to estimate the total amount of variance explained by all SNPs together without testing the SNPs individually for a quantitative trait [3], and subsequently extended it to the estimation of missing heritability for binary disease data from ascertained case-control studies [4]. The analyses until recently only included common SNPs (e.g. minor allele frequency >0.01). The estimate quantifies the overall contribution from the additive effects of all SNPs, which is the upper limit of the proportion of variance that is captured by the additive effects of the set of SNPs used in the estimation, and is also the lower limit of the narrow-sense heritability of the trait. We also extended the method to estimate the genetic correlation between two traits using SNP data [5], [6]. In contrast to the traditional (co)variance estimation methods that rely on pedigree information (family/twin studies), our method uses unrelated samples from a general population and the genetic (co)variance is estimated using a genetic relationship matrix (GRM) estimated from SNPs. The estimate of genetic variance using SNP data in unrelated individuals is free of confounding from common environment effects shared between close relatives that are difficult to model in family-based analyses, and is directly comparable to results
