Karl Schmid

This study identifies centromeric repeat turnovers in Arabidopsis species, revealing evolutionary mechanisms and the role of genetic drift and centromere drive in repeat replacement.

This study evaluates quinoa germination and phenotypic traits under cold stress in highland and coastal ecotypes, finding highland accessions have better germination under low temperatures, while coastal types mature earlier and yield more across sowing dates.

The paper uses GWAS and machine learning to develop molecular markers and a genomic prediction framework for improving maize DUS testing using historical data.

This study uses deep learning to predict geographic origins of barley genebank accessions and integrates imputed environmental data into genome-environment association studies, revealing both potential and limitations of this approach.

The paper tracks the replacement of native E. turcicum lineages by exotic tropical lineages in the Swiss Rhine Valley using metagenomic surveys and pooling strategies, showing a shift in population structure over time.

This study uses metagenomic sequencing to analyze the regional diversity of the fungal maize pathogen Exserohilum turcicum and its interactions with the leaf microbiome in Switzerland.

GGoutlieR identifies and visualizes geo-genetic outliers in biological samples to improve landscape genomics analyses.

The paper uses pool-based amplicon sequencing to analyze haplotype diversity at the ACCase resistance locus in blackgrass, revealing common soft sweeps and the persistence of TSR haplotypes.

This paper conducts a genome-wide association study on quinoa to identify genetic markers linked to agronomic traits and reveals selection patterns in an underutilized crop.

This study constructs a high-density genetic map of quinoa using Skim-seq and identifies fifteen QTL for ten agronomically important traits, including pleiotropic loci for flowering time and plant height.