Open AccessMultilocus Genetic Risk Scores for Venous Thromboembolism Risk Assessment
Author(s) -
Soria José Manuel,
Morange PierreEmmanuel,
Vila Joan,
Souto Juan Carlos,
Moyano Manel,
Trégouët DavidAlexandre,
Mateo José,
Saut Noémi,
Salas Eduardo,
Elosua Roberto
Publication year - 2014
Publication title -
journal of the american heart association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.494
H-Index - 85
ISSN - 2047-9980
DOI - 10.1161/jaha.114.001060
Subject(s) - medicine , receiver operating characteristic , factor v leiden , risk assessment , prothrombin g20210a , genetic testing , framingham risk score , area under the curve , venous thromboembolism , venous thrombosis , thrombosis , disease , computer security , computer science
Background Genetics plays an important role in venous thromboembolism ( VTE ). Factor V Leiden ( FVL or rs6025) and prothrombin gene G20210A ( PT or rs1799963) are the genetic variants currently tested for VTE risk assessment. We hypothesized that primary VTE risk assessment can be improved by using genetic risk scores with more genetic markers than just FVL ‐rs6025 and prothrombin gene PT ‐rs1799963. To this end, we have designed a new genetic risk score called Thrombo inCode (TiC). Methods and Results TiC was evaluated in terms of discrimination (Δ of the area under the receiver operating characteristic curve) and reclassification ( integrated discrimination improvement and net reclassification improvement ). This evaluation was performed using 2 age‐ and sex‐matched case–control populations: SANTPAU (248 cases, 249 controls) and the Marseille Thrombosis Association study ( MARTHA ; 477 cases, 477 controls). TiC was compared with other literature‐based genetic risk score s. TiC including F5 rs6025/rs118203906/rs118203905, F2 rs1799963, F12 rs1801020, F13 rs5985, SERPINC 1 rs121909548, and SERPINA 10 rs2232698 plus the A1 blood group (rs8176719, rs7853989, rs8176743, rs8176750) improved the area under the curve compared with a model based only on F5‐ rs6025 and F2‐ rs1799963 in SANTPAU (0.677 versus 0.575, P <0.001) and MARTHA (0.605 versus 0.576, P =0.008). TiC showed good integrated discrimination improvement of 5.49 ( P <0.001) for SANTPAU and 0.96 ( P =0.045) for MARTHA . Among the genetic risk score s evaluated, the proportion of VTE risk variance explained by TiC was the highest. Conclusions We conclude that TiC greatly improves prediction of VTE risk compared with other genetic risk score s. TiC should improve prevention, diagnosis, and treatment of VTE .