International meta-analysis of PTSD genome-wide association studies identifies sex- and ancestry-specific genetic risk loci

Nievergelt, Caroline M.; Maihofer, Adam X.; Klengel, Torsten; Atkinson, Elizabeth G.; Chen, Chia Yen; Choi, Karmel W.; Coleman, Jonathan R. I.; Dalvie, Shareefa; Duncan, Laramie; Gelernter, Joel; Levey, Daniel F.; Logue, Mark W.; Polimanti, Renato; Provost, Allison C.; Ratanatharathorn, Andrew; Stein, Murray B.; Torres, Katy; Aiello, Allison E.; Almli, Lynn M.; Amstadter, Ananda B.; Andersen, Søren Bo; Andreassen, Ole A.; Arbisi, Paul A.; Ashley–Koch, Allison; Austin, S. Bryn; Avdibegović, Esmina; Babić, Dragan; Bækvad‐Hansen, Marie; Baker, Dewleen G.; Beckham, Jean C.; Bierut, Laura J.; Bisson, Jonathan I.; Boks, Marco P.; Bolger, Elizabeth; Børglum, Anders D.; Bradley, Bekh; Brashear, Megan; Breen, Gerome; Bryant, Richard A.; Bustamante, Angela C.; Bybjerg‐Grauholm, Jonas; Calabrese, Joseph R.; Almeida, José Miguel Caldas de; Dale, Anders M.; Daly, Mark J.; Daskalakis, Nikolaos P.; Deckert, Jürgen; Delahanty, Douglas L.; Dennis, Michelle F.; Disner, Seth G.; Domschke, Katharina; Džubur-Kulenović, Alma; Erbes, Christopher R.; Evans, Alexandra; Farrer, Lindsay A.; Feeny, Norah C.; Flory, Janine D.; Forbes, David; Franz, Carol E.; Galea, Sandro; Garrett, Melanie E.; Gelaye, Bizu; Geuze, Elbert; Gillespie, Charles F.; Uka, Afërdita Goçi; Gordon, Scott D.; Guffanti, Guia; Hammamieh, Rasha; Harnal, Supriya; Hauser, Michael A.; Heath, Andrew C.; Hemmings, Sian; Hougaard, David M.; Jakovljević, Miro; Jett, Marti; Johnson, Eric O.; Jones, Ian; Jovanović, Tanja; Qin, Xue; Junglen, Angela; Karstoft, Karen‐Inge; Kaufman, Milissa L.; Kessler, Ronald C.; Khan, Alaptagin; Kimbrel, Nathan A.; King, Anthony P.; Koen, Nastassja; Kranzler, Henry R.; Kremen, William S.; Lawford, Bruce R.; Lebois, Lauren A. M.; Lewis, Catrin; Linnstaedt, Sarah D.; Lori, Adriana; Lugonja, Bozo; Luykx, Jurjen J.; Lyons, Michael J.; Maples‐Keller, Jessica L.; Marmar, Charles R.; Martin, Alicia R.; Martin, Nicholas G.; Maurer, Douglas; Mavissakalian, Matig; McFarlane, Alexander C.; McGlinchey, Regina E.; McLaughlin, Katie A.; McLean, Samuel A.; McLeay, Sarah; Mehta, Divya; Milberg, William; Miller, Mark W.; Morey, Rajendra A.; Morris, Charles P.; Mors, Ole; Mortensen, Preben Bo; Neale, Benjamin M.; Nelson, Elliot C.; Nordentoft, Merete; Norman, Sonya B.; O’Donnell, Meaghan; Orcutt, Holly K.; Panizzon, Matthew S.; Peters, Edward S.; Peterson, Alan L.; Peverill, Matthew; Pietrzak, Robert H.; Polusny, Melissa A.; Rice, John P.; Ripke, Stephan; Risbrough, Victoria B.; Roberts, Andrea L.; Rothbaum, Alex O.; Rothbaum, Barbara O.; Roy‐Byrne, Peter; Ruggiero, Ken; Rung, Ariane L.; Rutten, Bart P. F.; Saccone, Nancy L.; Sánchez, Sixto E.; Schijven, Dick; Seedat, Soraya; Seligowski, Antonia V.; Seng, Julia S.; Sheerin, Christina M.; Silove, Derrick; Smith, Alicia K.; Smoller, Jordan W.; Sponheim, Scott R.; Stein, Dan J.; Stevens, Jennifer; Sumner, Jennifer A.; Teicher, Martin H.; Thompson, Wesley K.; Trapido, Edward; Uddin, Monica; Ursano, Robert J.; Heuvel, Leigh Luella van den; Hooff, Miranda Van; Vermetten, Eric; Vinkers, Christiaan H.; Voisey, Joanne; Wang, Yunpeng; Wang, Zhewu; Werge, Thomas; Williams, Michelle A.; Williamson, Douglas E.; Winternitz, Sherry; Wolf, Christiane; Wolf, Erika J.; Wolff, Jonathan D.; Yehuda, Rachel; Young, Ross; Young, Keith A.; Zhao, Hongyu; Zoellner, Lori A.; Liberzon, Israel; Ressler, Kerry J.; Haas, Magali; Koenen, Karestan C.

doi:10.1038/s41467-019-12576-w

Cited by 387 publications

(396 citation statements)

References 87 publications

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“…We report 19 metabolite-phenotype associations that were identified at a false discovery rate (FDR) cutoff of 0.05 (Table 1, Supplementary Table 10, Supplementary Figure 26). The phenotypes (and significantly associated metabolites) were schizophrenia 32 31 (unknown metabolite X-24295), and attention deficit hyperactivity disorder (ADHD) 24 (orotate and malate). A two-sample MR analysis was performed for the 19 significant metabolite-phenotype associations.…”

