Accuracy of Genomic Prediction for Foliar Terpene Traits in Eucalyptus polybractea

Journal Article · 10 June 2018 · G3

DOI: https://doi.org/10.1534/g3.118.200443 · OSTI ID:1474631

Kainer, David  ^[1]; Stone, Eric A. ^[2]; Padovan, Amanda ^[2]; Foley, William J. ^[2]; Külheim, Carsten ^[2]

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1. The Australian National University, Acton (Australia). Research School of Biology; Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Center for BioEnergy Innovation, Biosciences Division
2. The Australian National University, Acton (Australia). Research School of Biology

Unlike agricultural crops, most forest species have not had millennia of improvement through phenotypic selection, but can contribute energy and material resources and possibly help alleviate climate change. Yield gains similar to those achieved in agricultural crops over millennia could be made in forestry species with the use of genomic methods in a much shorter time frame. Here we compare various methods of genomic prediction for eight traits related to foliar terpene yield in Eucalyptus polybractea, a tree grown predominantly for the production of Eucalyptus oil. The genomic markers used in this study are derived from shallow whole genome sequencing of a population of 480 trees. We compare the traditional pedigree-based additive best linear unbiased predictors (ABLUP), genomic BLUP (GBLUP), BayesB genomic prediction model, and a form of GBLUP based on weighting markers according to their influence on traits (BLUP|GA). Predictive ability is assessed under varying marker densities of 10,000, 100,000 and 500,000 SNPs. Our results show that BayesB and BLUP|GA perform best across the eight traits. Predictive ability was higher for individual terpene traits, such as foliar α-pinene and 1,8-cineole concentration (0.59 and 0.73, respectively), than aggregate traits such as total foliar oil concentration (0.38). This is likely a function of the trait architecture and markers used. BLUP|GA was the best model for the two biomass related traits, height and 1 year change in height (0.25 and 0.19, respectively). Predictive ability increased with marker density for most traits, but with diminishing returns. The results of this study are a solid foundation for yield improvement of essential oil producing eucalypts. New markets such as biopolymers and terpene-derived biofuels could benefit from rapid yield increases in undomesticated oil-producing species.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
• Grant/Contract Number: AC05-00OR22725
• OSTI ID: 1474631
• Journal Information: G3, Journal Name: G3 Journal Issue: 8 Vol. 8; ISSN 2160-1836
• Publisher: Genetics Society of AmericaCopyright Statement
• Country of Publication: United States
• Language: English