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JPMPH : Journal of Preventive Medicine and Public Health

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Special Article
Researches of Epigenetic Epidemiology for Infections and Radiation as Carcinogen
Jong-Myon Bae
J Prev Med Public Health. 2018;51(4):169-172.   Published online July 2, 2018
DOI: https://doi.org/10.3961/jpmph.18.070
  • 6,095 View
  • 148 Download
  • 2 Crossref
AbstractAbstract PDFSupplementary Material
In recent years, a number of studies have been reported on the various types of cancer arising from epigenetic alterations, including reports that these epigenetic alterations occur as a result of radiation exposure or infection. Thyroid cancer and breast cancer, in particular, have high cancer burden, and it has been confirmed that radiation exposure or onco-viral infection are linked to increased risk of development of these two types of cancer, respectively. Thus, the environment-epigenetic alteration-cancer occurrence (EEC) hypothesis has been suggested. This paper reviews the trends in research supporting this hypothesis for radiation exposure and onco-viral infection. If more evidences accumulate for the EEC hypothesis from future research, those findings may greatly aid in the prevention, early diagnosis, treatment, and prognosis of the thyroid cancer and breast cancer.
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Citations

Citations to this article as recorded by  
  • Expression level and function analysis of serum miRNAs in workers with occupational exposure to benzene series
    Kai Dai, Chen Wang, Wu Yao, Changfu Hao
    Chemosphere.2023; 313: 137460.     CrossRef
  • HDAC1 and HDAC2 Double Knockout Triggers Cell Apoptosis in Advanced Thyroid Cancer
    Ching-Ling Lin, Ming-Lin Tsai, Chun-Yu Lin, Kai-Wen Hsu, Wen-Shyang Hsieh, Wei-Ming Chi, Li-Chi Huang, Chia-Hwa Lee
    International Journal of Molecular Sciences.2019; 20(2): 454.     CrossRef
English Abstracts
A Review of Power and Sample Size Estimation in Genomewide Association Studies.
Ae Kyung Park, Ho Kim
J Prev Med Public Health. 2007;40(2):114-121.
DOI: https://doi.org/10.3961/jpmph.2007.40.2.114
  • 4,549 View
  • 74 Download
  • 2 Crossref
AbstractAbstract PDF
Power and sample size estimation is one of the crucially important steps in planning a genetic association study to achieve the ultimate goal, identifying candidate genes for disease susceptibility, by designing the study in such a way as to maximize the success possibility and minimize the cost. Here we review the optimal two-stage genotyping designs for genomewide association studies recently investigated by Wang et al(2006). We review two mathematical frameworks most commonly used to compute power in genetic association studies prior to the main study: Monte-Carlo and non-central chi-square estimates. Statistical powers are computed by these two approaches for case-control genotypic tests under one-stage direct association study design. Then we discuss how the linkagedisequilibrium strength affects power and sample size, and how to use empirically-derived distributions of important parameters for power calculations. We provide useful information on publicly available softwares developed to compute power and sample size for various study designs.
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Citations

Citations to this article as recorded by  
  • Sample Size and Statistical Power Calculation in Genetic Association Studies
    Eun Pyo Hong, Ji Wan Park
    Genomics & Informatics.2012; 10(2): 117.     CrossRef
  • The Effect of Increasing Control-to-case Ratio on Statistical Power in a Simulated Case-control SNP Association Study
    Moon-Su Kang, Sun-Hee Choi, In-Song Koh
    Genomics & Informatics.2009; 7(3): 148.     CrossRef
Statistical Issues in Genomic Cohort Studies.
Sohee Park
J Prev Med Public Health. 2007;40(2):108-113.
DOI: https://doi.org/10.3961/jpmph.2007.40.2.108
  • 3,312 View
  • 26 Download
AbstractAbstract PDF
When conducting large-scale cohort studies, numerous statistical issues arise from the range of study design, data collection, data analysis and interpretation. In genomic cohort studies, these statistical problems become more complicated, which need to be carefully dealt with. Rapid technical advances in genomic studies produce enormous amount of data to be analyzed and traditional statistical methods are no longer sufficient to handle these data. In this paper, we reviewed several important statistical issues that occur frequently in large-scale genomic cohort studies, including measurement error and its relevant correction methods, cost-efficient design strategy for main cohort and validation studies, inflated Type I error, gene-gene and gene-environment interaction and time-varying hazard ratios. It is very important to employ appropriate statistical methods in order to make the best use of valuable cohort data and produce valid and reliable study results.
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Original Article
Bioinformatics and Genomic Medicine.
Ju Han Kim
Korean J Prev Med. 2002;35(2):83-91.
  • 2,209 View
  • 37 Download
AbstractAbstract PDF
Bioinformatics is a rapidly emerging field of biomedical research. A flood of large-scale genomic and postgenomic data means that many of the challenges in biomedical research are now challenges in computational sciences. Clinical informatics has long developed methodologies to improve biomedical research and clinical care by integrating experimental and clinical information systems. The informatics revolutions both in bioinformatics and clinical informatics will eventually change the current practice of medicine, including diagnostics, therapeutics, and prognostics.Postgenome informatics, powered by high throughput technologies and genomic-scale databases, is likely to transform our biomedical understanding forever much the same way that biochemistry did a generation ago. The paper describes how these technologies will impact biomedical research and clinical care, emphasizing recent advances in biochip-based functional genomics and proteomics. Basic data preprocessing with normalization, primary pattern analysis, and machine learning algorithms will be presented. Use of integrated biochip informatics technologies, text mining of factual and literature databases, and integrated management of biomolecular databases will be discussed. Each step will be given with real examples in the context of clinical relevance. Issues of linking molecular genotype and clinical phenotype information will be discussed.
Summary

JPMPH : Journal of Preventive Medicine and Public Health