Michael A. Gore

The paper investigates the genetic and metabolic basis of the trade-off between carotenoids and dry matter in cassava roots, finding no direct transcriptional co-regulation but suggesting regulatory and metabolic links.

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.

This study analyzes the genetic diversity of farmer-maintained Theobroma cacao in Costa Rica, revealing previously uncharacterized local genetic groups and highlighting the importance of farmer-managed systems for conservation.

This paper develops a framework combining genomic prediction and a large language model to select climate-resilient pepper cultivars from germplasm collections.

This study introduces TriFlow-Seq, a method combining FACS and 16S rRNA sequencing to identify PHA-PAOs in EBPR systems and soil, revealing new candidates and their ecological roles.

This study examines a Zea mays slac1-2 mutant to assess its impact on water use efficiency and agronomic traits, finding no improvement in CO2 assimilation or yield despite increased stomatal conductance.

This study overexpresses NPQ genes in C4 plant Setaria viridis to compare photoprotection mechanisms with C3 plants, finding shared and unique traits that could improve crop resilience.

This study overexpresses NPQ-related genes in Setaria viridis to assess NPQ acclimation rates, finding faster NPQ induction but no photosynthetic benefit.

The paper identifies genes associated with cuticular wax biogenesis in maize leaves through integrative multi-omic analysis, linking wax composition to leaf cuticular conductance.

This study uses multi-spectral imaging to estimate genetic parameters of biomass accumulation and model growth curve stability in alfalfa across different irrigation treatments and cultivars.

The paper assesses the sufficiency of genetic diversity in sorghum germplasm repositories for climate adaptation by evaluating geographic distribution and genomic resilience scores.

The paper improves genomic prediction of agronomic traits in maize by integrating spatio-temporal modeling of multi-spectral aerial images with genetic data.

This study develops a genomic resource for Macadamia tetraphylla and examines its hybridization with cultivated Macadamia integrifolia in Hawaiian breeding lines and a remnant orchard.

This study evaluates prediction models for tocochromanol content in maize grain using genomic and transcriptomic data, finding that incorporating biological knowledge improves predictive ability.

The paper combines GWAS and TWAS to identify 13 candidate causal genes for tocochromanol levels in maize grain, including five previously unassociated genes.

The paper develops genomic prediction methods for plant metabolites using metabolite GWAS and kernel models, improving accuracy for LC-MS metabolites but not GC-MS.

This study uses transcriptome-wide association and prediction to identify genes related to carotenoid and tocochromanol levels in sweet corn and shows transcriptomic data improve predictive models.

The paper integrates GWAS and TWAS to identify 22 candidate genes regulating maize leaf cuticular conductance, with ISTL1 validated as a key gene through near-isogenic lines.

This study evaluates multi-omics prediction models for oat traits across environments and populations, showing improved accuracy over genomic prediction with SNP data.

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

The paper presents a genome-wide variation map of soybean accessions to study domestication-related mutations and finds a reduction in deleterious alleles in domesticated soybean.

The paper develops an open-source R package, waves, for spectral data analysis in plant breeding and integrates it into the Breedbase database to enhance spectral data handling and analysis.

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

This study evaluates aerial high-throughput phenotyping for indirect selection of grain yield in wheat breeding programs, finding vegetation indices predictive of yield and more effective than visual selection.

This study uses metabolomics and genomic approaches to improve prediction of healthful compounds in oat seeds by identifying latent factors associated with lipid metabolism.

This study identifies candidate genes associated with maize leaf cuticular conductance through genome-wide association and provides insights for improving drought-tolerant maize breeding.

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 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 identifies 120 QTL associated with domestication traits in cotton using an F2 population, revealing subgenome-specific distribution and environmental variability.