Fine-mapping of root-knot nematode response in common bean

• Grant number: 24/13064-1
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate
• Start date: November 18, 2024
• End date: November 17, 2025
• Field of knowledge: Biological Sciences - Genetics - Quantitative Genetics
• Principal Investigator: Maria Lúcia Carneiro Vieira
• Grantee: Guilherme Alexandre Luz da Costa
• Supervisor: Marcelo Mollinari
• Host Institution: Escola Superior de Agricultura Luiz de Queiroz (ESALQ). Universidade de São Paulo (USP). Piracicaba , SP, Brazil
• Institution abroad: North Carolina State University (NC State), United States
• Associated to the scholarship: 22/10841-1 - Common bean breeding aiming at root-knot nematode resistance, BP.DR

Abstract

Enhancing the genetic resolution of QTL mapping can aid in the identification of the shortest genomic regions affecting, for instance, plant-pathogen interactions, allowing for more precise mapping. A common method for achieving more precise mapping involves accumulating recombination events through the development and use of recombinant inbred lines. This approach breaks down large genomic regions into smaller segments, facilitating the accurate identification of candidate genes. Another strategy that has now being used to enhance the precision and understanding of the traits' genetic architecture is the use of joint analysis of multiple interconnected mapping populations. The objective of this project is firstly to perform a joint fine-mapping analysis considering the F2 population combined with a newly developed RIL population, and secondly to identify the genomic regions involved in the complex interaction between the common bean and the root-knot nematode. This combined approach leverages the strengths of both populations, increasing sample size and improving the robustness and resolution of QTL mapping efforts. For this, the map will be constructed using the hidden Markov model (HMM) technology, with specific transition probabilities tailored to each population using an adaptation of the MAPpoly2 software. A model selection technique, group LASSO, will be deployed to identify the most appropriate model. The search for and the consequent identification of candidate genes will be performed using bioinformatic tools. Therefore, we expect to integrate the results previously obtained by our research group in order to elucidate the genetic architecture and the common bean response mechanism to nematode infection.