12 research outputs found

    Descriptive statistics on the number of identical genotypes between all possible pairing of samples by group.

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    <p>Descriptive statistics on the number of identical genotypes between all possible pairing of samples by group.</p

    Detection of population structure in four HapMap populations.

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    <p>The first two principal components from EIGENSTRAT are plotted for all 3 SNP panels (A, SNP panel 93; B, SNP panel 52; C, SNP panel 19). As more AIMs are used in the analysis, the resolution improves. The 52 SNP panel appears to have some overlap between CEU and CHB+JPT though it should be noted that these datapoints are more clearly differentiated by considering the third and fourth principal components (not shown).</p

    Analysis of case-control study type I error rates from 3 simulated SNPs within <i>BDNF</i>.

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    <p>The three SNPs show allele frequency differences between CEU and YRI of 0.066 (rs11030108), 0.102 (rs10767658), and 0.233 (rs1013402). The y-axis is estimated type I error rate versus the simulated CEU proportion (x-axis). Panels on the left show data with a difference in disease prevalence ratio of 1.25 while a ratio of 1.5 is shown on the right.</p

    Estimated odds ratios (OR) from case-control analysis of 3 simulated SNPs within <i>BDNF</i>.

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    <p>Conventions are the same as <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0019699#pone-0019699-g002" target="_blank">Figure 2</a> except the y-axis is the average estimated OR from the same analysis as presented in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0019699#pone-0019699-g002" target="_blank">Figure 2</a>.</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

    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 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

    Validation of a microRNA target site polymorphism in <i>H3F3B</i> that is potentially associated with a broad schizophrenia phenotype

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    <div><p>Despite much progress, few genetic findings for schizophrenia have been assessed by functional validation experiments at the molecular level. We previously reported evidence for genetic linkage of broadly defined schizophrenia to chromosome 17q25 in a sample of 24 multiplex families. 2,002 SNPs under this linkage peak were analyzed for evidence of linkage disequilibrium using the posterior probability of linkage (PPL) framework. SNP rs1060120 produced the strongest evidence for association, with a PPLD|L score of 0.21. This SNP is located within the 3'UTR of the histone gene <i>H3F3B</i> and colocalizes with potential gene target miR-616. A custom miRNA target prediction program predicted that the binding of miR-616 to <i>H3F3B</i> transcripts would be altered by the allelic variants of rs1060120. We used dual luciferase assays to experimentally validate this interaction. The rs1060120 A allele significantly reduced luciferase expression, indicating a stronger interaction with miR-616 than the G allele (p = 0.000412). These results provide functional validation that this SNP could alter schizophrenia epigenetic mechanisms thereby contributing to schizophrenia-related disease risk.</p></div
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