used in a regression analysis to evaluate its association with the phenotype of interest in the target sample, as well as the proportion of variance explained (e.g., R2). This approach has been used to investigate the polygenicity of complex traits such as schizophrenia and bipolar disorder (International Schizophrenia Consortium et al., 2009), multiple sclerosis (International Multiple Sclerosis Genetics Consortium et al., 2010), height (Lango Allen et al., 2010), body mass index (Speliotes et al., 2010), and rheumatoid arthritis (Stahl et al., 2012). Frank et al. (2012) first applied this method to AD. They randomly divided their German GWAS sample into a discovery sample (667 cases and 1084 controls) and a target sample (666 cases and 1084 controls). The polygenic risk score was computed in the target sample using SNPs with P<0.5. The score significantly predicted case-control status in the target sample (P=9.66 × 10−9). In a similar analysis using the full German sample as the discovery sample and the COGA and SAGE samples as the target samples, profile scores significantly predicted case-control status in both samples (P=3.9 × 10−2 and P=1.1 × 10−4, respectively).
