Jennifer T Judy

Johns Hopkins Medicine, Baltimore, MD, United States

Are you Jennifer T Judy?

Claim your profile

Publications (9)30.47 Total impact

  • Source
    Molecular psychiatry 09/2013; · 15.05 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Numerous genome-wide gene expression studies of bipolar disorder (BP) have been carried out. These studies are heterogeneous, underpowered and use overlapping samples. We conducted a systematic review of these studies to synthesize the current findings. We identified all genome-wide gene expression studies on BP in humans. We then carried out a quantitative mega-analysis of studies done with post-mortem brain tissue. We obtained raw data from each study and used standardized procedures to process and analyze the data. We then combined the data and conducted three separate mega-analyses on samples from 1) any region of the brain (9 studies); 2) the prefrontal cortex (PFC) (6 studies); and 3) the hippocampus (2 studies). To minimize heterogeneity across studies, we focused primarily on the most numerous, recent and comprehensive studies. A total of 30 genome-wide gene expression studies of BP done with blood or brain tissue were identified. We included 10 studies with data on 211 microarrays on 57 unique BP cases and 229 microarrays on 60 unique controls in the quantitative mega-analysis. A total of 382 genes were identified as significantly differentially expressed by the three analyses. Eleven genes survived correction for multiple testing with a q-value < 0.05 in the PFC. Among these were FKBP5 and WFS1, which have been previously implicated in mood disorders. Pathway analyses suggested a role for metallothionein proteins, MAP Kinase phosphotases, and neuropeptides. We provided an up-to-date summary of results from gene expression studies of the brain in BP. Our analyses focused on the highest quality data available and provided results by brain region so that similarities and differences can be examined relative to disease status. The results are available for closer inspection on-line at Metamoodics [http://metamoodics.igm.jhmi.edu/], where investigators can look up any genes of interest and view the current results in their genomic context and in relation to leading findings from other genomic experiments in bipolar disorder.
    BMC Psychiatry 08/2013; 13(1):213. · 2.23 Impact Factor
  • Source
    Jennifer T Judy, Peter P Zandi
    [Show abstract] [Hide abstract]
    ABSTRACT: Although bipolar disorder (BP) is one of the most heritable psychiatric conditions, susceptibility genes for the disorder have yet to be conclusively identified. It is likely that variants in multiple genes across multiple pathways contribute to the genotype-phenotype relationship in the affected population. Recent evidence from genome-wide association studies implicates an entire class of genes related to the structure and regulation of ion channels, suggesting that the etiology of BP may arise from channelopathies. In this review, we examine the evidence for this hypothesis, with a focus on the potential role of voltage-gated potassium channels. We consider evidence from genetic and expression studies, and discuss the potential underlying biology. We consider animal models and treatment implications of the involvement of potassium ion channelopathy in BP. Finally, we explore intriguing parallels between BP and epilepsy, the signature channelopathy of the central nervous system.
    Frontiers in Genetics 01/2013; 4:105.
  • [Show abstract] [Hide abstract]
    ABSTRACT: Genome-wide association studies (GWAS) have implicated ANK3 as a susceptibility gene for bipolar disorder (BP). We examined whether epistasis with ANK3 may contribute to the "missing heritability" in BP. We first identified via the STRING database 14 genes encoding proteins with prior biological evidence that they interact molecularly with ANK3. We then tested for statistical evidence of interactions between SNPs in these genes in association with BP in a discovery GWAS dataset and two replication GWAS datasets. The most significant interaction in the discovery GWAS was between SNPs in ANK3 and KCNQ2 (p = 3.18 × 10(-8)). A total of 31 pair-wise interactions involving combinations between two SNPs from KCNQ2 and 16 different SNPs in ANK3 were significant after permutation. Of these, 28 pair-wise interactions were significant in the first replication GWAS. None were significant in the second replication GWAS, but the two SNPs from KCNQ2 were found to significantly interact with five other SNPs in ANK3, suggesting possible allelic heterogeneity. KCNQ2 forms homo- and hetero-meric complexes with KCNQ3 that constitute voltage-gated potassium channels in neurons. ANK3 is an adaptor protein that, through its interaction with KCNQ2 and KCNQ3, directs the localization of this channel in the axon initial segment (AIS). At the AIS, the KCNQ2/3 complex gives rise to the M-current, which stabilizes the neuronal resting potential and inhibits repetitive firing of action potentials. Thus, these channels act as "dampening" components and prevent neuronal hyperactivity. The interactions between ANK3 and KCNQ2 merit further investigation, and if confirmed, may motivate a new line of research into a novel therapeutic target for BP.
