Recent successes in genome-wide association studies (GWAS) are evidenced by a steady increase in the number of scientific publica- tions in the literature. This is likely to be a direct reflection of the level of efficiency with which investigators are now able to associate clinical phenotypes with SNPs. These successes have come despite complex challenges. Factors influencing the likelihood of a successful outcome include phenotypic heterogeneity, phenotype prevalence, popula- tion stratification, the minor allele frequencies of the genetic elements driving phenotypic variance, and the availability of adequate sample quantities.
In this context, it is not surprising that the choice of genotyping plat- form draws significant attention and discussion. The selection strategy and quantity of markers, as well as the robustness of data gener- ated by genotyping arrays, are known to affect study power and the likelihood of GWAS success. Illumina has designed its arrays with the intent of enabling investigators with a combination of high data quality and optimized genomic coverage. In many cases, this combination has provided the fastest path to significant associations and publica- tion. This document demonstrates multiple measures of coverage and data quality and describes how each contributes to the performance of Illumina’s genotyping platform.
