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Multi-omics-based prediction of hybrid performance in canola
Tuesday, 2021/04/06 | 08:20:43

Dominic KnochChristian R. WernerRhonda C. MeyerDavid RieweAmine AbbadiSophie LückeRod J. Snowdon & Thomas Altmann

Theoretical and Applied Genetics April 2021; vol. 134:1147–1165

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.

ABSTRACT

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.

 

See https://link.springer.com/article/10.1007/s00122-020-03759-x

Figure 2:

Visualisation of genotypes by t-distributed stochastic neighbour embedding. A Barnes-Hut Implementation of t-distributed stochastic neighbour embedding (t-SNE) was performed on 477 canola genotypes using a panel of 13,201 SNP and 3110 CNV markers. Sample colours indicate assignment of the genotypes to the three breeding pools. Plotting symbols correspond to the population types as indicated in the legend: ‘MS’ = male-sterile mother line and ‘o.p. DH’ = open-pollinated doubled haploid

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