Multi-omics-based prediction of hybrid performance in canola

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.

Unraveling the potential of phenomic selection within and among diverse breeding material of maize (Zea mays L.)

“…3 ). This performance was, however, also dependent on the trait ( Knoch et al 2021 ) and the genetic group. It should be mentioned here, that no general conclusion should be drawn solely from the results of the single landraces with their rather small population sizes.…”

“…The advantages of performing predictions based on NIRS data compared to genotypic data are the low requirements for infrastructure such as specialized laboratories and the strongly reduced costs, both coupled with the benefit of an increased speed and efficiency of selection in the breeding program. The first point challenges the assumption that became prevalent in the last years, namely that the most cost-efficient tool for trait prediction is found in genetic marker data ( Bernardo 2021b ; Knoch et al 2021 ). Supposed that seeds represent the most suitable material to obtain NIR spectra, we are much faster to capture all data with a state-of-the-art spectrometer compared to the DNA extraction and subsequent genotyping.…”

“…It should be noted though, that generating marker data by genotyping is still cost-intensive and - if not outsourced - requires a certain laboratory infrastructure. There have been several advances and attempts in different crop species to include omics data other than genomics as predictors, but to date these are more difficult to obtain and more expensive than genotypic data ( Riedelsheimer et al 2012 ; Westhues et al 2017 ; Schrag et al 2018 ; Stich et al 2020 ; Knoch et al 2021 ).…”

Terpene volatiles mediates the chemical basis of blueberry aroma and consumer acceptability

“…Similar ideas have also been used in other contexts in the plant literature, but not directly applied to fruit flavor improvements. Some recent examples are reported in canola (Knoch et al, 2021), oat (Hu et al, 2021), rice (Xu et al, 2021;Wang et al, 2019), wheat (Longin et al, 2020), and maize (Schrag et al, 2018).…”

High-throughput plant phenotyping: a role for metabolomics?

“…The free R package mixKernel is one example that implements machine learning methods for use in data integration [90]. For example, Knoch et al [91] compared the canola phenotype predictability for different data sets (genes, transcripts, metabolites) and their combinations. The metabolomics data showed low prediction accuracy.…”