Edwin van den Oord

Virginia Commonwealth University, Richmond, Virginia, United States

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Publications (48)394.06 Total impact

  • Andrey A Shabalin, Karolina A Aberg, Edwin Jcg van den Oord
    Epigenomics 02/2015; 7(1):13-5. · 2.43 Impact Factor
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    Molecular Psychiatry 02/2015; 20(2):207-214. · 15.15 Impact Factor
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    ABSTRACT: Anxiety disorders (ADs) are common mental disorders caused by a combination of genetic and environmental factors. Since ADs are highly comorbid with each other, partially due to shared genetic basis, studying AD phenotypes in a coordinated manner may be a powerful strategy for identifying potential genetic loci for ADs. To detect these loci, we performed genome-wide association studies (GWAS) of ADs. In addition, as a complementary approach to single-locus analysis, we also conducted gene- and pathway-based analyses. GWAS data were derived from the control sample of the Molecular Genetics of Schizophrenia (MGS) project (2,540 European American and 849 African American subjects) genotyped on the Affymetrix GeneChip 6.0 array. We applied two phenotypic approaches: (1) categorical case-control comparisons (CC) based upon psychiatric diagnoses, and (2) quantitative phenotypic factor scores (FS) derived from a multivariate analysis combining information across the clinical phenotypes. Linear and logistic models were used to analyse the association with ADs using FS and CC traits, respectively. At the single locus level, no genome-wide significant association was found. A trans-population gene-based meta-analysis across both ethnic subsamples using FS identified three genes (MFAP3L on 4q32.3, NDUFAB1 and PALB2 on 16p12) with genome-wide significance (false discovery rate (FDR] <5%). At the pathway level, several terms such as transcription regulation, cytokine binding, and developmental process were significantly enriched in ADs (FDR <5%). Our approaches studying ADs as quantitative traits and utilizing the full GWAS data may be useful in identifying susceptibility genes and pathways for ADs.
    PLoS ONE 11/2014; 9(11):e112559. · 3.53 Impact Factor
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    ABSTRACT: Genome-wide association studies (GWAS) of schizophrenia have identified multiple risk variants with robust association signals for schizophrenia. However, these variants could explain only a small proportion of schizophrenia heritability. Furthermore, the effect size of these risk variants is relatively small (eg, most of them had an OR less than 1.2), suggesting that additional risk variants may be detected when increasing sample size in analysis. Here, we report the identification of a genome-wide significant schizophrenia risk locus at 22q13.1 by combining 2 large-scale schizophrenia cohort studies. Our meta-analysis revealed that 7 single nucleotide polymorphism (SNPs) on chromosome 22q13.1 reached the genome-wide significance level (P < 5.0×10(-8)) in the combined samples (a total of 38441 individuals). Among them, SNP rs6001946 had the most significant association with schizophrenia (P = 2.04×10(-8)). Interestingly, all 7 SNPs are in high linkage disequilibrium and located in the MKL1 gene. Expression analysis showed that MKL1 is highly expressed in human and mouse brains. We further investigated functional links between MKL1 and proteins encoded by other schizophrenia susceptibility genes in the whole human protein interaction network. We found that MKL1 physically interacts with GSK3B, a protein encoded by a well-characterized schizophrenia susceptibility gene. Collectively, our results revealed that genetic variants in MKL1 might confer risk to schizophrenia. Further investigation of the roles of MKL1 in the pathogenesis of schizophrenia is warranted.
