PT  - JOURNAL ARTICLE
AU  - Haixiao Hu
AU  - Malachy T. Campbell
AU  - Trevor H. Yeats
AU  - Xuying Zheng
AU  - Daniel E. Runcie
AU  - Giovanny Covarrubias-Pazaran
AU  - Corey Broeckling
AU  - Linxing Yao
AU  - Melanie Caffe-Treml
AU  - Lucía Gutiérrez
AU  - Kevin P. Smith
AU  - James Tanaka
AU  - Owen A. Hoekenga
AU  - Mark E. Sorrells
AU  - Michael A. Gore
AU  - Jean-Luc Jannink
TI  - Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations
AID  - 10.1101/2021.05.03.442386
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.05.03.442386
4099  - http://biorxiv.org/content/early/2021/05/03/2021.05.03.442386.short
4100  - http://biorxiv.org/content/early/2021/05/03/2021.05.03.442386.full
AB  - Multi-omics prediction has been shown to be superior to genomic prediction with genome-wide DNA-based genetic markers (G) for predicting phenotypes. However, most of the existing studies were based on historical datasets from one environment; therefore, they were unable to evaluate the efficiency of multi-omics prediction in multi-environment trials and distantly-related populations. To fill those gaps, we designed a systematic experiment to collect omics data and evaluate 17 traits in two oat breeding populations planted in single and multiple environments. In the single-environment trial, transcriptomic BLUP (T), metabolomic BLUP (M), G+T, G+M and G+T+M models showed greater prediction accuracy than GBLUP for 5, 10, 11, 17 and 17 traits, respectively, and metabolites generally performed better than transcripts when combined with SNPs. In the multi-environment trial, multi-trait models with omics data outperformed both counterpart multi-trait GBLUP models and single-environment omics models, and the highest prediction accuracy was achieved when modeling genetic covariance as an unstructured covariance model. We also demonstrated that omics data can be used to prioritize loci from one population with omics data to improve genomic prediction in a distantly-related population using a two-kernel linear model that accommodated both likely casual loci with large-effect and loci that explain little or no phenotypic variance. We propose that the two-kernel linear model is superior to most genomic prediction models that assume each variant is equally likely to affect the trait and can be used to improve prediction accuracy for any trait with prior knowledge of genetic architecture.Competing Interest StatementThe authors have declared no competing interest.