Vernell S. Williamson

MicroRNA expression profiling in the prefrontal cortex of individuals affected with schizophrenia and bipolar disorders

MicroRNAs (miRNAs) are a large family of small non-coding RNAs which negatively control gene expression at both the mRNA and protein levels. The number of miRNAs identified is growing rapidly and approximately one-third is expressed in the brain where they have been shown to affect neuronal differentiation, synaptosomal complex localization and synapse plasticity, all functions thought to be disrupted in schizophrenia. Here we investigated the expression of 667 miRNAs (miRBase v.13) in the prefrontal cortex of individuals with schizophrenia (SZ, N = 35) and bipolar disorder (BP, N = 35) using a real-time PCR-based Taqman Low Density Array (TLDA). After extensive QC steps, 441 miRNAs were included in the final analyses. At a FDR of 10%, 22 miRNAs were identified as being differentially expressed between cases and controls, 7 dysregulated in SZ and 15 in BP. Using in silico target gene prediction programs, the 22miRNAs were found to target brain specific genes contained within networks overrepresented for neurodevelopment, behavior, and SZ and BP disease development. In an initial attempt to corroborate some of these predictions, we investigated the extent of correlation between the expressions of hsa-mir-34a, -132 and -212 and their predicted gene targets. mRNA expression of tyrosine hydroxylase (TH), phosphogluconate dehydrogenase (PGD) and metabotropic glutamate receptor 3 (GRM3) was measured in the SMRI sample. Hsa-miR-132 and -212 were negatively correlated with TH (p = 0.0001 and 0.0017) and with PGD (p = 0.0054 and 0.017, respectively).

Variants in nicotinic acetylcholine receptors α5 and α3 increase risks to nicotine dependence.

Nicotinic acetylcholine receptors bind to nicotine and initiate the physiological and pharmacological responses to tobacco smoking. In this report, we studied the association of α5 and α3 subunits with nicotine dependence and with the symptoms of alcohol and cannabis abuse and dependence in two independent epidemiological samples (n = 815 and 1,121, respectively). In this study, seven single nucleotide polymorphisms were genotyped in the CHRNA5 and CHRNA3 genes. In both samples, we found that the same alleles of rs16969968 (P = 0.0068 and 0.0028) and rs1051730 (P = 0.0237 and 0.0039) were significantly associated with the scores of Fagerström test for nicotine dependence (FTND). In the analyses of the symptoms of abuse/dependence of alcohol and cannabis, we found that rs16969968 and rs1051730 were significantly associated with the symptoms of alcohol abuse or dependence (P = 0.0072 and 0.0057) in the combined sample, but the associated alleles were the opposite of that of FTND. No association with cannabis abuse/dependence was found. These results suggested that the α5 and α3 subunits play a significant role in both nicotine dependence and alcohol abuse/dependence. However, the opposite effects with nicotine dependence and alcohol abuse/dependence were puzzling and future studies are necessary to resolve this issue.

Experimental validation of candidate schizophrenia gene ZNF804A as target for hsa-miR-137

MicroRNAs (miRNAs) are small non-coding RNAs that mainly function as negative regulators of gene expression (Lai, 2002) and have been shown to be involved in schizophrenia etiology through genetic and expression studies (Burmistrova et al., 2007; Hansen et al., 2007a; Perkins et al., 2007; Beveridge et al., 2010; Kim et al., 2010). In a mega analysis of genome-wide association study (GWAS) of schizophrenia (SZ) and bipolar disorders (BP), a polymorphism (rs1625579) located in the primary transcript of a miRNA gene, hsa-miR-137, was reported to be strongly associated with SZ. Four SZ loci (CACNA1C, TCF4, CSMD1, C10orf26) achieving genome-wide significance in the same study were predicted and later experimentally validated (Kwon et al., 2011) as hsa-miR-137 targets. Here, using in silico, cellular and luciferase based approaches we also provide evidence that another well replicated candidate schizophrenia gene, ZNF804A, is also target for hsa-miR-137.

Cannabinoid Receptor 1 Gene Association With Nicotine Dependence

CONTEXT The endogenous cannabinoid system has been implicated in drug addiction in animal models. The cannabinoid receptor 1 (CNR1) gene is 1 of the 2 receptors expressed in the brain. It has been reported to be associated with alcoholism and multiple drug abuse and dependence. OBJECTIVE To test the hypothesis that the CNR1 gene is associated with nicotine dependence. DESIGN Genotype-phenotype association study. Ten single-nucleotide polymorphisms were genotyped in the CNR1 gene in 2 independent samples. For the first sample (n = 688), a 3-group case-control design was used to test allele association with smoking initiation and nicotine dependence. For the second sample (n = 961), association was assessed with scores from the Fagerström Test for Nicotine Dependence (FTND). Settings Population samples selected from the Mid-Atlantic Twin Registry. PARTICIPANTS White patients aged 18 to 65 years who met the criteria of inclusion. MAIN OUTCOME MEASURES Fagerström Tolerance Questionnaire and FTND scores. RESULTS Significant single-marker and haplotype associations were found in both samples, and the associations were female specific. Haplotype 1-1-2 of markers rs2023239-rs12720071-rs806368 was associated with nicotine dependence and FTND score in the 2 samples (P < .001 and P = .009, respectively). CONCLUSION Variants and haplotypes in the CNR1 gene may alter the risk for nicotine dependence, and the associations are likely sex specific.

