Currently there is great emphasis on increasing sample size, on the order of 50,000–200,000 subjects, for GWAS of alcohol dependence through meta-analyses to provide sufficient power to detect variants at the genome-wide significant threshold of p < 10–8 (Agrawal et al., 2016; Begum et al., 2012; Tawa et al., 2016). Meta-analysis efforts will take a considerable amount of time and involve coordinated efforts among many different laboratories and large consortia such as the Psychiatric Genetics Consortium. However, a plethora of novel biological information can be useful to consider if a plausible functional hypothesis can be generated for candidate genes from the existing GWAS, even when the majority of the identified SNPs do not meet the stringent genome-wide significance threshold (p < 10–8). In particular, attempting to make connections between GWAS candidates, where functional significance is uncertain, with gene variants with strong evidence of functionality identified via candidate gene association or linkage, may provide clues for ultimate validation for GWAS (see Fig. 1). Further, hypothesis-generation is one of the core features of the GWAS design and puts first a primary objective