    Schizophrenia Bulletin 11/2014; · 8.61 Impact Factor
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    ABSTRACT: Current catalogs of brain expression quantitative trait loci (eQTL) are incomplete and the findings do not replicate well across studies. All existing cortical eQTL studies are small and emphasize the need for a meta-analysis. We performed a meta-analysis of 424 brain samples across five studies to identify regulatory variants influencing gene expression in human cortex. We identified 3584 genes in autosomes and chromosome X with false discovery rate q<0.05 whose expression was significantly associated with DNA sequence variation. Consistent with previous eQTL studies, local regulatory variants tended to occur symmetrically around transcription start sites and the effect was more evident in studies with large sample sizes. In contrast to random SNPs, we observed that significant eQTLs were more likely to be near 5'-untranslated regions and intersect with regulatory features. Permutation-based enrichment analysis revealed that SNPs associated with schizophrenia and bipolar disorder were enriched among brain eQTLs. Genes with significant eQTL evidence were also strongly associated with diseases from OMIM (Online Mendelian Inheritance in Man) and the NHGRI (National Human Genome Research Institute) genome-wide association study catalog. Surprisingly, we found that a large proportion (28%) of ~1000 autosomal genes encoding proteins needed for mitochondrial structure or function were eQTLs (enrichment P-value=1.3 × 10(-)(9)), suggesting a potential role for common genetic variation influencing the robustness of energy supply in brain and a possible role in the etiology of some psychiatric disorders. These systematically generated eQTL information should be a valuable resource in determining the functional mechanisms of brain gene expression and the underlying biology of associations with psychiatric disorders.
    Translational psychiatry. 10/2014; 4:e459.
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    ABSTRACT: Clozapine is a particularly effective antipsychotic medication but its use is curtailed by the risk of clozapine-induced agranulocytosis/granulocytopenia (CIAG), a severe adverse drug reaction occurring in up to 1% of treated individuals. Identifying genetic risk factors for CIAG could enable safer and more widespread use of clozapine. Here we perform the largest and most comprehensive genetic study of CIAG to date by interrogating 163 cases using genome-wide genotyping and whole-exome sequencing. We find that two loci in the major histocompatibility complex are independently associated with CIAG: a single amino acid in HLA-DQB1 (126Q) (P ¼ 4.7 Â 10 À 14 , odds ratio (OR) ¼ 0.19, 95% confidence interval (CI) ¼ 0.12–0.29) and an amino acid change in the extracellular binding pocket of HLA-B (158T) (P ¼ 6.4 Â 10 À 10 , OR ¼ 3.3, 95% CI ¼ 2.3–4.9). These associations dovetail with the roles of these genes in immunogenetic phenotypes and adverse drug responses for other medications, and provide insight into the pathophysiology of CIAG.
    Nature Communications 09/2014; 1718192123252628(27). · 10.74 Impact Factor
  • Karolina A Aberg, Edwin Jcg van den Oord
    Biomarkers in Medicine 06/2014; 8(5):607-11. · 3.22 Impact Factor
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    ABSTRACT: IMPORTANCE We investigated the variation in neuropsychological function explained by risk alleles at the psychosis susceptibility gene ZNF804A and its interacting partners using single nucleotide polymorphisms (SNPs), polygenic scores, and epistatic analyses. Of particular importance was the relative contribution of the polygenic score vs epistasis in variation explained. OBJECTIVES To (1) assess the association between SNPs in ZNF804A and the ZNF804A polygenic score with measures of cognition in cases with psychosis and (2) assess whether epistasis within the ZNF804A pathway could explain additional variation above and beyond that explained by the polygenic score. DESIGN, SETTING, AND PARTICIPANTS Patients with psychosis (n = 424) were assessed in areas of cognitive ability impaired in schizophrenia including IQ, memory, attention, and social cognition. We used the Psychiatric GWAS Consortium 1 schizophrenia genome-wide association study to calculate a polygenic score based on identified risk variants within this genetic pathway. Cognitive measures significantly associated with the polygenic score were tested for an epistatic component using a training set (n = 170), which was used to develop linear regression models containing the polygenic score and 2-SNP interactions. The best-fitting models were tested for replication in 2 independent test sets of cases: (1) 170 individuals with schizophrenia or schizoaffective disorder and (2) 84 patients with broad psychosis (including bipolar disorder, major depressive disorder, and other psychosis). MAIN OUTCOMES AND MEASURES Participants completed a neuropsychological assessment battery designed to target the cognitive deficits of schizophrenia including general cognitive function, episodic memory, working memory, attentional control, and social cognition. RESULTS Higher polygenic scores were associated with poorer performance among patients on IQ, memory, and social cognition, explaining 1% to 3% of variation on these scores (range, P = .01 to .03). Using a narrow psychosis training set and independent test sets of narrow phenotype psychosis (schizophrenia and schizoaffective disorder), broad psychosis, and control participants (n = 89), the addition of 2 interaction terms containing 2 SNPs each increased the R2 for spatial working memory strategy in the independent psychosis test sets from 1.2% using the polygenic score only to 4.8% (P = .11 and .001, respectively) but did not explain additional variation in control participants. CONCLUSIONS AND RELEVANCE These data support a role for the ZNF804A pathway in IQ, memory, and social cognition in cases. Furthermore, we showed that epistasis increases the variation explained above the contribution of the polygenic score.
