Genome-wide association studies (GWASs) seek to identify common variants, usually single nucleotide polymorphisms (SNPs), that are associated with common phenotypes (for review of GWAS, see: Bush and Moore, 2012). Most GWAS data have been obtained through chip-based microarray technologies such as Illumina® or Affymetrix®, while targeted sequencing approaches have been used to follow up regions of interest identified in chip microarrays (DiStefano and Taverna, 2011). GWAS studies are correlational and any effects of most SNPs on disease phenotype are small, so large sample sizes are necessary to produce enough power to meet a stringent P value (e.g., 5 × 10−8) and to estimate SNP effect size. Currently, meta-analysis, combining multiple datasets, is used to generate the largest possible sample.
