Invited critical review Cardiovascular diseases and genome-wide association studies Ndeye Coumba Ndiaye 1 , Mohsen Azimi Nehzad 1 , Said El Shamieh 1 , Maria G. Stathopoulou 1 , Sophie Visvikis-Siest ⁎ , 1 “Cardiovascular Genetics” Research Unit EA4373, 30 rue Lionnois, Université Henri Poincaré, Nancy 1, F-54000 Nancy, France abstract article info Article history: Received 25 May 2011 Received in revised form 31 May 2011 Accepted 31 May 2011 Available online 7 June 2011 Keywords: Genome-Wide Association Studies Cardiovascular diseases Cardiovascular-related quantitative traits Gene–gene interactions Gene–environment interactions Genome-Wide Association Studies (GWAS) on cardiovascular diseases and related quantitative traits revealed numerous genetic variants, which however have been partially replicated, probably due to the heterogeneity of the clinical phenotypes and the populations studied. Even if novel biological pathways have been identified through these studies, there is still a long way until the validation of causal variants and their use in clinical practice as factors for prevention, risk assessment and as targets for the development of new medications. GWAS methodologies should, in the following years, integrate gene–gene and gene–environment interaction analyses in a global research strategy and also involve subsequent transcriptomic and proteomic investigations. The GWAS era is very promising but it is just at the beginning. © 2011 Elsevier B.V. All rights reserved. Contents 1. Introduction: cardiovascular diseases: common disorders with important genetic component . . . . . . . . . . . . . . . . . . . . . . . 1697 2. Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1698 2.1. Literature search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1698 2.2. Data extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1698 3. Brief overview of GWAS on cardiovascular diseases and their related quantitative traits . . . . . . . . . . . . . . . . . . . . . . . . . . 1698 4. GWAS strengths and limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1699 4.1. Current GWAS designs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1699 4.2. Future challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1699 4.3. Missing heritability in cardiovascular diseases, an Achilles' heel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1700 5. Concluding remarks and perspectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1700 Disclosures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1700 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1700 1. Introduction: cardiovascular diseases: common disorders with important genetic component Cardiovascular diseases (CVDs) are the leading cause of death in the world. Based on World Health Organization, 17.1 million people died from CVDs in 2004 and it is estimated that by 2030, approximately 23.6 million deaths will be recorded due to CVDs, mainly from heart disease and stroke [1]. The aetiology of CVDs is multifactorial, where a complex combination of environmental, genetic and clinical risk factors seems to play determinant role [2]. In fact, the pathogenesis of coronary heart disease (CHD) is known to be influenced by smoking, diabetes, hypertension, obesity, physical inactivity, alcohol intake and psycho- social conditions [3–5]. Genetic elements contribute to the develop- ment of CAD [6,7]. This complex architecture and its genetic background [8,9] are still poorly understood and substantial discrep- ancies remain in estimating the heritability of numerous CVDs-related quantitative traits (QTs) [10]. Risk algorithms such as the Framingham Risk Score [11] have traditionally incorporated classical clinical and environmental risk factors such as age, gender, blood lipid concentra- tions, blood pressure (BP), body mass index, family history and Clinica Chimica Acta 412 (2011) 1697–1701 ⁎ Corresponding author at: Université Henri Poincaré, Faculté de Pharmacie, "Cardiovascular Genetics" Research Unit EA4373, 30, rue Lionnois, 54000 Nancy. Tel.: +33 607602569; fax: +33 383321322. E-mail addresses: coumba.ndiaye@inserm.fr (N.C. Ndiaye), mohsen.azimi-nezhad@pharma.uhp-nancy.fr (M. Azimi Nehzad), said.shamieh@gmail.com (S. El Shamieh), maria.stathopoulou@inserm.fr (M.G. Stathopoulou), Sophie.Visvikis-Siest@inserm.fr (S. Visvikis-Siest). 1 All authors contributed equally to this work. 0009-8981/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.cca.2011.05.035 Contents lists available at ScienceDirect Clinica Chimica Acta journal homepage: www.elsevier.com/locate/clinchim