| Autor: |
Agaz H. Wani, Seyma Katrinli, Xiang Zhao, Nikolaos P. Daskalakis, Anthony S. Zannas, Allison E. Aiello, Dewleen G. Baker, Marco P. Boks, Leslie A. Brick, Chia-Yen Chen, Shareefa Dalvie, Catherine Fortier, Elbert Geuze, Jasmeet P. Hayes, Ronald C. Kessler, Anthony P. King, Nastassja Koen, Israel Liberzon, Adriana Lori, Jurjen J. Luykx, Adam X. Maihofer, William Milberg, Mark W. Miller, Mary S. Mufford, Nicole R. Nugent, Sheila Rauch, Kerry J. Ressler, Victoria B. Risbrough, Bart P. F. Rutten, Dan J. Stein, Murray B. Stein, Robert J. Ursano, Mieke H. Verfaellie, Eric Vermetten, Christiaan H. Vinkers, Erin B. Ware, Derek E. Wildman, Erika J. Wolf, Caroline M. Nievergelt, Mark W. Logue, Alicia K. Smith, Monica Uddin |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
BMC Medical Genomics, Vol 17, Iss 1, Pp 1-14 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1755-8794 |
| DOI: |
10.1186/s12920-024-02002-6 |
| Popis: |
Abstract 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 |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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