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Circ Cardiovasc Genet. 2009 Feb;2(1):7-15. doi: 10.1161/CIRCGENETICS.108.833392. Epub 2009 Jan 23.

Prediction of cardiovascular disease outcomes and established cardiovascular risk factors by genome-wide association markers.

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  • 1Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece.



Genome-wide association (GWA) platforms have yielded a rapidly increasing number of new genetic markers. The ability of these markers to improve prediction of clinically important outcomes is debated.


A systematic review was performed of GWA-derived markers associated with cardiovascular outcomes or other phenotypes that represent common established risk factors for cardiovascular outcomes. Sources of information included the National Human Genome Research Institute catalog of published GWA studies, and perusal of the eligible GWA articles, meta-analyses on the respective associations, and articles on the incremental predictive performance of common variants in the GWA era. A total of 95 eligible associations were retrieved from the National Human Genome Research Institute catalogue of published GWA studies as of September 2008. Of those 36 have statistical support of P<10(-7). In depth evaluation of the respective articles shows 28 independent associations with such statistical support, pertaining to coronary artery disease, myocardial infarction, atrial fibrillation/flutter, prolongation of QT interval, as well as type 2 diabetes, body mass index, high-density lipoprotein levels, low-density lipoprotein levels, and nicotine dependence. Between-study heterogeneity is not taken into account usually, but it seems common and it would pose a challenge to generalizability across different populations for these markers. Still limited data are available in non-white populations. Effect sizes are small and may be even smaller in subsequent replications and meta-analysis. Population attributable fractions are substantial, given the large frequency of the risk alleles. However, individualized risk measures are typically very small (proportion of variance explained <1% per marker). When used in conjunction with traditional predictors, improvement in overall prediction (eg, area under the curve) or risk reclassification is limited, and subject to methodological caveats.


Despite very promising signals in terms of statistical significance, evidence for improvement in cardiovascular prediction by currently available markers derived from GWA studies is sparse. Clinical use of such markers currently would be premature.

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