Levey et al. (2014) used a Convergent Functional Genomics (CFG) approach to conduct a risk profile scoring analysis for AD. Here, they used the German GWAS dataset (Treutlein et al., 2009) and information from other association and linkage studies of AD, gene expression (including post-mortem brain expression and peripheral tissue expression), and genetic studies in animal models to generate a list of 135 candidate genes, and assembled risk profile scores from 713 nominally significant SNPs (P≤0.05) within these genes. They then tested for the ability of this score to predict case-control status in an independent German target sample (Frank et al., 2012). Overall, they found that they were unable to predict case-control status significantly with the risk prediction score, similar to the findings from the candidate gene study-derived risk prediction scores in Yan et al. (2014). However, when they prioritized the genes in the risk prediction score to a set of 11 cross-validated findings from the DBP stress-reactive knockout mouse model for alcoholism, they found significant prediction in the German target sample (P=0.041) and alcohol abuse and dependence samples from the United States (Gelernter et al., 2014a).
