AA IDO1 Variant Genotype (G2431A, rs3739319) Is Associated with Severe Dengue Risk Development in a DEN-3 Brazilian Cohort.

Dengue is considered one of the most challenging public health threats in the world. Infection may be clinically asymptomatic but can result in severe forms. The indoleamine 2,3 dioxygenase (IDO) gene encodes one of first enzymes (IDO) of the kynurenine pathway. This study aimed to verify the association between G2431A IDO1 gene single nucleotide polymorphism (SNP) (rs3739319) and dengue fever development. We included 299 dengue-infected individuals in the study and 96 dengue-free controls. We collected clinical and diagnostic test data and divided the patients with dengue infection into three groups, based on World Health Organization (WHO) criteria: 131 Dengue without warning signs (DWOS), 143 Dengue with warning signs (DWS), and 25 severe dengue (SD). We genotyped 193 of the dengue cases using quantitative polymerase chain reaction to the SNP rs3739319. The other 106 dengue cases and 96 dengue-free controls had previously been genotyped using the Illumina Genotyping Kit. Genotyping of the infected patients revealed frequencies of 106 GG (35.4%), 126 GA (42.1%), and 67 AA (22.4%), whereas the nondengue exposed control group showed similar frequencies, 29 GG (30.2%), 52 GA (54.2%), and 15 AA (15.6%). Under risk analysis we found that AA genotype patients had a higher risk of developing SD in a codominant model (AA × GG; odds ratio [OR] = 11.5-fold in comparison to non-SD group -DWOS and -DWS patients; confidence interval [CI] = 0.02-0.32; Yates correction = 1.9e-05) and in a recessive model (AA × AG+GG; OR = 9.41; CI = 3.62-26.7; Yates correction = 4.8e-08). An allelic model reinforced the association between A allele and SD phenotype development that was found in the SD versus DWOS+DWS analysis (OR = 3.59; CI = 1.50-9.56; Yates correction = 0.0033). Our data show an association between the IDO G2431A variant and the risk for SD. This SNP may be relevant for further investigation into disease mechanisms and host factors in future genetic and pathophysiological studies.

Severe Dengue Prognosis Using Human Genome Data and Machine Learning.

Dengue has become one of the most important worldwide arthropod-borne diseases. Dengue phenotypes are based on laboratorial and clinical exams, which are known to be inaccurate. OBJECTIVE: We present a machine learning approach for the prediction of dengue fever severity based solely on human genome data. METHODS: One hundred and two Brazilian dengue patients and controls were genotyped for 322 innate immunity single nucleotide polymorphisms (SNPs). Our model uses a support vector machine algorithm to find the optimal loci classification subset and then an artificial neural network (ANN) is used to classify patients into dengue fever or severe dengue. RESULTS: The ANN trained on 13 key immune SNPs selected under dominant or recessive models produced median values of accuracy greater than 86%, and sensitivity and specificity over 98% and 51%, respectively. CONCLUSION: The proposed classification method, using only genome markers, can be used to identify individuals at high risk for developing the severe dengue phenotype even in uninfected conditions. SIGNIFICANCE: Our results suggest that the genetic context is a key element in phenotype definition in dengue. The methodology proposed here is extendable to other Mendelian based and genetically influenced diseases.