Improving polygenic risk prediction of renal function by removing biomarker-specific effects

Biomarkers are frequently used in clinical practice; however, it is essential to consider the genetics that may independently impact their baseline levels in-turn impacting interpretation of results. For example, estimated glomerular filtration rate (eGFR) equations are based on two biomarkers, cystatin C and creatinine, and widely employed in clinical practice. In this work we demonstrate how genetics of the underlying biomarkers impact measurement variability and may explain some of the discrepancies among eGFR equations. To do this we used shared genetic-architecture to identify "shared" (renal-specific) and "biomarker-specific" regions of the genome. The shared polygenic risk score (PRS) explained 60% more of the variability in the most-common creatinine-derived eGFR estimates than either the biomarker-specific PRS or a PRS including all regions. Our findings highlight the necessity of considering biomarker-specific genetics when constructing PRS for eGFR and other biomarker-derived clinical risk estimates.