Evaluating coverage of genome-wide association studies. Nat Genet

University of Oxford, Oxford, England, United Kingdom
Nature Genetics (Impact Factor: 29.35). 07/2006; 38(6):659-62. DOI: 10.1038/ng1801
Source: PubMed


Genome-wide association studies involving hundreds of thousands of SNPs in thousands of cases and controls are now underway. The first of many analytical challenges in these studies involves the choice of SNPs to genotype. It is not practical to construct a different panel of tag SNPs for each study, so the first generation of genome-wide scans will use predefined, commercially available marker panels, which will in part dictate their success or failure. We compare different approaches in use today, and show that although many of them provide substantial coverage of common variation in non-African populations, the precise extent is strongly dependent on the frequencies of alleles of interest and on specific considerations of study design. Overall, despite substantial differences in genotyping technologies, marker selection strategies and number of markers assayed, the first-generation high-throughput platforms all offer similar levels of genome coverage.

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Available from: Lon Cardon, Apr 27, 2015
    • "HapMap created a detailed mapping of ancestral chromosome segments in four populations, resulting in a catalogue of ten million SNPs from which certain SNPs can serve as markers ('tag-SNPs') for genetic variation. Thus, a few hundred thousand SNPs could be used as markers for most of the common variation of the human genome (Barrett & Cardon, 2006). Currently, the Catalog of Published GWASs from the National Human Genome Institute (http://www.genome.gov/gwastudies/) "
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    • "Consequently, genotypes at the millions of common variants throughout the genome are strongly correlated with each other, and thus can be captured by a subset of “tag” SNPs. The most efficient GWAS genotyping products, therefore, have been designed to include these tag SNPs, maximising coverage of common variation, but reducing costs without a corresponding loss in power for detecting association with complex traits [18]. However, although this design is beneficial for the discovery of GWAS loci, it poses substantial challenges to the dissection of association signals for the purposes of fine mapping because (i) the causal variant(s) will not necessarily have been directly typed and (ii) multiple variants may demonstrate equivalent statistical evidence of association because of the LD between them. "
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