Leveraging large-scale biobank EHRs to enhance pharmacogenetics of cardiometabolic disease medications.

Autor:	Sadler MC; University Center for Primary Care and Public Health, Lausanne, Switzerland.; Swiss Institute of Bioinformatics, Lausanne, Switzerland.; Department of Computational Biology, University of Lausanne, Lausanne, Switzerland., Apostolov A; Department of Computational Biology, University of Lausanne, Lausanne, Switzerland., Cevallos C; Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland., Ribeiro DM; Department of Computational Biology, University of Lausanne, Lausanne, Switzerland., Altman RB; Department of Bioengineering, Stanford University, Stanford, CA, USA., Kutalik Z; University Center for Primary Care and Public Health, Lausanne, Switzerland.; Swiss Institute of Bioinformatics, Lausanne, Switzerland.; Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.
Jazyk:	angličtina
Zdroj:	MedRxiv : the preprint server for health sciences [medRxiv] 2024 Apr 07. Date of Electronic Publication: 2024 Apr 07.
DOI:	10.1101/2024.04.06.24305415
Abstrakt:	Electronic health records (EHRs) coupled with large-scale biobanks offer great promises to unravel the genetic underpinnings of treatment efficacy. However, medication-induced biomarker trajectories stemming from such records remain poorly studied. Here, we extract clinical and medication prescription data from EHRs and conduct GWAS and rare variant burden tests in the UK Biobank (discovery) and the All of Us program (replication) on ten cardiometabolic drug response outcomes including lipid response to statins, HbA1c response to metformin and blood pressure response to antihypertensives (N = 740-26,669). Our findings at genome-wide significance level recover previously reported pharmacogenetic signals and also include novel associations for lipid response to statins (N = 26,669) near LDLR and ZNF800 . Importantly, these associations are treatment-specific and not associated with biomarker progression in medication-naive individuals. Furthermore, we demonstrate that individuals with higher genetically determined low-density and total cholesterol baseline levels experience increased absolute, albeit lower relative biomarker reduction following statin treatment. In summary, we systematically investigated the common and rare pharmacogenetic contribution to cardiometabolic drug response phenotypes in over 50,000 UK Biobank and All of Us participants with EHR and identified clinically relevant genetic predictors for improved personalized treatment strategies. Competing Interests: Competing interests The authors declare that they have no competing interests.
Databáze:	MEDLINE
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