Evidence map›Paper›PMID 30217207›Full record

ArticleInternational journal of health geographics2018

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

Juliano André Boquett, Marcelo Zagonel-Oliveira, Luis Fernando Jobim, Mariana Jobim, Luiz Gonzaga, Maurício Roberto Veronez, Nelson Jurandi Rosa Fagundes, Lavínia Schüler-Faccini

Open access · goldAbstract read
In one paragraph

Article in International journal of health geographics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.8field-weighted citation impact, top 30% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 11 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 3 institutions in 1 country.

Juliano André BoquettInstituto Nacional de Genética Médica Populacional (INaGeMP), Porto Alegre, Brazil.ORCID 0000-0002-6437-789X
Marcelo Zagonel-OliveiraInstituto Nacional de Genética Médica Populacional (INaGeMP), Porto Alegre, Brazil.
Luis Fernando JobimDepartment of Immunology, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil.
Mariana JobimDepartment of Immunology, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil.
Luiz GonzagaInstituto Nacional de Genética Médica Populacional (INaGeMP), Porto Alegre, Brazil.
Maurício Roberto VeronezInstituto Nacional de Genética Médica Populacional (INaGeMP), Porto Alegre, Brazil.
Nelson Jurandi Rosa FagundesInstituto Nacional de Genética Médica Populacional (INaGeMP), Porto Alegre, Brazil.
Lavínia Schüler-FacciniInstituto Nacional de Genética Médica Populacional (INaGeMP), Porto Alegre, Brazil. lavinia.faccini@ufrgs.br.
National Institute on Population Medical Genetics · BRUniversidade Federal do Rio Grande do Sul · BRHospital de Clínicas de Porto Alegre · BR

Funding

Instituto Nacional de Genética Médica Populacional 476978/2008-4
6 · The paper itself

Abstract

backgroundHLA genes are the most polymorphic of the human genome and have distinct allelic frequencies in populations of different geographical regions of the world, serving as genetic markers in ancestry studies. In addition, specific 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.

methodsIn 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.

resultsThe results indicate a HLA genetic structure compatible with the history of RS colonization, where it is possible to observe differentiation between regions that underwent different colonization processes. Spatial analyzes of autoimmune disease hospitalization data were performed revealing clusters for different regions of the state for each disease analyzed. The correlation test between allelic frequency and the occurrence of autoimmune diseases indicated a significant correlation between the HLA-B*08 allele and rheumatoid arthritis.

conclusionsGenetic 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.

Indexed as

Databases, GeneticSpatial AnalysisAutoimmune DiseasesBrazilChromosome MappingHLA AntigensHumansHLA AntigensAutoimmune diseasesCorrelationGenetic structureGeoreferencingHLA

Identifiers

PMID30217207
PMCPMC6137739
OpenAlexW2889606636

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.