Functional characterization of structural variations through gene expression and tissue culture in skeletal muscle of Nelore cattle

• Grant number: 24/17468-0
• Support Opportunities: Scholarships in Brazil - Doctorate (Direct)
• Start date: March 01, 2025
• End date: July 31, 2028
• Field of knowledge: Agronomical Sciences - Animal Husbandry - Genetics and Improvement of Domestic Animals
• Principal Investigator: Luiz Lehmann Coutinho
• Grantee: Renato Duarte de Araújo
• Host Institution: Escola Superior de Agricultura Luiz de Queiroz (ESALQ). Universidade de São Paulo (USP). Piracicaba , SP, Brazil
• Associated research grant: 19/04089-2 - The Nelore hologenome: implications in beef quality and feed efficiency, AP.ESCIENCE.TEM

Abstract

Brazil is one of the largest beef producers in the world, with the Nelore breed being predominant in the national herd. Structural variations (SVs) are a class of polymorphisms ¿ 50 bp, which include translocations, inversions, and CNVs, i.e., deletions and duplications. Recent studies have shown that CNVs are associated with phenotypic traits important for production, e.g., feed efficiency and meat quality, as well as gene expression. However, these studies identified CNVs using SNP chips, which have low sensitivity and are unable to detect other types of SVs. The use of WGS data stands out for detecting different types and sizes of SVs. However, it presents a high number of false positives and false negatives. An alternative strategy is the combination of multiple molecular techniques (e.g., WGS and SNP chips) for identifying high-confidence CNVs, allowing for higher precision values but with low sensitivity. As an alternative, it is necessary to use SV calling programs with better precision and sensitivity, such as deep learning-based programs, which have shown great potential but are still underutilized and tested in practice. The identification of SVs is crucial to understanding which polymorphisms influence economically important traits, leading to the identification of functional variations that require validation through specific experiments. This project proposes the identification of SVs in 86 Nelore bulls using WGS data, aiming for high-confidence SVs. Additionally, a comparison will be made between deep learning-based programs and conventional methods for SV detection. Finally, the functional activity of CNVs will be validated through gene editing experiments in vitro in muscle cell lines.