• Journal of Preventive Medicine and Public Health
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• 제36권3호
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• pp.209-212
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• 2003

Because of advances of technologies in the field of genmic epidemiology in the recent years, specimen collection, storage and analysis became an essential part of research methodologies. DNA is now being used in epidemiologic studies to evaluate genetic risk factors and specimens other than the fresh whole blood can De used for PCR. Therefore, All nucleated cells, such as buccal swabs and urine specimens, are suitable for DNA analysis. For an unlimited source of genomic DNA, EBV transformation of lymphocytes can be used for immortalization. However, the type of specimen collected in genomic epidemiologic studies will depend on the study where the epidemiologist play a leading role for the design. We also briefly described various finds of analysis for SNP that is an essential part of the genomic epidemiology.

• 미생물학회지
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• 제54권3호
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• pp.188-199
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• 2018

다양한 미생물학 연구 분야의 발전에 힘입어 유전체역학은 발전되어 왔다. 예를 들어, 대용량서열화 기술의 발전으로 미생물 유전체의 수는 급속도로 증가해 오고 있다. 이러한 풍부한 유전체 데이터는 전에는 보지 못한 보다 더 정확한 미생물종의 동정에 도움을 주는 균주종 타이핑에 새로운 기회를 제공한다. 유전체역학은 유전체에 일반적인 유전자를 찾고 표기하는 것 뿐만 아니라 항균 저항성 유전자를 찾을 수 있다. 균주종 타이핑과 항균 저항성 유전자 찾기는 각각 종을 구분하고 유전체내의 유전자 위치를 결정하는 유전체 역학의 방법들로 시간에 따른 변화가 없는 측면이다. 이에 반하여, 하나의 숙주가 어떤 숙주를 감염시켰는지 알아내기 위해서는 감염된 숙주들 사이의 시간에 따른 동적인 전염 경로를 추론해야 한다. 이렇게, 균주종 타이핑, 항균 저항성 유전자 찾기, 전염 계통수 추론을 통하여 유전체역학의 궁극적인 목표 중 하나인 미생물성 전염병을 보다 효율적으로 감시할 수 있을 것으로 기대된다. 그리고, 대용량서열화 기술 중, 3세대 서열화기술 중 하나인 옥스포드 나노포어 MinION의 보다 나은 휴대성과 빠른 서열화의 성능 덕분에 유전체역학은 더 많은 발전을 거듭할 것으로 보인다. 이에, 본 연구는 항균 저항성 유전자를 찾고 전염병 경로를 추론하는 계산적인 방법에 대하여 살펴보고, 미생물 유전체역학에서 MinION이 응용된 예들에 대하여 논하였다.

• Journal of Preventive Medicine and Public Health
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• 제36권3호
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• pp.213-222
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• 2003

Genomic epidemiology is defined as 'an evoking field of inquiring that uses the systematic application of epidemiologic methods are approaches in population-based studies of the impact of human genetic variation on health and disease (Khoury, 1998)'. Most human diseases are caused by the intricate interaction among environmental exposures and genetic susceptibility factors. Susceptibility genes involved in disease pathogenesis are categorized into two groups: high penetrance genes (i.e., BRAC1, RB, etc.) and lour penetranoe genes (i.e., GSTs, Cyps, XRCC1, ets.), and low penetrance susceptibility genes has the higher priority for epidemiological research due to high population attributable risk. In this paper, the summarized results of the association study between single nucleotide polymorphisms (SNPs) and breast cancer in Korea were introduced and the international trends of genomic epidemiology research were reviewed with an emphasis on internee-based case-control and cohort consortium.

• 한국동물위생학회지
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• 제46권1호
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• pp.93-106
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• 2023

Emerging and re-emerging zoonotic viruses become critical public health, economic, societal, and cultural burdens. The Coronavirus disease-19 (COVID-19) pandemic reveals needs for effective preparedness and responsiveness against the emergence of variants and the next virus outbreak. The targeted next-generation sequencing (NGS) significantly contributes to the acquisition of viral genome sequences directly from clinical specimens. Using this advanced NGS technology, the genomic epidemiology and surveillance play a critical role in identifying of infectious source and origin, tracking of transmission chains and virus evolution, and characterizing the virulence and developing of vaccines during the outbreak. In this review, we highlight the platforms and preparation of targeted NGS for the viral genomics. We also demonstrate the application of this strategy to take advantage of the responsiveness and prevention of emerging zoonotic viruses. This article provides broad and deep insights into the preparedness and responsiveness for the next zoonotic virus outbreak.

