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PHRP : Osong Public Health and Research Perspectives

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2 "single nucleotide polymorphism"
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Original Articles
The Prevalence of CYP2B6 Gene Polymorphisms in Malaria-endemic Population of Timor in East Nusa Tenggara Indonesia
Linawati Hananta, Indwiani Astuti, Ahmad Hamim Sadewa, Josephine Alice, Jontari Hutagalung, Mustofa
Osong Public Health Res Perspect. 2018;9(4):192-196.   Published online August 31, 2018
DOI: https://doi.org/10.24171/j.phrp.2018.9.4.08
  • 3,286 View
  • 39 Download
  • 7 Citations
AbstractAbstract PDF
Objectives

The CYP2B6 is one of the most polymorphic CYP genes in humans that has the potential to modify the pharmacological and toxicological responses to clinically important drugs such as antimalarial artemisinin and its derivatives. The aim of the study was to determine the frequency of CYP2B6 polymorphisms in Timor malaria endemic area, East Nusa Tenggara, Indonesia where Artemisin-based Combination Therapy (ACT) has been used to treat uncomplicated malaria.

Methods

A total of 109 healthy subjects were participated in this study. CYP2B6*4, *6 and *9 polymorphisms were analyzed using PCR-RFLP to confirm the SNPs prevalence of 516G>T and 785A>G in exon 4 and 5.

Results

There were 96 subjects included in the analysis. In the exon 4 of CYP2B6 516G>T, the frequency of the T mutation was 37.5% (39/96), and the wildtype 27.1% (26/96). In the exon 5, CYP2B6 785A>G mutant was detected in 29.2% (28/96) of individuals, and the wildtype allele in 35.4% (34/96). The frequency of CYP2B6*9 (516G>T), CYP2B6*4 (785A>G) and CYP2B6*6 (516G>T and 785A>G) were 40.6%, 29.2% and 22.9%, respectively. The prevalence of these CYP2B6 gene polymorphisms in Timorian ethnic were higher than that in Malay, Han Chinese, Indian, and Egyptian populations.

Conclusion

The prevalence of these CYP2B6 516G>T and 785A>G polymorphisms in Timorian ethnic is higher than that in other populations. These polymorphisms may affect the metabolism of artemisinin and its derivatives.

Development of a Predictive Model for Type 2 Diabetes Mellitus Using Genetic and Clinical Data
Juyoung Lee, Bhumsuk Keam, Eun Jung Jang, Mi Sun Park, Ji Young Lee, Dan Bi Kim, Chang-Hoon Lee, Tak Kim, Bermseok Oh, Heon Jin Park, Kyu-Bum Kwack, Chaeshin Chu, Hyung-Lae Kim
Osong Public Health Res Perspect. 2011;2(2):75-82.   Published online June 30, 2011
DOI: https://doi.org/10.1016/j.phrp.2011.07.005
  • 1,723 View
  • 13 Download
  • 11 Citations
AbstractAbstract PDFSupplementary Material
Objectives
Recent genetic association studies have provided convincing evidence that several novel loci and single nucleotide polymorphisms (SNPs) are associated with the risk of developing type 2 diabetes mellitus (T2DM). The aims of this study were: 1) to develop a predictive model of T2DM using genetic and clinical data; and 2) to compare misclassification rates of different models.
Methods
We selected 212 individuals with newly diagnosed T2DM and 472 controls aged in their 60s from the Korean Genome and Epidemiology Study. A total of 499 known SNPs from 87 T2DM-related genes were genotyped using germline DNA. SNPs were analyzed for significant association with T2DM using various classification algorithms including Quest (Quick, Unbiased, Efficient, Statistical tree), Support Vector Machine, C4.5, logistic regression, and K-nearest neighbor.
Results
We tested these models using the complete Korean Genome and Epidemiology Study cohort (n = 10,038) and computed the T2DM misclassification rates for each model. Average misclassification rates ranged at 28.2–52.7%. The misclassification rates for the logistic and machine-learning algorithms were lower than the statistical tree algorithms. Using 1-to-1 matched data, the misclassification rate of the statistical tree QUEST algorithm using body mass index and SNP variables was the lowest, but overall the logistic regression performed best.
Conclusions
The K-nearest neighbor method exhibited more robust results than other algorithms. For clinical and genetic data, our “multistage adjustment” model outperformed other models in yielding lower rates of misclassification. To improve the performance of these models, further studies using warranted, strategies to estimate better classifiers for the quantification of SNPs need to be developed.

PHRP : Osong Public Health and Research Perspectives