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Multivariate genome-wide association identify loci for complex growth traits by considering additive and over-dominance effects in hybrid Eucalyptus
Umeå University, Faculty of Science and Technology, Department of Ecology and Environmental Sciences. Stora Enso AB.
Umeå University, Faculty of Science and Technology, Department of Ecology and Environmental Sciences. Department of Plant Biology, Swedish University of Agricultural Sciences.
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Genome-wide association studies are a powerful and widely used approach to decipher the genetic control of quantitative traits. One of the major challenges for traits in hybrid forest trees, such as hybrid Eucalyptus, is dissecting also non-additive effects for complex traits using a traditional linear mixed model. These non-additive effects, especially over-dominance effects, are one of most important hypotheses for the genetic basis of heterosis. In this study, we used a population including 949 F1 hybrids and their 174 parents, that were phenotyped for circumference at breast height and height at age of three years and six years, and also genotyped at 37,832 informative SNPs. Here we use and compare single-trait and multi-trait association models by accounting for additive and over-dominance effects, to evaluate genomic regions associated with the growth traits. For additive effect-based association model, nine significant SNPs were observed in multi-trait analyses, whereas only two unique SNPs were detected in single-trait analyses. These two SNPs were also identified in the multi-trait model. When evaluating over-dominance effects, 17 and 13 SNPs were identified from multi-trait and single-trait models, respectively. Moreover, more phenotypic variation can be explained by SNPs identified from multi-trait GWAS when including over-dominance effects. Overall, this study shows the added values of including over-dominance and considering multiple traits for identifying genomic regions that control traits of interest and that could contribute to heterosis in hybrids.

Keyword [en]
Eucalyptus grandis, E. urophylla, heterosis, multivariate mixed linear model, univariate mixed linear model
National Category
Genetics Forest Science
Identifiers
URN: urn:nbn:se:umu:diva-145494OAI: oai:DiVA.org:umu-145494DiVA, id: diva2:1188299
Available from: 2018-03-07 Created: 2018-03-07 Last updated: 2018-06-09Bibliographically approved
In thesis
1. Genomic selection and genome-wide association studies to dissect quantitative traits in forest trees
Open this publication in new window or tab >>Genomic selection and genome-wide association studies to dissect quantitative traits in forest trees
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The convergence of quantitative genetics of complex traits with genomic technologies is quickly becoming an innovative approach to explore fundamental genetic questions and also have practical consequences for implementations in tree breeding. In this thesis, I used genomic selection and genome-wide association studies (GWAS) to dissect the genetic basis of quantitative traits, i.e. growth, phenology and wood property traits. I also assessed the importance of dominance and epistatic effects in hybrid Eucalyptus. Both dominance and epistasis are important in hybrids, as they are the likely contributing to the genetic basis of heterosis. To successfully implement genomic selection models, several important factors have to be considered. I found that for a good model establishment, both the size and composition of the training population, as well as the number of SNPs to be important considered. Based on the optimal models, additive, dominance and epistasis genetic effects of growth and wood traits have been estimated to evaluate genetic parameters and how these influence the prediction accuracy, which can be used in selecting elite breeding individuals or clones. I also addressed the advantage of genotyping-based analyses by showing that we could accurately correct pedigree information errors. More importantly, genotyping-based analyses capture both Mendelian segregation variation within full-sib families and cryptic genetic links through unknown common ancestors, which are not available from traditional pedigree data. GWAS were used to analyse growth and phenology related traits. Using a single-trait GWAS method, we identified a region strongly associated with the timing of bud set in Populus tremula, a trait with high heritability. For the growth related traits, we found that a multi-traits GWAS approach was more powerful than single-trait analyses as it identified more associated SNPs in hybrid Eucalyptus. Moreover, many more novel associated SNPs were identified from considering over-dominance effects in the GWAS analyses. After annotating the associated SNPs I show that these functional candidate genes were related to growth and responding to abiotic and biotic stress. In summary, the results of genomic selection and GWAS provided a deeper understanding of the genetic backgrounds of quantitative traits in forest trees.

Place, publisher, year, edition, pages
Umeå: Umeå University, 2018. p. 38
Keyword
Genomic prediction, genome-wide association study, additive effects, dominance effects, epistasis effects, realized relationship matrix, Eucalyptus grandis, Eucalyptus urophylla
National Category
Genetics Evolutionary Biology Forest Science
Identifiers
urn:nbn:se:umu:diva-145497 (URN)978-91-7601-849-1 (ISBN)
Public defence
2018-04-06, Lilla hörsalen (KB.E3.01), KBC-byggnaden, Umeå, 10:00 (English)
Opponent
Supervisors
Available from: 2018-03-16 Created: 2018-03-07 Last updated: 2018-06-09Bibliographically approved

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Tan, BiyueIngvarsson, Pär K.

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