Genomewide Association Study of Movement-Related Adverse Antipsychotic Effects

Center for Biomarker Research and Personalized Medicine, School of Pharmacy, Medical College of Virginia of Virginia Commonwealth University, Richmond, Virginia 23298, USA.
Biological psychiatry (Impact Factor: 10.26). 10/2009; 67(3):279-82. DOI: 10.1016/j.biopsych.2009.08.036
Source: PubMed


Understanding individual differences in the development of extrapyramidal side effects (EPS) as a response to antipsychotic therapy is essential to individualize treatment.
We performed genomewide association studies to search for genetic susceptibility to EPS. Our sample consisted of 738 schizophrenia patients, genotyped for 492K single nucleotide polymorphisms (SNPs). We studied three quantitative measures of antipsychotic adverse drug reactions-the Simpson-Angus Scale (SAS) for Parkinsonism, the Barnes Akathisia Rating Scale, and the Abnormal Involuntary Movement Scale (AIMS)-as well as a clinical diagnosis of probable tardive dyskinesia.
Two SNPs for SAS, rs17022444 and rs2126709 with p = 1.2 x 10(-10) and p = 3.8 x 10(-7), respectively, and one for AIMS, rs7669317 with p = 7.7 x 10(-8), reached genomewide significance (Q value < .1). rs17022444 and rs7669317 were located in intergenic regions and rs2126709 was located in ZNF202 on 11q24. Fourteen additional signals were potentially interesting (Q value < .5). The ZNF202 is a transcriptional repressor controlling, among other genes, PLP1, which is the major protein in myelin. Mutations in PLP1 cause Pelizaeus-Merzbacher disease, which has Parkinsonism as an occurring symptom. Altered mRNA expression of PLP1 is associated with schizophrenia.
Although our findings require replication and validation, this study demonstrates the potential of genomewide association studies to discover genes and pathways that mediate adverse effects of antipsychotics.

