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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Unravelling Rubber Tree Growth by Integrating GWAS and Biological Network-Based Approaches

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Francisco, Felipe Roberto [1] ; Aono, Alexandre Hild [1] ; da Silva, Carla Cristina [1] ; Goncalves, Paulo S. [2] ; Scaloppi Junior, Erivaldo J. J. [2] ; Le Guen, Vincent [3, 4] ; Fritsche-Neto, Roberto [5] ; Souza, Livia Moura [1, 6] ; Souza, Anete Pereira de [1, 7]
Total Authors: 9
[1] Univ Campinas UNICAMP, Mol Biol & Genet Engn Ctr CBMEG, Campinas - Brazil
[2] Agron Inst IAC, Ctr Rubber Tree & Agroforestry Syst, Votuporanga - Brazil
[3] Ctr Cooperat Int Rech Agron Dev CIRAD, UMR AGAP, Montpellier - France
[4] Univ Montpellier, Inst Agro, AGAP, CIRAD, INRAE, Montpellier - France
[5] Univ Sao Paulo, Dept Genet, Luiz Queiroz Coll Agr ESALQ, Piracicaba - Brazil
[6] Sao Francisco Univ USF, Itatiba - Brazil
[7] Univ Campinas UNICAMP, Biol Inst, Dept Plant Biol, Campinas - Brazil
Total Affiliations: 7
Document type: Journal article
Source: FRONTIERS IN PLANT SCIENCE; v. 12, DEC 21 2021.
Web of Science Citations: 0

Hevea brasiliensis (rubber tree) is a large tree species of the Euphorbiaceae family with inestimable economic importance. Rubber tree breeding programs currently aim to improve growth and production, and the use of early genotype selection technologies can accelerate such processes, mainly with the incorporation of genomic tools, such as marker-assisted selection (MAS). However, few quantitative trait loci (QTLs) have been used successfully in MAS for complex characteristics. Recent research shows the efficiency of genome-wide association studies (GWAS) for locating QTL regions in different populations. In this way, the integration of GWAS, RNA-sequencing (RNA-Seq) methodologies, coexpression networks and enzyme networks can provide a better understanding of the molecular relationships involved in the definition of the phenotypes of interest, supplying research support for the development of appropriate genomic based strategies for breeding. In this context, this work presents the potential of using combined multiomics to decipher the mechanisms of genotype and phenotype associations involved in the growth of rubber trees. Using GWAS from a genotyping-by-sequencing (GBS) Hevea population, we were able to identify molecular markers in QTL regions with a main effect on rubber tree plant growth under constant water stress. The underlying genes were evaluated and incorporated into a gene coexpression network modelled with an assembled RNA-Seq-based transcriptome of the species, where novel gene relationships were estimated and evaluated through in silico methodologies, including an estimated enzymatic network. From all these analyses, we were able to estimate not only the main genes involved in defining the phenotype but also the interactions between a core of genes related to rubber tree growth at the transcriptional and translational levels. This work was the first to integrate multiomics analysis into the in-depth investigation of rubber tree plant growth, producing useful data for future genetic studies in the species and enhancing the efficiency of the species improvement programs. (AU)

FAPESP's process: 18/18985-7 - Integrated genetic map and wider genomic selection looking at characters of economic impotence in seringueira
Grantee:Felipe Roberto Francisco
Support type: Scholarships in Brazil - Doctorate (Direct)
FAPESP's process: 19/03232-6 - Genome wide selection in sugarcane using machine learning and complex networks for economically important traits
Grantee:Alexandre Hild Aono
Support type: Scholarships in Brazil - Doctorate (Direct)