Genomic selection simulations for improvement in the breeding programme of rubber tree

• Grant number: 18/14305-1
• Support type: Scholarships abroad - Research Internship - Doctorate (Direct)
• Effective date (Start): November 01, 2018
• Effective date (End): October 31, 2019
• Field of knowledge: Biological Sciences - Genetics - Plant Genetics
• Principal Investigator: Anete Pereira de Souza
• Grantee: Luciano Henrique Braz dos Santos
• Supervisor abroad: John Micheal Hickey
• Home Institution: Centro de Biologia Molecular e Engenharia Genética (CBMEG). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Local de pesquisa : University of Edinburgh, Scotland
• Associated to the scholarship: 17/07908-9 - Genetic and genomic analysis in the Hevea genus aiming to contribute to the genetic improvement of Hevea brasiliensis, BP.DD

Abstract

ABSTRACTThe rubber tree (Hevea brasiliensis) is the main commercial source of natural rubber, which is used in many products, including tires and medical devices. Despite its importance, this tree has a long generation time, which explains the slow progress in breeding, usually 30 years per cycle using traditional methods. Considering the breeding time, any tool that reduces this time, such as marker-assisted selection, may represent an advantage for the crop. With the development of next generation sequencing technology, the cost of DNA sequencing methods has lowered, making the use of genotyping by sequencing (GBS) possible in many studies. Genomic selection can reduce the time of rubber tree breeding by using prediction equations with parameters based on genotyped and phenotyped individuals to predict the values in the individuals that are candidates for selection and that have only genotypic data, thus avoiding the long phenotyping time used for this crop. Preliminary results were generated in a PhD. project (2017/07908-9) using GBS methods in studies on linkage disequilibrium, making it possible to use this dataset to simulate genomic selection cycles and compare them with traditional breeding methods used in this crop. To analyse these data or use only simulated data, it is important to collaborate with a research group that has expertise in the use of simulation for the design of a better breeding programme with genomic selection.