Christine Diepenbrock

This study examines how genotype and environment affect seed coat patterns and nutritional content in common bean, finding significant environmental impacts on macronutrients and associations between seed coat pigmentation and mineral concentrations.

This study identifies genomic hotspots and improves genomic prediction for cowpea traits using a MAGIC population across environmental gradients.

This study identifies three QTLs conferring resistance to root-knot nematodes in lima beans, with two affecting both nematode species and one specific to M. incognita.

The paper evaluates genotypic and phenotypic traits of lima bean accessions across environments to support breeding, identifying genomic associations and establishing a core collection.

This study evaluates simulated digestion models to assess bioaccessible nutrient levels in common bean, finding dynamic models outperform static ones in predicting nutrient release.

This study evaluates genomic prediction strategies for carotenoid traits in maize, finding that genome-wide markers outperform gene-specific markers and that GP methods are effective across environments.

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

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.