Andrew Ratanatharathorn, Marco P. Boks, Adam X. Maihofer, Allison E. Aiello, Ananda B. Amstadter, Allison E. Ashley-Koch, Dewleen G. Baker, Jean C. Beckham, Evelyn Bromet, Michelle Dennis, Melanie E. Garrett, Elbert Geuze, Guia Guffanti, Michael A. Hauser, Varun Kilaru, Nathan A. Kimbrel, Karestan C. Koenen, Pei-Fen Kuan, Mark W. Logue, Benjamin J. Luft, Mark W. Miller, Colter Mitchell, Nicole R. Nugent, Kerry J. Ressler, Bart P. F. Rutten, Murray B. Stein, Eric Vermetten, Christiaan H. Vinkers, Nagy A. Youssef, VA Mid-Atlantic MIRECC Workgroup, PGC PTSD Epigenetics Workgroup, Monica Uddin, Caroline M. Nievergelt, Alicia K. Smith. Epigenome-wide association of PTSD from heterogeneous cohorts with a common multi-site analysis pipeline. Am J Med Genet. 174B:619–630. 2017
Compelling evidence suggests that epigenetic mechanisms such as DNA methylation play a role in stress regulation and in the etiologic basis of stress related disorders such as Post traumatic Stress Disorder (PTSD). Here we describe the purpose and methods of an international consortium that was developed to study the role of epigenetics in PTSD. Inspired by the approach used in the Psychiatric Genomics Consortium, we brought together investigators representing seven cohorts with a collective sample size of N = 1147 that included detailed information on trauma exposure, PTSD symptoms, and genome-wide DNA methylation data. The objective of this consortium is to increase the analytical sample size by pooling data and combining expertise so that DNA methylation patterns associated with PTSD can be identified. Several quality control and analytical pipelines were evaluated for their control of genomic inflation and technical artifacts with a joint analysis procedure established to derive comparable data over the cohorts for meta-analysis. We propose methods to deal with ancestry population stratification and type I error inflation and discuss the advantages and disadvantages of applying robust error estimates. To evaluate our pipeline, we report results from an epigenome-wide association study (EWAS) of age, which is a well-characterized phenotype with known epigenetic associations. Overall, while EWAS are highly complex and subject to similar challenges as genome-wide association studies (GWAS), we demonstrate that an epigenetic meta-analysis with a relatively modest sample size can be wellpowered to identify epigenetic associations. Our pipeline can be used as a framework for consortium efforts for EWAS.
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