380 research outputs found
Imaging phenotypes and genotypes in schizophrenia
No full textSchizophrenia is associated with subtle structural and functional brain abnormalities. Both recent and classical data suggest that it is a heterogeneous disorder that is clearly heritable. The cause and course of schizophrenia are poorly understood, and classical categories of clinical symptoms have not been particularly useful in identifying its pathophysiology or predicting its treatment. The possible genetic risk factors for schizophrenia are numerous; however, the connection between the genotype and the time-course, or the multifaceted symptoms of the disease, has yet to be established. Brain imaging methods that study the structure or function of the cortical and subcortical regions have also identified distinct patterns that distinguish schizophrenics from controls, and that may identify meaningful subtypes of schizophrenia. The predictive relationship between these imaging phenotypes and disease characteristics such as treatment response is only beginning to be revealed. The emergence of the field of imaging genetics, combining genetic, and neuroimaging data, holds much promise for the deeper understanding and improved treatment of diseases such as schizophrenia. In this article we review some of the key findings in imaging phenotyping and genotyping of schizophrenia, and the initial endeavors at their combination into more meaningful and predictive patterns, or endophenotypes identifying the relationships among clinical symptoms, course, genes, and the underlying pathophysiology
Imaging genetics approaches to identify mechanisms in severe mental illness
No full textComment on:
Hippocampal and frontolimbic function as intermediate phenotype for psychosis: evidence from healthy relatives and a common risk variant in CACNA1C. Biol Psychiatry. 2014 Sep 15;76(6):466-75. doi: 10.1016/j.biopsych.2013.11.025. Epub 2013 Dec 8
Transposable elements and psychiatric disorders
No full textTransposable Elements (TEs) or transposons are low-complexity elements (e.g., LINEs, SINEs, SVAs, and HERVs) that make up to two-thirds of the human genome. There is mounting evidence that TEs play an essential role in genomic architecture and regulation related to both normal function and disease states. Recently, the identification of active TEs in several different human brain regions suggests that TEs play a role in normal brain development and adult physiology and quite possibly in psychiatric disorders. TEs have been implicated in hemophilia, neurofibromatosis, and cancer. With the advent of next-generation whole-genome sequencing approaches, our understanding of the relationship between TEs and psychiatric disorders will greatly improve. We will review the biology of TEs and early evidence for TE involvement in psychiatric disorders
Dopamine D2 receptor gene variants and quantitative measures of positive and negative symptom response following clozapine treatment
No full textDopamine D2 receptor blockade is the major basis for the antipsychotic action of typical antipsychotic drugs (AP) and a necessary but not sufficient basis for the antipsychotic action of atypical APs such as clozapine and other multireceptor antagonists which rely, in part, upon 5-HT2A antagonism. Genetic factors affecting the density and/or function of D2 receptors may therefore affect AP response.
Objectives This exploratory study investigates the effect of 12 single nucleotide polymorphisms (SNPs) spanning the entire dopamine D2 gene on clozapine response in two distinct schizophrenic populations (Caucasian and Africanâ American) refractory or intolerant to conventional APs.
Methods This study included 183 Caucasian and 49 Africanâ American DSM-III-R or DSM-IV schizophrenics. Genotyping was determined by 5prime-exonuclease fluorescence assays. Within each population genotype, allele, allele +/â , and haplotype frequencies were compared between responders and non-responders by X2 tests. Linkage disequilibrium analysis was also performed.
Results In the Caucasian sample, no significant associations were found for individual SNP tests; however, two haplotypes were identified as having significant protective effects on treatment outcome. In the Africanâ American sample, individual SNP tests identified the Taq1A, Taq1B, and rs1125394 markers as being predictive of clozapine response. Haplotype analyses identified four protective haplotypes containing these SNPs. In addition, no association between the â 141C Ins/Del site and clozapine response was found in either population.