    Frontiers in Genetics 01/2013; 4:87.
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Numerous candidate gene association studies of bipolar disorder (BP) have been carried out, but the results have been inconsistent. Individual studies are typically underpowered to detect associations with genes of small effect sizes. We conducted a meta-analysis of published candidate gene studies to evaluate the cumulative evidence. We systematically searched for all published candidate gene association studies of BP. We then carried out a random-effects meta-analysis on all polymorphisms that were reported on by three or more case-control studies. The results from meta-analyses of these genes were compared with the findings from a recent mega-analysis of eleven genome-wide association studies (GWAS) in BP performed by the Psychiatric GWAS Consortium (PGC). A total of 487 articles were included in our review. Among these, 33 polymorphisms in 18 genes were reported on by three or more case-control studies and included in the random-effects meta-analysis. Polymorphisms in BDNF, DRD4, DAOA, and TPH1, were found to be nominally significant with a P-value < 0.05. However, none of the findings were significant after correction for multiple testing. Moreover, none of these polymorphisms were nominally significant in the PGC-BP GWAS. A number of plausible candidate genes have been previously associated with BP. However, the lack of robust findings in our review of these candidate genes highlights the need for more atheoretical approaches to study the genetics of BP afforded by GWAS. The results of this meta-analysis and from other on-going genomic experiments in BP are available online at Metamoodics (http://metamoodics.igm.jhmi.edu).
    American Journal of Medical Genetics Part B Neuropsychiatric Genetics 05/2012; 159B(5):508-18. · 3.23 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Epidemiological studies, such as family, twin, and adoption studies, demonstrate the presence of a heritable component to both attempted and completed suicide. Some of this heritability is accounted for by the presence of comorbid psychiatric disorders, but the evidence also indicates that a portion of this heritability is specific to suicidality. The serotonergic system has been studied extensively in this phenotype, but findings have been inconsistent, possibly due to the presence of multiple susceptibility variants and/or gene-gene interactions. In this study, we genotyped 174 tag and coding single nucleotide polymorphisms (SNPs) from 17 genes within the serotonin pathway on 516 subjects with a major mood disorder and a history of a suicide attempt (cases) and 515 healthy controls, with the goal of capturing the common genetic variation across each of these candidate genes. We tested the 174 markers in single-SNP, haplotype, gene-based, and epistasis analyses. While these association analyses identified multiple marginally significant SNPs, haplotypes, genes, and interactions, none of them survived correction for multiple testing. Additional studies, including assessment in larger sample sets and deep resequencing to identify rare causal variants, may be required to fully understand the role that the serotonin pathway plays in suicidal behavior.
    American Journal of Medical Genetics Part B Neuropsychiatric Genetics 01/2012; 159B(1):112-9. · 3.23 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Mood disorders are highly heritable forms of major mental illness. A major breakthrough in elucidating the genetic architecture of mood disorders was anticipated with the advent of genome-wide association studies (GWAS). However, to date few susceptibility loci have been conclusively identified. The genetic etiology of mood disorders appears to be quite complex, and as a result, alternative approaches for analyzing GWAS data are needed. Recently, a polygenic scoring approach that captures the effects of alleles across multiple loci was successfully applied to the analysis of GWAS data in schizophrenia and bipolar disorder (BP). However, this method may be overly simplistic in its approach to the complexity of genetic effects. Data mining methods are available that may be applied to analyze the high dimensional data generated by GWAS of complex psychiatric disorders. We sought to compare the performance of five data mining methods, namely, Bayesian