Meta-analysis of genome-wide association studies of anxiety disorders
John Hettema, Takeshi Otowa, Karin Hek, Minyoung Lee, Enda Byrne, Saira Mirza, Michel
Nivard, T Bigdeli, Steven Aggen, Daniel E Adkins, Aaron Wolen, Ayman Fanous, Matthew Keller, Enrique Castelao, Zoltan Kutalik, Sandra
Van der Auwera, Georg Homuth, Matthias Nauck, Alexander Teumer, Jouke-Jan Hottenga,
Nese Direk, Albert Hofman, André Uitterlinden, Cornelis Mulder, Anjali Henders, Sarah
Medland, Scott Gordon, Andrew Heath, Pamela Madden, Michelle Pergadia, Peter van der
Most, Ilja Nolte, Floor van Oort, Catharina Hartman, Albertine Oldehinkel, Martin
Preisig, Hans Joergen Grabe, Christel Middeldorp, Brenda WJH Penninx, Dorret Boomsma,
Nicholas Martin, Grant Montgomery, Brion Maher, Edwin van den Oord, Naomi Wray, and
Henning Tiemeier. (2016). “Meta-analysis of genome-wide association studies of anxiety
disorders." Molecular Psychiatry, 21 (10): 1391-9
https://doi.org/10.1038/mp.2015.197
Anxiety disorders (ADs), namely generalized AD, panic disorder and phobias, are common, etiologically complex conditions with a partially genetic basis. Despite differing on diagnostic definitions based on clinical presentation, ADs likely represent various expressions of an underlying common diathesis of abnormal regulation of basic threat-response systems. We conducted genome-wide association analyses in nine samples of European ancestry from seven large, independent studies. To identify genetic variants contributing to genetic susceptibility shared across interview-generated DSM-based ADs, we applied two phenotypic approaches: (1) comparisons between categorical AD cases and supernormal controls, and (2) quantitative phenotypic factor scores (FS) derived from a multivariate analysis combining information across the clinical phenotypes. We used logistic and linear regression, respectively, to analyze the association between these phenotypes and genome-wide single nucleotide polymorphisms. Meta-analysis for each phenotype combined results across the nine samples for over 18 000 unrelated individuals. Each meta-analysis identified a different genome-wide significant region, with the following markers showing the strongest association: for case-control contrasts, rs1709393 located in an uncharacterized non-coding RNA locus on chromosomal band 3q12.3 (P=1.65 × 10(-8)); for FS, rs1067327 within CAMKMT encoding the calmodulin-lysine N-methyltransferase on chromosomal band 2p21 (P=2.86 × 10(-9)). Independent replication and further exploration of these findings are needed to more fully understand the role of these variants in risk and expression of ADs.