Separating the genetics of disease, treatment and treatment response using graphical modeling and large-scale electronic health records

Genetic variants affect baseline health and biomarker values, which in turn impact both the therapy selected for an individual and the magnitude of change induced by the medication. Here, we propose an approach for complex longitudinal repeated measures biobank data, which separates genetic effects for disease from the genetic effects for medication usage and those for treatment response. For 211,845 individuals, we construct a pre-post study design from 1,420,443 repeated blood pressure (BP) measurements and 1,117,900 prescription records for common BP influencing drugs, using electronic health records. We model these jointly alongside 8,430,446 imputed single nucleotide polymorphism (SNP) markers and 17,852 whole-exome sequence loss-of-function (LoF) variants, all within a single novel graphical modeling framework. We identify pharmacogenetic candidate SNPs and LoF variants in genes SLC35F2, PKD1 and KCNIP4 , which are associated with angiotensin receptor blocker therapy and response after controlling for hypertensive disease status. We additionally detect and replicate established clinically relevant variants for statin treatment across multiple biobanks. We find that genetic variation for BP is predominantly shaped prior to the age of 50, but that hundreds of independent loci associate with age-specific BP changes through life. Finally, once post-treatment measures are conditioned on pre-treatment measures and therapy, we find no evidence for genetic variants influencing treatment-response, despite power to detect a genetic variant that contributes > 0.017% of explained variance. Our graphical modeling and pre-post study design provides a robust way of detecting time-, treatment- and treatment response-specific genetic associations within large-scale biobank studies.