Chris‐Carolin Schön

The paper develops a maize MAGIC population to dissect grain yield and proxy traits under stress, identifying QTL and improving genomic prediction models.

This study identifies QTL for maize seedling root traits under chilling conditions that influence early plant development in the field.

The paper analyzes a maize landrace selection experiment to identify loci under selection and track genetic changes over time.

The paper identifies QTL for lateral root length in maize across developmental stages and examines their correlation with biomass under different nutrient conditions.

The paper identifies a structural variation in lhcb6 in maize landraces that affects photosystem II maximum quantum efficiency through differential accumulation of LHCII antenna components.

The paper identifies a rare allele of ndhm1 in maize that improves cold tolerance and photosynthetic efficiency through NDH-mediated cyclic electron transport.

The paper introduces HBimpute, an imputation pipeline that improves calling accuracy, coverage, and read depth from low-read-depth sequencing data, achieving results comparable to high-density arrays.

The paper compares genomic prediction models for maize traits, finding that selecting top SNP interactions improves prediction accuracy compared to additive models.

This paper compares bivariate genomic prediction models for plant traits across two growing seasons, finding that models incorporating epistasis improve prediction accuracy.

The paper identifies novel haplotypes in maize landraces associated with quantitative traits and demonstrates their superior performance compared to breeding lines.

The paper reports high-quality genome sequences of European flint maize lines and analyzes genomic dynamics between European and US maize germplasm.

The paper demonstrates that integrating transcriptomic data with genomic data improves prediction of hybrid maize combinations, particularly for complex traits like dry matter yield.

The paper reports the de novo assembly of two European Flint maize lines to enhance the maize pan-genome.

This study identifies independent temperate adaptations in maize flowering time across different populations using GWAS and machine learning.