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Genomewide Pharmacogenomic Analysis of Response to Treatment with Antipsychotics

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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 schizophrenic patients to the most effective and safe medication. Here we use a genomewide 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 (CATIE). Subjects were genotyped using the Affymetrix 500K genotyping platform plus a custom 164K chip to improve genomewide coverage. Treatment outcome was measured using the Positive and Negative Syndrome Scale (PANSS). Our criterion for genomewide significance was a pre-specified threshold that ensures, on average, only 10% of the significant findings are false discoveries. The top statistical result reached significance at our pre-specified threshold and involved a SNP in an intergenic region on chromosome 4p15. In addition, SNPs in ANKS1B and CNTNAP5 that 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 GWAS to discover novel genes that mediate effects of antipsychotics, which eventually could help to tailor drug treatment to schizophrenic patients.

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      Most cited references 81

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      PLINK: a tool set for whole-genome association and population-based linkage analyses.

      Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.
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        Statistical significance for genomewide studies.

        With the increase in genomewide experiments and the sequencing of multiple genomes, the analysis of large data sets has become commonplace in biology. It is often the case that thousands of features in a genomewide data set are tested against some null hypothesis, where a number of features are expected to be significant. Here we propose an approach to measuring statistical significance in these genomewide studies based on the concept of the false discovery rate. This approach offers a sensible balance between the number of true and false positives that is automatically calibrated and easily interpreted. In doing so, a measure of statistical significance called the q value is associated with each tested feature. The q value is similar to the well known p value, except it is a measure of significance in terms of the false discovery rate rather than the false positive rate. Our approach avoids a flood of false positive results, while offering a more liberal criterion than what has been used in genome scans for linkage.
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          Principal components analysis corrects for stratification in genome-wide association studies.

          Population stratification--allele frequency differences between cases and controls due to systematic ancestry differences-can cause spurious associations in disease studies. We describe a method that enables explicit detection and correction of population stratification on a genome-wide scale. Our method uses principal components analysis to explicitly model ancestry differences between cases and controls. The resulting correction is specific to a candidate marker's variation in frequency across ancestral populations, minimizing spurious associations while maximizing power to detect true associations. Our simple, efficient approach can easily be applied to disease studies with hundreds of thousands of markers.
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            Author and article information

            Affiliations
            [a ] Center for Biomarker Research and Personalized Medicine, Department of Pharmacy, Medical College of Virginia of Virginia Commonwealth University, Richmond, VA, USA
            [b ] Department of Psychiatry, University of North Carolina at Chapel Hill, NC, USA
            [c ] Virginia Institute for Psychiatric and Behavioral Genetics, Department of Psychiatry, Medical College of Virginia of Virginia Commonwealth University, Richmond, VA, USA
            [d ] Department of Psychiatry, Columbia University, New York, NY, USA
            [e ] Departments of Genetics, Psychiatry, & Epidemiology, University of North Carolina at Chapel Hill, NC, USA
            [f ] Department of Medical Epidemiology & Biostatistics, Karolinska Institutet, Stockholm, Sweden
            Author notes
            [* ]Corresponding author ( jlmcclay@ 123456vcu.edu )
            Journal
            9607835
            20545
            Mol Psychiatry
            Molecular psychiatry
            1359-4184
            1476-5578
            11 August 2009
            1 September 2009
            January 2011
            1 July 2011
            : 16
            : 1
            : 76-85
            2888895
            19721433
            10.1038/mp.2009.89
            nihpa135249

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            Funding
            Funded by: National Institute of Mental Health : NIMH
            Funded by: National Human Genome Research Institute : NHGRI
            Award ID: R01 MH078069-01A2 ||MH
            Funded by: National Institute of Mental Health : NIMH
            Funded by: National Human Genome Research Institute : NHGRI
            Award ID: R01 MH077139-01A1 ||MH
            Funded by: National Institute of Mental Health : NIMH
            Funded by: National Human Genome Research Institute : NHGRI
            Award ID: R01 MH074027-01A1 ||MH
            Funded by: National Institute of Mental Health : NIMH
            Funded by: National Human Genome Research Institute : NHGRI
            Award ID: R01 HG004240-02 ||HG
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