11 research outputs found

    Meta-Analysis of Repository Data: Impact of Data Regularization on NIMH Schizophrenia Linkage Results

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    <div><p>Human geneticists are increasingly turning to study designs based on very large sample sizes to overcome difficulties in studying complex disorders. This in turn almost always requires multi-site data collection and processing of data through centralized repositories. While such repositories offer many advantages, including the ability to return to previously collected data to apply new analytic techniques, they also have some limitations. To illustrate, we reviewed data from seven older schizophrenia studies available from the NIMH-funded Center for Collaborative Genomic Studies on Mental Disorders, also known as the Human Genetics Initiative (HGI), and assessed the impact of data cleaning and regularization on linkage analyses. Extensive data regularization protocols were developed and applied to both genotypic and phenotypic data. Genome-wide nonparametric linkage (NPL) statistics were computed for each study, over various stages of data processing. To assess the impact of data processing on aggregate results, Genome-Scan Meta-Analysis (GSMA) was performed. Examples of increased, reduced and shifted linkage peaks were found when comparing linkage results based on original HGI data to results using post-processed data within the same set of pedigrees. Interestingly, reducing the number of affected individuals tended to increase rather than decrease linkage peaks. But most importantly, while the effects of data regularization within individual data sets were small, GSMA applied to the data in aggregate yielded a substantially different picture after data regularization. These results have implications for analyses based on other types of data (e.g., case-control GWAS or sequencing data) as well as data obtained from other repositories.</p></div

    Overview of Included Studies and Sample Sizes.

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    <p><sup>1</sup> Total sample sizes by Population Group are N = 346 African American, N = 352 European American, N = 574 Han Chinese and N = 280 Hispanic.</p><p><sup>2</sup> This count omits 100 pedigree IDs dropped prior to processing, primarily due to uninformativeness for linkage or duplication across Studies 6, 7.</p><p><sup>3</sup> 15 families were dropped (and 3 subsumed by joining) prior to this stage, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084696#pone.0084696.s001" target="_blank">Appendix S1</a> & <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0084696#pone.0084696.s005" target="_blank">Table S1</a> for details.</p><p><sup>4</sup> Families used in this paper to compare results across the four data configurations are those remaining after genotype processing with at least two schizophrenia cases according to either the HGI or CAPS clinical criteria, omitting 16 such families with bitsize larger than 24 for computational reasons.</p><p><sup>5</sup> Study 7 included trios in the published total.</p

    Effects of data processing on (a) number of affected individuals<sup>1</sup> and (b) number of multiplex families<sup>2</sup>.

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    <p>S1 through S7 indicate study numbers. The bars represent Human Genetics Initiative (HGI) and Combined Analysis of Psychiatric Studies (CAPS) data. <sup>1</sup>HGI diagnosis includes SZ, SA, SADD, NSPECT and BSPECT; CAPS diagnosis includes Schizophrenia and Schizophrenia/Affective as defined in the text. <sup>2</sup>HGI includes all 1,413 families with at least two affected individuals by HGI criteria; CAPS includes all 1,046 families with at least two affected individuals by CAPS criteria. Note that analyses presented in the main text utilized the subset of pedigrees satisfying both criteria.</p

    Effects of data processing on linkage results based on meta-analysis.

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    <p>The lines refer to Human Genetics Initiative (HGI) or Combined Analysis of Psychiatric Studies (CAPS) data.</p

    Examples of the effects of data processing on linkage results within individual data subsets.

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    <p>The labels for each line indicate state of phenotype (Pheno) and genotype (Geno) data, which can be Human Genetics Initiative (HGI) or Combined Analysis of Psychiatric Studies (CAPS).</p

    Membrane-associated (ma) IL-15 of DC and CD40L expression of CD4<sup>+</sup> T cells in 5 groups of macaques.

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    <p>(A) Membrane-associated (ma) IL-15 of DC and (B) CD40L expression on CD4<sup>+</sup> T cells in 5 groups of macaques pre- and post-4<sup>th</sup> immunization, presented as % mean (±sem). The significance between pre- and post-immunization was analysed by paired “t” test and differences between the 5 groups after immunization was analysed by ANOVA.</p

    AID and A3G expression in CD20<sup>+</sup> B cells pre- and post-2<sup>nd</sup> immunization and their correlation.

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    <p>Comparative investigation of (A) AID and (B) A3G expression in CD20<sup>+</sup> B cells before and after the 2<sup>nd</sup> immunization in the 4 groups of immunized macaques; group 5 unimmunized controls remained unchanged (data not presented). Correlation between A3G and AID expression in CD20<sup>+</sup> B cells in the 5 groups after 2<sup>nd</sup> immunization is presented in (C). Representative flow cytometry of AID and A3G in pre- and post 2<sup>nd</sup> immunization is shown in (D). * p<0.05 and ** p<0.01. In all figures the 2 uninfected macaques in group 3 are indicated by a solid circle.</p

    Correlation between DC maIL-15 and A3G mRNA or protein in CD4<sup>+</sup> central and effector T or B memory cells.

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    <p>Correlation between maIL-15 on DC and A3G mRNA in PBMC (A<b>–</b>C), intracellular A3G protein in CD4<sup>+</sup>CD95<sup>+</sup>CCR7<sup>+</sup> central memory cells (D<b>–</b>F), CD4<sup>+</sup>CD95<sup>+</sup>CCR7<sup>−</sup> effector memory cells (G,I), CD20<sup>+</sup> B cells (J<b>–</b>L) or CD20<sup>+</sup>CD27<sup>+</sup> memory B cells (M<b>–</b>O) in the combined groups (1<b>–</b>5), group 1 or group 3 macaques, respectively. IL-15 and A3G were assayed after the last immunization and before the animals were challenged with SHIV SF162.P4. In all figures the 2 uninfected macaques in group 3 are indicated by a solid circle.</p

    A3G in CD20<sup>+</sup> and CD27<sup>+</sup> memory B cells pre- and post-immunization in the 4 groups.

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    <p>Expression of A3G in (A) CD20<sup>+</sup> B cells and (B) CD20<sup>+</sup>CD27<sup>+</sup> memory B cells in 5 groups of macaques before and after the 4<sup>th</sup> immunization assayed by flow cytometry with MAb to A3G, CD20 and CD27 and (C) representative illustration; (n = 8 per group, except gp4 n = 6). * p<0.05. In all figures the 2 uninfected macaques in group 3 are indicated by a solid circle.</p

    Correlation between A3G memory B and CD4<sup>+</sup> T cells, and A3G mRNA with HLA or neutralizing antibodies.

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    <p>Correlation between A3G in CD20<sup>+</sup>CD27<sup>+</sup> memory B cells and CD4<sup>+</sup>CD95<sup>+</sup>CCR7<sup>−</sup> effector memory T cells (A) in all 5 groups, (B) in group 1 (without SHIV antigens) and (C) in group 3 macaques. Correlation between A3G mRNA in PBMC and serum anti-HLA class I antibodies (D<b>–</b>F), anti-HLA class II antibodies (MFI) (G<b>–</b>I) assayed by the Luminex HLA antibody method and neutralizing activity (J<b>–</b>L) determined by using a TZM-b1 assay in the corresponding groups. In all figures the 2 uninfected macaques in group 3 are indicated by a solid circle.</p
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