Overview of Included Studies and Sample Sizes.

<p>¹ 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>² This count omits 100 pedigree IDs dropped prior to processing, primarily due to uninformativeness for linkage or duplication across Studies 6, 7.</p><p>³ 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>⁴ 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>⁵ Study 7 included trios in the published total.</p

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

<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

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

<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.

<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

Effects of data processing on (a) number of affected individuals¹ and (b) number of multiplex families².

<p>S1 through S7 indicate study numbers. The bars represent Human Genetics Initiative (HGI) and Combined Analysis of Psychiatric Studies (CAPS) data. ¹HGI diagnosis includes SZ, SA, SADD, NSPECT and BSPECT; CAPS diagnosis includes Schizophrenia and Schizophrenia/Affective as defined in the text. ²HGI 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

Different molecular interactions in models M1–M7 produce different temporal profiles of PIP₃ binding to Itk.

<p>(<b>A</b>) Kinetics of PIP₃ association of Itk for fixed initial PIP₃ and Itk concentrations (100 and 370 molecules, respectively) in models with feedbacks (M1–M4, and M7, left panel) and no feedbacks (M5–M6, right panel). (B) The shapes of the temporal profiles can be characterized by the parameters peak time (<i>τ</i>_p), peak width (<i>τ</i>_w), and peak value or amplitude (<i>A</i>). The dimensionless asymmetry ratio <i>R</i> = <i>τ</i>_w/<i>τ</i>_p quantifies how symmetric the shape of the time profile is. A larger R value indicates larger asymmetry. (C) Variations in R in models M1–M7 for different initial concentrations of Itk and PIP₃. Color scales for R values are shown on the right of each panel.</p

Experimentally measured PLCγ1activation kinetics in DP thymocytes stimulated with TCR ligands of different affinities and robustness of <i>in silico</i> models.

<p>(A) Immunoblots showing Y₇₈₃-phosphorylated (upper panels) and total (lower panels) PLCγ1 protein amounts in <i>RAG2^−/−MHC^−/− OT1 TCR-transgenic</i> DP thymocytes stimulated for the indicated times with MHCI tetramers presenting the indicated altered peptide ligands (APL). (B) Phospho-PLCγ1 levels normalized to total PLCγ1 protein amounts plotted over time for the indicated APLs. Their TCR affinity decreases in the order OVA (black)>Q4R7 (red)>Q4H7 (blue)>G4 (green). Band intensities were quantified via scanning and analysis with <i>ImageJ</i> software. Representative of several independent experiments. (C) Variation of the Kulback-Leibler distance D_KL with <i>R</i> for models M1–M3 (blue, red and black, respectively), M7 (yellow), and M4–M6 (orange, purple, and maroon, respectively) at high initial Itk (Itk⁰ = 140 molecules) and PIP₃ concentrations (PIP₃⁰ = 530 molecules), representing high-affinity OVA stimulation for <i>τ</i>_p = 2 min and <i>A</i> (shown as <i>A</i>_avg) = 40 molecules. Note we use <i>A</i> to represent the amplitude <i>A</i>^expt in experiments measuring fold change in Itk phosphorylation (see the main text for further details). The vertical orange bar indicates R<i>^expt</i> for OVA. Color legend in (D). (D) The color map shows which model is most robust (has the lowest D_KL) as <i>R^expt</i> and <i>A</i> (shown as <i>A</i>_avg) are varied for the same parameters as in (C). The color legend is depicted on the right.</p

Models containing Itk dimers and dueling feedbacks also show higher robustness for polyclonal T cells stimulated by anti-CD3 antibodies.

<p>PLCγ1 phosphorylation kinetics in <i>MHC^−/−</i> T cells stimulated by antibodies against (A) CD3 or (B) CD3 and CD4 at 1 µg/ml versus 5 µg/ml. (C) Variation of D_KL with R for the <i>in silico models</i> M1–M3 (blue, red and black, respectively), M7 (yellow), and M5–M6 (purple and maroon, respectively) at initial Itk (Itk⁰ = 100 molecules) and PIP₃ concentrations (PIP₃⁰ = 370 molecules) at <i>τ</i>_p = 1 min and <i>A</i>_avg = 60 molecules, representing anti-CD3 stimulation at 5 µg/ml. The orange bar indicates R<i>^expt</i>. Note we use <i>A</i>_avg to represent the amplitude A^expt in experiments measuring fold change in Itk phosphorylation (see the main text for further details). (D) Variation of D_KL with R for anti-CD3/CD4 stimulation at 5 µg/ml at <i>τ</i>_p = 1 min and <i>A</i>_avg = 80 molecules. The initial Itk (Itk⁰ = 140 molecules) and PIP₃ concentrations (PIP₃⁰ = 530 molecules) were used. The orange bar indicates R<i>^expt</i>. (E) and (F) show maps of the most robust models (with the lowest D_KL) as R<i>^expt</i> and <i>A</i> (shown as <i>A</i>_avg) are varied for the same parameters as in (C) and (D), respectively.</p

Relevant basic interactions between Itk, PIP₃ and IP₄.

<p>Following TCR-pMHC binding, Itk molecules are bound by the LAT signalosome via SLP-76 (not shown). Itk molecules (monomers or dimers, blue diamonds), bind the membrane lipid PIP₃ with low affinity through their PH domains. PIP₃ bound Itk phosphorylates and thereby activates LAT-bound PLCγ1. Activated PLCγ1 then hydrolyzes the membrane lipid PIP₂ into the soluble second messenger IP₃, a key mediator of Ca²⁺ mobilization. IP₃ 3-kinase B (ItpkB) converts IP₃ into IP₄ (red filled circle). For our <i>in silico</i> models, we simplified this series of reactions, encircled by the orange oval, into a single second order reaction where PIP₃ bound Itk converts PIP₂ into IP₄. In models M1–M4 and M7, IP₄ modifies the Itk PH domain (denoted as Itk^C, purple diamonds) to promote PIP₃ and IP₄ binding to the Itk PH domain. At the onset of the signaling, when the concentration of IP₄ is smaller than that of PIP₃, IP₄ helps Itk^C to bind to PIP₃ (left lower panel). However, as the concentration of IP₄ is increased at later times, IP₄ outcompetes PIP₃ for binding to Itk^C and sequesters Itk^C to the cytosol (right lower panel). In models M5/M6, IP₄ and PIP₃ do not augment each other’s binding to Itk. However, IP₄ still outcompetes PIP₃ for Itk PH domain binding when the number of IP₄ molecules becomes much larger than that of PIP₃ molecules at later times.</p