Development of a polygenic risk score for predicting selenium deficiency in an admixed population

• Grant number: 21/15196-4
• Support Opportunities: Scholarships abroad - Research Internship - Post-doctor
• Effective date (Start): September 29, 2022
• Effective date (End): September 28, 2023
• Field of knowledge: Health Sciences - Nutrition
• Principal Investigator: Carla Barbosa Nonino
• Grantee: Lígia Moriguchi Watanabe
• Supervisor: Lisete Maria Ribeiro de Sousa
• Host Institution: Faculdade de Medicina de Ribeirão Preto (FMRP). Universidade de São Paulo (USP). Ribeirão Preto , SP, Brazil
• Research place: Universidade de Lisboa, Portugal
• Associated to the scholarship: 20/08687-9 - Selenium deficiency: identification of a risk profile considering genetic, clinical and environmental factors of patients with obesity in the pre and postoperative period of bariatric surgery, BP.PD

Abstract

Genetic variants related to selenium (Se) metabolism can directly influence the individual's blood levels, increasing the risk of developing diseases related to its deficiency. Thus, estimating the probabilistic susceptibility of an individual to Se deficiency, considering their genetic profile - polygenic risk score (PRS) - has shown to be an important tool in clinical management, aiming to reduce the risk of associated diseases and to personalize the mineral intake recommendations. Furthermore, there is a paucity of studies in the literature regarding blood Se levels and genetic variants in a mixed population, such as the Brazilian one. Therefore, this project aims to: (a) Determine the correlation between ancestry components and serum Se levels; (b) Determine associations of single nucleotide polymorphisms (SNPs) in candidate genes (related to Se); (c) Determine a PRS for Se deficiency using the variables that were significantly associated with serum Se levels. Genomic data, serum Se concentration, and anthropometric parameters were previously collected (FAPESP project number 2020/08687-9). These data will be used in specific statistical analysis to meet the planned aims. Thus, the following analyzes will be performed: (a) Efficient Local Ancestry Inference (ELAI), using quantile regression and a k-means classifier to infer local ancestry for mixed-race individuals; (b) generalized estimating equations (GEE) to identify the most significant SNPs, which will be considered as the SNP representative of the cluster (SNPref), using sex, BMI, age, and ancestry of each individual as covariates; (c) PRS will be calculated from the selected SNPref. Statistical analysis will be performed using the R software (R Core Team, 2017), available free from the CRAN - Comprehensive R Archive Network (http://cran.r-project.org/). (AU)