Kyunga Kim
성균관대학교 의과대학 · 의학
Kyunga Kim 교수의 연구실은 복합질환의 유전적 기반을 규명하고, 다유전자 상호작용 및 유전자-환경 상호작용을 분석하는 데 초점을 맞추고 있습니다. 특히, 전장 게놈 연관 분석(GWAS)과 다변량 분석 기법을 활용해 유전적 요소 간의 상호작용을 보다 정밀하게 탐색하며, 임상적 예측 모델 개발에도 기여하고 있습니다. 연구는 유전적 위험 요소의 조합을 기반으로 암이나 알츠하이머병과 같은 복합질환의 전조기 예측에 응용됩니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
Unraveling the genetic background of common complex traits is a major goal in modern genetics. In recent years, genome-wide association (GWA) studies have been conducted with large-scale data sets of genetic variants. Most of those studies have relied on single-marker approaches that identify single genetic factors individually and can be limited in considering fully the joint effects of multiple genetic factors on complex traits. Joint identification of multiple genetic factors would be more po
In this article, we introduce two types of new evaluation measures. First, we develop weighted BA (wBA) that utilizes the quantitative information on the effect size of each multi-locus genotype on a trait. Second, we employ ordinal association measures to assess the performance of MDR classifiers. Simulation studies were conducted to compare the proposed measures with BA, a current measure. Our results showed that the wBA and tau(b) improved the power of MDR in detecting gene-gene interactions.
We were able to propose a predictive algorithm for each aMCI individual's conversion to dementia using the IML technique. This algorithm is expected to be useful in clinical practice and the research field, as it can suggest conversion with high accuracy and identify the degree of influence of risk factors for each patient.
In this cohort study of 10 years of clinical data, survival outcomes were improved across all stages, with larger increases in patients with stage III to IV disease. The incidence of never-smokers and the use of molecular testing increased.
