Timothy M. Beissinger

Genome-wide association study in European beech (Fagus sylvatica L.) for drought stress traits

The paper conducts a GWAS in European beech to identify genetic markers associated with drought stress traits, finding significant SNPs linked to stomatal density and water use efficiency.

Experimental evolution in maize with replicated divergent selection identifies plant-height associated SNPs

This study uses replicated divergent selection in maize to identify plant-height associated SNPs by applying a false discovery rate method to detect selection signals.

Ghat: An R package for identifying adaptive polygenic traits

Ghat identifies selection on polygenic traits by analyzing allele frequency changes and effect sizes, demonstrating its power on wheat traits and simulated populations.

Imputation of Low-density Marker Chip Data in Plant Breeding: Evaluation of Methods Based on Sugar Beet

This paper evaluates imputation methods for low-density marker data in sugar beet breeding, finding that AlphaPlantImpute2 performs well with default settings while Beagle benefits from parameter tuning, and combining both tools yields the highest accuracy.

learnMET: an R package to apply machine learning methods for genomic prediction using multi-environment trial data

learnMET is an R package that uses machine learning to predict genomic traits from multi-environment trial data by integrating genomic and environmental factors.

Genetic Architecture of Maize Rind Strength Revealed by the Analysis of Divergently Selected Populations

The paper identifies genetic factors influencing maize rind strength through divergent selection and transcriptome analysis, revealing key genes and pathways related to cell wall composition.

Genomic prediction informed by biological processes expands our understanding of the genetic architecture underlying free amino acid traits in dry Arabidopsis seeds

The paper uses genomic prediction to identify biological processes influencing free amino acid traits in Arabidopsis seeds, revealing new trait-pathway associations and improving predictive ability for specific amino acids.