Genomic Control for Association Studies

TLDR

A dense set of SNPs covering the genome and efficient genotype assessment methods are expected soon, raising the question of how to use these technologies to identify genes influencing complex disorder liability. The study aims to develop a statistical method that can be applied to case–control data and, like family‑based designs, controls for population heterogeneity. The method is insensitive to violations of model assumptions, uses Bayesian outlier techniques to bypass Bonferroni correction, and thus offers better performance while limiting false positives. The genomic control method performs well for plausible liability gene effects, suggesting promise for future complex disorder genetic analyses. Summary.

Abstract

Summary. 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.