Figure 5.

Using $Pr\left(\stackrel{\sim }{PG{S}_i}>t\right)$ for risk stratification can improve the rates of high array PGS individuals in a simulated mixed sequencing depth cohort

Risk stratification based on $Pr\left(\stackrel{\mathit{\sim }}{\mathit{P}\mathit{G}{\mathit{S}}_{\mathit{i}}}\mathit{>}\mathit{t}\right)$ yields a higher rate of high array PGS individuals compared with risk stratification based on dosage PGS. In this experiment, we take individuals with the highest scores by $Pr\left(\stackrel{\sim }{PG{S}_i}>t\right)$ and by dosage PGS and calculate the proportion of individuals with an array PGS value in the top two deciles. We changed the number of individuals inspected from 0 to 160 (20% of the entire cohort) and found we select slightly more individuals with high array PGS by using the $Pr\left(\stackrel{\sim }{PG{S}_i}>t\right)$ metric. Error bars represend one standard error of the mean percent of true positives and change in precision.