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Available from: Joseph L. McClay, Oct 02, 2015
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    • "Refinement of the GWAS approach takes a two-step design, using high-density array to discover the SNP associations in a population cohort followed by replicating the initial findings above the genome-wide significance with additional patient sets in a more hypothesis-driven study of sufficient sample size. While this approach has been successfully applied in the pharmacogenomics of clopidogrel, flucloxacillin, simvastatin, and warfarin [17–22], the implications of the results are less clear for other drugs such as the psychotropics [23–30]. "
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    ABSTRACT: The mapping of the human genome and subsequent advancements in genetic technology had provided clinicians and scientists an understanding of the genetic basis of altered drug pharmacokinetics and pharmacodynamics, as well as some examples of applying genomic data in clinical practice. This has raised the public expectation that predicting patients' responses to drug therapy is now possible in every therapeutic area, and personalized drug therapy would come sooner than later. However, debate continues among most stakeholders involved in drug development and clinical decision-making on whether pharmacogenomic biomarkers should be used in patient assessment, as well as when and in whom to use the biomarker-based diagnostic tests. Currently, most would agree that achieving the goal of personalized therapy remains years, if not decades, away. Realistic application of genomic findings and technologies in clinical practice and drug development require addressing multiple logistics and challenges that go beyond discovery of gene variants and/or completion of prospective controlled clinical trials. The goal of personalized medicine can only be achieved when all stakeholders in the field work together, with willingness to accept occasional paradigm change in their current approach.
    02/2013; 2013(8):641089. DOI:10.1155/2013/641089
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    • "Such genes include polymorphisms of the dopamine D2 receptor (Bakker et al., 2008; Park et al., 2011; Zai et al., 2007a,b), polymorphisms of catechol-O-methyl-transferase (COMT) and cytochrome-P450-1A2 (Bakker et al., 2008), single nucleotide polymorphisms rs17022444, rs2126709 (in the zinc finger gene ZNF202; Aberg et al., 2010), rs3943552 (in the GL12 gene; Greenbaum et al., 2010), rs2445142 (in the heat shock protein gene HSPG2; Greenbaum et al., 2011) and other genes such as Taq1A, dopamine D3 receptor gene region Serine9Glycine, rs7669317, and GABA pathway genes. However, despite the intensive genetic studies, the findings indicate that the genetic associations with tardive dyskinesia are small (dopamine D2 variants; Bakker et al., 2008), may show potential (SNPs rs1702244 and rs2126709; Aberg et al., 2010), may provide a possible contribution (GL12; Greenbaum et al., 2010), or are " only nominally significant " (HSPG2; Greenbaum et al., 2011). Currently, there is no information on any associations between tardive dyskinesia and the transporter gene DAT1/SLC6A3 or the vesicular monoamine transporter-2 (VMAT2). "
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    ABSTRACT: The in vivo labeling and brain imaging of dopamine transporters measures the density of dopamine neuron terminals in the human caudate/putamen. A review of such studies shows that the long-term use of antipsychotics had no major effect on the density of the dopamine terminals in individuals who had no tardive dyskinesia, but had reduced the density in those patients with tardive dyskinesia. In addition, the normal loss of dopamine terminals in healthy individuals was approximately 5% per decade. However, this rate of cell loss was apparently increased by approximately three-fold, to about 15% per decade, in schizophrenia patients using antipsychotics on a long-term basis, as measured by the in vivo imaging of the dopamine transporters in the dopamine neuron terminals. While an apparent reduction in dopamine transporters may result from reduced expression of the transporters secondary to antipsychotic treatment, the seemingly increased loss rate is consistent with the accumulation of antipsychotics in the neuromelanin of the substantia nigra, subsequent injury to the dopamine-containing neurons, and the development of extrapyramidal motor disturbances such as tardive dyskinesia or Parkinson's disease.
    Progress in Neuro-Psychopharmacology and Biological Psychiatry 02/2013; 44. DOI:10.1016/j.pnpbp.2013.02.011 · 3.69 Impact Factor
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    • "As our approach condenses all information collected during the trials in an optimal, empirical fashion, it results in more precise estimates than traditional approaches (e.g., subtracting pre- from post-treatment observations) that estimate treatment effects using only two assessments. We have successfully applied this method in several genome-wide association studies performed on CATIE and STAR*D samples [17], [18], [19], [20], [21], [22], [23], [29]. "
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    ABSTRACT: Only a subset of patients will typically respond to any given prescribed drug. The time it takes clinicians to declare a treatment ineffective leaves the patient in an impaired state and at unnecessary risk for adverse drug effects. Thus, diagnostic tests robustly predicting the most effective and safe medication for each patient prior to starting pharmacotherapy would have tremendous clinical value. In this article, we evaluated the use of genetic markers to estimate ancestry as a predictive component of such diagnostic tests. We first estimated each patient's unique mosaic of ancestral backgrounds using genome-wide SNP data collected in the Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) (n = 765) and the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) (n = 1892). Next, we performed multiple regression analyses to estimate the predictive power of these ancestral dimensions. For 136/89 treatment-outcome combinations tested in CATIE/STAR*D, results indicated 1.67/1.84 times higher median test statistics than expected under the null hypothesis assuming no predictive power (p<0.01, both samples). Thus, ancestry showed robust and pervasive correlations with drug efficacy and side effects in both CATIE and STAR*D. Comparison of the marginal predictive power of MDS ancestral dimensions and self-reported race indicated significant improvements to model fit with the inclusion of MDS dimensions, but mixed evidence for self-reported race. Knowledge of each patient's unique mosaic of ancestral backgrounds provides a potent immediate starting point for developing algorithms identifying the most effective and safe medication for a wide variety of drug-treatment response combinations. As relatively few new psychiatric drugs are currently under development, such personalized medicine offers a promising approach toward optimizing pharmacotherapy for psychiatric conditions.
    PLoS ONE 02/2013; 8(2):e55239. DOI:10.1371/journal.pone.0055239 · 3.23 Impact Factor
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