Expressive

rs10936879 - TMEM38BP1 - LINC01208

Magnitude 2.2 · 2 studies on file

Reported associations

  • Symptom-level modelling unravels the shared genetic architecture of anxiety and depression. - Nature human behaviour (2021) · Thorp JG, Campos AI, Grotzinger AD, Gerring ZF, An J, Ong JS, Wang W, Shringarpure S, Byrne EM, MacGregor S, Martin NG, Medland SE, Middeldorp CM, Derks EM · PubMed 33859377

    Depression and anxiety are highly prevalent and comorbid psychiatric traits that cause considerable burden worldwide. Here we use factor analysis and genomic structural equation modelling to investigate the genetic factor structure underlying 28 items assessing depression, anxiety and neuroticism, a closely related personality trait. Symptoms of depression and anxiety loaded on two distinct, although highly genetically correlated factors, and neuroticism items were partitioned between them. We used this factor structure to conduct genome-wide association analyses on latent factors of depressive symptoms (89 independent variants, 61 genomic loci) and anxiety symptoms (102 variants, 73 loci) in the UK Biobank. Of these associated variants, 72% and 78%, respectively, replicated in an independ

  • Multivariate genome-wide analyses of the well-being spectrum. - Nature genetics (2019) · Baselmans BML, Jansen R, Ip HF, van Dongen J, Abdellaoui A, van de Weijer MP, Bao Y, Smart M, Kumari M, Willemsen G, Hottenga JJ, Boomsma DI, de Geus EJC, Nivard MG, Bartels M · PubMed 30643256

    We introduce two novel methods for multivariate genome-wide-association meta-analysis (GWAMA) of related traits that correct for sample overlap. A broad range of simulation scenarios supports the added value of our multivariate methods relative to univariate GWAMA. We applied the novel methods to life satisfaction, positive affect, neuroticism, and depressive symptoms, collectively referred to as the well-being spectrum (N = 2,370,390), and found 304 significant independent signals. Our multivariate approaches resulted in a 26% increase in the number of independent signals relative to the four univariate GWAMAs and in an ~57% increase in the predictive power of polygenic risk scores. Supporting transcriptome- and methylome-wide analyses (TWAS and MWAS, respectively) uncovered an addition

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