Identification of Variants (rs11571707, rs144848, and rs11571769) in the BRCA2 Gene Associated with Hereditary Breast Cancer in Indigenous Populations of the Brazilian Amazon.
Elizabeth Ayres Fragoso DobbinJéssyca Amanda Gomes MedeirosMarta Solange Camarinha Ramos CostaJuliana Carla Gomes RodriguesJoão Farias GuerreiroJosé Eduardo KrollSandro José de SouzaPaulo Pimentel de AssumpçãoÂndrea Kely Campos Ribeiro Dos SantosSidney Emanuel Batista Dos SantosRommel Mario Rodríguez BurbanoMarianne Rodrigues FernandesNey Pereira Carneiro Dos SantosPublished in: Genes (2021)
Estimates show that 5-10% of breast cancer cases are hereditary, caused by genetic variants in autosomal dominant genes; of these, 16% are due to germline mutations in the BRCA1 and BRCA2 genes. The comprehension of the mutation profile of these genes in the Brazilian population, particularly in Amazonian Amerindian groups, is scarce. We investigated fifteen polymorphisms in the BRCA1 and BRCA2 genes in Amazonian Amerindians and compared the results with the findings of global populations publicly available in the 1000 Genomes Project database. Our study shows that three variants (rs11571769, rs144848, and rs11571707) of the BRCA2 gene, commonly associated with hereditary breast cancer, had a significantly higher allele frequency in the Amazonian Amerindian individuals in comparison with the African, American, European, and Asian groups analyzed. These data outline the singular genetic profiles of the indigenous population from the Brazilian Amazon region. The knowledge about BRCA1 and BRCA2 variants is critical to establish public policies for hereditary breast cancer screening in Amerindian groups and populations admixed with them, such as the Brazilian population.
Keyphrases
- breast cancer risk
- genome wide
- copy number
- genome wide identification
- african american
- bioinformatics analysis
- healthcare
- dna methylation
- gene expression
- mental health
- public health
- emergency department
- genome wide analysis
- machine learning
- electronic health record
- big data
- adverse drug
- genetic diversity
- atomic force microscopy
- high resolution