Armin C. Hölker

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

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.