Spatial analyzes of HLA data in Rio Grande do Sul, south Brazil : genetic structure and possible correlation with autoimmune diseases

• Autor(a) principal: Boquett, Juliano André
• Data de Publicação: 2018
• Outros Autores: Oliveira, Marcelo Zagonel de, Jobim, Luiz Fernando Job, Wilson, Mariana de Sampaio Leite Jobim, Gonzaga Junior, Luiz, Veronez, Maurício Roberto, Fagundes, Nelson Jurandi Rosa, Faccini, Lavinia Schuler
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UFRGS
• Texto Completo: http://hdl.handle.net/10183/199339
• topic: Doenças autoimunes; Análise espacial; Mapeamento cromossomico; Base de dados; Rio Grande do Sul; HLA; Autoimmune diseases; Genetic structure; Correlation; Georeferencing

Background: HLA genes are the most polymorphic of the human genome and have distinct allelic frequencies in populations of diferent geographical regions of the world, serving as genetic markers in ancestry studies. In addition, specifc HLA alleles may be associated with various autoimmune and infectious diseases. The bone marrow donor registry in Brazil is the third largest in the world, and it counts with genetic typing of HLA-A, -B, and -DRB1. Since 1991 Brazil has maintained the DATASUS database, a system fed with epidemiological and health data from compulsory registration throughout the country. Methods: In this work, we perform spatial analysis and georeferencing of HLA genetic data from more than 86,000 bone marrow donors from Rio Grande do Sul (RS) and data of hospitalization for rheumatoid arthritis, multiple sclerosis and Crohn’s disease in RS, comprising the period from 1995 to 2016 obtained through the DATASUS system. The allele frequencies were georeferenced using Empirical Bayesian Kriging; the diseases prevalence were georeferenced using Inverse Distance Weighted and cluster analysis for both allele and disease were performed using Getis-Ord Gi* method. Spearman’s test was used to test the correlation between each allele and disease. Results: The results indicate a HLA genetic structure compatible with the history of RS colonization, where it is possible to observe diferentiation between regions that underwent diferent colonization processes. Spatial analyzes of autoimmune disease hospitalization data were performed revealing clusters for diferent regions of the state for each disease analyzed. The correlation test between allelic frequency and the occurrence of autoimmune diseases indicated a signifcant correlation between the HLA-B*08 allele and rheumatoid arthritis. Conclusions: Genetic mapping of populations and the spatial analyzes such as those performed in this work have great economic relevance and can be very useful in the formulation of public health campaigns and policies, contributing to the planning and adjustment of clinical actions, as well as informing and educating professionals and the population.