Application of genomewide SNP arrays for detection of simulated susceptibility loci

The prospect of SNP-based genomewide association analysis has been extensively discussed, but practical experiences remain limited. We performed an association study using a recently developed array of 11,555 SNPs distributed throughout the human genome. A total of 104 DNA samples were hybridized to these chips with an average call rate of 97% (range 85.3-98.6%). The resulting genomewide scans were applied to distinguish between carriers and noncarriers of 37 test variants, used as surrogates for monogenic disease traits. The test variants were not contained in the chip and had been determined by other methods. Without adjustment for multiple testing, the procedure detected 24% of the test variants, but the positive predictive value was low (2%). Adjustment for multiple testing eliminated most false-positive associations, but the share of true positive associations decreased to 10-12%. We also simulated fine-mapping of susceptibility loci by restricting testing to the immediate neighborhood of test variants (+/-5 Mb). This increased the proportion of correctly identified test variants to 22-27%. Simulation of a bigenic inheritance reduced the sensitivity to 1%. Similarly adverse effect had reduction of allelic penetrance. In summary, we demonstrate the feasibility and considerable specificity of SNP array-based association studies to detect variants underlying monogenic, highly penetrant traits. The outcome is affected by allelic frequencies of chip SNPs, by the ratio between simulated "cases" and "controls," and by the degree of linkage disequilibrium. A major improvement is expected from raising the density of the SNP array.