Study of genetic variants associated with overweight and obesity in partially isolated Brazilian populations from remnants of quilombos communities

• Grant number: 25/07484-0
• Support Opportunities: Scholarships abroad - Research Internship - Master's degree
• Start date: August 01, 2025
• End date: January 31, 2026
• Field of knowledge: Biological Sciences - Genetics - Human and Medical Genetics
• Principal Investigator: Regina Célia Mingroni Netto
• Grantee: Sophia Lincoln Cardoso de Azevedo
• Supervisor: Andrea Roseli Vancan Russo Horimoto
• Host Institution: Instituto de Biociências (IB). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Institution abroad: University of North Carolina at Chapel Hill (UNC), United States
• Associated to the scholarship: 24/16454-5 - Study of genetic variants associated with overweight and obesity in semi-isolated Brazilian populations from quilombo remnants, BP.MS

Abstract

Overweight and obesity (OWO) are risk factors for noncommunicable diseases that are among the leading causes of death in the world. These are highly polygenic, complex phenotypes whose genetic architecture is being characterized. However, most genomic studies are focused on populations with European ancestry. Our knowledge about genetic variants with relevant effects in admixed and underrepresented populations is, thus, limited. In this sense, we aim to identify genes and genetic variants associated with anthropometric measures used to assess body adiposity in a cohort of three-way admixed individuals with a high proportion of African ancestry from partially isolated Quilombos communities from Vale do Ribeira, São Paulo state. To this end, we have inferred global and local ancestry estimates for 243 OWO cases and 351 controls, applying different algorithms and comparing the results. We are currently optimizing the pipeline to apply these results in admixture mapping analysis, which will also consider adjustments for principal components and pairwise kinship coefficients to account for population structure and genetic relatedness, respectively. Genomic regions displaying association signals will be fine-mapped using functional annotation data of adipocytes and other analytical strategies to identify the putative causal variants. Additionally, we will build polygenic risk score models for the referred population to leverage the summary statistics resulting from our association mapping approach and to evaluate if our findings can be applied to risk stratification of admixed populations with a high African ancestry component. (AU)