Section: Figure 1 Gwas Meta-analysis Of the Csf Metabolomementioning

confidence: 99%

Cerebrospinal fluid metabolomics identifies 19 brain-related phenotype associations

Panyard

Kim

Darst

et al. 2020

Preprint

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Advances in technology have allowed for the study of metabolomics in the context of disease, enabling the discovery of new potential risk factors, diagnostic markers, and drug targets. For neurological and psychiatric phenotypes, the cerebrospinal fluid (CSF) is of particular biomedical importance as it is in direct contact with the brain and spinal cord. However, the CSF metabolome is difficult to study on a large scale due to the relative complexity of the procedure needed to collect the fluid compared to blood or urine studies. Here, we present a metabolome-wide association study (MWAS), an analysis using individual-level genetic and metabolomic data from two cohorts to impute metabolites into large samples with genome-wide association summary statistics. We conducted a metabolome-wide genome-wide association analysis with 338 CSF metabolites, identifying 16 genotype-metabolite associations, 6 of which were novel. Using these results, we then built prediction models for all available CSF metabolites and tested for associations with 27 neurological and psychiatric phenotypes in large cohorts, identifying 19 significant CSF metabolite-phenotype associations. Our results demonstrate the potential of MWAS to overcome the logistic challenges inherent in cerebrospinal fluid research to study the role of metabolomics in brain-related phenotypes and the feasibility of this framework for similar studies of omic data in scarce sample types.

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Section: Figure 1 Gwas Meta-analysis Of the Csf Metabolomementioning

confidence: 99%

Cerebrospinal fluid metabolomics identifies 19 brain-related phenotype associations

Panyard

Kim

Darst

et al. 2020

Preprint

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“…Largescale molecular platforms, such as those involved with genome-wide association studies (GWAS) allow for the examination of millions of variants in association with traits, and afford an agnostic approach to discover genes and biologic systems involved with disorders. The most recent meta-analyses of GWAS data from the PGC PTSD workgroup (n~200,000) revealed numerous significant variants and quantified the molecular heritability of PTSD (Nievergelt et al, 2019). Collaborative efforts from the PGC have led to progress on finding epigenome-wide significant sites related to risk for PTSD (Smith et al, 2019).…”

Section: Ten Years Of Progress In Genomicsmentioning

confidence: 99%

A decennial review of psychotraumatology: what did we learn and where are we going?

Olff

Amstadter

Armour

et al. 2019

European Journal of Psychotraumatology

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On 6 December 2019 we start the 10th year of the European Journal of Psychotraumatogy (EJPT), a full Open Access journal on psychotrauma. This editorial is part of a special issue/ collection celebrating the 10 years anniversary of the journal where we will acknowledge some of our most impactful articles of the past decade (also discussed below and marked with * in the reference list). In this editorial the editors present a decennial review of the field addressing a range of topics that are core to both the journal and to psychotraumatology as a discipline. These include neurobiological developments (genomics, neuroimaging and neuroendocrine research), forms of trauma exposure and impact across the lifespan, mass trauma and early interventions, work-related trauma, trauma in refugee populations, and the potential consequences of trauma such as PTSD or Complex PTSD, but also resilience. We address innovations in psychological, medication (enhanced) and technologyassisted treatments, mediators and moderators like social support and finally how new research methods help us to gain insights in symptom structures or to better predict symptom development or treatment success. We aimed to answer three questions 1. Where did we stand in 2010? 2. What did we learn in the past 10 years? 3. What are our knowledge gaps? We conclude with a number of recommendations concerning top priorities for the future direction of the field of psychotraumatology and correspondingly the journal.

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“…The risk factors for developing PTSD are multifactorial [6][7][8][9][10][11][12] , and include history of prior stress exposure, level of social support, and premorbid psychological traits. PTSD is 30-40% heritable [13][14][15][16][17][18] and recent studies provide evidence of genome-wide significant risk loci associated with PTSD [18][19][20] .…”

Section: Introductionmentioning

confidence: 99%

Transcriptomic Organization of Human Posttraumatic Stress Disorder

Girgenti

Wang

et al. 2020

Preprint

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Posttraumatic stress disorder (PTSD) affects approximately 8% of the general population, with higher rates in extreme stress groups, including combat veterans or victims of sexual assault. Despite extensive study of the neurobiological correlates of PTSD, little is known about its molecular substrates. Here differential gene expression and network analyses of 4 prefrontal cortex (PFC) postmortem subregions of male and female PTSD subjects demonstrates extensive remodeling of the transcriptomic landscape. The data revealed a highly connected down-regulated set of interneuron transcripts in the most significant gene network associated with PTSD and integration of this data with genotype data from the largest PTSD GWAS identified the interneuron synaptic gene ELFN1 as conferring significant genetic liability for PTSD. We also identified marked sexual dimorphism in the transcriptomic signatures that could contribute to the higher rates of PTSD in women. Comparison with a matched major depressive disorder (MDD) cohort revealed significant divergence between the molecular profiles of subjects with PTSD and depression despite their high comorbidity. Our analysis provides convergent systems-level evidence of genomic networks within the PFC that contribute to the pathophysiology of PTSD in humans.

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International meta-analysis of PTSD genome-wide association studies identifies sex- and ancestry-specific genetic risk loci

Cited by 387 publications

References 87 publications

Cerebrospinal fluid metabolomics identifies 19 brain-related phenotype associations

Cerebrospinal fluid metabolomics identifies 19 brain-related phenotype associations

A decennial review of psychotraumatology: what did we learn and where are we going?

Transcriptomic Organization of Human Posttraumatic Stress Disorder

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