Using genetic information from candidate gene and genome-wide association studies in risk prediction for alcohol dependence

Family‐based and genome‐wide association studies (GWAS) of alcohol dependence (AD) have reported numerous associated variants. The clinical validity of these variants for predicting AD compared with family history information has not been reported. Using the Collaborative Study on the Genetics of Alcoholism (COGA) and the Study of Addiction: Genes and Environment (SAGE) GWAS samples, we examined the aggregate impact of multiple single nucleotide polymorphisms (SNPs) on risk prediction. We created genetic sum scores by adding risk alleles associated in discovery samples, and then tested the scores for their ability to discriminate between cases and controls in validation samples. Genetic sum scores were assessed separately for SNPs associated with AD in candidate gene studies and SNPs from GWAS analyses that met varying P‐value thresholds. Candidate gene sum scores did not exhibit significant predictive accuracy. Family history was a better classifier of case‐control status, with a significant area under the receiver operating characteristic curve (AUC) of 0.686 in COGA and 0.614 in SAGE. SNPs that met less stringent P‐value thresholds of 0.01–0.50 in GWAS analyses yielded significant AUC estimates, ranging from mean estimates of 0.549 for SNPs with P < 0.01 to 0.565 for SNPs with P < 0.50. This study suggests that SNPs currently have limited clinical utility, but there is potential for enhanced predictive ability with better understanding of the large number of variants that might contribute to risk.

Detecting miRNAs in deep-sequencing data: a software performance comparison and evaluation

Deep sequencing has become a popular tool for novel miRNA detection but its data must be viewed carefully as the state of the field is still undeveloped. Using three different programs, miRDeep (v1, 2), miRanalyzer and DSAP, we have analyzed seven data sets (six biological and one simulated) to provide a critical evaluation of the programs performance. We selected these software based on their popularity and overall approach toward the detection of novel and known miRNAs using deep-sequencing data. The program comparisons suggest that, despite differing stringency levels they all identify a similar set of known and novel predictions. Comparisons between the first and second version of miRDeep suggest that the stringency level of each of these programs may, in fact, be a result of the algorithm used to map the reads to the target. Different stringency levels are likely to affect the number of possible novel candidates for functional verification, causing undue strain on resources and time. With that in mind, we propose that an intersection across multiple programs be taken, especially if considering novel candidates that will be targeted for additional analysis. Using this approach, we identify and performed initial validation of 12 novel predictions in our in-house data with real-time PCR, six of which have been previously unreported.

Association of CRHR1 variants and posttraumatic stress symptoms in hurricane exposed adults

Posttraumatic stress disorder (PTSD) is a moderately heritable anxiety disorder that may develop after exposure to trauma. However, only few genetic variants that relate to PTSD have been studied. This study examined the relationship between 12 single nucleotide polymorphisms (SNPs) in the corticotropin-releasing hormone receptor 1 gene (CRHR1) and post-disaster PTSD symptoms and diagnosis in adults exposed to 2004 Florida hurricanes. CRHR1 regulates the hypothalamic-pituitary-adrenal (HPA) axis; dysregulation of the HPA axis is characteristic of stress phenotypes. Final analyses were conducted in the European-American (EA) subsample (n=564) due to population stratification. After correction for multiple testing, rs12938031 and rs4792887 remained associated with post-hurricane PTSD symptoms. Additionally, rs12938031 was associated with post-hurricane diagnosis of PTSD. This study is the first to examine CRHR1 in relation to PTSD in adults, and provides evidence for the importance of CRHR1 variation in the etiology of PTSD. Although results are preliminary and require replication, they justify follow-up efforts to characterize how this gene relates to PTSD.

