Predicting venous thrombosis in women using a combination of genetic markers and clinical risk factors

Published on Feb 1, 2015in Journal of Thrombosis and Haemostasis4.157
· DOI :10.1111/JTH.12808
Maria Bruzelius10
Estimated H-index: 10
(KI: Karolinska Institutet),
Matteo Bottai61
Estimated H-index: 61
(KI: Karolinska Institutet)
+ 7 AuthorsJacob Odeberg33
Estimated H-index: 33
(KI: Karolinska Institutet)
Background: Family history of venous thromboembolism (VTE) has been suggested to be more useful in risk assessment than thrombophilia testing. Objectives: We investigated established genetic susceptibility variants for association with VTE and evaluated a genetic risk score in isolation and combined with known trigger factors, including family history of VTE. Patients/Method: A total of 18 single nucleotide polymorphisms (SNPs) selected from the literature were genotyped in 2835 women participating in a Swedish nationwide case-control study (the ThromboEmbolism Hormone Study [TEHS]). Association with VTE was assessed by odds ratios (ORs) with 95% confidence interval (CI) using logistic regression. Clinical and genetic predictors that contributed significantly to the fit of the logistic regression model were included in the prediction models. SNP-SNP interactions were investigated and incorporated into the models if found significant. Risk scores were evaluated by calculating the area under the receiver-operating characteristics curve (AUC). Results: Seven SNPs (F5 rs6025, F2 rs1799963, ABO rs514659, FGG rs2066865, F11 rs2289252, PROC rs1799810 and KNG1 rs710446) with four SNP-SNP interactions contributed to the genetic risk score for VTE, with an AUC of 0.66 (95% CI, 0.64–0.68). After adding clinical risk factors, which included family history of VTE, the AUC reached 0.84 (95% CI, 0.82–0.85). The goodness of fit of the genetic and combined scores improved when significant SNP-SNP interaction terms were included. Conclusion: Prediction of VTE in high-risk individuals was more accurate when a combination of clinical and genetic predictors with SNP-SNP interactions was included in a risk score.
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