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.
Bibliographical noteFunding Information:
Metabolomic data were generated by HH, TY, KS, LG, and MC-T. Analyses were performed by MC under the guidance of MG and J-LJ. MC wrote the manuscript with guidance from J-LJ and MG. Comments were provided by HH, LG, LB, MS, MG, and J-LJ. This study was supported by grants secured by KS, LG, MC-T, MS, MG, and J-LJ. All authors read and approved the manuscript.
Funding. Funding for this research was provided by USDA-NIFA-AFRI 2017-67007-26502. The USDA is an equal opportunity provider and employer.
© Copyright © 2021 Campbell, Hu, Yeats, Brzozowski, Caffe-Treml, Gutiérrez, Smith, Sorrells, Gore and Jannink.
- Bayesian regression
- genomic prediction
- plant breeding