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

• Egile Nagusiak: Agaz H. Wani (Egilea), Seyma Katrinli (Egilea), Xiang Zhao (Egilea), Nikolaos P. Daskalakis (Egilea), Anthony S. Zannas (Egilea), Allison E. Aiello (Egilea), Dewleen G. Baker (Egilea), Marco P. Boks (Egilea), Leslie A. Brick (Egilea), Chia-Yen Chen (Egilea), Shareefa Dalvie (Egilea), Catherine Fortier (Egilea), Elbert Geuze (Egilea), Jasmeet P. Hayes (Egilea), Ronald C. Kessler (Egilea), Anthony P. King (Egilea), Nastassja Koen (Egilea), Israel Liberzon (Egilea), Adriana Lori (Egilea), Jurjen J. Luykx (Egilea), Adam X. Maihofer (Egilea), William Milberg (Egilea), Mark W. Miller (Egilea), Mary S. Mufford (Egilea), Nicole R. Nugent (Egilea), Sheila Rauch (Egilea), Kerry J. Ressler (Egilea), Victoria B. Risbrough (Egilea), Bart P. F. Rutten (Egilea), Dan J. Stein (Egilea), Murray B. Stein (Egilea), Robert J. Ursano (Egilea), Mieke H. Verfaellie (Egilea), Eric Vermetten (Egilea), Christiaan H. Vinkers (Egilea), Erin B. Ware (Egilea), Derek E. Wildman (Egilea), Erika J. Wolf (Egilea), Caroline M. Nievergelt (Egilea), Mark W. Logue (Egilea), Alicia K. Smith (Egilea), Monica Uddin (Egilea)
• Formatua: Liburua
• Argitaratua: BMC, 2024-09-01T00:00:00Z.
• Gaiak: article
• Sarrera elektronikoa: Connect to this object online.

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 distin...