Methylation risk scores are associated with a collection of phenotypes within electronic health record systems
Year: 2022
Type: article
Study design: Observational Study
Abstract: Inference of clinical phenotypes is a fundamental task in precision medicine, and has therefore been heavily investigated in recent years in the context of electronic health records (EHR) using a large arsenal of machine learning techniques, as well as in the context of genetics using polygenic risk scores (PRS). In this work, we considered the epigenetic analog of PRS, methylation risk scores (MRS), a linear combination of methylation states. Since methylation states are influenced by both environmental and genetic factors, we hypothesized that MRS would complement PRS and EHR-based machine-learning methods, improving overall prediction accuracy. To evaluate this hypothesis, we performed the largest assessment of methylation risk scores in clinical datasets to be conducted to date. We measured methylation across a large cohort (n=831) of diverse samples in the UCLA Health biobank, for which both genetic and complete EHR data are available. We constructed MRS for 607 phenotypes spanning diagnoses, clinical lab tests, and medication prescriptions. When added to a baseline set of predictive features, MRS significantly improved the imputation of 139 outcomes, whereas the PRS improved only 22 (median improvement for methylation 10.74%, 141.52%, and 15.46% in medications, labs and diagnosis codes, respectively, whereas genotypes only improved the labs at a median increase of 18.42%). We added significant MRS to state-of-the-art EHR imputation methods that leverage the entire set of medical records, and found that including MRS as a medical feature in the algorithm significantly improves EHR imputation in 37% of lab tests examined (median R 2 increase 47.6%). Finally, we replicated several MRS in multiple external studies of methylation (minimum p-value of 2.72 × 10 − 7 ) and replicated 22 of 30 tested MRS internally in two separate cohorts of different ethnicity. In summary, our work provides a comprehensive evaluation of MRS in comparison to PRS and EHR imputation on the largest dataset consisting of methylation, genotype, and EHR data. Our publicly available results and weights show promise for methylation risk scores as clinical and scientific tools.
Source: npj Genomic Medicine
Institution: University of California, Los Angeles
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,690
Funders: National Science Foundation, U.S. Department of Health and Human Services, National Institutes of Health, Directorate for Computer and Information Science and Engineering, National Heart, Lung, and Blood Institute, National Human Genome Research Institute, National Institute of General Medical Sciences, National Institute of Environmental Health Sciences, National Institute of Biomedical Imaging and Bioengineering
Awards: III: Medium: Detecting Low Dimensional Structures in Genomic Data, Rarely Common: Uncovering the dominant role of rare variants in the genetic architecture of complex human traits., Methods for Genomic Analysis in Heterogeneous Tissues, Leveraging genetic variation to dissect gene regulatory networks of reprogramming to pluripotency, G2355553396, Improved methods for inference of genotype-specific response to environmental toxins, Epigenomics of asthma risk factors and clinical subtypes in minority children, G3992357357, Genomic Approaches to Population Health in Multi-Ethnic Hospital Systems, G4166665927, G4363072978, G4604973645, Leveraging functional data to predict disease risk in multi-ethnic populations, G4765065348, G5345772220, Machine Learning of Physiological Waveforms and Electronic Health Record Data to Predict, Diagnose, and Treat Hemodynamic Instability in Surgical Patients, G6506185064, G6532411786, G7159390087, G7569451492, Biomedical Informatics Tools for Applied Perioperative Physiology, Center for Multi- and Trans-ethnic Mapping of Mendelian and Complex Diseases, G8271477110, Training Grant in Genomic Analysis and Interpretation, G9000070190, G9000078506, G9000094210, G9000221242, G9000232488
OpenAlex ID: W4293052083