title: Genomic control for association studies.
abstract: A dense set of single nucleotide polymorphisms (SNP) covering the genome and an efficient method to assess SNP genotypes are expected to be available in the near future. An outstanding question is how to use these technologies efficiently to identify genes affecting liability to complex disorders. To achieve this goal, we propose a statistical method that has several optimal properties: It can be used with case control data and yet, like family-based designs, controls for population heterogeneity; it is insensitive to the usual violations of model assumptions, such as cases failing to be strictly independent; and, by using Bayesian outlier methods, it circumvents the need for Bonferroni correction for multiple tests, leading to better performance in many settings while still constraining risk for false positives. The performance of our genomic control method is quite good for plausible effects of liability genes, which bodes well for future genetic analyses of complex disorders.
authors: Department of Psychiatry, University of Pittsburgh, Pennsylvania 15213, USA. devlinbj@msx.upmc.edu
authors: B
authors: B
authors: Devlin
authors: K
authors: K
authors: Roeder
mesh_terms: D001499:Bayes Theorem; D001699:Biometry; D016022:Case-Control Studies; D005821:Genetic Techniques; D015894:Genome, Human; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical