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Minji Kim 3 Articles
Estimation of heritability attributable to single-locus effects with a regression of offspring on mid-parent (ROMP) method for cardiovascular risk factors.
Sun Ha Jee, Jung Yong Park, Ji Eun Yoon, Minji Kim, Eun Young Cho, Yang soo Jang
Korean J Epidemiol. 2003;25(1):24-31.
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Abstract
PURPOSE
The objective of this study was to estimate the heritability attributable to single-locus effects with a regression of offspring on mid-parent (ROMP) method for cardiovascular risk factors.
METHODS
The regression of offspring on mid-parent is determined with and without the inclusion of a single-locus effect, and the difference between the slopes of these two regression is an estimate of the heritability attributable to the single-locus effect. The study population included 1,550 family members of 295 patients, derived from cardiovascular genome center. The risk factors considered were total serum cholesterol, triglyceride, LDL cholesterol, apoAI and apoB. Heritability was estimated from the slope of the linear regression of offspring on mid-parents.
RESULTS
Estimated heritability was 35 to 46% for total cholesterol with 6.2% attributable to polymorphism S128R. For triglyceride, the estimated heritability was 47.6% with 2% attributable to polymorphism G-217A. The heritability was 36-46% for LDL-cholesterol. For LDL cholesterol, S128R specific effect was 8.7%. Estimated heritability was 62.2% for apoAI with 3.2% attributable to polymorphism G-217A and 58 to 75% for apoB with 5.4% attributable to polymorphism S128R.
CONCLUSIONS
These traits were significantly associated with polymorphism S128R. These results highlight the importance of considering genetic factors in studies of cardiovascular risk factors. Unlike traditional population-based tests of association, ROMP appears to be robust with respect to population stratification.
Summary
Korean summary
Key Message
A Review Study on Confounding Effect: Case-control Study.
Seonwoo Kim, Minji Kim, Soon Young Lee
Korean J Epidemiol. 1999;21(2):248-253.
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AbstractAbstract PDF
Abstract
Confounding is the distortion of a disease/exposure association brought about by other factors which are not considered in the study design or the data analysis. These factors are called confounding factors. We should be cautious in data analysis of observational study of association of disease/exposure, since confounding often occurred in observational study. This study examines confounding effect according to data pattern (the ratio of controls to cases, the ratio of exposures to non-exposures for each level of confounding factor), criteria for treating a variable as a confounding variable, and some notes for the analysis in case-control study.
Summary
Korean summary
Key Message
A Review Study on Comparing Treatment Effects among Subgroups.
Seonwoo Kim, Minji Kim, Soon Young Lee
Korean J Epidemiol. 1999;21(1):104-110.
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  • 14 Download
AbstractAbstract PDF
Abstract
It is interested in examining treatment effect on a particular category of subjects or in comparing treatment effects among different subgroups as well as overall treatment effect due to heterogeneity of study subjects. Subgroup analyses are exceedingly common, but they are also often misleading. Conclusions based on subgroup analyses can do harm both when a particular category of people is denied effective treatment (a "false-negative" conclusion), and when ineffective or even harmful treatment is given to a subgroup of people (a "false-positive" conclusion). Because of the frequency and the importance of clinical application of subgroup analysis, researchers need to be cautious about doing subgroup analyses. This study presents guidelines to help conducting subgroup analyses correctly.
Summary
Korean summary
Key Message

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