    JAMA Psychiatry 05/2014; · 12.01 Impact Factor
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    ABSTRACT: Most psychiatric disorders are moderately to highly heritable. The degree to which genetic variation is unique to individual disorders or shared across disorders is unclear. To examine shared genetic etiology, we use genome-wide genotype data from the Psychiatric Genomics Consortium (PGC) for cases and controls in schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorders (ASD) and attention-deficit/hyperactivity disorder (ADHD). We apply univariate and bivariate methods for the estimation of genetic variation within and covariation between disorders. SNPs explained 17–29% of the variance in liability. The genetic correlation calculated using common SNPs was high between schizophrenia and bipolar disorder (0.68 ± 0.04 s.e.), moderate between schizophrenia and major depressive disorder (0.43 ± 0.06 s.e.), bipolar disorder and major depressive disorder (0.47 ± 0.06 s.e.), and ADHD and major depressive disorder (0.32 ± 0.07 s.e.), low between schizophrenia and ASD (0.16 ± 0.06 s.e.) and non-significant for other pairs of disorders as well as between psychiatric disorders and the negative control of Crohn’s disease. This empirical evidence of shared genetic etiology for psychiatric disorders can inform nosology and encourages the investigation of common pathophysiologies for related disorders.
    Nature Genetics 08/2013; AOP. · 29.65 Impact Factor
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    ABSTRACT: Aim: As the primary relevant tissue (brain) for psychiatric disorders is commonly not available, we aimed to investigate whether blood can be used as a proxy in methylation studies on the basis of two models. In the 'signature' model methylation-disease associations occur because a disease-causing factor affected methylation in the blood. In the 'mirror-site' model the methylation status in the blood is correlated with the corresponding disease-causing site in the brain. Materials, methods & results: Methyl-binding domain enrichment and next-generation sequencing of the blood, cortex and hippocampus from four haloperidol-treated and ten untreated C57BL/6 mice revealed high levels of correlation in methylation across tissues. Despite the treatment inducing a large number of methylation changes, this correlation remains high. Conclusion: Our results show that, consistent with the signature model, factors that affect brain processes (i.e., haloperidol) leave biomarker signatures in the blood and, consistent with the mirror-site model, the methylation status of many sites in the blood mirror those in the brain.
    Epigenomics 08/2013; 5(4):367-77. · 5.22 Impact Factor
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    PLoS ONE 07/2013; 8(7):67776-. · 3.53 Impact Factor
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    ABSTRACT: Schizophrenia (SCZ) is a devastating psychiatric condition. Identifying the specific genetic variants and pathways that increase susceptibility to SCZ is critical to improve disease understanding and address the urgent need for new drug targets. To identify SCZ susceptibility genes. We integrated results from a meta-analysis of 18 genome-wide association studies (GWAS) involving 1,085,772 single-nucleotide polymorphisms (SNPs) and 6 databases that showed significant informativeness for SCZ. The 9380 most promising SNPs were then specifically genotyped in an independent family-based replication study that, after quality control, consisted of 8107 SNPs. Linkage meta-analysis, brain transcriptome meta-analysis, candidate gene database, OMIM, relevant mouse studies, and expression quantitative trait locus databases. We included 11,185 cases and 10,768 control subjects from 6 databases and, after quality control 6298 individuals (including 3286 cases) from 1811 nuclear families. Case-control status for SCZ. Replication results showed a highly significant enrichment of SNPs with small P values. Of the SNPs with replication values of P.01, the proportion of SNPs that had the same direction of effects as in the GWAS meta-analysis was 89% in the combined ancestry group (sign test, P < 2.20 x 10(-16) and 93% in subjects of European ancestry only (P < 2.20 < 10(-16)). Our results supported the major histocompatibility complex region showing a3.7-fold overall enrichment of replication values of P < .01 in subjects from European ancestry. We replicated SNPs in TCF4 (P = 2.53 x 10(-10)) and NOTCH4 (P = 3.16 x 10(-7)) that are among the most robust SCZ findings. More novel findings included POM121L2 (P = 3.51 x 10(-7)), AS3MT (P = 9.01 x 10(-7)), CNNM2 (P = 6.07 = 10(-7)), and NT5C2(P = 4.09 x 10(-7)). To explore the many small effects, we performed pathway analyses. The most significant pathways involved neuronal function (axonal guidance, neuronal systems, and L1 cell adhesion molecule interaction)and the immune system (antigen processing, cell adhesion molecules relevant to T cells, and translocation to immunological synapse). We replicated novel SCZ disease genes and pathogenic pathways. Better understanding the molecular and biological mechanisms involved with schizophrenia may improve disease management and may identify new drug targets.
