Incorporating gene expression and environment for genomic prediction in wheat

JIA LIU^1,2*Andrew Gock²Kerrie Ramm²Sandra Stops²Tanya Phongkham²Adam Norman³Russell Eastwood³Eric Stone¹Shannon Dillon²

• ¹Australian National University, Canberra, Australia
• ²Commonwealth Scientific and Industrial Research Organisation (CSIRO), Canberra, Australian Capital Territory, Australia
• ³Australian Grain Technologies (AGT), Urrbrae, Australia

The adoption of novel molecular strategies such as genomic selection (GS) in crop breeding have been key to maintaining rates of genetic gain through increased efficiency and shortening the cycle of evaluation relative to conventional selection. In the search for improved methodologies that incorporate novel sources of variation for the assessment of genetic merit, GS remains a focus of crop breeding research globally. Here we explored the role transcriptome data could play in enhancing GS in wheat. Across 286 wheat lines, we integrated phenotype and multi-omic data from controlled environment and field experiments including ca. 40K single nucleotide polymorphisms (SNP), abundance data for ca. 50K transcripts as well as meta-data (e.g. categorical environments) predicted individual genetic merit for two agronomic traits, flowering time and height. Using this integrated data, we predicted individual genetic merit for the agronomic traits. We evaluated the performance of different model scenarios based on linear (GBLUP) and Gaussian/nonlinear (RKHS) regression in the Bayesian analytical framework. These models explored the relative contributions of different combinations of additive genomic (G), transcriptomic (T) and environment (E), with and without considering non-additive epistasis, dominance and the G × E random effects. In controlled environments, where traits were measured under contrasting daylength regimes (long and short days), transcriptome abundance outperformed other random effects when considered independently, while the model combining SNP, environment and G × E marginally outperformed the transcriptome. The best performing model for prediction of both flowering and height combined all data types, where the GBLUP framework showed slightly better performance overall compared with RKHS across all tests. Under field conditions, we similarly found that models combining all variables were superior. However, the relative contribution of the transcriptome was reduced. Our results show there is a predictive advantage to direct inclusion of the transcriptome for genomic evaluation in wheat breeding. However, the complexity and cost of generating transcriptome data are likely to limit its feasibility for commercial breeding. We demonstrate that combining less costly environmental covariates with conventional genomic data 1 Liu et al.provide a practical alternative with similar gains to the transcriptome when environments are well characterised.