Two complementary methods have been proposed to investigate the genetic architecture of complex traits by considering large sets of SNPs simultaneously. The first, the SNP (or sometimes chip) heritability method, estimates the proportion of additive genetic variance attributable to all measured SNPs in a GWAS. This analysis is most often performed using a Genome-wide Complex Trait Analysis (GCTA; Yang et al., 2011). GCTA uses a mixed linear model to fit the effects of all SNPs as random effects and estimates the variance attributable to the aggregate random effect. A genetic relationship matrix (GRM) is estimated from the SNP data of all pairs of individuals. GCTA is typically performed on a set of unrelated subjects to estimate the variance attributable to common SNPs in the absence of effects due to shared environment or non-common SNPs (e.g., rare variants). It can be extended to compute the genetic correlation between traits measured in the same study or different studies.
