Geoffrey P. Morris

This study uses pangenomics and machine learning to identify genetic markers associated with carotenoid accumulation in sorghum, revealing structural variation and multi-locus genetic architecture.

The paper evaluates how phenotyping replication, training population size, and genomic relatedness affect genomic predictive ability in sweet sorghum breeding programs.

This study identifies genomic regions and variant types associated with local adaptation in sorghum using genotype-environment associations, highlighting the importance of cis-regulatory elements.

The paper develops field and lab phenotyping methods to detect genetic variation for iron deficiency chlorosis tolerance in sorghum, showing improved genetic signal detection with filtered data and controlled-environment assays.

The paper identifies stable genomic loci associated with photoperiodic flowering and grain mold resistance in sorghum, providing tools for breeding climate-resilient varieties.

The paper simulates breeding metapopulations to show how epistasis increases with genetic admixture and highlights its importance in crop breeding.

This study analyzes the population genomic structure of sorghum landraces to determine how environmental and cultural factors influence genetic diversity across different regions and time periods.

The paper develops genomic approaches to build elite gene pools from locally-adapted landraces, finding that QGI effectively identifies elite pairs while cis-elite crosses yield better genetic gain.

The paper analyzes interchromosomal linkage disequilibrium in wheat breeding families to detect sign epistasis, finding 19 regions with strong disequilibrium that support the hypothesis over additive selection.

The paper identifies the pangenomic origins and molecular basis of RMES1, a noncanonical NLR gene that enabled evolutionary rescue in sorghum by disrupting aphid feeding through global immunity activation.

The paper demonstrates how a global pangenomic breeding network can develop and deploy drought and pest-resistant sorghum varieties for smallholder farmers in Niger.

This paper constructs a sorghum pangenome and identifies trait-associated variants to inform climate-aware genome-enabled breeding strategies.

The paper investigates the pleiotropic regulatory functions of Tannin1 in sorghum, finding no conserved pleiotropic effects in leaf tissue compared to Arabidopsis TTG1.

This study improves genomic selection in hexaploid wheat by incorporating additive and epistatic effects, leading to a 3% increase in prediction accuracy for grain yield.

The paper tests an extended omnigenic model to explain the geometric distribution of effect sizes in complex traits using sorghum yield data, finding evidence for regulatory effects both in space and time.

The paper assesses the vulnerability of the sorghum aphid resistance gene RMES1 to biotype shifts and finds it is bolstered by the presence of RMES2 and cyanogenesis.

The paper uses a crop model to show that breeding sorghum for limited transpiration could increase yield in drought-prone US regions without tradeoffs.

The paper tests genetic variation for limited transpiration in sorghum under field conditions and evaluates canopy temperature as a surrogate trait.

This study uses phenomics and genomics to improve early-season chilling tolerance in sorghum through marker-assisted breeding, showing up to 24% performance improvement in lines with the CT allele.

The paper develops a genomic resource for West African sorghum by characterizing a diverse panel and identifying QTLs for flowering time and plant height.

The paper identifies quantitative trait loci (QTL) for plant architecture traits in sorghum using a nested association mapping population and evaluates genomic prediction accuracy.

This study identifies genetic factors controlling inflorescence morphology in sorghum using nested association mapping, revealing QTL associated with precipitation adaptation.

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.

The paper identifies genetic loci associated with chilling tolerance in sorghum using nested association mapping and finds some loci co-localize with historically selected genes.