Improving genomic prediction for seed quality traits in oat (Avena sativa L.) using trait-specific relationship matrices

Authors: CAMPBELL, MALACHY - Cornell University; HU, HAIXIAO - Cornell University; YEATS, TREVOR - Cornell University; BZOZOWSKI, LAUREN - Cornell University; CAFFE-TREML, MELANIE - South Dakota State University; GUTIERREZ, LUCIA - University Of Wisconsin; SMITH, KEVIN - University Of Minnesota; SORRELLS, MARK - Cornell University; GORE, MICHAEL - Cornell University; Jannink, Jean-Luc

Submitted to: Frontiers in Genetics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/4/2021
Publication Date: 3/31/2021
Citation: Campbell, M.T., Hu, H., Yeats, T.H., Bzozowski, L.J., Caffe-Treml, M., Gutierrez, L., Smith, K.P., Sorrells, M.E., Gore, M.A., Jannink, J. 2021. Improving genomic prediction for seed quality traits in oat (Avena sativa L.) using trait-specific relationship matrices. Frontiers in Genetics. 12:643733. https://doi.org/10.3389/fgene.2021.643733.
DOI: https://doi.org/10.3389/fgene.2021.643733

Interpretive Summary: Plant traits depend on mechanisms at different organization levels from DNA to RNA to enzymes in a biological system. The widespread use of technologies to observe these levels (RNA-sequencing, metabolomics, etc.) has provided plant genetics and breeders with a new wealth of information that may help explain variation in important traits such as yield, seed quality, and fitness. A major challenge is effectively using these data to help predict unobserved individuals for conventional agronomic traits. Standard prediction involves using markers to calculate the relationships between individuals, such that highly related individuals should be more similar than distantly related individuals. If markers are prioritized based on whether they are close to genes that affect a specific trait, the relationships calculated are trait-specific. Thus, we can calculate Trait-specific genomic relationship matrices (TGRMs). Using these matrices should improve predictions for the focal trait. In this study we evaluated the use of TGRMs to incorporate information on RNA-sequencing and metabolomics to predict an agronomic trait, total lipid content, in oat seed. Nine lipids were quantified in a panel of 336 oat lines. Marker effects on RNA-sequencing and metabolomics were used to construct TGRMs. The TGRM approach significantly improved predictions for total lipid content when compared to a conventional approach. Collectively, these results highlight the utility of using TGRM to accommodate information new information sources and improve genomic prediction for a conventional agronomic trait.

Technical Abstract: The observable phenotype is the manifestation of information that is passed along different organization levels (transcriptional, translational, and metabolic) of a biological system. The widespread use of various omic technologies (RNA-sequencing, metabolomics, etc.) has provided plant genetics and breeders with a wealth of information on pertinent intermediate molecular processes that may help explain variation in conventional traits such as yield, seed quality, and fitness, among others. A major challenge is effectively using these data to help predict the genetic merit of new, unobserved individuals for conventional agronomic traits. Trait-specific genomic relationship matrices (TGRMs) model the relationships between individuals using genome-wide markers (SNPs) and place greater emphasis on markers that most relevant to the trait compared to conventional genomic relationship matrices. Given that these approaches define relationships based on putative causal loci, it is expected that these approaches should improve predictions for related traits. In this study we evaluated the use of TGRMs to accommodate information on intermediate molecular phenotypes (referred to as endophenotypes) and to predict an agronomic trait, total lipid content, in oat seed. Nine fatty acids were quantified in a panel of 336 oat lines. Marker effects were estimated for each endophenotype, and were used to construct TGRMs. A multikernel TRGM model (MK-TRGM-BLUP) was used to predict total seed lipid content in an independent panel of 210 oat lines. The MK-TRGM-BLUP approach significantly improved predictions for total lipid content when compared to a conventional genomic BLUP (gBLUP) approach. Given that the MK-TGRM-BLUP approach leverages information on the nine fatty acids to predict genetic values for total lipid content in unobserved individuals, we compared the MK-TGRM-BLUP approach to a multi-trait gBLUP (MT-gBLUP) approach that jointly fits phenotypes for fatty acids and total lipid content. The MK-TGRM-BLUP approach significantly outperformed MT-gBLUP. Collectively, these results highlight the utility of using TGRM to accommodate information on endophenotypes and improve genomic prediction for a conventional agronomic trait.