Analyses of GWAS data beyond traditional SNP association testing have increased our understanding of the genetic architecture of complex traits, including AD. As described above, results from polygenic analyses of GWAS data are consistent with the polygenic nature of AD and have shown that the many individual loci that influence AD risk exert small effects. Nonetheless, overall, estimates of the variance in AD risk attributable to common variants have been as high as 23% (Yang et al., 2014). Polygenic methods can be extended to answer questions such as the degree of genetic sharing between AD and other traits, similar to what has been done for major psychiatric disorders in the PGC dataset using the bivariate GCTA method (Lee et al., 2013) and risk profile scoring (Smoller et al., 2013). Common genetic variation may regulate or encode proteins found within important biological pathways for addiction. Motivated by this hypothesis, pathway analysis of GWAS data has led to new discoveries in the genetics of AD, and will likely become more important as pathway annotations and other resources continue to improve. The integration
