Genomic control for association studies

A dense set of single nucleotide polymorphisms (SNP) covering the genome an d an efficient method to assess SNP genotypes are expected to be available in the near future. An outstanding question is how to use these technologie s efficiently to identify genes affecting liability to complex disorders. T o achieve this goal, we propose a statistical method that has several optim al properties: It can be used with case-control data and yet, like family-b ased designs, controls for population heterogeneity; it is insensitive to t he usual violations of model assumptions, such as cases failing to be stric tly independent; and, by using Bayesian outlier methods, it circumvents the need for Bonferroni correction for multiple tests, leading to better perfo rmance in many settings while still constraining risk for false positives. The performance of our genomic control method is quite good for plausible e ffects of liability genes, which bodes well for future genetic analyses of complex disorders.