Multiple SNP testing improves risk prediction of first venous thrombosis

There are no risk models available yet that accurately predict a person's risk for developing venous thrombosis. Our aim was therefore to explore whether inclusion of established thrombosis-associated single nucleotide polymorphisms (SNPs) in a venous thrombosis risk model improves the risk pre...

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Published inBlood Vol. 120; no. 3; pp. 656 - 663
Main Authors de Haan, Hugoline G., Bezemer, Irene D., Doggen, Carine J.M., Le Cessie, Saskia, Reitsma, Pieter H., Arellano, Andre R., Tong, Carmen H., Devlin, James J., Bare, Lance A., Rosendaal, Frits R., Vossen, Carla Y.
Format Journal Article
LanguageEnglish
Published Washington, DC Elsevier Inc 19.07.2012
Americain Society of Hematology
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Summary:There are no risk models available yet that accurately predict a person's risk for developing venous thrombosis. Our aim was therefore to explore whether inclusion of established thrombosis-associated single nucleotide polymorphisms (SNPs) in a venous thrombosis risk model improves the risk prediction. We calculated genetic risk scores by counting risk-increasing alleles from 31 venous thrombosis-associated SNPs for subjects of a large case-control study, including 2712 patients and 4634 controls (Multiple Environmental and Genetic Assessment). Genetic risk scores based on all 31 SNPs or on the 5 most strongly associated SNPs performed similarly (areas under receiver-operating characteristic curves [AUCs] of 0.70 and 0.69, respectively). For the 5-SNP risk score, the odds ratios for venous thrombosis ranged from 0.37 (95% confidence interval [CI], 0.25-0.53) for persons with 0 risk alleles to 7.48 (95% CI, 4.49-12.46) for persons with more than or equal to 6 risk alleles. The AUC of a risk model based on known nongenetic risk factors was 0.77 (95% CI, 0.76-0.78). Combining the nongenetic and genetic risk models improved the AUC to 0.82 (95% CI, 0.81-0.83), indicating good diagnostic accuracy. To become clinically useful, subgroups of high-risk persons must be identified in whom genetic profiling will also be cost-effective.
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ISSN:0006-4971
1528-0020
DOI:10.1182/blood-2011-12-397752