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Bioinformatics analysis for genomic characterization of familial hypercholesterolemia patients in the Brazilian population

Grant number: 23/02730-8
Support Opportunities:Scholarships in Brazil - Post-Doctoral
Effective date (Start): April 01, 2023
Effective date (End): January 31, 2024
Field of knowledge:Biological Sciences - Genetics - Human and Medical Genetics
Principal Investigator:Mario Hiroyuki Hirata
Grantee:Paula Paccielli Freire-Barguil
Host Institution: Faculdade de Ciências Farmacêuticas (FCF). Universidade de São Paulo (USP). São Paulo , SP, Brazil
Associated research grant:16/12899-6 - Genomics, epigenomics and pharmacogenomics characterization of familial hypercholesterolemia in the Brazilian population, AP.TEM


Familial hypercholesterolemia (FH) is a genetic disease characterized by high concentrations of low-density protein cholesterol (LDL-c), increasing cardiovascular risk. The molecular diagnosis of HF is based on analyzing genes that cause autosomal dominant HF. However, although several functional mutations associated with HF have already been described in the LDLR, APOB, and PCSK9, other genes involved in cholesterol metabolic pathways have also been associated, characterizing polygenic HF. Understanding the genetic and epigenetic mechanisms involved in the development of FH is fundamental for comprehending this complex condition. Its further elucidation may lead to the development of more effective treatments that positively impact the quality of life of these patients and reduce the morbidity and mortality attributed to it. The present study proposes the development of a specific bioinformatics pipeline automating the characterization and genomic analysis of monogenic and polygenic HF carriers. Patients with phenotypically diagnosed FH will be recruited from six research centers in different regions of Brazil. The methods used include: (i) identification of genes related to cholesterol metabolism from samples of patients with FH sequenced with Next Generation Sequencing (NGS); (ii) identification and characterization of new variants in the genes involved with HF; (iii) development of specific pipelines to automate data analysis. The bioinformatics analyses will be carried out using prediction programs and search for variants in the genome, in addition to proprietary algorithms written in the Python commands. The results of this study aim to contribute to the knowledge of the molecular basis of FH, provide elements for directing the genetic diagnosis and personalized therapy of affected patients, and enable the creation of a national bank of genomic data that helps guide the molecular diagnostic procedure for patients with the HF phenotype and their families.

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