networks, support vector machine, random forest, radial basis function network, and logistic regression, against the polygenic scoring approach in the analysis of GWAS data on BP. The different classification methods were trained on GWAS datasets from the Bipolar Genome Study (2191 cases with BP and 1434 controls) and their ability to accurately classify case/control status was tested on a GWAS dataset from the Wellcome Trust Case Control Consortium. The performance of the classifiers in the test dataset was evaluated by comparing area under the receiver operating characteristic curves. Bayesian networks performed the best of all the data mining classifiers, but none of these did significantly better than the polygenic score approach. We further examined a subset of single-nucleotide polymorphisms (SNPs) in genes that are expressed in the brain, under the hypothesis that these might be most relevant to BP susceptibility, but all the classifiers performed worse with this reduced set of SNPs. The discriminative accuracy of all of these methods is unlikely to be of diagnostic or clinical utility at the present time. Further research is needed to develop strategies for selecting sets of SNPs likely to be relevant to disease susceptibility and to determine if other data mining classifiers that utilize other algorithms for inferring relationships among the sets of SNPs may perform better.
    Psychiatric genetics 11/2011; 22(2):55-61. · 2.33 Impact Factor
  • Source
    Peter P Zandi, Jennifer T Judy
    [Show abstract] [Hide abstract]
    ABSTRACT: Existing psychotropic medications for the treatment of mental illnesses, including antidepressants, mood stabilizers, and antipsychotics, are clinically suboptimal. They are effective in only a subset of patients or produce partial responses, and they are often associated with debilitating side effects that discourage adherence. There is growing enthusiasm in the promise of pharmacogenetics to personalize the use of these treatments to maximize their efficacy and tolerability; however, there is still a long way to go before this promise becomes a reality. This article reviews the progress that has been made in research toward understanding how genetic factors influence psychotropic drug responses and the challenges that lie ahead in translating the research findings into clinical practices that yield tangible benefits for patients with mental illnesses.
    Clinics in laboratory medicine 12/2010; 30(4):931-74. · 1.17 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Family, twin, and adoption studies provide convincing evidence for a genetic contribution to suicidal behavior. The heritability for suicidal behavior depends in part on the transmission of psychiatric disorders, such as mood disorders and substance use disorders, but is also partly independent of them. Three linkage studies using the attempted suicide phenotype in pedigrees with bipolar disorder, major depression, or alcoholism have provided consistent evidence that 2p11-12 harbors a susceptibility gene for attempted suicide. A microarray expression study using postmortem brain samples has implicated a gene from the 2p11-12 candidate region, the trans-Golgi network protein 2 (TGOLN2) gene, as being consistently up-regulated in suicide cases as compared to controls. Here, we present a TGOLN2 case-control association study using nine single nucleotide polymorphisms (SNPs). These nine SNPs, which include seven tag SNPs and two coding SNPs, have been genotyped in 517 mood disorder subjects with a history of attempted suicide and 515 normal controls. Allelic and genotypic analyses of the case-control sample did not provide evidence for association with the attempted suicide phenotype. Eight of the nine SNPs provided supportive evidence for association (P-values ranging from 0.008 to 0.03) when we compared the attempted suicide cases with a history of alcoholism to the attempted suicide cases without a history of alcoholism. However, this association finding was not replicated in an independent sample. Taken together, these analyses do not provide support for the hypothesis that common genetic variation in TGOLN2 contributes significantly to the risk for attempted suicide in subjects with major mood disorders.
    American Journal of Medical Genetics Part B Neuropsychiatric Genetics 03/2010; 153B(5):1016-23. · 3.23 Impact Factor

Publication Stats

43 Citations
30.47 Total Impact Points

Institutions

  • 2012–2013
    • Johns Hopkins Medicine
      • Department of Psychiatry and Behavioral Sciences
      Baltimore, MD, United States
  • 2011
    • Johns Hopkins University
      • Department of Medicine
      Baltimore, MD, United States
  • 2010
    • Johns Hopkins Bloomberg School of Public Health
      • Department of Mental Health
      Baltimore, MD, United States