Integrating mRNA and miRNA Weighted Gene Co-Expression Networks with eQTLs in the Nucleus Accumbens of Subjects with Alcohol Dependence

Alcohol consumption is known to lead to gene expression changes in the brain. After performing weighted gene co-expression network analyses (WGCNA) on genome-wide mRNA and microRNA (miRNA) expression in Nucleus Accumbens (NAc) of subjects with alcohol dependence (AD; N = 18) and of matched controls (N = 18), six mRNA and three miRNA modules significantly correlated with AD were identified (Bonferoni-adj. p≤ 0.05). Cell-type-specific transcriptome analyses revealed two of the mRNA modules to be enriched for neuronal specific marker genes and downregulated in AD, whereas the remaining four mRNA modules were enriched for astrocyte and microglial specific marker genes and upregulated in AD. Gene set enrichment analysis demonstrated that neuronal specific modules were enriched for genes involved in oxidative phosphorylation, mitochondrial dysfunction and MAPK signaling. Glial-specific modules were predominantly enriched for genes involved in processes related to immune functions, i.e. cytokine signaling (all adj. p≤ 0.05). In mRNA and miRNA modules, 461 and 25 candidate hub genes were identified, respectively. In contrast to the expected biological functions of miRNAs, correlation analyses between mRNA and miRNA hub genes revealed a higher number of positive than negative correlations (χ2 test p≤ 0.0001). Integration of hub gene expression with genome-wide genotypic data resulted in 591 mRNA cis-eQTLs and 62 miRNA cis-eQTLs. mRNA cis-eQTLs were significantly enriched for AD diagnosis and AD symptom counts (adj. p = 0.014 and p = 0.024, respectively) in AD GWAS signals in a large, independent genetic sample from the Collaborative Study on Genetics of Alcohol (COGA). In conclusion, our study identified putative gene network hubs coordinating mRNA and miRNA co-expression changes in the NAc of AD subjects, and our genetic (cis-eQTL) analysis provides novel insights into the etiological mechanisms of AD.

DISTMIX: direct imputation of summary statistics for unmeasured SNPs from mixed ethnicity cohorts

Motivation: To increase the signal resolution for large-scale meta-analyses of genome-wide association studies, genotypes at unmeasured single nucleotide polymorphisms (SNPs) are commonly imputed using large multi-ethnic reference panels. However, the ever increasing size and ethnic diversity of both reference panels and cohorts makes genotype imputation computationally challenging for moderately sized computer clusters. Moreover, genotype imputation requires subject-level genetic data, which unlike summary statistics provided by virtually all studies, is not publicly available. While there are much less demanding methods which avoid the genotype imputation step by directly imputing SNP statistics, e.g. Directly Imputing summary STatistics (DIST) proposed by our group, their implicit assumptions make them applicable only to ethnically homogeneous cohorts. Results: To decrease computational and access requirements for the analysis of cosmopolitan cohorts, we propose DISTMIX, which extends DIST capabilities to the analysis of mixed ethnicity cohorts. The method uses a relevant reference panel to directly impute unmeasured SNP statistics based only on statistics at measured SNPs and estimated/user-specified ethnic proportions. Simulations show that the proposed method adequately controls the Type I error rates. The 1000 Genomes panel imputation of summary statistics from the ethnically diverse Psychiatric Genetic Consortium Schizophrenia Phase 2 suggests that, when compared to genotype imputation methods, DISTMIX offers comparable imputation accuracy for only a fraction of computational resources. Availability and implementation: DISTMIX software, its reference population data, and usage examples are publicly available at http://code.google.com/p/distmix. Contact: dlee4@vcu.edu Supplementary information: Supplementary Data are available at Bioinformatics online.

JEPEG: a summary statistics based tool for gene-level joint testing of functional variants

Motivation: Gene expression is influenced by variants commonly known as expression quantitative trait loci (eQTL). On the basis of this fact, researchers proposed to use eQTL/functional information univariately for prioritizing single nucleotide polymorphisms (SNPs) signals from genome-wide association studies (GWAS). However, most genes are influenced by multiple eQTLs which, thus, jointly affect any downstream phenotype. Therefore, when compared with the univariate prioritization approach, a joint modeling of eQTL action on phenotypes has the potential to substantially increase signal detection power. Nonetheless, a joint eQTL analysis is impeded by (i) not measuring all eQTLs in a gene and/or (ii) lack of access to individual genotypes. Results: We propose joint effect on phenotype of eQTL/functional SNPs associated with a gene (JEPEG), a novel software tool which uses only GWAS summary statistics to (i) impute the summary statistics at unmeasured eQTLs and (ii) test for the joint effect of all measured and imputed eQTLs in a gene. We illustrate the behavior/performance of the developed tool by analysing the GWAS meta-analysis summary statistics from the Psychiatric Genomics Consortium Stage 1 and the Genetic Consortium for Anorexia Nervosa. Conclusions: Applied analyses results suggest that JEPEG complements commonly used univariate GWAS tools by: (i) increasing signal detection power via uncovering (a) novel genes or (b) known associated genes in smaller cohorts and (ii) assisting in fine-mapping of challenging regions, e.g. major histocompatibility complex for schizophrenia. Availability and implementation: JEPEG, its associated database of eQTL SNPs and usage examples are publicly available at http://code.google.com/p/jepeg/. Contact: dlee4@vcu.edu Supplementary information: Supplementary data are available at Bioinformatics online.