Estimating coverage and power for genetic association studies using near-complete variation data.
Although studies suggest that SNPs derived from HapMap provide promising coverage and power for association studies, the lack of alternative variation datasets limits independent analysis. Using near-complete variation data for 76 genes resequenced in HapMap samples, we find that coverage of common variation by commercial genotyping arrays is substantially lower compared to the HapMap-based estimates. We quantify the power offered by these arrays for a range of disease models.
Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA.
Tushar R
TR
Bhangale
Mark J
MJ
Rieder
Deborah A
DA
Nickerson
D002874:Chromosome Mapping / Q000379:methods*; D005787:Gene Frequency; D008040:Genetic Linkage; D005838:Genotype; D006801:Humans; D015588:Observer Variation; D020411:Oligonucleotide Array Sequence Analysis; D020641:Polymorphism, Single Nucleotide; D012680:Sensitivity and Specificity; D017422:Sequence Analysis, DNA / Q000379:methods