Conclusions Interindividual variability in clozapine response among treatment refractory/intolerant patients is still not fully understood and likely involves multiple factors. This exploratory analysis suggests that the D2 receptor gene may be one such factor
Analysis of miR-137 expression and rs1625579 in dorsolateral prefrontal cortex
No full textMicroRNAs (miRNAs) are small non-coding RNAs that act as potent regulators of gene expression. A recent GWAS reported the rs1625579 SNP, located downstream of miR-137, as the strongest new association with schizophrenia [Ripke S, Sanders AR, Kendler KS, Levinson DF, Sklar P, Holmans PA, etal. Genome-wide association study identifies five new schizophrenia loci. Nat Genet 2011;43:969-76.]. Prior to this GWAS finding, a schizophrenia imaging-genetic study found miR-137 target genes significantly enriched for association with activation in the dorsolateral prefrontal cortex (DLPFC) [Potkin SG, Macciardi F, Guffanti G, Fallon JH, Wang Q, Turner JA, etal. Identifying gene regulatory networks in schizophrenia. Neuroimage 2010;53:839-47.].We investigated the expression levels of miR-137 and three candidate target genes (ZNF804A, CACNA1C, TCF4) in the DLPFC of postmortem brain tissue from 2 independent cohorts: (1) 26 subjects (10 control (CTR), 7 schizophrenia (SZ), 9 bipolar disorder (BD)) collected at the UCI brain bank; and (2) 99 subjects (33 CTR, 35 SZ, 31 BD) obtained from the Stanley Medical Research Institute (SMRI). MiR-137 expression in the DLPFC did not differ between diagnoses. We also explored the relationship between rs1625579 genotypes and miR-137 expression. Significantly lower miR-137 expression levels were observed in the homozygous TT subjects compared to TG and GG subjects in the control group (30% decrease, p-value=0.03). Moreover, reduced miR-137 levels in TT subjects corresponded to increased levels of the miR-137 target gene TCF4. The miR-137 expression pattern in 9 brain regions was significant for regional effect (ANOVA p-value=1.83E-12), with amygdala and hippocampus having the highest miR-137 expression level. In conclusion, decreased miR-137 expression is associated with the SZ risk allele of rs1625579, and potential regulation of TCF4, another SZ candidate gene. This study offers additional support for involvement of miR-137 and downstream targets as mechanisms of risk for psychiatric disorders
Role of ethnicity in antipsychotic-induced weight gain and tardive dyskinesia: genes or environment?
No full textAim: This study explored the role of self-reported ethnicity and genetic ancestry on antipsychotic (AP)-induced weight gain and tardive dyskinesia (TD) in schizophrenia. Patients and methods: Ethnicity was determined by self-report as well as Structure analysis of 190 SNPs selected from HapMap3, genotyped using a customized Illumina BeadChip. Age, gender, baseline weight and AP response using Brief Psychiatric Rating Scale were assessed. Multivariate regression models for AP-induced weight gain and TD, based on the Abnormal Involuntary Movement Scale were constructed. Results: African-American ethnicity (self-report, p = 0.021 and Structure analysis, p = 0.042) predicted AP-induced weight gain but not TD (self-report, p = 0.408 and Structure analysis, p = 0.714). Conclusion: Self-reported African-American ethnicity seemed to better predict AP-induced weight gain in schizophrenia compared with genetic ancestry, suggesting a possible role of environmental in addition to genetic factors. Future larger studies are needed to clarify specific gene-environment mechanisms mediating the effect of ethnicity on AP-induced weight gain. © 2013 Future Medicine Ltd
Comparison of different methods to estimate genetic ancestry and control for stratification in genome-wide association studies
No full textIn case-control association studies, population subdivision or admixture can lead to spurious associations between a phenotype and unlinked candidate loci. Population stratification can occur in case-control association studies when allele frequencies differ between cases and controls because of ancestry.
