Expressive

rs10818400 - BRINP1 - LINC01613

Magnitude 2.2 · 5 studies on file

Reported associations

  • Shared genetics of psychiatric disorders and type 2 diabetes:a large-scale genome-wide cross-trait analysis. - Journal of psychiatric research (2023) · Ding H, Xie M, Wang J, Ouyang M, Huang Y, Yuan F, Jia Y, Zhang X, Liu N, Zhang N · PubMed 36738649

    Individuals with psychiatric disorders have elevated rates of type 2 diabetes comorbidity. Although little is known about the shared genetics and causality of this association. Thus, we aimed to investigate shared genetics and causal link between different type 2 diabetes and psychiatric disorders. We conducted a large-scale genome-wide cross-trait association study(GWAS) to investigate genetic overlap between type 2 diabetes and anorexia nervosa, attention deficit/hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depressive disorder, obsessive-compulsive disorder, schizophrenia, anxiety disorders and Tourette syndrome. By post-GWAS functional analysis, we identify variants genes expression in various tissues. Enrichment pathways, potential protein interaction and m

  • Multivariate genetic analysis of personality and cognitive traits reveals abundant pleiotropy. - Nature human behaviour (2023) · Hindley G, Shadrin AA, van der Meer D, Parker N, Cheng W, O'Connell KS, Bahrami S, Lin A, Karadag N, Holen B, Bjella T, Deary IJ, Davies G, Hill WD, Bressler J, Seshadri S, Fan CC, Ueland T, Djurovic S, Smeland OB, Frei O, Dale AM, Andreassen OA · PubMed 37365406

    Personality and cognitive function are heritable mental traits whose genetic foundations may be distributed across interconnected brain functions. Previous studies have typically treated these complex mental traits as distinct constructs. We applied the 'pleiotropy-informed' multivariate omnibus statistical test to genome-wide association studies of 35 measures of neuroticism and cognitive function from the UK Biobank (n = 336,993). We identified 431 significantly associated genetic loci with evidence of abundant shared genetic associations, across personality and cognitive function domains. Functional characterization implicated genes with significant tissue-specific expression in all tested brain tissues and brain-specific gene sets. We conditioned independent genome-wide association

  • 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

  • The genetics of the mood disorder spectrum: genome-wide association analyses of over 185,000 cases and 439,000 controls - Unknown journal (n.d.) · Unknown authors · PubMed 31926635

    ABSTRACT: Background Mood disorders (including major depressive disorder and bipolar disorder) affect 10-20% of the population. They range from brief, mild episodes to severe, incapacitating conditions that markedly impact lives. Despite their diagnostic distinction, multiple approaches have shown considerable sharing of risk factors across the mood disorders. Methods To clarify their shared molecular genetic basis, and to highlight disorder-specific associations, we meta-analysed data from the latest Psychiatric Genomics Consortium (PGC) genome-wide association studies of major depression (including data from 23andMe) and bipolar disorder, and an additional major depressive disorder cohort from UK Biobank (total: 185,285 cases, 439,741 controls; non-overlapping N = 609,424). Results Sev

  • Phenotype integration improves power and preserves specificity in biobank-based genetic studies of major depressive disorder - Unknown journal (n.d.) · Unknown authors · PubMed 37985818

    ABSTRACT: Biobanks often contain several phenotypes relevant to diseases such as major depressive disorder (MDD), with partly distinct genetic architectures. Researchers face complex tradeoffs between shallow (large sample size, low specificity/sensitivity) and deep (small sample size, high specificity/sensitivity) phenotypes, and the optimal choices are often unclear. Here we propose to integrate these phenotypes to combine the benefits of each. We use phenotype imputation to integrate information across hundreds of MDD-relevant phenotypes, which significantly increases genome-wide association study (GWAS) power and polygenic risk score (PRS) prediction accuracy of the deepest available MDD phenotype in UK Biobank, LifetimeMDD. We demonstrate that imputation preserves specificity in its g

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