Author Affiliations: Biostatistics Department, Boston University School of Public Health, Boston, Mass (Dr Dupuis); National Heart, Lung, and Blood Institute (NHLBI) and NHLBI's Framingham Heart Study (Dr O’Donnell); and Cardiology Division, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston (Dr O’Donnell).
The completion of the Human Genome sequence1 has been accompanied by the rapid appearance of genetic association studies using large numbers of genetic markers to search for genetic variation underlying common, major health problems such as cardiovascular disease and cancer. Physicians will increasingly encounter articles in the literature analyzing screens of ever larger numbers of common genetic markers. An excellent example is the study of 280 single nucleotide polymorphisms (SNPs) in 24 venous thrombosis candidate genes reported by Smith and colleagues in this issue of JAMA.2 Such studies are currently focused on the role of genetic variation in candidate gene regions, but a “gold rush” now under way of unbiased genome-wide association studies (GWAS) using hundreds of thousands of SNPs will generate a vast number of potentially valid genetic associations. Clinicians and scientists alike will need to exercise care in distinguishing gold from fool's gold when interpreting the results of these studies. To do so requires an understanding of the rationale for large-scale studies of SNPs as well as important elements of study design and, in particular, major statistical considerations in the interpretation of results (see Box for list of terms commonly used in such studies).
Dupuis J, O’Donnell CJ. Interpreting Results of Large-Scale Genetic Association Studies: Separating Gold From Fool's Gold. JAMA. 2007;297(5):529–531. doi:10.1001/jama.297.5.529
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