As the GWAS method has been reviewed extensively, only a brief description is given here and Table 1 provides ample opportunities for further reading. A GWAS for a human disease is usually a variant of a cross-sectional case-control study, the familiar workhorse in biomedicine. Cases meet lifetime criteria for a disease (e.g., schizophrenia) and controls should have never met criteria and, ideally, be through the period of risk. Each individual in the sample is genotyped for a pre-defined set of a million or more genetic markers spaced across the genome. The genetic markers are single nucleotide polymorphisms (SNPs, “snips”) which are relatively straight-forward to assay in a highly multiplexed fashion. After careful quality control, each SNP is tested for association with disease. In effect, these tests compare the allele frequencies in cases versus controls, and a large case-control difference suggests an etiological role for a particular SNP or its genomic region. Because of the large numbers of statistical comparisons, the laws of probability mandate correction for multiple comparisons. A typical type 1 error threshold for genome-wide significance is often taken to be 5×10-8 (akin to a Bonferroni correction of 0.05 divided by 1 million tests) (Pe'er et al. 2008).
