Much of the foundational work on G × E in alcohol outcomes has been conducted in twin studies [6–9,12]. Most G × E studies to date using measured genotypes on alcohol use outcomes have focused on candidate genes or single nucleotide polymorphisms (SNPs), where the effect of a specific candidate gene or single SNP varies as a function of the environment [6]. However, candidate gene research has generated inconsistent results, probably a reflection of being underpowered to robustly detect moderations, false positives and publication bias [13,14]. Furthermore, the use of single genes in G × E studies does not align with our current molecular genetic understanding that complex behaviors, including alcohol use [15], problems [16] and dependence [17], have a polygenic architecture, driven by many genetic variants of very small effect [18,19]. Large sample sizes are needed to detect robust genetic associations for complex behavioral outcomes in genome-wide association studies (GWAS), which use data from the entire genome rather than relying on pre-defined SNPs [20,21].
