Post-traumatic stress disorder (PTSD) is a common and debilitating disorder. The risk of PTSD following trauma is heritable, but robust common variants have yet to be identified by genome-wide association studies (GWAS). We have collected a multi-ethnic cohort including over 30,000 PTSD cases and 170,000 controls. We first demonstrate significant genetic correlations across 60 PTSD cohorts to evaluate the comparability of these phenotypically heterogeneous studies. In this largest GWAS meta-analysis of PTSD to date we identify a total of 6 genome-wide significant loci, 4 in European and 2 in African-ancestry analyses. Follow-up analyses incorporated local ancestry and sex-specific effects, and functional studies. Along with other novel genes, a non-coding RNA (ncRNA) and a Parkinson's Disease gene, PARK2, were associated with PTSD. Consistent with previous reports, SNP-based heritability estimates for PTSD range between 10-20%. Despite a significant shared liability between PTSD and major depressive disorder, we show evidence that some of our loci may be specific to PTSD. These results demonstrate the role of genetic variation contributing to the biology of differential risk for PTSD and the necessity of expanding GWAS beyond European ancestry.Comparability of PGC2 studies PGC2 compiled the largest collection of global PTSD GWAS to date, with subjects recruited from both clinically deeply characterized, small patient groups and large cohorts with self-reported PTSD symptoms. We did not restrict the type of trauma subjects were exposed to, and trauma included both civilian and/or military events, often with pre-existing exposure to childhood trauma. To evaluate the comparability of these phenotypically heterogeneous studies we first estimated genetic correlations with LDSC, 15 a method that leverages GWAS summary results, the only data type available to PGC-PTSD for several of the larger military and non-US cohorts. We found significant genetic correlations (r g ) between studies using a cross-validation approach including all PGC2 EUA subjects (10 runs with studies randomly placed into 2 groups; mean r g = 0.56, mean SE = 0.23, mean p = 0.029, Supplementary Table 8).Next, additional analyses on the UK Biobank cohort (UKBB) were performed. This cohort comprises a very large proportion of the data, with almost as many EUA cases as the rest of the EUA PGC2 combined (referred to as PGC1.5). PTSD screening in UKBB was based on self-reported symptoms from a mental health survey. 16 We found a considerable genetic correlation between the UKBB and PGC1.5 EUA subjects (r g = 0.73, SE = 0.21, p = 0.0005; Supplementary Table 9). Further, sensitivity analyses in the UKBB using 3 alternative inclusion criteria for PTSD cases and controls showed stable correlations with PGC1.5 (P1 -P3; r g = 0.72 -0.79; Supplementary Table 10). Subsequent analyses were based on the UKBB phenotype including the largest number of subjects (P1; N = 126,188). Sex-stratified genetic correlations support the findings of a significant genetic signal...
Differences in susceptibility to posttraumatic stress disorder (PTSD) may be related to epigenetic differences between PTSD cases and trauma-exposed controls. Such epigenetic differences may provide insight into the biological processes underlying the disorder. Here we describe the results of the largest DNA methylation meta-analysis of PTSD to date with data from the Psychiatric Genomics Consortium (PGC) PTSD Epigenetics Workgroup. Ten cohorts, military and civilian, contributed blood-derived DNA methylation data (HumanMethylation450 BeadChip) from 1,896 PTSD cases (42%) and trauma-exposed controls (58%). Utilizing a common QC and analysis strategy, we identified ten CpG sites associated with PTSD (9.61E-07
38Admixed populations are routinely excluded from medical genomic studies due to concerns over 39 population structure. Here, we present a statistical framework and software package, Tractor, to facilitate the 40 inclusion of admixed individuals in association studies by leveraging local ancestry. We test Tractor with 41 simulations and empirical data focused on admixed African-European individuals. Tractor generates ancestry-42 specific effect size estimates, can boost GWAS power, and improves the resolution of association signals. 43Using a local ancestry aware regression model, we replicate known hits for blood lipids in admixed 44 populations, discover novel hits missed by standard GWAS procedures, and localize signals closer to putative 45 causal variants. 46 47 48 Introduction 49Admixed groups, whose genomes contain more than one ancestral population such as African 50American and Hispanic/Latino individuals, make up more than a third of the US populace, and the population 51 is becoming increasingly mixed over time 1 . Many common, heritable, diseases including prostate cancer 2-5 , 52 asthma 6-9 , and several cardiovascular disorders such as atherosclerosis 10,11 are enriched in admixed 53 populations of the US. However, only a minute proportion of association studies address the genetic 54 architecture of complex traits in such groups 12,13 ; admixed individuals are systematically removed from many 55 studies due to the lack of methods and pipelines to effectively account for their ancestry such that population 56 substructure can infiltrate analyses and bias results [14][15][16][17][18][19][20][21] . Large-scale efforts to collect genetic data alongside 57 medically-relevant phenotypes are beginning to focus more on non-Eurasian ethnic groups that contain higher 58 amounts of admixture 22-27 , motivating the timely development of scalable methods to allow well-calibrated 59 statistical genomic work on these populations. If not addressed, this inability to analyze admixed people will 60 limit the clinical utility of large-scale data-collection efforts for minorities, exacerbating the concerning health 61 disparities that already exist 28-32 . 62In GWAS, the specific concern regarding including admixed participants is obtaining false positive hits due 63 to alleles being at different frequencies across populations. Most studies currently attempt to control for this by 64 using Principle Components (PCs) in a linear or linear mixed model framework. However, PCs capture broader 65 admixture fractions, and individuals' local ancestry makeup may differ between case and control cohorts even 66 if their global fractions are identical. Even including PCs as covariates, then, still leaves open the possibility for 67 false positive associations, as well as absorbing power. 68Studying diverse populations in gene discovery efforts not only reduces disparities but also benefits 69 genetic analysis for individuals of all ancestries. Perhaps the most notable example of this is in multi-ethnic 70 fine-mapping, which ca...
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.