Multi-omics-based prediction of hybrid performance in canola

Dominic Knoch, Christian R Werner, Rhonda C Meyer, David Riewe, Amine Abbadi, Sophie Lücke, Rod J Snowdon, Thomas Altmann

Research output: Contribution to journalArticlepeer-review

Abstract

KEY MESSAGE: Complementing or replacing genetic markers with transcriptomic data and use of reproducing kernel Hilbert space regression based on Gaussian kernels increases hybrid prediction accuracies for complex agronomic traits in canola. In plant breeding, hybrids gained particular importance due to heterosis, the superior performance of offspring compared to their inbred parents. Since the development of new top performing hybrids requires labour-intensive and costly breeding programmes, including testing of large numbers of experimental hybrids, the prediction of hybrid performance is of utmost interest to plant breeders. In this study, we tested the effectiveness of hybrid prediction models in spring-type oilseed rape (Brassica napus L./canola) employing different omics profiles, individually and in combination. To this end, a population of 950 F1 hybrids was evaluated for seed yield and six other agronomically relevant traits in commercial field trials at several locations throughout Europe. A subset of these hybrids was also evaluated in a climatized glasshouse regarding early biomass production. For each of the 477 parental rapeseed lines, 13,201 single nucleotide polymorphisms (SNPs), 154 primary metabolites, and 19,479 transcripts were determined and used as predictive variables. Both, SNP markers and transcripts, effectively predict hybrid performance using (genomic) best linear unbiased prediction models (gBLUP). Compared to models using pure genetic markers, models incorporating transcriptome data resulted in significantly higher prediction accuracies for five out of seven agronomic traits, indicating that transcripts carry important information beyond genomic data. Notably, reproducing kernel Hilbert space regression based on Gaussian kernels significantly exceeded the predictive abilities of gBLUP models for six of the seven agronomic traits, demonstrating its potential for implementation in future canola breeding programmes.

Original languageEnglish
JournalTAG Theoretical and Applied Genetics
Early online date1 Feb 2021
DOIs
Publication statusE-pub ahead of print - 1 Feb 2021

Keywords

  • Spring-type brassica napus (canola)
  • Hybrid prediction
  • Genomic best linear unbiased prediction (gBLUP)
  • Reproducing kernel Hilbert space regression (RKHS)
  • Heterosis
  • Agronomic traits

Fingerprint

Dive into the research topics of 'Multi-omics-based prediction of hybrid performance in canola'. Together they form a unique fingerprint.

Cite this