We evaluated five methods (Fst, Genomic Control, STRUCTURE, PLINK and EIGENSTRAT) using 317K SNPs (Illumina HumanHap300) in a case-control sample of 200 American subjects with different races (Caucasian, African and Asian) in order to identify and to correct for stratification. Fst, Structure and Genomic Control are based on the usage of few genetic markers while PLINK and EIGENSTRAT are computationally tractable on a genome-wide scale. Fst, STRUCTURE and Genomic Control did not detect a significant stratification in our sample, as well as EIGENSTRAT and PLINK. However, these last two methods, using a much larger information from the whole set of SNPs, graphically suggested the presence of a partial stratification, due to African and Asian individuals while the estimated inflation factor of 1 didn't statistically confirm stratification. This brought to the decision to further enlarge the sample with hundreds of controls coming from Caucasian populations. When we enlarged the sample to 650 individuals we found a high value of inflation factor as statistical confirmation of the population stratification. The substructure still depends only on African and Asian subjects that are separated from the Caucasian homogeneous sample. Therefore the sample size is crucial to get enough power to detect a possible stratification
Gene discovery through imaging genetics : identification of two novel genes associated with schizophrenia
No full textWe have discovered two genes, RSRC1 and ARHGAP18, associated with schizophrenia and in
an independent study provided additional support for this association. We have both
discovered and verified the association of two genes, RSRC1 and ARHGAP18, with
schizophrenia. We combined a genome-wide screening strategy with neuroimaging measures
as the quantitative phenotype and identified the single nucleotide polymorphisms (SNPs)
related to these genes as consistently associated with the phenotypic variation. To control for
the risk of false positives, the empirical P-value for association significance was calculated
using permutation testing. The quantitative phenotype was Blood-Oxygen-Level Dependent
(BOLD) Contrast activation in the left dorsal lateral prefrontal cortex measured during a working
memory task. The differential distribution of SNPs associated with these two genes in cases
and controls was then corroborated in a larger, independent sample of patients with
schizophrenia (n = 82) and healthy controls (n = 91), thus suggesting a putative etiological
function for both genes in schizophrenia. Up until now these genes have not been linked to any
neuropsychiatric illness, although both genes have a function in prenatal brain development.
We introduce the use of functional magnetic resonance imaging activation as a quantitative phenotype in conjunction with genome-wide association as a gene discovery tool
SNP-based pathway enrichment analysis for genome-wide association studies
No full textAbstract Background Recently we have witnessed a surge of interest in using genome-wide association studies (GWAS) to discover the genetic basis of complex diseases. Many genetic variations, mostly in the form of single nucleotide polymorphisms (SNPs), have been identified in a wide spectrum of diseases, including diabetes, cancer, and psychiatric diseases. A common theme arising from these studies is that the genetic variations discovered by GWAS can only explain a small fraction of the genetic risks associated with the complex diseases. New strategies and statistical approaches are needed to address this lack of explanation. One such approach is the pathway analysis, which considers the genetic variations underlying a biological pathway, rather than separately as in the traditional GWAS studies. A critical challenge in the pathway analysis is how to combine evidences of association over multiple SNPs within a gene and multiple genes within a pathway. Most current methods choose the most significant SNP from each gene as a representative, ignoring the joint action of multiple SNPs within a gene. This approach leads to preferential identification of genes with a greater number of SNPs. Results We describe a SNP-based pathway enrichment method for GWAS studies. The method consists of the following two main steps: 1) for a given pathway, using an adaptive truncated product statistic to identify all representative (potentially more than one) SNPs of each gene, calculating the average number of representative SNPs for the genes, then re-selecting the representative SNPs of genes in the pathway based on this number; and 2) ranking all selected SNPs by the significance of their statistical association with a trait of interest, and testing if the set of SNPs from a particular pathway is significantly enriched with high ranks using a weighted Kolmogorov-Smirnov test. We applied our method to two large genetically distinct GWAS data sets of schizophrenia, one from European-American (EA) and the other from African-American (AA). In the EA data set, we found 22 pathways with nominal P-value less than or equal to 0.001 and corresponding false discovery rate (FDR) less than 5%. In the AA data set, we found 11 pathways by controlling the same nominal P-value and FDR threshold. Interestingly, 8 of these pathways overlap with those found in the EA sample. We have implemented our method in a JAVA software package, called SNP Set Enrichment Analysis (SSEA), which contains a user-friendly interface and is freely available at http://cbcl.ics.uci.edu/SSEA. Conclusions The SNP-based pathway enrichment method described here offers a new alternative approach for analysing GWAS data. By applying it to schizophrenia GWAS studies, we show that our method is able to identify statistically significant pathways, and importantly, pathways that can be replicated in large genetically distinct samples.</p
Correlation of qEEG with PET in schizophrenia
No full textPET relative metabolism was correlated with quantitative EEG in 9 schizophrenic patients. The PET metabolic regions of interest were the frontal lobes, thalamus and basal ganglia, and right and left temporal lobes. Significant positive correlations were seen for the frontal lobes and delta EEG power, and alpha power with subcortical metabolism. The physiologic plausibility of those correlations is discussed with reference to the possible effect of neuroleptic medication
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