 In an effort to increase the amount of phenotypic variance accounted for by GWAS results, Yang et al. (2015) developed genome complex trait analysis (GCTA), now referred to as ‘SNP heritability’ (or h2SNP). While traditional heritability is estimated from coarse-grain genetic similarity of related individuals, SNP heritability is estimated from fine-grain genetic similarity of unrelated individuals. SNP heritability now comprises a family of highly sophisticated statistical techniques that seek to maximize proportions of phenotypic variance attributable to observed or imputed SNP variants. SNP heritability exhibits a few features worth attention. First, instead of limiting analysis to SNPs who meet the strict p-value GWAS significance threshold, SNP heritability is derived by analyzing the complete set of SNPs for each participant sample—even those that are not associated with the trait of interest. To this end, SNP heritability is biologically non-obvious. Second, SNP heritability assumes linear additivity of SNP effects. Third, SNP heritability represents the current limit on the total variance that could be explained by SNPs, for any given phenotype. So, for example, if a polygenic score were maximally predictive, it would be equivalent to SNP heritability. Fourth, SNP heritability is consistently lower than twin heritability, for any behavioral trait. This gap between twin heritability and SNP heritability is sometimes referred to as the missing heritability problem, which we discuss in Section 5.