In addition to using polygenic scores as predictors in statistical models testing the impact of many genetic influences on a particular outcome, approaches have been developed to estimate what could accurately be termed “marker-based” or “molecular” heritability. The general approach of this method uses genome-wide markers to examine deviation from expected genetic-phenotypic similarity (Visscher et al., 2006). Methods such as genome-wide complex trait analysis (GCTA) rely on population-based samples with available GWAS data (Yang et al., 2011). In GCTA, a genetic relationship matrix is derived from all available SNPs and used to estimate the proportion of phenotypic variation accounted for by the genome-wide genetic differences. Importantly, while this method can generate an estimate of the phenotypic variation accounted for by genetic differences, it does not identify specifically which variants or pathways account for it. Extensions of the method claim to improve accuracy by correcting for linkage disequilibrium, as opposed to pruning out correlated (but possibly true independent effect) SNPs (Vilhjalmsson et al., 2015) or by relying on HaploSNPs (Bhatia et al., 2015), large shared segments in high LD that can
