Omics-based models for prediction of hybrid performance in oilseed rape

DFG funded project, in cooperation with the University of Gießen, NPZ and DSV


Based on previous work on biomass and heterosis prediction in Arabidopsis (Meyer et al., 2007; Steinfath et al., 2010) and maize (Riedelsheimer et al., 2012), the project is built on the hypothesis that specific allelic combinations of regulatory genes, their downstream gene expression, as well as elicited metabolite profiles, are associated with improved vegetative growth and seed yield in hybrids.

The project pursues two goals: on the one hand to effectively predict hybrid performance in spring oilseed rape by combining information of multiple omics-layers, and on the other hand to identify genetic loci causal for trait variation and to elucidate links between vegetative growth, transcript and metabolite levels. For this purpose, comprehensive datasets have been generated at an early vegetative stage for a collection of 475 genetically diverse pollinator lines from a commercial canola breeding programme and two elite male-sterile testers.

A F1 hybrid population with 950 individuals was generated and evaluated in the field.

Detailed phenotyping data were generated by growing the parental lines and selected hybrids in the automated IPK high-throughput phenotyping platform for large plants. Image-derived phenotype data were complemented by global metabolite (GC-MS) and transcriptome (mRNA-Seq) profiles of pools of the same plants. These data were utilised for correlation analyses, and in combination with array-derived SNP and CNV data for genome-wide-association studies. Multiple co-localized marker-trait-associations for different omics-layers were detected, including metabolites, transcripts and growth-related traits.

A time resolved analysis revealed dynamic contributions of loci for the accumulation of biomass with certain loci being particularly active in either an early, intermediate and late phase.

Finally, the individual and combined data sets were used to for prediction of hybrid performance in field using gBLUP and RHKS models.


Meyer RC, Steinfath M, Lisec J, et al. 2007. The metabolic signature related to high plant growth rate in Arabidopsis thaliana. Proceedings of the National Academy of Sciences of the United States of America 104, 4759–4764.

Riedelsheimer C, Czedik-Eysenberg A, Grieder C, Lisec J, Technow F, Sulpice R, Altmann T, Stitt M, Willmitzer L, Melchinger AE. 2012. Genomic and metabolic prediction of complex heterotic traits in hybrid maize. Nature Genetics 44, 217–220.

Steinfath M, Gärtner T, Lisec J, Meyer RC, Altmann T, Willmitzer L, Selbig J. 2010. Prediction of hybrid biomass in Arabidopsis thaliana by selected parental SNP and metabolic markers. TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik 120, 239–247.


Knoch D, A Abbadi, F Grandke, RC Meyer, B Samans, CR Werner, R Snowdon & T Altmann (2018) High-throughput phenotyping reveals dynamic QTL action on plant growth progression in canola. In preparation.