Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations.

Autor: Hu H; Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA. haixiao.work@gmail.com., Campbell MT; Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA., Yeats TH; Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA., Zheng X; Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA., Runcie DE; Department of Plant Sciences, University of California Davis, Davis, CA, 95616, USA., Covarrubias-Pazaran G; International Maize and Wheat Improvement Center (CIMMYT), Km. 45, Carretera México-Veracruz, El Batán, 56130, Texcoco, Edo. de México, México., Broeckling C; Proteomics and Metabolomics Facility, Colorado State University, C130 Microbiology, 2021 Campus Delivery, Fort Collins, CO, 80521, USA., Yao L; Proteomics and Metabolomics Facility, Colorado State University, C130 Microbiology, 2021 Campus Delivery, Fort Collins, CO, 80521, USA., Caffe-Treml M; Department of Agronomy, Horticulture & Plant Science, South Dakota State University, Brookings, SD, 57007, USA., Gutiérrez LA; Department of Agronomy, University of Wisconsin-Madison, Madison, WI, 53706, USA., Smith KP; Department of Agronomy & Plant Genetics, University of Minnesota, St. Paul, MN, 55108, USA., Tanaka J; Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA., Hoekenga OA; Cayuga Genetics Consulting Group LLC, Ithaca, NY, 14850, USA., Sorrells ME; Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA., Gore MA; Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA., Jannink JL; Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY, 14853, USA.; USDA-ARS, Robert W. Holley Center for Agriculture and Health, Ithaca, NY, 14853, USA.
Jazyk: angličtina
Zdroj: TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik [Theor Appl Genet] 2021 Dec; Vol. 134 (12), pp. 4043-4054. Date of Electronic Publication: 2021 Oct 13.
DOI: 10.1007/s00122-021-03946-4
Abstrakt: Key Message: Integration of multi-omics data improved prediction accuracies of oat agronomic and seed nutritional traits in multi-environment trials and distantly related populations in addition to the single-environment prediction. 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.
(© 2021. This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.)
Databáze: MEDLINE
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