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.
"There has since been a wave of large genome-wide association studies (GWAS) of BD that have used high-density SNP microarrays to look for common shared genotypes and haplotypes. Although such studies seem to suffer from many of the same problems as the family-based whole genome scans performed using microsatellite markers, i.e. failure to replicate across different sample sets and a realization that much larger sample sizes are necessary, the use of standard SNPs and genotyping methodologies has allowed the pooling of data from large patient cohorts, and this has led to some exciting findings of possible susceptibility loci and genes such as DGKH, CACNA1C and ANK3 (reviewed in Lee et al., 2012). "
[Show abstract][Hide abstract] ABSTRACT: Recently, genome-wide association studies (GWAS) for cases versus controls using single nucleotide polymorphism microarray data have shown promising findings for complex neuropsychiatric disorders, including bipolar disorder (BD).
Here we describe a comprehensive genome-wide study of bipolar disorder (BD), cross-referencing analysis from a family-based study of 229 small families with association analysis from over 950 cases and 950 ethnicity-matched controls from the UK and Canada. Further, loci identified in these analyses were supported by pathways identified through pathway analysis on the samples.
Although no genome-wide significant markers were identified, the combined GWAS findings have pointed to several genes of interest that support GWAS findings for BD from other groups or consortia, such as at SYNE1 on 6q25, PPP2R2C on 4p16.1, ZNF659 on 3p24.3, CNTNAP5 (2q14.3), and CDH13 (16q23.3). This apparent corroboration across multiple sites gives much confidence to the likelihood of genetic involvement in BD at these loci. In particular, our two-stage strategy found association in both our combined case/control analysis and the family-based analysis on 1q21.2 (closest gene: sphingosine-1-phosphate receptor 1 gene, S1PR1) and on 1q24.1 near the gene TMCO1, and at CSMD1 on 8p23.2, supporting several previous GWAS reports for BD and for schizophrenia. Pathway analysis suggests association of pathways involved in calcium signalling, neuropathic pain signalling, CREB signalling in neurons, glutamate receptor signalling and axonal guidance signalling.
The findings presented here show support for a number of genes previously implicated genes in the etiology of BD, including CSMD1 and SYNE1, as well as evidence for previously unreported genes such as the brain-expressed genes ADCY2, NCALD, WDR60, SCN7A and SPAG16.
BMC Medical Genetics 01/2014; 15(1):2. DOI:10.1186/1471-2350-15-2 · 2.08 Impact Factor
"There is a strong genetic component involved in the pathogenesis of schizophrenia. In recent years multiple genetic markers have been identified as conferring increased risk for schizophrenia from genome wide association studies [1,2]. One of these markers is the rs12807809 (T/C) single nucleotide polymorphism (SNP) in the neurogranin (NRGN) gene . "
[Show abstract][Hide abstract] ABSTRACT: Although the genome wide supported psychosis susceptibility neurogranin (NRGN) gene is expressed in human brains, it is unclear how it impacts brain morphology in schizophrenia. We investigated the influence of NRGN rs12807809 on cortical thickness, subcortical volumes and shapes in patients with schizophrenia. One hundred and fifty six subjects (91 patients with schizophrenia and 65 healthy controls) underwent structural MRI scans and their blood samples were genotyped. A brain mapping algorithm, large deformation diffeomorphic metric mapping, was used to perform group analysis of subcortical shapes and cortical thickness. Patients with risk TT genotype were associated with widespread cortical thinning involving frontal, parietal and temporal cortices compared with controls with TT genotype. No volumetric difference in subcortical structures (hippocampus, thalamus, amygdala, basal ganglia) was observed between risk TT genotype in patients and controls. However, patients with risk TT genotype were associated with thalamic shape abnormalities involving regions related to pulvinar and medial dorsal nuclei. Our results revealed the influence of the NRGN gene on thalamocortical morphology in schizophrenia involving widespread cortical thinning and thalamic shape abnormalities. These findings help to clarify underlying NRGN mediated pathophysiological mechanisms involving cortical-subcortical brain networks in schizophrenia.
PLoS ONE 12/2013; 8(12):e85603. DOI:10.1371/journal.pone.0085603 · 3.23 Impact Factor
"CNAs and single nucleotide polymorphisms (SNPs) in humans have revealed extensive genetic diversity in populations (Fu et al., 2010; Sudmant et al., 2010). A framework based on evolutionary genetics has been adopted to understand the disease-causing deleterious CNAs or beneficial CNAs present in human populations (Cooper et al., 2007; Elia et al., 2011; Lee et al., 2011; Nozawa et al., 2007; Perry et al., 2007). "
[Show abstract][Hide abstract] ABSTRACT: Copy number alteration (CNA) is one type of genomic aberration that is often induced by genome instability and is associated with diseases such as cancer. Determination of the genome-wide CNA profile is an important step in identifying the underlying mutation mechanisms. Genomic data based on next-generation sequencing technology is particularly suitable for determination of high-quality CNA profile. Now is an important time to reevaluate the use of sequencing techniques for CNA analysis, especially with the rapid growth of the different targeted genome and whole-genome sequencing strategies.
In this study, we provide a comparison of resequencing strategies, with regard to their utility, applied to the same hepatocellular carcinoma (HCC) sample for copy number determination. These strategies include whole-genome, exome, and restriction site-associated DNA (RAD) sequencing. The last of these strategies is a targeted sequencing technique that involves cutting the genome with a restriction enzyme and isolating the targeted sequences. Our data demonstrate that RAD sequencing is an efficient and comprehensive strategy that allows the cost-effective determination of CNAs. Further investigation of RAD sequencing data led to the finding that a precise measurement of the allele frequency would be a helpful complement to the read depth for CNA analysis for two reasons. First, knowledge of the allele frequency helps to resolve refined calculations of allele-specific copy numbers, which in turn identify the functionally important CNAs that are under natural selection on the parental alleles. Second, this knowledge enables deconvolution of CNA patterns in complex genomic regions.
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