Variantes en los genes TNFA, IL6 e IFNG asociadas con la gravedad del dengue en una muestra de población colombiana

Introduction: The genetic makeup of the host contributes to the clinical profile of dengue. This could be due to the effect of variants in the genes encoding pro-inflammatory cytokines.Objective: To evaluate the association between the variants of three polymorphisms in TNFA, IL6 and IFNG candidate genes with dengue severity in a sample of Colombian population.Materials and methods: We evaluated the rs1800750, rs2069843, and rs2069705 polymorphisms in TNFA, IL6 and IFNG candidate genes, respectively, in 226 patients with dengue infection. The genotypes were typed using both polymerase chain reaction (PCR) and restriction fragment length polymorphism (RFLP). To determine the risk of different dengue phenotypes, we compared allele frequencies with chi-square and genotypes and haplotypes using logistic regression. Finally, these analyzes were adjusted with data from self-identification or the ancestral genetic component.Results: The A allele in the rs2069843 polymorphism, adjusted by self-identification, was associated with dengue hemorrhagic fever cases in Afro-Colombians. In the entire sample, this polymorphism, adjusted by the ancestral genetic component, was reproducible. In addition, there were significant associations between GGT and GAC allelic combinations of rs1800750, rs2069843, and rs2069705 in dengue hemorrhagic fever patients, with and without adjustment by ancestral genetic component. Additionally, the AGC allelic combination produced 58.03 pg/ml of interleukin-6 more than the GGC combination, regardless of European, Amerindian and African genetic components.Conclusions: The variants of GGT and GAC polymorphisms of rs1800750, rs2069843, and rs2069705 in the TNFA, IL6 and IFNG genes, respectively, were correlated with the susceptibility to dengue severity in a sample of Colombian population.Introducción. La composición genética del huésped determina, entre otros aspectos, el perfil clínico del dengue, lo cual se debería al efecto de variantes en los genes que codifican citocinas proinflamatorias.Objetivo. Evaluar la asociación entre las variantes de tres polimorfismos en los genes candidatos TNFA, IL6 e IFNG con la gravedad del dengue en una población colombiana.Materiales y métodos. Se evaluaron los polimorfismos rs1800750, rs2069843 y rs2069705 de los genes TNFA, IL6 e IFNG, respectivamente, en 226 pacientes con dengue. Los genotipos se tipificaron usando la reacción en cadena de la polimerasa (PCR) y los polimorfismos de la longitud de los fragmentos de restricción (Restriction Fragment Length Polymorphism, RFLP). Para determinar el riesgo de diferentes fenotipos del dengue, se compararon las frecuencias alélicas con la prueba de ji al cuadrado, y los genotipos y los haplotipos, con regresión logística. Por último, los análisis se ajustaron utilizando datos de autoidentificación o del componente genético ancestral.Resultados. El alelo A del rs2069843, ajustado por autoidentificación, se asoció con casos de dengue hemorrágico en afrocolombianos. En la muestra completa, dicho polimorfismo, ajustado por componente genético ancestral, fue reproducible. Además, hubo asociaciones significativas entre las combinaciones alélicas GGT y GAC de los rs1800750, rs2069843 y rs2069705 en pacientes con dengue hemorrágico, con ajuste por componente genético ancestral y sin él. Además, la combinación alélica AGC produjo 58,03 pg/ml más de interleucina 6 que la GGC, independientemente de los componentes genéticos europeo, amerindio y africano.Conclusión. Las variantes de los polimorfismos GGT y GAC de los rs1800750, rs2069843 y rs2069705 en los genes TNFA, IL6 e IFNG, respectivamente, se correlacionaron con la gravedad del dengue en esta muestra de población colombiana

Predicting haplogroups using a versatile machine learning program (PredYMaLe) on a new mutationally balanced 32 Y-STR multiplex (CombYplex): Unlocking the full potential of the human STR mutation rate spectrum to estimate forensic parameters

We developed a new mutationally well-balanced 32 Y-STR multiplex (CombYplex) together with a machine learning (ML) program PredYMaLe to assess the impact of STR mutability on haplogourp prediction, while respecting forensic community criteria (high DC/HD). We designed CombYplex around two sub-panels M1 and M2 characterized by average and high-mutation STR panels. Using these two sub-panels, we tested how our program PredYmale reacts to mutability when considering basal branches and, moving down, terminal branches. We tested first the discrimination capacity of CombYplex on 996 human samples using various forensic and statistical parameters and showed that its resolution is sufficient to separate haplogroup classes. In parallel, PredYMaLe was designed and used to test whether a ML approach can predict haplogroup classes from Y-STR profiles. Applied to our kit, SVM and Random Forest classifiers perform very well (average 97 %), better than Neural Network (average 91 %) and Bayesian methods (< 90 %)

