations.

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 analyzes Tripsacum transcriptomes to compare gene evolution with maize after a shared polyploidy event, finding similar patterns of gene loss and expression conservation.

The paper identifies enhancer regions in cassava using RNA polymerase mapping and shows they are enriched with causal variants affecting fitness and root composition.

This study estimates deleterious mutation burden in sorghum and assesses its impact on agronomic traits, finding moderate genetic variance explanation but limited improvement in breeding accuracy due to linkage.

This study quantitatively analyzes the heritability of the maize leaf microbiome, finding few bacterial clades and diversity metrics are heritable, while many metabolic functions are, with Methylobacteria playing a key role.

The paper investigates the role of deleterious alleles in trait variation and heterosis in maize by integrating sequence conservation and genomic prediction models.

This study identifies quantitative trait loci (QTL) for lodging in maize by evaluating diverse inbred lines across multiple environments, finding overlaps with genes involved in lignin and cellulose synthesis.

Primers anchored in conserved repeat sequence amplify thousands of maize middle-repetitive loci at once, with markers called from the low-copy sequence between copies for under $5 per sample.

The paper constructs a cassava haplotype map to evaluate deleterious mutations, finding increased fixed deleterious variants, limited purging due to low recombination, and masking of harmful mutations in breeding.