Assessing agreement between different polygenic risk scores in the UK Biobank

Abstract

Polygenic risk scores (PRS) are proposed for use in clinical and research settings for risk stratification. However, there are limited investigations on how different PRS diverge from each other in risk prediction of individuals. We compared two recently published PRS for each of three conditions, breast cancer, hypertension and dementia, to assess the stability of using these algorithms for risk prediction in a single large population. We used imputed genotyping data from the UK Biobank prospective cohort, limited to the White British subset. We found that: (1) 20% or more of SNPs in the first PRS were not represented in the more recent PRS for all three diseases, by the same SNP or a surrogate with R2 > 0.8 by linkage disequilibrium (LD). (2) Although the difference in the area under the receiver operating characteristic curve (AUC) obtained using the two PRS is hardly appreciable for all three diseases, there were large differences in individual risk prediction between the two PRS. For instance, for each disease, of those classified in the top 5% of risk by the first PRS, over 60% were not so classified by the second PRS. We found substantial discordance between different PRS for the same disease, indicating that individuals could receive different medical advice depending on which PRS is used to assess their genetic susceptibility. It is desirable to resolve this uncertainty before using PRS for risk stratification in clinical settings.

Epistatic Features and Machine Learning Improve Alzheimer’s Risk Prediction Over Polygenic Risk Scores

“…Ensembling of epistatic models with PRS did not give the same improvement as was observed in the cross-validation analysis, likely because the PRS model performed poorly on the holdout dataset in general. We suspect this is due to the well-known difficulties in applying PRS models trained on one dataset to another [36]. However, ensembling with a PRS model did improve model specificity and positive predictive value on the holdout set.…”

“…PRS model performed poorly on the holdout dataset in general. We suspect this is due to the well-known difficulties in applying PRS models trained on one dataset to another [36]. However, ensembling with a PRS model did improve model specificity and positive predictive value on the holdout set.…”

Combining machine learning with Cox models to identify predictors for incident post-menopausal breast cancer in the UK Biobank

“…Due to the substantial discordance in individual-level risk categorisation between different PRS for the same disease 35 , we included two breast cancer PRS as potential genetic features: PRS 313 9 and PRS 120k 36 . Neither PRS used UKB data in its derivation stage, hence both are suitable for calculation within the UKB population without the concern of inflated effect estimates due to sample overlap.…”

Future implications of polygenic risk scores for life insurance underwriting

“…The lack of ancestry diversity in GWAS, resulting in reduced predictive performance of PGS in non-European populations, is widely recognized as a major limitation of PGS 33 . As such, an individual’s PGS today may differ from one calculated in the future due to changes to the methodology, new GWAS data, and improvements in ancestry data, which could result in different risk classifications and altered medical advice for individuals 52 .…”