Evaluation of variants in IL6R, TLR3, and DC-SIGN genes associated with dengue in sampled Colombian population

Introduction: Host genetics is recognized as an influential factor for the development of dengue disease. Objective: This study evaluated the association of dengue with the polymorphisms rs8192284 for gene IL6R, rs3775290 for TLR3, and rs7248637 for DC-SIGN. Materials and methods: Of the 292 surveyed subjects, 191 were confirmed for dengue fever and the remaining 101 were included as controls. The genotypes were resolved using polymerase chain reaction and restriction fragment length polymorphism (PCRRFLP). In an attempt to determine the risk (Odds Ratio) of suffering dengue fever, data were analyzed using chi-square for alleles and logistic regression for both genotypes and allelic combinations. Confidence intervals were set to 95% for all tests regardless of the adjustment by either self-identification or ancestry. Results: For Afro-Colombians, the allele rs8192284 C offered protection against dengue [OR=0.425,(0.204-0.887), p=0.020]. The alleles rs7248637 A and rs3775290 A posed, respectively, an increased risk of dengue for Afro-Colombians [OR=2.389, (1.170-4.879), p=0.015] and Mestizos [OR=2.329, (1.283-4.226), p=0.005]. The reproducibility for rs8192284 C/C [OR=2.45, (1.05-5.76), p=0.013] remained after adjustment by Amerindian ancestry [OR=2.52, (1.04-6.09), p=0.013]. The reproducibility for rs3775290 A/A [OR=2.48, (1.09-5.65), p=0.033] remained after adjustment by European [OR=2.34, (1.02-5.35), p=0.048], Amerindian [OR=2.49, (1.09-5.66), p=0.035], and African ancestry [OR=2.37, (1.04-5.41), p=0.046]. Finally, the association of dengue fever with the allelic combination CAG [OR=2.07, (1.06-4.05), p=0.033] remained after adjustment by Amerindian ancestry [OR=2.16, (1.09-4.28), p=0.028]. Conclusions: Polymorphisms rs8192284 for IL6R, rs3775290 for TLR3, and rs7248637 for DC-SIGN were associated with the susceptibility to suffer dengue fever in the sampled Colombian population

Predicting haplogroups using a versatile machine learning program (PredYMaLe) on a new mutationally balanced 32 Y-STR multiplex (CombYplex): unlocking the full potential of the human STR mutation rate spectrum to estimate forensic parameters

We developed a new mutationally well-balanced 32 Y-STR multiplex (CombYplex) together with a machine learning (ML) program PredYMaLe to assess the impact of STR mutability on haplogourp prediction, while respecting forensic community criteria (high DC/HD). We designed CombYplex around two sub-panels M1 and M2 characterized by average and high-mutation STR panels. Using these two sub-panels, we tested how our program PredYmale reacts to mutability when considering basal branches and, moving down, terminal branches. We tested first the discrimination capacity of CombYplex on 996 human samples using various forensic and statistical parameters and showed that its resolution is sufficient to separate haplogroup classes. In parallel, PredYMaLe was designed and used to test whether a ML approach can predict haplogroup classes fromY-STR profiles. Applied to our kit, SVM and Random Forest classifiers perform very well (average 97%), better than Neural Network (average 91%) and Bayesian methods (<90%). We observe heterogeneity in haplogroup assignation accuracy among classes, with most haplogroups having high prediction scores (99-100%) and two (E1b1b and G) having lower scores (67%). The small sample sizes of these classes explain the high tendency to misclassify the Y-profiles of these haplogroups; results were measurably improved as soon as more training data were added. We provide evidence that our ML approach is a robust method to accurately predict haplogroups when it is combined with a sufficient number of markers, well-balanced mutation rate Y-STR panels, and large ML training sets. Further research on confounding factors (such as gene conversion) and ideal STR panels in regard to the branches analysed can be developed to help classifiers further optimize prediction scores.Depto. de Biodiversidad, Ecología y EvoluciónFac. de Ciencias BiológicasTRUEpu