    Archives of General Psychiatry 06/2013; 70(6):573-81. · 13.75 Impact Factor
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    ABSTRACT: The importance of including developmental and environmental measures in genetic studies of human pathology is widely acknowledged, but few empirical studies have been published. Barriers include the need for longitudinal studies that cover relevant developmental stages and for samples large enough to deal with the challenge of testing gene-environment-development interaction. A solution to some of these problems is to bring together existing data sets that have the necessary characteristics. As part of the National Institute on Drug Abuse-funded Gene-Environment-Development Initiative, our goal is to identify exactly which genes, which environments, and which developmental transitions together predict the development of drug use and misuse. Four data sets were used of which common characteristics include (1) general population samples, including males and females; (2) repeated measures across adolescence and young adulthood; (3) assessment of nicotine, alcohol, and cannabis use and addiction; (4) measures of family and environmental risk; and (5) consent for genotyping DNA from blood or saliva. After quality controls, 2,962 individuals provided over 15,000 total observations. In the first gene-environment analyses, of alcohol misuse and stressful life events, some significant gene-environment and gene-development effects were identified. We conclude that in some circumstances, already collected data sets can be combined for gene-environment and gene-development analyses. This greatly reduces the cost and time needed for this type of research. However, care must be taken to ensure careful matching across studies and variables.
    Twin Research and Human Genetics 03/2013; · 1.92 Impact Factor
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    ABSTRACT: BACKGROUND: In methylome-wide association studies (MWAS) there are many possible differences between cases and controls (e.g. related to life style, diet, and medication use) that may affect the methylome and produce false positive findings. An effective approach to control for these confounders is to first capture the major sources of variation in the methylation data and then regress out these components in the association analyses. This approach is, however, computationally very challenging due to the extremely large number of methylation sites in the human genome.Result: We introduce MethylPCA that is specifically designed to control for potential confounders in studies where the number of methylation sites is extremely large. MethylPCA offers a complete and flexible data analysis including 1) an adaptive method that performs data reduction prior to PCA by empirically combining methylation data of neighboring sites, 2) an efficient algorithm that performs a principal component analysis (PCA) on the ultra high-dimensional data matrix, and 3) association tests. To accomplish this MethylPCA allows for parallel execution of tasks, uses C++ for CPU and I/O intensive calculations, and stores intermediate results to avoid computing the same statistics multiple times or keeping results in memory. Through simulations and an analysis of a real whole methylome MBD-seq study of 1,500 subjects we show that MethylPCA effectively controls for potential confounders. CONCLUSIONS: MethylPCA provides users a convenient tool to perform MWAS. The software effectively handles the challenge in memory and speed to perform tasks that would be impossible to accomplish using existing software when millions of sites are interrogated with the sample sizes required for MWAS.
