Methylation risk scores are associated with a collection of phenotypes within electronic health record systems

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 R2 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.

A methylation clock model of mild SARS‐CoV‐2 infection provides insight into immune dysregulation

“…An individual's pattern of DNA methylation contains a lifetime record of environmental exposures and has been associated with increased risk for various autoimmune, neurological, and metabolic diseases. Methylation‐based signatures have been reported to have higher predictive value for future health outcomes than polygenic risk scores (preprint: Thompson et al , 2022 ; Yousefi et al , 2022 ). DNA methylation has been used to construct lifelong methylation clocks that predict chronological age and all‐cause mortality (Horvath & Raj, 2018 ; Lu et al , 2019 ).…”

Identification of Sjögren’s syndrome patient subgroups by clustering of labial salivary gland DNA methylation profiles

“…While there is evidence to suggest that LSG removal does not lead to significant morbidity in SS cases [ 49 , 50 ], if epigenetic profiles from whole blood can provide similar disease subtype information as LSG tissue, then more efficient ways of using DNA methylation for disease classification may be feasible. DNA methylation arrays could be utilized in a clinical setting, similar to SNP arrays [ 51 ], and in combination with SNP arrays to improve phenotype classification as recently described [ 52 ].…”

A unified hypothesis-free feature extraction framework for diverse epigenomic data

“…Because methylation is a covalent modification that is relatively stable and simple to assay, methylation models are reproducible across cohorts, tissues, and assay platforms [13, 14, 15] and can be constructed for large numbers of phenotypes [16]. The properties of methylation make it an attractive front-line epigenetic assay.…”