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Predicting Stroke Through Genetic Risk Functions : The CHARGE Risk Score Project 원문보기

Stroke, v.45 no.2, 2014년, pp.403 - 412  

Ibrahim-Verbaas, Carla A. ,  Fornage, Myriam ,  Bis, Joshua C. ,  Choi, Seung Hoan ,  Psaty, Bruce M. ,  Meigs, James B. ,  Rao, Madhu ,  Nalls, Mike ,  Fontes, Joao D. ,  O’Donnell, Christopher J. ,  Kathiresan, Sekar ,  Ehret, Georg B. ,  Fox, Caroline S. ,  Malik, Rainer ,  Dichgans, Martin ,  Schmidt, Helena ,  Lahti, Jari ,  Heckbert, Susan R. ,  Lumley, Thomas ,  Rice, Kenneth ,  Rotter, Jerome I. ,  Taylor, Kent D. ,  Folsom, Aaron R. ,  Boerwinkle, Eric ,  Rosamond, Wayne D. ,  Shahar, Eyal ,  Gottesman, Rebecca F. ,  Koudstaal, Peter J. ,  Amin, Najaf ,  Wieberdink, Renske G. ,  Dehghan, Abbas ,  Hofman, Albert ,  Uitterlinden, André ,  G. ,  DeStefano, Anita L. ,  Debette, Stephanie ,  Xue, Luting ,  Beiser, Alexa ,  Wolf, Philip A. ,  DeCarli, Charles ,  Ikram, M. Arfan ,  Seshadri, Sudha ,  Mosley Jr, Thomas H. ,  Longstreth Jr, W.T. ,  van Duijn, Cornelia M. ,  Launer, Lenore J.

Abstract AI-Helper 아이콘AI-Helper

Background and Purpose—Beyond the Framingham Stroke Risk Score, prediction of future stroke may improve with a genetic risk score (GRS) based on single-nucleotide polymorphisms associated with stroke and its risk factors.Methods—The study includes 4 population-based cohorts with 2047 f...

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참고문헌 (32)

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