The most commonly applied approach for predicting genetic risk of human disease is computing polygenic risk scores (PRS) from GWAS summary statistics (Figure 2A) (14). This approach was introduced early in the GWAS era, developed first in the context of psychiatric disease. Researchers recognized that insufficient sample sizes in early studies produced few robust associations, but the aggregation of many loci below the genome-wide significance threshold could significantly predict disease risk in new studies. These analyses were consistent with a polygenic mode of inheritance from variants tagging causal risk (15; 16). At their core, PRS are simply calculated by multiplying the number of risk alleles a person carries by the effect size of each variant, and then summing each of these products across all risk loci (Figure 2A) (17). To ensure the validity of these scores, it is essential that the effect sizes are estimated in an independent cohort. This approach has now provided an empirical demonstration of early theoretical models from Gottesman & Shields for schizophrenia and even earlier from Fisher for quantitative traits (1; 5).
