Assessment of genomic prediction capabilities of transcriptome data in a barley multi-parent RIL population

The field of genomic selection (GS) is advancing rapidly on many fronts including the utilization of multi-omics datasets with the goal to increase prediction ability (PA) and to become an integral part of an increasing number of breeding programs ensuring future food security. In this study, we used RNA sequencing (RNA-Seq) data to perform genomic prediction (GP) on three related barley RIL populations investigating the potential of increasing PA by combining genomic and transcriptomic datasets, adding whole genome sequencing (WGS) SNP data, functional parameter filtering, and empirical quality filtering. Our RNA-Seq data were generated cost-efficiently using small footprint plant cultivation, high-throughput RNA extraction, and library preparation miniaturization. We also examined the depth of the sequencing as an additional cost-saving measure. We used five-fold cross-validation to evaluate the PA of the gene expression dataset, the RNA-Seq SNP dataset, and the consensus SNP dataset between the RNA-Seq and parental WGS data, resulting in PAs between 0.73 and 0.78. The consensus SNP dataset performed best, with five out of eight traits performing significantly better compared to a 50K SNP array, which served as a benchmark. The advantage of the consensus SNP dataset was most prominent in the inter-population predictions, in which the training- and validation-set originated from different RIL sub-populations. We could therefore not only show that RNA-Seq data alone are able to predict various complex traits in barley using RIL, but also that the performance can be further increased by WGS data for which the public availability will steadily increase.