Agaz Wani, Seyma Katrinli, Xiang Zhao, Nikolaos Daskalakis, Anthony Zannas, Allison Aiello, Dewleen Baker, Marco Boks, Leslie Brick, Chia-Yen Chen, Shareefa Dalvie, Catherine Fortier, Elbert Geuze, Jasmeet Hayes, Ronald Kessler, Anthony King, Nastassja Koen, Israel Liberzon, Adriana Lori, Jurjen Luykx, Adam Maihofer, William Milberg, Mark Miller, Mary Mufford, Nicole Nugent, Sheila Rauch, Kerry Ressler, Victoria Risbrough, Bart Rutten, Dan Stein, Murrary Stein, Robert Ursano, Mieke Verfaellie, Erin Ware, Derek Wildman, Erika Wolf, Caroline Nievergelt, Mark Logue, Alicia Smith, Monica Uddin, Eric Vermetten, Christiaan Vinkers
Background Incorporating genomic data into risk prediction has become an increasingly useful 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...
February 15, 2024: Research Square