Traditional and Genetic Risk Score and Stroke Risk Prediction in Korea
- Author:
Keum Ji JUNG
1
;
Semi HWANG
;
Sunmi LEE
;
Hyeon Chang KIM
;
Sun Ha JEE
Author Information
- Publication Type:Original Article
- Keywords: Risk factors; Epidemiologic methods; Genetics; Stroke
- MeSH: Alleles; Epidemiologic Methods; Genetics; Genome-Wide Association Study; Korea; Odds Ratio; Polymorphism, Single Nucleotide; Risk Factors; ROC Curve; Stroke
- From:Korean Circulation Journal 2018;48(8):731-740
- CountryRepublic of Korea
- Language:English
- Abstract: BACKGROUND AND OBJECTIVES: Whether using both traditional risk factors and genetic variants for stroke as opposed to using either of the 2 alone improves the prediction of stroke risk remains unclear. The purpose of this study was to compare the predictability of stroke risk between models using traditional risk score (TRS) and genetic risk score (GRS). METHODS: We used a case-cohort study from the Korean Cancer Prevention Study-II (KCPS-II) Biobank (n=156,701). We genotyped 72 single nucleotide polymorphisms (SNPs) identified in genome-wide association study (GWAS) on the KCPS-II sub-cohort members and stroke cases. We calculated GRS by summing the number of risk alleles. Prediction models with or without GRS were evaluated in terms of the area under the receiver operating characteristic curve (AUROC). RESULTS: Sixteen out of 72 SNPs identified in GWAS showed significant associations with stroke, with an odds ratio greater than 2.0. For participants aged < 40 years, AUROCs for incident stroke were 0.58, 0.65, and 0.67 in models using modifiable TRS only, GRS only, and TRS plus GRS, respectively, showing that GRS only model had better prediction than TRS only. For participants aged ≥40 years, however, TRS only model had better prediction than GRS only model. Favorable levels of traditional risk were associated with significantly lower stroke risks within each genetic risk category. CONCLUSIONS: TRS and GRS were both independently associated with stroke risk. Using genetic variants in addition to traditional risk factors may be the most accurate way of predicting stroke risk, particularly in relatively younger individuals.
