J . A . Sumner, L . E . Duncan , E . J. Wolf , A . B . Amstadter, D . G . Baker, J . C . Beckham, B . Gelaye , S . Hemmings , N . A . Kimbrel , M . W. Logue , V. Michopoulos , K . S . Mitchell , C . Nievergelt, A . Rothbaum, S . Seedat , G . Shinozaki , and E . Vermetten. Posttraumatic stress disorder has genetic overlap with cardiometabolic traits. Psychological Medicine (2017), doi: 10.1017/S0033291717000733
Growing research links posttraumatic stress disorder (PTSD) to a range of cardiometabolic conditions, including metabolic syndrome, heart disease, stroke, and type 2 diabetes (Heppner et al. 2009; Roberts et al. 2015; Sumner et al. 2015; Wolf et al. 2016; Koenen et al. 2017). A number of behavioral (e.g. poor diet, physical inactivity, cigarette smoking, substance use) and physiological (e.g. dysregulation of biological stress response systems, such as the hypothalamic–pituitary–adrenal axis, autonomic nervous system, and immune system) pathways have been proposed to underlie associations between PTSD and cardiometabolic risk (Dedert et al. 2010; van Liempt et al. 2013; Wentworth et al. 2013; Koenen et al. 2017). Additionally, overlapping genetic factors may predispose individuals to both PTSD and cardiometabolic disease. Initial evidence for shared genetic effects across PTSD and cardiometabolic traits comes from studies based on twin designs (Vaccarino et al. 2013, 2014) and candidate gene approaches (Pollard et al. 2016). However, to date, research has not examined the genetic overlap of PTSD and cardiometabolic disease using a genome-wide design. Recent developments in genetic computational methods now permit the estimation of genetic correlations between complex traits from genome-wide association study (GWAS) summary statistics, referred to as crosstrait LD score regression (LDSR) (Bulik-Sullivan et al. 2015). This cross-trait LDSR approach can be applied flexibly by utilizing summary statistics as input (Bulik-Sullivan et al. 2015). In contrast, other methods designed to identify shared genetic effects (i.e. polygenic scores and restricted maximum likelihood) require individual genotype data, which often cannot be released due to data-sharing restrictions. Another advantage of the LDSR method is that it is not biased by sample overlap across studies (Bulik-Sullivan et al. 2015). The LDSR approach also incorporates the effects of all SNPs, including those that do not reach genomewide significance, thereby improving the accuracy and power of genetic prediction (Dudbridge, 2016). However, one limitation of this method is that it can only be used with samples without recent admixture and for whom suitable large-scale genetic data resources are available. Given these restrictions, it can only be applied to European ancestry (EA) samples, and not African-American or Latino populations, at this time.
The Psychiatric Genomics Consortium (PGC)-PTSD working group has been leading efforts to identify genetic risk variants associated with PTSD (Logue et al. 2015), and the first meta-analysis of PTSD GWASs (N = 20 070) has been recently completed by this group (Duncan et al. 2017). In the present study, we used summary statistics from the PGC-PTSD meta-analysis of Duncan et al. (2017) to conduct the first GWAS-based investigation of potential genetic overlap between PTSD and cardiometabolic traits.
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