    BMC Bioinformatics 03/2013; 14(1):74. · 2.67 Impact Factor
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    ABSTRACT: BACKGROUND: Methylation studies are a promising complement to genetic studies of DNA sequence. However, detailed prior biological knowledge is typically lacking, so methylome-wide association studies (MWAS) will be critical to detect disease relevant sites. A cost-effective approach involves the next-generation sequencing (NGS) of single-end libraries created from samples that are enriched for methylated DNA fragments. A limitation of single-end libraries is that the fragment size distribution is not observed. This hampers several aspects of the data analysis such as the calculation of enrichment measures that are based on the number of fragments covering the CpGs. RESULTS: We developed a non-parametric method that uses isolated CpGs to estimate sample-specific fragment size distributions from the empirical sequencing data. Through simulations we show that our method is highly accurate. While the traditional (extended) read count methods resulted in severely biased coverage estimates and introduces artificial inter-individual differences, through the use of the estimated fragment size distributions we could remove these biases almost entirely. Furthermore, we found correlations of 0.999 between coverage estimates obtained using fragment size distributions that were estimated with our method versus those that were "observed" in paired-end sequencing data. CONCLUSIONS: We propose a non-parametric method for estimating fragment size distributions that is highly precise and can improve the analysis of cost-effective MWAS studies that sequence single-end libraries created from samples that are enriched for methylated DNA fragments.
    BMC Bioinformatics 02/2013; 14(1):50. · 2.67 Impact Factor
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    ABSTRACT: Aim: We studied the use of methyl-CpG binding domain (MBD) protein-enriched genome sequencing (MBD-seq) as a cost-effective screening tool for methylome-wide association studies (MWAS). Materials & methods: Because MBD-seq has not yet been applied on a large scale, we first developed and tested a pipeline for data processing using 1500 schizophrenia cases and controls plus 75 technical replicates with an average of 68 million reads per sample. This involved the use of technical replicates to optimize quality control for multi- and duplicate-reads, an in silico experiment to identify CpGs in loci with alignment problems, CpG coverage calculations based on multiparametric estimates of the fragment size distribution, a two-stage adaptive algorithm to combine data from correlated adjacent CpG sites, principal component analyses to control for confounders and new software tailored to handle the large data set. Results: We replicated MWAS findings in independent samples using a different technology that provided single base resolution. In an MWAS of age-related methylation changes, one of our top findings was a previously reported robust association involving GRIA2. Our results also suggested that owing to the many confounding effects, a considerable challenge in MWAS is to identify those effects that are informative about disease processes. Conclusion: This study showed the potential of MBD-seq as a cost-effective tool in large-scale disease studies.
    Epigenomics 12/2012; 4(6):605-21. · 5.22 Impact Factor
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    ABSTRACT: Genetic factors underlying trait neuroticism, reflecting a tendency towards negative affective states, may overlap genetic susceptibility for anxiety disorders and help explain the extensive comorbidity amongst internalizing disorders. Genome-wide linkage (GWL) data from several studies of neuroticism and anxiety disorders have been published, providing an opportunity to test such hypotheses and identify genomic regions that harbor genes common to these phenotypes. In all, 11 independent GWL studies of either neuroticism (n=8) or anxiety disorders (n=3) were collected, which comprised of 5341 families with 15 529 individuals. The rank-based genome scan meta-analysis (GSMA) approach was used to analyze each trait separately and combined, and global correlations between results were examined. False discovery rate (FDR) analysis was performed to test for enrichment of significant effects. Using 10 cM intervals, bins nominally significant for both GSMA statistics, P(SR) and P(OR), were found on chromosomes 9, 11, 12, and 14 for neuroticism and on chromosomes 1, 5, 15, and 16 for anxiety disorders. Genome-wide, the results for the two phenotypes were significantly correlated, and a combined analysis identified additional nominally significant bins. Although none reached genome-wide significance, an excess of significant P(SR)P-values were observed, with 12 bins falling under a FDR threshold of 0.50. As demonstrated by our identification of multiple, consistent signals across the genome, meta-analytically combining existing GWL data is a valuable approach to narrowing down regions relevant for anxiety-related phenotypes. This may prove useful for prioritizing emerging genome-wide association data for anxiety disorders.
