bioRxivGeneticsPreprint
Largest genome-wide association study for PTSD identifies genetic risk loci in European and African ancestries and implicates novel biological pathways
Caroline M. NievergeltiD, Adam X. MaihoferiD, Torsten KlengeliD, Elizabeth G. AtkinsoniD, Chia‐Yen CheniD, Karmel W. ChoiiD, Jonathan R. I. ColemaniD, Shareefa DalvieiD, Laramie E. DuncaniD, Mark W. LogueiD, Allison C. ProvostiD, Andrew D. RatanatharathorniD, Murray B. SteiniD, Katy TorresiD, Allison E. AielloiD, Lynn M. AlmliiD, Ananda B. AmstadteriD, Søren Bo AnderseniD, Ole A. AndreasseniD, Paul A. ArbisiiD, Allison E. Ashley‐KochiD, Sydney Bryn AustiniD, Esmina AvdibegovićiD, Dragan BabićiD, Marie Bækvad‐HanseniD, , Douglas MaureriD
Abstract
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 Parkinsons 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.
- organism: human
- assay: GWAS summary statistics
- research focus: GWAS / association mapping
- approach: statistical model
- Citations
- 21
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