Genomic Association in Rubber Tree: Bases for Increasing Productivity of the Main Source of Natural Rubber

• Grant number: 24/06647-0
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: September 01, 2024
• End date: April 30, 2028
• Field of knowledge: Agronomical Sciences - Agronomy - Crop Science
• Principal Investigator: Anete Pereira de Souza
• Grantee: Wanderson Lima Cunha
• Host Institution: Centro de Biologia Molecular e Engenharia Genética (CBMEG). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil

Abstract

Natural rubber is a critical raw material for the industry, and its primary commercial source is the rubber tree (Hevea brasiliensis). The growing demand for natural rubber and the shortage in international supply expected for the coming years have encouraged the search for new strategies to boost the production of the material. Brazil, the centre of origin of the rubber tree and once the largest producer in the world, contributes only 1.5% of global production today. After decades of decline, national production began to grow again in the 1980s, with the implementation of rubber plantations in the state of São Paulo. However, it is still insufficient to meet the country's internal demand. To enable a sustainable increase in natural rubber production does not appear, it is necessary to invest in the improvement of rubber trees to obtain more productive varieties in the colder and drier regions of the Southeast and South of the country, which are less favourable to the spread of natural rubber diseases and pests species. The rubber tree is commercially produced by grafting, and the improvement of the species has focused on the selection of elite clones from the upper part of the plant (graft).On the other hand, rootstocks have received little attention despite their known influence on latex productivity. Despite the opportunities and demands for rubber tree improvement, developing new varieties still needs to be faster, taking 20 to 30 years. Therefore, new strategies that reduce these cycles must be evaluated, and the most promising ones involve using high-resolution genomic data. In this project, we will apply genome-wide association (GWAS) and machine learning (ML) methods, integrated with the co-expression network, to accelerate rubber tree improvement efforts through 1) the determination of genetic architecture and 2) the identification of genes and pathways metabolic characteristics associated with productivity in rootstocks. To this end, we will use an experimental population established in 1989 in Pindorama-SP, composed of graft/rootstock combinations of six rubber tree varieties, and for which phenotypic data collected for more than ten years are available, therefore treating an exceptional opportunity for genotype-phenotype association studies in the species. This project will considerably expand the availability of genomic resources for the main rubber tree varieties produced in the country. It will provide the basis for marker-assisted selection programs on rootstocks, thus increasing the efficiency of breeding programs for this species.