of low frequency variants. Also is more costly than genotyping common SNPs through genotyping arrays, but the cost is rapidly decreasingIncrease the range of phenotypes studiedMay be able to find a high effect size phenotype, but also need to correct for the number of measures assessed, which may be large (e.g., in “voxel-based” GWAS; Stein et al. 2010; Ge et al. 2012); if too many are assessed, power is low2. Data reductionFocus on candidate SNPs/genes, candidate pathways, candidate phenotypesAvoids heavy statistical correction, but may miss unexpected variants or phenotypes2.1. Based on classical genetics principlesHeritability screening—remove or de-emphasise measures with low heritabilityThis may empower genomic screens of complex phenotypes (e.g., genome-wide connectome-wide screens; Jahanshad et al. 2013a), see ENIGMA-DTI (Jahanshad et al., Jahanshad et al. 2013b).Genetic Clustering—find parts of an image or 3D cortical surface with common underlying genetic determinationGWAS on the resulting “genetic clusters” appears to have higher power than standard voxel-based approaches (Chiang et al. 2011, 2012; Chen et al. 2011; 2012)2.2. Based on relevance to diseaseEndophenotype ranking value (ERV; Glahn et al. 2012), aims to rank biomarkers in terms of their promise as endophenotypes for any heritable illness.Balances the strength of the genetic signal for the endophenotype and
