Our analytic strategy encompassed two naïve extremes, one represented by tests of each individual SNP in isolation and the other by a single GCTA test of all in aggregate. The genome-wide scan might be improved upon by differentially weighting groups of SNPs (Roeder, Devlin, & Wasserman, 2007), such as those expressed in brain versus those not, or those implicated by prior research. Examining multiple markers, rather than one SNP at a time, is another possibility, which can provide increased power (Pan, 2008) and which also permits pathway or network analysis. Such methods incorporate prior biological knowledge or information about the topological relationship among genes in a network. This might be valuable for assessing matrix-based endophenotypes (Schumann, 2014, this issue). These methods also provide a means to evaluate gene-gene interactions while constraining the search space. It is now possible to focus on those genetic variants known to influence gene expression (so-called expression quantitative trait loci, or eQTLs), rather than examine all SNPs. Massive publicly available datasets now provide comprehensive maps of enhancers, insulators, promoters, and eQTLs, all part of the genetic
