Background: Genome-wide association (GWA) studies have led to a paradigm shift in how researchers study the genetics underlying disease. Many GWA studies are now publicly available and can be used to examine whether or not previously proposed candidate genes are supported by GWA data. This approach is particularly important for the field of alcoholism because the contribution of many candidate genes remains controversial. Methods: Using the Human Genome Epidemiology (HuGE) Navigator, we selected candidate genes for alcoholism that have been frequently examined in scientific articles in the past decade. Specific candidate loci as well as all the reported single nucleotide polymorphisms (SNPs) in candidate genes were examined in the Study of Addiction: Genetics and Environment (SAGE), a GWA study comparing alcohol-dependent and nondependent subjects. Results: Several commonly reported candidate loci, including rs1800497 in DRD2, rs698 in ADH1C, rs1799971 in OPRM1, and rs4680 in COMT, are not replicated in SAGE (p > 0.05). Among candidate loci available for analysis, only rs279858 in GABRA2 (p = 0.0052, OR = 1.16) demonstrated a modest association. Examination of all SNPs reported in SAGE in over 50 candidate genes revealed no SNPs with large frequency differences between cases and controls, and the lowest p-value of any SNP was 0.0006. Conclusions: We provide evidence that several extensively studied candidate loci do not have a strong contribution to risk of developing alcohol dependence in European and African ancestry populations. Owing to the lack of coverage, we were unable to rule out the contribution of other variants, and these genes and particular loci warrant further investigation. Our analysis demonstrates that publicly available GWA results can be used to better understand which if any of previously proposed candidate genes contribute to disease. Furthermore, we illustrate how examining the convergence of candidate gene and GWA studies can help elucidate the genetic architecture of alcoholism and more generally complex diseases.
- Alcohol dependence
- Candidate genes
- Genome-wide association studies