    European journal of human genetics: EJHG 04/2012; 20(10):1078-84. · 3.56 Impact Factor
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    ABSTRACT: Background A variety of species and experimental designs have been used to study genetic influences on alcohol dependence, ethanol response, and related traits. Integration of these heterogeneous data can be used to produce a ranked target gene list for additional investigation. Results In this study, we performed a unique multi-species evidence-based data integration using three microarray experiments in mice or humans that generated an initial alcohol dependence (AD) related genes list, human linkage and association results, and gene sets implicated in C. elegans and Drosophila. We then used permutation and false discovery rate (FDR) analyses on the genome-wide association studies (GWAS) dataset from the Collaborative Study on the Genetics of Alcoholism (COGA) to evaluate the ranking results and weighting matrices. We found one weighting score matrix could increase FDR based q-values for a list of 47 genes with a score greater than 2. Our follow up functional enrichment tests revealed these genes were primarily involved in brain responses to ethanol and neural adaptations occurring with alcoholism. Conclusions These results, along with our experimental validation of specific genes in mice, C. elegans and Drosophila, suggest that a cross-species evidence-based approach is useful to identify candidate genes contributing to alcoholism.
    BMC Genomics 01/2012; 13(8). · 4.04 Impact Factor
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    ABSTRACT: Tardive dyskinesia (TD) is a debilitating, unpredictable, and often irreversible side effect resulting from chronic treatment with typical antipsychotic agents such as haloperidol. TD is characterized by repetitive, involuntary, purposeless movements primarily of the orofacial region. In order to investigate genetic susceptibility to TD, we used a validated mouse model for a systems genetics analysis geared toward detecting genetic predictors of TD in human patients. Phenotypic data from 27 inbred strains chronically treated with haloperidol and phenotyped for vacuous chewing movements were subject to a comprehensive genomic analysis involving 426,493 SNPs, 4,047 CNVs, brain gene expression, along with gene network and bioinformatic analysis. Our results identified ~50 genes that we expect to have high prior probabilities for association with haloperidol-induced TD, most of which have never been tested for association with human TD. Among our top candidates were genes regulating the development of brain motor control regions (Zic4 and Nkx6-1), glutamate receptors (Grin1 and Grin2a), and an indirect target of haloperidol (Drd1a) that has not been studied as well as the direct target, Drd2.
    Mammalian Genome 12/2011; 23(5-6):322-35. · 2.88 Impact Factor
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    ABSTRACT: SIRT1 is a NAD(+)-dependent deacetylase that governs a number of genetic programs to cope with changes in the nutritional status of cells and organisms. Behavioral responses to food abundance are important for the survival of higher animals. Here we used mice with increased or decreased brain SIRT1 to show that this sirtuin regulates anxiety and exploratory drive by activating transcription of the gene encoding the monoamine oxidase A (MAO-A) to reduce serotonin levels in the brain. Indeed, treating animals with MAO-A inhibitors or selective serotonin reuptake inhibitors (SSRIs) normalized anxiety differences between wild-type and mutant animals. SIRT1 deacetylates the brain-specific helix-loop-helix transcription factor NHLH2 on lysine 49 to increase its activation of the MAO-A promoter. Both common and rare variations in the SIRT1 gene were shown to be associated with risk of anxiety in human population samples. Together these data indicate that SIRT1 mediates levels of anxiety, and this regulation may be adaptive in a changing environment of food availability.
    Cell 12/2011; 147(7):1459-72. · 31.96 Impact Factor

Publication Stats

2k Citations
394.06 Total Impact Points

Institutions

  • 2003–2014
    • Virginia Commonwealth University
      • • Center for Biomarker Research and Personalized Medicine,
      • • School of Pharmacy
      • • Department of Psychiatry
      • • Department of Pharmacology and Toxicology
      • • Virginia Institute for Psychiatric and Behavioral Genetics
      Richmond, Virginia, United States
  • 2013
    • Duke University Medical Center
      • Department of Psychiatry and Behavioral Science
      Durham, NC, United States
  • 2011
    • University of North Carolina at Chapel Hill
      • Department of Genetics
      Chapel Hill, NC, United States
  • 2005
    • U.S. Department of Veterans Affairs
      Washington, Washington, D.C., United States