Biomedical subjects

Mark W Miller

Publications and source records attributed to Mark W Miller.

4 recordsLinked to original sources

PubMed2025

Genetic and epigenetic analysis of plasma glial fibrillary acidic protein (GFAP) levels in PTSD.

Glial fibrillary acidic protein (GFAP) is an astrocytic marker that can be assessed in blood using single molecule array technology. Recent studies suggest that individuals with posttraumatic stress disorder (PTSD) have suppressed circulating levels of this CNS biomarker. This study examined the hypothesis that PTSD and plasma GFAP levels share common genetic and epigenetic pathways. Using data from 1096 veterans and civilians, we computed a PTSD polygenic risk score (PRS) derived from a prior PTSD genomewide association study (GWAS) and found that PTSD severity and the PRS were each associated with reduced levels of GFAP. To clarify the basis of the PRS association, we performed a GWAS of GFAP which identified 20 genomewide-significant loci including genes implicated in independent GWASs of PTSD and neurodegenerative disease (e.g., PRKN, NFIA). Comparison of the PTSD and GFAP GWAS results showed that PTSD-associated genes were significantly enriched in the GFAP results with notable overlap involving NPSR1 and the protocadherin alpha (PCDHA) gene cluster. Similarly, we performed an epigenomewide association study (EWAS) of GFAP, which identified 4 genomewide-significant associations (including loci in MCT4 and SREBF1) and then compared those results to the findings of a PTSD EWAS. Results again showed significantly greater overlap than would be expected by chance and included loci implicated in prior studies of depression, dementia, and inflammation. This study clarifies the genetic and epigenetic basis of the association between PTSD and plasma GFAP levels and should encourage future research into the role of GFAP in the pathophysiology of PTSD.

Humans↗

PubMed2024

Blood-based DNA methylation and exposure risk scores predict PTSD with high accuracy in military and civilian cohorts.

BACKGROUND: Incorporating genomic data into risk prediction has become an increasingly popular approach for rapid identification of individuals most at risk for complex disorders such as PTSD. Our goal was to develop and validate Methylation Risk Scores (MRS) using machine learning to distinguish individuals who have PTSD from those who do not. METHODS: Elastic Net was used to develop three risk score models using a discovery dataset (n = 1226; 314 cases, 912 controls) comprised of 5 diverse cohorts with available blood-derived DNA methylation (DNAm) measured on the Illumina Epic BeadChip. The first risk score, exposure and methylation risk score (eMRS) used cumulative and childhood trauma exposure and DNAm variables; the second, methylation-only risk score (MoRS) was based solely on DNAm data; the third, methylation-only risk scores with adjusted exposure variables (MoRSAE) utilized DNAm data adjusted for the two exposure variables. The potential of these risk scores to predict future PTSD based on pre-deployment data was also assessed. External validation of risk scores was conducted in four independent cohorts. RESULTS: The eMRS model showed the highest accuracy (92%), precision (91%), recall (87%), and f1-score (89%) in classifying PTSD using 3730 features. While still highly accurate, the MoRS (accuracy = 89%) using 3728 features and MoRSAE (accuracy = 84%) using 4150 features showed a decline in classification power. eMRS significantly predicted PTSD in one of the four independent cohorts, the BEAR cohort (beta = 0.6839, p=0.006), but not in the remaining three cohorts. Pre-deployment risk scores from all models (eMRS, beta = 1.92; MoRS, beta = 1.99 and MoRSAE, beta = 1.77) displayed a significant (p < 0.001) predictive power for post-deployment PTSD. CONCLUSION: The inclusion of exposure variables adds to the predictive power of MRS. Classification-based MRS may be useful in predicting risk of future PTSD in populations with anticipated trauma exposure. As more data become available, including additional molecular, environmental, and psychosocial factors in these scores may enhance their accuracy in predicting PTSD and, relatedly, improve their performance in independent cohorts.

Humans↗

PubMed2020

Epigenome-wide meta-analysis of PTSD across 10 military and civilian cohorts identifies methylation changes in AHRR.

Epigenetic differences may help to distinguish between PTSD cases and trauma-exposed controls. Here, we describe the results of the largest DNA methylation meta-analysis of PTSD to date. Ten cohorts, military and civilian, contribute blood-derived DNA methylation data from 1,896 PTSD cases and trauma-exposed controls. Four CpG sites within the aryl-hydrocarbon receptor repressor (AHRR) associate with PTSD after adjustment for multiple comparisons, with lower DNA methylation in PTSD cases relative to controls. Although AHRR methylation is known to associate with smoking, the AHRR association with PTSD is most pronounced in non-smokers, suggesting the result was independent of smoking status. Evaluation of metabolomics data reveals that AHRR methylation associated with kynurenine levels, which are lower among subjects with PTSD. This study supports epigenetic differences in those with PTSD and suggests a role for decreased kynurenine as a contributor to immune dysregulation in PTSD.

Basic Helix-Loop-Helix Proteins↗

PubMed2020

PTSD is associated with increased DNA methylation across regions of HLA-DPB1 and SPATC1L.

Posttraumatic stress disorder (PTSD) is characterized by intrusive thoughts, avoidance, negative alterations in cognitions and mood, and arousal symptoms that adversely affect mental and physical health. Recent evidence links changes in DNA methylation of CpG cites to PTSD. Since clusters of proximal CpGs share similar methylation signatures, identification of PTSD-associated differentially methylated regions (DMRs) may elucidate the pathways defining differential risk and resilience of PTSD. Here we aimed to identify epigenetic differences associated with PTSD. DNA methylation data profiled from blood samples using the MethylationEPIC BeadChip were used to perform a DMR analysis in 187 PTSD cases and 367 trauma-exposed controls from the Grady Trauma Project (GTP). DMRs were assessed with R package bumphunter. We identified two regions that associate with PTSD after multiple test correction. These regions were in the gene body of HLA-DPB1 and in the promoter of SPATC1L. The DMR in HLA-DPB1 was associated with PTSD in an independent cohort. Both DMRs included CpGs whose methylation associated with nearby sequence variation (meQTL) and that associated with expression of their respective genes (eQTM). This study supports an emerging literature linking PTSD risk to genetic and epigenetic variation in the HLA region.

Cytoskeletal Proteins↗