Christina Lehermeier

TL;DR: The results demonstrate that due to long-range LD, medium heritabilities, and small sample sizes, superiority of variable selection methods cannot be expected in plant breeding populations even for traits like FRIGIDA gene expression in Arabidopsis and flowering time in rice.

TL;DR: This work evaluated testcross performance of 1652 doubled-haploid maize lines that were genotyped with 56,110 single nucleotide polymorphism markers and phenotyped for five agronomic traits in four to six European environments and theoretically and empirically investigated marker linkage phases across multiparental populations.

TL;DR: Two new nested association mapping designs adapted to European conditions were derived from the complementary dent and flint heterotic groups of maize, with favorable allelic effects detected in both groups open perspectives for improving biomass production.

TL;DR: Genomic prediction accuracy can be significantly increased by model calibration across multiple breeding cycles as long as selection cycles are connected by a sufficient number of common ancestors and prediction accuracy has not reached a plateau when increasing sample size.

TL;DR: An assessment of prediction accuracy features cases where ignoring population structure leads to a parsimonious more powerful model as well as others where the multivariate and stratified approaches have higher predictive power.