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  • Identifying DNA methylation sites affecting drug response using electronic health record-derived GWAS summary statistics. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Smith, D. A., Arteaga, S. A., Sadler, M. C., Altman, R. B. 2025; 30: 457-472

    Abstract

    Adverse drug responses (ADRs) result in over 7,000 deaths annually. Pharmacogenomic studies have shown that many ADRs are partially attributable to genetics. However, emerging data suggest that epigenetic mechanisms, such as DNA methylation (DNAm) also contribute to this variance. Understanding the impact of DNA methylation on drug response may minimize ADRs and improve the personalization of drug regimens. In this work, we identify DNA methylation sites that likely impact drug response phenotypes for anticoagulant and cardiometabolic drugs. We use instrumental variable analysis to integrate genome-wide association study (GWAS) summary statistics derived from electronic health records (EHRs) within the U.K. Biobank (UKBB) with methylation quantitative trait loci (mQTL) data from the Genetics of DNA Methylation Consortium (GoDMC). This approach allows us to achieve a robust sample size using the largest publicly available pharmacogenomic GWAS. For warfarin, we find 71 DNAm sites. Of those, 8 are near the gene VKORC1 and 48 are on chromosome 6 near the human leukocyte antigen (HLA) gene family. We also find 2 warfarin DNAm sites near the genes CYP2C9 and CYP2C19. For statins, we identify 17 DNAm sites. Eight are near the APOB gene, which encodes a carrier protein for low-density lipoprotein cholesterol (LDL-C). We find no novel significant epigenetic results for metformin.

    View details for PubMedID 39670389

  • Identifying DNA methylation sites affecting drug response using electronic health record-derived GWAS summary statistics. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Smith, D. A., Arteaga, S. A., Sadler, M. C., Altman, R. B. 2025; 30: 457-472

    Abstract

    Adverse drug responses (ADRs) result in over 7,000 deaths annually. Pharmacogenomic studies have shown that many ADRs are partially attributable to genetics. However, emerging data suggest that epigenetic mechanisms, such as DNA methylation (DNAm) also contribute to this variance. Understanding the impact of DNA methylation on drug response may minimize ADRs and improve the personalization of drug regimens. In this work, we identify DNA methylation sites that likely impact drug response phenotypes for anticoagulant and cardiometabolic drugs. We use instrumental variable analysis to integrate genome-wide association study (GWAS) summary statistics derived from electronic health records (EHRs) within the U.K. Biobank (UKBB) with methylation quantitative trait loci (mQTL) data from the Genetics of DNA Methylation Consortium (GoDMC). This approach allows us to achieve a robust sample size using the largest publicly available pharmacogenomic GWAS. For warfarin, we find 71 DNAm sites. Of those, 8 are near the gene VKORC1 and 48 are on chromosome 6 near the human leukocyte antigen (HLA) gene family. We also find 2 warfarin DNAm sites near the genes CYP2C9 and CYP2C19. For statins, we identify 17 DNAm sites. Eight are near the APOB gene, which encodes a carrier protein for low-density lipoprotein cholesterol (LDL-C). We find no novel significant epigenetic results for metformin.

    View details for DOI 10.1142/9789819807024_0033

    View details for PubMedID 40299609

  • Which social media platforms facilitate monitoring the opioid crisis? PLOS digital health Carpenter, K. A., Nguyen, A. T., Smith, D. A., Samori, I. A., Humphreys, K., Lembke, A., Kiang, M. V., Eichstaedt, J. C., Altman, R. B. 2025; 4 (4): e0000842

    Abstract

    Social media can provide real-time insight into trends in substance use, addiction, and recovery. Prior studies have used platforms such as Reddit and X (formerly Twitter), but evolving policies around data access have threatened these platforms' usability in research. We evaluate the potential of a broad set of platforms to detect emerging trends in the opioid use disorder and overdose epidemic. From these, we identified 11 high-potential platforms, for which we documented policies regulating drug-related discussion, data accessibility, geolocatability, and prior use in opioid-related studies. We quantified their volume of opioid discussion, including in informal language by including slang generated using a large language model. Beyond the most commonly used Reddit and X/Twitter, the platforms with high potential for use in opioid-related surveillance are TikTok, YouTube, and Facebook. Leveraging a variety of social platforms, instead of merely one, yields broader subpopulation representation and safeguards against reduced data access in any single platform.

    View details for DOI 10.1371/journal.pdig.0000842

    View details for PubMedID 40293990

    View details for PubMedCentralID PMC12036940

  • Promises and challenges in pharmacoepigenetics. Cambridge prisms, Precision medicine Smith, D. A., Sadler, M. C., Altman, R. B. 2023; 1: e18

    Abstract

    Pharmacogenetics, the study of how interindividual genetic differences affect drug response, does not explain all observed heritable variance in drug response. Epigenetic mechanisms, such as DNA methylation, and histone acetylation may account for some of the unexplained variances. Epigenetic mechanisms modulate gene expression and can be suitable drug targets and can impact the action of nonepigenetic drugs. Pharmacoepigenetics is the field that studies the relationship between epigenetic variability and drug response. Much of this research focuses on compounds targeting epigenetic mechanisms, called epigenetic drugs, which are used to treat cancers, immune disorders, and other diseases. Several studies also suggest an epigenetic role in classical drug response; however, we know little about this area. The amount of information correlating epigenetic biomarkers to molecular datasets has recently expanded due to technological advances, and novel computational approaches have emerged to better identify and predict epigenetic interactions. We propose that the relationship between epigenetics and classical drug response may be examined using data already available by (1) finding regions of epigenetic variance, (2) pinpointing key epigenetic biomarkers within these regions, and (3) mapping these biomarkers to a drug-response phenotype. This approach expands on existing knowledge to generate putative pharmacoepigenetic relationships, which can be tested experimentally. Epigenetic modifications are involved in disease and drug response. Therefore, understanding how epigenetic drivers impact the response to classical drugs is important for improving drug design and administration to better treat disease.

    View details for DOI 10.1017/pcm.2023.6

    View details for PubMedID 37560024