Due to the individually small effect sizes of contributing loci and stringent statistical significance criterion of GWAS most modest effect size polymorphisms will not be deemed “significant” and “replicated” in many GWAS (Purcell et al., 2009). Multiple approaches to aggregating those effects have been developed, thereby increasing the power to detect a polygenic signal and aiding in understanding the nature of complex traits. Purcell and colleagues (Purcell et al., 2009) used an approach in which two stage GWAS data were used to select a set of “independent” SNPs in linkage equilibrium that generated p-values below some arbitrary threshold (PT) in one sample as a discovery stage. Those SNPs were then used to create polygenic sum scores, with each allele weighted by the logarithm of the odds ratio from the discovery sample, to be tested in a second sample. The terms “polygenic scores” (PGS), “genetic risk scores” (GRS) and “polygenic risk scores” (PRS) are now used interchangeably to describe metrics comprising a large number of SNPs pooled together to represent a measured set of variants underlying a particular trait or disease.
