Accuracy of Genomic Prediction for Foliar Terpene Traits in Eucalyptus polybractea

Kainer, D., Stone, E. A., Padovan, A., Foley, W. J., Kulheim, C.
Genetics Society of America (GSA)
Published 2018
Publication Date:
2018-08-01
Publisher:
Genetics Society of America (GSA)
Electronic ISSN:
2160-1836
Topics:
Biology
Published by:
_version_ 1836399018388750336
autor Kainer, D., Stone, E. A., Padovan, A., Foley, W. J., Kulheim, C.
beschreibung 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.
citation_standardnr 6312074
datenlieferant ipn_articles
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feed_publisher Genetics Society of America (GSA)
feed_publisher_url http://www.genetics-gsa.org/
insertion_date 2018-08-01
journaleissn 2160-1836
publikationsjahr_anzeige 2018
publikationsjahr_facette 2018
publikationsjahr_intervall 7984:2015-2019
publikationsjahr_sort 2018
publisher Genetics Society of America (GSA)
quelle G3: Genes, Genomes, Genetics
relation http://www.g3journal.org/cgi/content/short/8/8/2573?rss=1
search_space articles
shingle_author_1 Kainer, D., Stone, E. A., Padovan, A., Foley, W. J., Kulheim, C.
shingle_author_2 Kainer, D., Stone, E. A., Padovan, A., Foley, W. J., Kulheim, C.
shingle_author_3 Kainer, D., Stone, E. A., Padovan, A., Foley, W. J., Kulheim, C.
shingle_author_4 Kainer, D., Stone, E. A., Padovan, A., Foley, W. J., Kulheim, C.
shingle_catch_all_1 Accuracy of Genomic Prediction for Foliar Terpene Traits in Eucalyptus polybractea
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.
Kainer, D., Stone, E. A., Padovan, A., Foley, W. J., Kulheim, C.
Genetics Society of America (GSA)
2160-1836
21601836
shingle_catch_all_2 Accuracy of Genomic Prediction for Foliar Terpene Traits in Eucalyptus polybractea
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.
Kainer, D., Stone, E. A., Padovan, A., Foley, W. J., Kulheim, C.
Genetics Society of America (GSA)
2160-1836
21601836
shingle_catch_all_3 Accuracy of Genomic Prediction for Foliar Terpene Traits in Eucalyptus polybractea
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.
Kainer, D., Stone, E. A., Padovan, A., Foley, W. J., Kulheim, C.
Genetics Society of America (GSA)
2160-1836
21601836
shingle_catch_all_4 Accuracy of Genomic Prediction for Foliar Terpene Traits in Eucalyptus polybractea
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.
Kainer, D., Stone, E. A., Padovan, A., Foley, W. J., Kulheim, C.
Genetics Society of America (GSA)
2160-1836
21601836
shingle_title_1 Accuracy of Genomic Prediction for Foliar Terpene Traits in Eucalyptus polybractea
shingle_title_2 Accuracy of Genomic Prediction for Foliar Terpene Traits in Eucalyptus polybractea
shingle_title_3 Accuracy of Genomic Prediction for Foliar Terpene Traits in Eucalyptus polybractea
shingle_title_4 Accuracy of Genomic Prediction for Foliar Terpene Traits in Eucalyptus polybractea
timestamp 2025-06-30T23:36:18.190Z
titel Accuracy of Genomic Prediction for Foliar Terpene Traits in Eucalyptus polybractea
titel_suche Accuracy of Genomic Prediction for Foliar Terpene Traits in Eucalyptus polybractea
topic W
uid ipn_articles_6312074