This study has several limitations. Despite the size of the COGA discovery samples, power to detect association with low frequency variants is modest. Likewise, sample sizes of the replication datasets were small, raising the possibility that true findings were not replicated. Despite this, one association was confirmed by the NIAAA samples, and meta-analyses of all datasets resulted in increased significance of genomewide significant signals. Additionally, we used hard-called genotypes as necessitated by our family data requiring rigorous Mendelian error checking, which is not straightforward with dosage data (see also Walters et al., 2018 for a similar approach to family data (Walters et al., 2018)). Nonetheless, results for our top loci are similar when dosage data were used for a confirmatory analysis (e.g., rs10647170 P=1.53E-08). Another limitation inherent in both in silico functional analyses and RNA expression analysis is the source of the data, which are largely from subjects of European ancestry. As a result, conclusions from these functional data may be limited when analyzing associated variants identified in the COGA-AA sample. Nonetheless, this represents an important first step towards characterizing
