Genome wide association studies (GWAS) and copy number variation (CNV) studies of the major psychoses: What have we learnt?
ABSTRACT Schizophrenia (SZ) and bipolar disorder (BPD) have high heritabilities and are clinically and genetically complex. Genome wide association studies (GWAS) and studies of copy number variations (CNV) in SZ and BPD have allowed probing of their underlying genetic risks. In this systematic review, we assess extant genetic signals from published GWAS and CNV studies of SZ and BPD up till March 2011. Risk genes associated with SZ at genome wide significance level (p value<7.2 × 10(-8)) include zinc finger binding protein 804A (ZNF804A), major histocompatibility (MHC) region on chromosome 6, neurogranin (NRGN) and transcription factor 4 (TCF4). Risk genes associated with BPD include ankyrin 3, node of Ranvier (ANK3), calcium channel, voltage dependent, L type, alpha 1C subunit (CACNA1C), diacylglycerol kinase eta (DGKH), gene locus on chromosome 16p12, and polybromo-1 (PBRM1) and very recently neurocan gene (NCAN). Possible common genes underlying psychosis include ZNF804A, CACNA1C, NRGN and PBRM1. The CNV studies suggest that whilst CNVs are found in both SZ and BPD, the large deletions and duplications are more likely found in SZ rather than BPD. The validation of any genetic signal is likely confounded by genetic and phenotypic heterogeneities which are influenced by epistatic, epigenetic and gene-environment interactions. There is a pressing need to better integrate the multiple research platforms including systems biology computational models, genomics, cross disorder phenotyping studies, transcriptomics, proteomics, metabolomics, neuroimaging and clinical correlations in order to get us closer to a more enlightened understanding of the genetic and biological basis underlying these potentially crippling conditions.
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ABSTRACT: Today multinational studies using genome-wide association scan (GWAS) for >1000,000 polymorphisms on >100,000 cases with major psychiatric diseases versus controls, combined with next-generation sequencing have found ~100 genetic polymorphisms associated with schizophrenia (SCZ), bipolar disorder (BD), autism, attention deficit and hyperactivity disorder (ADHD), etc. However, the effect size of each genetic mutation has been generally low (<1%), and altogether could portray a tiny fraction of these mental diseases. Furthermore, none of these polymorphisms was specific to disease phenotypes indicating that they are simply genetic risk factors rather than causal mutations. The lack of identification of the major gene(s) in huge genetic studies increased the tendency for reexamining the roles of environmental factors in psychiatric and other complex diseases. However, this time at cellular/molecular levels mediated by epigenetic mechanisms that are heritable, but reversible while interacting with the environment. Now, gene-specific or whole-genome epigenetic analyses have introduced hundreds of aberrant epigenetic marks in the blood or brain of individuals with psychiatric diseases that include aberrations in DNA methylation, histone modifications and microRNA expression. Interestingly, most of the current psychiatric drugs such as valproate, lithium, antidepressants, antipsychotics and even electroconvulsive therapy (ECT) modulate epigenetic codes. The existing data indicate that, the impacts of environment/nurture, including the uterine milieu and early-life events might be more significant than genetic/nature in most psychiatric diseases. The lack of significant results in large-scale genetic studies led to revise the bolded roles of genetics and now we are at the turning point of genomics for reconsidering environmental factors that through epigenetic mechanisms may impact the brain development/functions causing disease phenotypes.Iranian Journal of Psychiatry and Behavioral Sciences 01/2014; 8(3):1-10.
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ABSTRACT: eLife digest Many individuals who have diabetes also have other diseases that affect the heart and blood vessels. It is not uncommon for human diseases to occur together like this; and understanding the relationships between diseases and other traits can make it easier to diagnose conditions. Furthermore, it can also help researchers develop treatments that are more precisely targeted to each condition and cause fewer side effects. Two conditions or traits tend to occur together if they are caused by mutations in the same gene or genes; or if they involve processes within cells that share the same proteins and other molecules. However, in most cases the genes and molecular mechanisms involved are not yet known so it is more difficult to work out how the traits are connected. Computing techniques make it possible to assess the relationships between hundreds or thousands of traits at the same time. These high volume analyses can also allow scientists to identify less obvious relationships that might be missed in more traditional types of study. Here, Oren et al. created a new computer algorithm to identify related traits, their shared genetic basis, and the molecular mechanisms behind them. The algorithm is called GEMOT and uses a three-step approach to sift through a large amount of data. Oren et al. tested GEMOT using a database of 1738 documented traits—including diseases and behaviors—in laboratory mice. Oren et al. identified many clusters of traits in the mice and the underlying genetic and molecular mechanisms that link them. For example, they found that a mutation in a gene called Klf7 affected the expression of other genes that are involved in making new cells in the bone marrow. In turn, these changes influenced 17 different behaviors in the mice after they were injected with the painkiller morphine. In humans, the same genes that underlie behaviors related to morphine treatment have been linked to the survival rate of patients with a form of brain cancer. This suggests that—alongside providing pain-relief—morphine may influence how the tumor grows. The algorithm developed by Oren et al. can now be used to further explore the impact of the environment on the relationships between traits. DOI: http://dx.doi.org/10.7554/eLife.04346.002eLife Sciences 03/2015; 4. DOI:10.7554/eLife.04346 · 8.52 Impact Factor
Indian Journal of Psychological Medicine 01/2015; 37(1):1. DOI:10.4103/0253-7176.150796