Reference-Based Standardization Approach Stabilizing Small Batch Risk Prediction via Polygenic Score

Abstract

The polygenic score (PGS) holds promise for motivating preventive behavioral changes. However, no clinically validated standardization methodology currently exists. Here, we demonstrate the efficacy of a “reference-based” approach for standardization. This method uses the PGS distribution in the general population as a reference for normalization and percentile determination; however, it has not been validated. We investigated three potential influences on PGS computation: (1) the size of the reference population, (2) biases associated with different genotyping platforms, and (3) inclusion of kinship ties within the reference group. Our results indicate that the reference size affects the bootstrap estimate of standard error for PGS percentiles, peaking around the 50th percentile and diminishing at extreme percentiles (1st or 100th). Discrepancies between genotyping platforms, such as different microarrays and whole-genome sequencing, resulted in deviations in PGS (p < 0.05 in Kolmogorov–Smirnov test). However, these deviations were reduced to a nonsignificant level using shared genetic variants in the calculations when the ancestry of the samples and reference were matched. This approach recovered approximately 9.6% of the positive predictive value of PGS by naïve genotype. Our results provide fundamental insights for establishing clinical guidelines for implementing PGS to communicate reliable risks to individuals.