Shared genetics among major psychiatric disorders

Department of Biomedical and NeuroMotor Sciences, University of Bologna, Bologna 40123, Italy. Electronic address: .
The Lancet (Impact Factor: 45.22). 02/2013; 381(9875). DOI: 10.1016/S0140-6736(13)60223-8
Source: PubMed
12 Reads
  • Source
    • "Genetic association and genome-wide association studies (GWAS) (Huang et al., 2010; Liu et al., 2011; Purcell et al., 2009; Smoller et al., 2013) suggest there is some degree of genetic overlap among specific disorders such as affective disorders and psychosis, but also specific genetic diversity. Genetic pleiotrophy, or the impact of one gene on multiple phenotypes , has been reported to account for 17% of the genes or 5% of the single nucleotide polymorphisms (SNPs) associated with complex traits (Serretti and Fabbri, 2013; Sivakumaran et al., 2011). Identification of such genes between disorders can help identify shared molecular pathways between the disorders. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Major efforts have been directed at family-based association and case-control studies to identify the involvement of candidate genes in the major disorders of mental health. What remains unknown is whether candidate genes are associated with multiple disorders via pleiotropic mechanisms, and/or if other genes are specific to susceptibility for individual disorders. Here we undertook a review of genes that have been identified in prior meta-analyses examining specific genes and specific mental disorders that have core disruptions to emotional and cognitive function and contribute most to burden of illness- major depressive disorder (MDD), anxiety disorders (AD, including panic disorder and obsessive compulsive disorder), schizophrenia (SZ) and bipolar disorder (BD) and attention deficit hyperactivity disorder (ADHD). A literature review was conducted up to end-March 2013 which included a total of 1519 meta-analyses across 157 studies reporting multiple genes implicated in one or more of the five disorders studied. A total of 134 genes (206 variants) were identified as significantly associated risk variants for MDD, AD, ADHD, SZ or BD. Null genetic effects were also reported for 195 genes (426 variants). 13 genetic variants were shared in common between two or more disorders (APOE e4, ACE Ins/Del, BDNF Val66Met, COMT Val158Met, DAOA G72/G30 rs3918342, DAT1 40-bp, DRD4 48-bp, SLC6A4 5-HTTLPR, HTR1A C1019G, MTHR C677T, MTHR A1298C, SLC6A4 VNTR and TPH1 218A/C) demonstrating evidence for pleiotrophy. Another 12 meta-analyses of GWAS studies of the same disorders were identified, with no overlap in genetic variants reported. This review highlights the progress that is being made in identifying shared and unique genetic mechanisms that contribute to the risk of developing several major psychiatric disorders, and identifies further steps for progress.
    Journal of Psychiatric Research 09/2014; 60. DOI:10.1016/j.jpsychires.2014.09.014 · 3.96 Impact Factor
  • Source
    • "Increasing evidence suggests that ASD has common genetic risk factors and neuroanatomical overlap with schizophrenia (Carroll & Owen, 2009; Cheung et al., 2010; Serretti & Fabbri, 2013). Intriguingly , a recent theory of schizophrenia (Adams, Stephan, Brown, Frith, & Friston, 2013; Fletcher & Frith, 2009) invoked undue high precision of prediction errors to explain positive symptoms in schizophrenia (hallucinations and delusions). "
    [Show abstract] [Hide abstract]
    ABSTRACT: There have been numerous attempts to explain the enigma of autism, but existing neurocognitive theories often provide merely a refined description of 1 cluster of symptoms. Here we argue that deficits in executive functioning, theory of mind, and central coherence can all be understood as the consequence of a core deficit in the flexibility with which people with autism spectrum disorder can process violations to their expectations. More formally we argue that the human mind processes information by making and testing predictions and that the errors resulting from violations to these predictions are given a uniform, inflexibly high weight in autism spectrum disorder. The complex, fluctuating nature of regularities in the world and the stochastic and noisy biological system through which people experience it require that, in the real world, people not only learn from their errors but also need to (meta-)learn to sometimes ignore errors. Especially when situations (e.g., social) or stimuli (e.g., faces) become too complex or dynamic, people need to tolerate a certain degree of error in order to develop a more abstract level of representation. Starting from an inability to flexibly process prediction errors, a number of seemingly core deficits become logically secondary symptoms. Moreover, an insistence on sameness or the acting out of stereotyped and repetitive behaviors can be understood as attempts to provide a reassuring sense of predictive success in a world otherwise filled with error. (PsycINFO Database Record (c) 2014 APA, all rights reserved).
    Psychological Review 08/2014; In press. DOI:10.1037/a0037665 · 7.97 Impact Factor
  • Source
    • "The Cross-Disorder Group of the Psychiatric Genomics Consortium [Cross-Disorder Group of the Psychiatric Genomics Consortium et al., 2013] recently described the joint analysis of five major psychiatric disorders, revealing potential common underlying biological mechanisms [Cross-Disorder Group of the Psychiatric Genomics Consortium et al., 2013, Serretti and Fabbri, 2013]. Some genetic variants were associated with a number of (early or late-onset) psychiatric disorders, thus substantiating the usefulness of moving from specific syndromes to mechanisms common to a variety of diseases. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Deviations from normal body weight are observed prior to and after the onset of Alzheimer's disease (AD). Midlife obesity confers increased AD risk in later life, whereas late-life obesity is associated with decreased AD risk. The role of underweight and weight loss for AD risk is controversial. Based on the hypothesis of shared genetic variants for both obesity and AD, we analyzed the variants identified for AD or obesity from genome-wide association meta-analyses of the GERAD (AD, cases = 6,688, controls = 13,685) and GIANT (body mass index [BMI] as measure of obesity, n = 123,865) consortia. Our cross-disorder analysis of genome-wide significant 39 obesity SNPs and 23 AD SNPs in these two large data sets revealed that: (1) The AD SNP rs10838725 (pAD = 1.1 × 10−08) at the locus CELF1 is also genome-wide significant for obesity (pBMI = 7.35 × 10−09). (2) Four additional AD risk SNPs were nominally associated with obesity (rs17125944 at FERMT2, pBMI = 4.03 × 10−05, pBMI corr = 2.50 × 10−03; rs3851179 at PICALM; pBMI = 0.002, rs2075650 at TOMM40/APOE, pBMI = 0.024, rs3865444 at CD33, pBMI = 0.024). (3) SNPs at two of the obesity risk loci (rs4836133 downstream of ZNF608; pAD = 0.002 and at rs713586 downstream of RBJ/DNAJC27; pAD = 0.018) were nominally associated with AD risk. Additionally, among the SNPs used for confirmation in both studies the AD risk allele of rs1858973, with an AD association just below genome-wide significance (pAD = 7.20 × 10−07), was also associated with obesity (SNP at IQCK/GPRC5B; pBMI = 5.21 × 10−06; pcorr = 3.24 × 10−04). Our first GWAS based cross-disorder analysis for AD and obesity suggests that rs10838725 at the locus CELF1 might be relevant for both disorders. © 2014 Wiley Periodicals, Inc.
    American Journal of Medical Genetics Part B Neuropsychiatric Genetics 06/2014; 165(4). DOI:10.1002/ajmg.b.32234 · 3.42 Impact Factor
Show more