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암 발생예측 모형과 유전위험점수에 관한 고찰

Other Titles
 Review on Genetic Risk Score and Cancer Prediction Models 
Authors
 정금지  ;  김소리울  ;  윤미욱  ;  전티나  ;  지선하 
Citation
 Journal of Health Informatics and Statistics (보건정보통계학회지), Vol.39(1) : 1-14, 2014 
Journal Title
 Journal of Health Informatics and Statistics (보건정보통계학회지) 
ISSN
 2287-3708 
Issue Date
2014
Keywords
Single-nucleotide polymorphisms ; Genetic risk score ; Prediction model
Abstract
Objectives: In genome-wide association studies (GWASs), single-nucleotide polymorphisms (SNPs) that have been identified as cancer-associated loci are common, but they confer only small increases in risk. The question was whether combining multiple disease-related SNPs and the modest effects within Genetic Risk Score (GRS) may be useful in identifying subgroups that are at high risk of cancer. Methods: In this paper, we first reviewed articles that examined the predictability of GRS on cancer prediction models. Our data sources included a PubMed search of the literature published until February 2014. Secondly, we have calculated the GRS using the data example data with five SNPs related colorectal cancer (CRC) obtained from the Korean cancer prevention study II. Two approaches were used to calculate the GRS: a simple risk alleles count method (counted GRS) and a weighted method based on the genotype frequencies for each SNP and the effect sizes (allelic odds ratio or beta coefficient) from our study (weighted GRS). Results: Of 31 studies initially identified, 16 (135,110 participants) met the inclusion criteria. Among 16 articles, 7 studies were related to prostate cancer, 6 studies to breast cancer, and 3 studies to colon cancer and lung cancer. Fifteen studies except for one study concluded that in general, a genetic score may be helpful or useful in identifying the high risk group and particularly to determining the high risk individual among patients within a ‘‘gray zone’’ of cancer risk. The weighted GRS with age and sex (AUC=0.9333) had higher predictability on the CRC risk than the model with GRS alone (AUC=0.816). Conclusions: Although adding GRS improves prediction model performance, the clinical utility of these genetic risk models is limited. Nonetheless, the modelling suggests public health potential since it is possible to stratify the population into cancer risk categories, thereby informing targeted prevention and management.
Files in This Item:
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Appears in Collections:
4. Graduate School of Public Health (보건대학원) > Graduate School of Public Health (보건대학원) > 1. Journal Papers
Yonsei Authors
Jung, Keum Ji(정금지) ORCID logo https://orcid.org/0000-0003-4993-0666
Jee, Sun Ha(지선하) ORCID logo https://orcid.org/0000-0001-9519-3068
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/138851
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