but modest effects of these genes. Forth, variants that are uncommon (1%-5%) or rare (<1%) in the study population may also not be detected in SAGE because of their individually small contribution to overall alcoholism. Fifth, the SAGE dataset primarily consists of European Americans (69.5%), African Americans (30.3%) and a few Hispanics (3.4%) (Table 1) and association findings may be different in other populations such as Asians. Some of the genes and variants examined in this analysis are more well studied and have a higher frequency in Asian Ancestry than in European and African Ancestry populations, such as the Asp40 allele of the candidate variant rs1799971 in OPRM1 (Arias et al., 2006), and therefore may have a more impressive effect in studies that focus on Asian ancestry populations. Sixth, our analysis did not examine the effects of combinations of genes or the effect of different environmental factors. Analysis of multiple genes and populations enriched for specific environmental risk factors will likely explain a greater degree of the genetic risk of alcoholism. Despite these limitations, this analysis demonstrates that GWA studies are a powerful technique for verifying the importance of genes and particular variants that have been previously identified in the
