COGA has conducted a series of analyses that evaluate the predictive utility of GWAS data for alcoholism and related phenotypes (Yan et al. 2011). Here, we have expanded the scope of this work by examining what this information tells us about the disorder’s underlying genetic architecture. Using a two-stage, risk prediction framework similar to the one employed by Purcell et al. (2009) to characterize the polygenic basis of schizophrenia, we aggregated variation across nominally associated GWAS loci into quantitative scores or “genomic profiles” and correlated these predictors with observed AD status in independent target samples from SAGE (Fig. S1).