• Journal of Preventive Medicine and Public Health
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• 제42권6호
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• pp.349-355
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• 2009

Biomarkers are characteristic biological properties that can be detected and measured in a variety of biological matrices in the human body, including the blood and tissue, to give an indication of whether there is a threat of disease, if a disease already exists, or how such a disease may develop in an individual case. Along the continuum from exposure to clinical disease and progression, exposure, internal dose, biologically effective dose, early biological effect, altered structure and/or function, clinical disease, and disease progression can potentially be observed and quantified using biomarkers. While the traditional discovery of biomarkers has been a slow process, the advent of molecular and genomic medicine has resulted in explosive growth in the discovery of new biomarkers. In this review, issues in evaluating biomarkers will be discussed and the biomarkers of environmental exposure, early biologic effect, and susceptibility identified and validated in epidemiological studies will be summarized. The spectrum of genomic approaches currently used to identify and apply biomarkers and strategies to validate genomic biomarkers will also be discussed.

• Journal of Preventive Medicine and Public Health
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• 제42권6호
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• pp.356-359
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• 2009

In the more than 100 genome wide association studies (GWAS) conducted in the past 5 years, more than 250 genetic loci contributing to more than 40 common diseases and traits have been identified. Whilst many genes have been linked to a trait, both their individual and combined effects are small and unable to explain earlier estimates of heritability. Given the rapid changes in disease incidence that cannot be accounted for by changes in diagnostic practises, there is need to have well characterized exposure information in addition to genomic data for the study of gene-environment interactions. The case-control and cohort study designs are most suited for studying associations between risk factors and occurrence of an outcome. However, the case control study design is subject to several biases and hence the preferred choice of the prospective cohort study design in investigating geneenvironment interactions. A major limitation of utilising the prospective cohort study design is the long duration of follow-up of participants to accumulate adequate outcome data. The GWAS paradigm is a timely reminder for traditional epidemiologists who often perform one- or few-at-a-time hypothesis-testing studies with the main hallmarks of GWAS being the agnostic approach and the massive dataset derived through large-scale international collaborations.

• 대한예방의학회:학술대회논문집
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• 대한예방의학회 2003년도 하계워크샵 및 전공의 연수교육 강의집
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• pp.1-14
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• 2003

• 한국유전체학회:학술대회논문집
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• 한국유전체학회 2005년도 The 14th Korea Genome Conference
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• pp.46-46
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• 2005

• Genomics & Informatics
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• 제11권2호
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• pp.83-92
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• 2013

Genetic studies on facial morphology targeting healthy populations are fundamental in understanding the specific genetic influences involved; yet, most studies to date, if not all, have been focused on congenital diseases accompanied by facial anomalies. To study the specific genetic cues determining facial morphology, we estimated familial correlations and heritabilities of 14 facial measurements and 3 latent factors inferred from a factor analysis in a subset of the Korean population. The study included a total of 229 individuals from 38 families. We evaluated a total of 14 facial measurements using 2D digital photographs. We performed factor analysis to infer common latent variables. The heritabilities of 13 facial measurements were statistically significant (p < 0.05) and ranged from 0.25 to 0.61. Of these, the heritability of intercanthal width in the orbital region was found to be the highest ($h^2$ = 0.61, SE = 0.14). Three factors (lower face portion, orbital region, and vertical length) were obtained through factor analysis, where the heritability values ranged from 0.45 to 0.55. The heritability values for each factor were higher than the mean heritability value of individual original measurements. We have confirmed the genetic influence on facial anthropometric traits and suggest a potential way to categorize and analyze the facial portions into different groups.

• Journal of Preventive Medicine and Public Health
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• 제40권2호
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• pp.108-113
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• 2007

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.