암 발생예측 모형과 유전위험점수에 관한 고찰

정금지; 김소리울; 윤미욱; 전티나; 지선하

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자료유형: 학술저널

저자정보: 정금지 김소리울 윤미욱 전티나 지선하

저널정보: 한국보건정보통계학회 보건정보통계학회지 보건정보통계학회지 제39권 제1호

발행연도: 2014.1

수록면: 1 - 14 (14page)

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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.

#Single-nucleotide polymorphisms #Genetic risk score #Prediction model

참고문헌 (21)

참고문헌 신청

Aly M / 2011 / Polygenic risk score improves prostate cancer risk prediction: results from the Stockholm-1 cohort study / European Urology 60 (1) : 21 ~ 28

Amos CI / 2008 / Genome-wide association scan of tag SNPs identifies a susceptibility locus for lung cancer at 15q25.1 / Nature Genetics 40 (5) : 616 ~ 622

Blakely T / 2013 / The association of active smoking with multiple cancers:national census-cancer registry cohorts with quantitative bias analysis / Cancer Causes & Contr

Cerhan JR / 2014 / A pooled analysis of waist circumference and mortality in 650,000 adults / Mayo Clinic Proceedings 89 (3) : 335 ~ 345

Chu YH / 2014 / MicroRNA gene polymorphisms and environmental factors increase patient susceptibility to hepatocellular carcinoma / PloS One 9 (2) : e89930 ~