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      Genome-wide pharmacogenomic analysis of response to treatment with antipsychotics.

      Molecular Psychiatry
      Antipsychotic Agents, classification, therapeutic use, Benzodiazepines, Chromosomes, Human, Pair 4, Dibenzothiazepines, Double-Blind Method, Follow-Up Studies, Genome-Wide Association Study, Humans, Perphenazine, Pharmacogenetics, Piperazines, Polymorphism, Single Nucleotide, Risperidone, Schizophrenia, drug therapy, genetics, Thiazoles, Treatment Outcome

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          Abstract

          Schizophrenia is an often devastating neuropsychiatric illness. Understanding the genetic variation affecting response to antipsychotics is important to develop novel diagnostic tests to match individual schizophrenia patients to the most effective and safe medication. In this study, we use a genome-wide approach to detect genetic variation underlying individual differences in response to treatment with the antipsychotics olanzapine, quetiapine, risperidone, ziprasidone and perphenazine. Our sample consisted of 738 subjects with DSM-IV schizophrenia who took part in the Clinical Antipsychotic Trials of Intervention Effectiveness. Subjects were genotyped using the Affymetrix 500 K genotyping platform plus a custom 164 K chip to improve genome-wide coverage. Treatment outcome was measured using the Positive and Negative Syndrome Scale. Our criterion for genome-wide significance was a prespecified threshold that ensures that, on an average, only 10% of the significant findings are false discoveries. The top statistical result reached significance at our prespecified threshold and involved a single-nucleotide polymorphism (SNP) in an intergenic region on chromosome 4p15. In addition, SNPs in Ankyrin Repeat and Sterile Alpha Motif Domain-Containing Protein 1B (ANKS1B) and in the Contactin-Associated Protein-Like 5 gene (CNTNAP5), which mediated the effects of olanzapine and risperidone on Negative symptoms, were very close to our threshold for declaring significance. The most significant SNP in CNTNAP5 is nonsynonymous, giving rise to an amino-acid substitution. In addition to highlighting our top results, we provide all P-values for download as a resource for investigators with the requisite samples to carry out replication. This study demonstrates the potential of genome-wide association studies to discover novel genes that mediate the effects of antipsychotics, which could eventually help to tailor drug treatment to schizophrenic patients.

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            The positive and negative syndrome scale (PANSS) for schizophrenia.

            The variable results of positive-negative research with schizophrenics underscore the importance of well-characterized, standardized measurement techniques. We report on the development and initial standardization of the Positive and Negative Syndrome Scale (PANSS) for typological and dimensional assessment. Based on two established psychiatric rating systems, the 30-item PANSS was conceived as an operationalized, drug-sensitive instrument that provides balanced representation of positive and negative symptoms and gauges their relationship to one another and to global psychopathology. It thus constitutes four scales measuring positive and negative syndromes, their differential, and general severity of illness. Study of 101 schizophrenics found the four scales to be normally distributed and supported their reliability and stability. Positive and negative scores were inversely correlated once their common association with general psychopathology was extracted, suggesting that they represent mutually exclusive constructs. Review of five studies involving the PANSS provided evidence of its criterion-related validity with antecedent, genealogical, and concurrent measures, its predictive validity, its drug sensitivity, and its utility for both typological and dimensional assessment.
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              Association mapping in structured populations.

              The use, in association studies, of the forthcoming dense genomewide collection of single-nucleotide polymorphisms (SNPs) has been heralded as a potential breakthrough in the study of the genetic basis of common complex disorders. A serious problem with association mapping is that population structure can lead to spurious associations between a candidate marker and a phenotype. One common solution has been to abandon case-control studies in favor of family-based tests of association, such as the transmission/disequilibrium test (TDT), but this comes at a considerable cost in the need to collect DNA from close relatives of affected individuals. In this article we describe a novel, statistically valid, method for case-control association studies in structured populations. Our method uses a set of unlinked genetic markers to infer details of population structure, and to estimate the ancestry of sampled individuals, before using this information to test for associations within subpopulations. It provides power comparable with the TDT in many settings and may substantially outperform it if there are conflicting associations in different subpopulations.
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