31 research outputs found

    A comparison of founder-only and all-pedigree-members genotype-expression association by regression analysis-0

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    <p><b>Copyright information:</b></p><p>Taken from "A comparison of founder-only and all-pedigree-members genotype-expression association by regression analysis"</p><p>http://www.biomedcentral.com/1753-6561/1/S1/S8</p><p>BMC Proceedings 2007;1(Suppl 1):S8-S8.</p><p>Published online 18 Dec 2007</p><p>PMCID:PMC2367500.</p><p></p

    Concordance rates using various reference panels.

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    <p>SNPs present in 9 different commercial arrays were extracted for imputing the HLA alleles from the HapMap3 r2 dataset of East Asian (CHB + JPT) individuals. Each subset was imputed for <i>HLA-A</i>, <i>-B</i>, <i>-C</i>, <i>-DQB1</i> and <i>-DRB1</i> using the Korean reference panel. Concordance rates (y-axis) were plotted against the proportion of reference-panel SNPs that were present in each subset (x-axis).</p

    Additional file 1: of The beneficial effects of Tai Chi exercise on endothelial function and arterial stiffness in elderly women with rheumatoid arthritis

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    Correlation plot of change in flow-mediated dilatation (FMD) versus change in brachial-ankle pulse wave velocity (baPWV) after 3 months of Tai Chi exercise. (JPG 337 kb

    Number of classical HLA alleles and polymorphic amino acid positions in the HLA reference panel.

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    <p>Number of classical HLA alleles and polymorphic amino acid positions in the HLA reference panel.</p

    Frequencies and disease effect sizes of imputed and genotyped <i>HLA-DRB1</i> alleles.

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    <p>We revisited our previous rheumatoid arthritis association studies using either typed SNPs <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112546#pone.0112546-Kim1" target="_blank">[16]</a> or HLA alleles <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112546#pone.0112546-Bang1" target="_blank">[17]</a>. After imputing HLA variants from the SNP-based dataset, (A) frequencies and (B) disease effect sizes of the imputed classical alleles of <i>HLA-DRB1</i> were compared with those of genotyped classical alleles in the HLA-based dataset.</p

    Concordance rates between imputed and genotyped alleles.

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    <p>* Reference panel and test datasets were randomly assigned 100 times from all 413 Korean subjects and the mean concordance rate was calculated by 100 independent imputations using each of the 100 reference panel and test dataset pairs.</p><p>** For consistency of panel size between Korean and European panels, the mean concordance rate was calculated by 100 independent imputations using 100 different subsets (n = 413) of the European reference panel.</p><p>Concordance rates between imputed and genotyped alleles.</p

    Determinants of quality of life in patients with fibromyalgia: A structural equation modeling approach

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    <div><p>Objective</p><p>Health-related quality of life (HRQOL) in patients with fibromyalgia (FM) is lower than in patients with other chronic diseases and the general population. Although various factors affect HRQOL, no study has examined a structural equation model of HRQOL as an outcome variable in FM patients. The present study assessed relationships among physical function, social factors, psychological factors, and HRQOL, and the effects of these variables on HRQOL in a hypothesized model using structural equation modeling (SEM).</p><p>Methods</p><p>HRQOL was measured using SF-36, and the Fibromyalgia Impact Questionnaire (FIQ) was used to assess physical dysfunction. Social and psychological statuses were assessed using the Beck Depression Inventory (BDI), the State-Trait Anxiety Inventory (STAI), the Arthritis Self-Efficacy Scale (ASES), and the Social Support Scale. SEM analysis was used to test the structural relationships of the model using the AMOS software.</p><p>Results</p><p>Of the 336 patients, 301 (89.6%) were women with an average age of 47.9±10.9 years. The SEM results supported the hypothesized structural model (χ<sup>2</sup> = 2.336, df = 3, p = 0.506). The final model showed that Physical Component Summary (PCS) was directly related to self-efficacy and inversely related to FIQ, and that Mental Component Summary (MCS) was inversely related to FIQ, BDI, and STAI.</p><p>Conclusions</p><p>In our model of FM patients, HRQOL was affected by physical, social, and psychological variables. In these patients, higher levels of physical function and self-efficacy can improve the PCS of HRQOL, while physical function, depression, and anxiety negatively affect the MCS of HRQOL.</p></div
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