due to the large intersubject variability. As noted below, one goal of ENIGMA has been to screen brain measures for reproducibility, heritability, and association with disease, to see which ones are likely to be promising for genetic analysis (we return to this topic below; see also Table 1).Table 1Selection of brain measures for genetic analysis. In ENIGMA, the brain measures chosen for analysis had to be feasible to measure consistently and efficiently at a large number of sites, according to agreed protocols (available at enigma.ini.usc.edu). As power is limited in GWAS, various tactics may be useful in the future to boost the power to find genetic associations. Some of these are categorized herePower enhancement approachPrinciplePros and cons1. Enhance the datasetIncrease the sample size (some GWAS studies now assess 100,000+ subjects (Lango Allen et al. 2010; Speliotes et al. 2010))Identifies variants with smaller effect sizes, but is more costlyIncrease genomic coverage/sequencingPicks up rarer variants, but requires even more subjects for power 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
