150 research outputs found

    Ethics Reporting in Biospecimen and Genetic Research: Current Practice and Suggestions for Changes

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    <div><p>Modern approaches for research with human biospecimens employ a variety of substantially different types of ethics approval and informed consent. In most cases, standard ethics reporting such as “consent and approval was obtained” is no longer meaningful. A structured analysis of 120 biospecimen studies recently published in top journals revealed that more than 85% reported on consent and approval, but in more than 90% of cases, this reporting was insufficient and thus potentially misleading. Editorial policies, reporting guidelines, and material transfer agreements should include recommendations for meaningful ethics reporting in biospecimen research. Meaningful ethics reporting is possible without higher word counts and could support public trust as well as networked research.</p></div

    Bayesian Independent Component Analysis Recovers Pathway Signatures from Blood Metabolomics Data

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    Interpreting the complex interplay of metabolites in heterogeneous biosamples still poses a challenging task. In this study, we propose independent component analysis (ICA) as a multivariate analysis tool for the interpretation of large-scale metabolomics data. In particular, we employ a Bayesian ICA method based on a mean-field approach, which allows us to statistically infer the number of independent components to be reconstructed. The advantage of ICA over correlation-based methods like principal component analysis (PCA) is the utilization of higher order statistical dependencies, which not only yield additional information but also allow a more meaningful representation of the data with fewer components. We performed the described ICA approach on a large-scale metabolomics data set of human serum samples, comprising a total of 1764 study probands with 218 measured metabolites. Inspecting the <i>source matrix</i> of statistically independent metabolite profiles using a weighted enrichment algorithm, we observe strong enrichment of specific metabolic pathways in all components. This includes signatures from amino acid metabolism, energy-related processes, carbohydrate metabolism, and lipid metabolism. Our results imply that the human blood metabolome is composed of a distinct set of overlaying, statistically independent signals. ICA furthermore produces a <i>mixing matrix</i>, describing the strength of each independent component for each of the study probands. Correlating these values with plasma high-density lipoprotein (HDL) levels, we establish a novel association between HDL plasma levels and the branched-chain amino acid pathway. We conclude that the Bayesian ICA methodology has the power and flexibility to replace many of the nowadays common PCA and clustering-based analyses common in the research field

    Bayesian Independent Component Analysis Recovers Pathway Signatures from Blood Metabolomics Data

    No full text
    Interpreting the complex interplay of metabolites in heterogeneous biosamples still poses a challenging task. In this study, we propose independent component analysis (ICA) as a multivariate analysis tool for the interpretation of large-scale metabolomics data. In particular, we employ a Bayesian ICA method based on a mean-field approach, which allows us to statistically infer the number of independent components to be reconstructed. The advantage of ICA over correlation-based methods like principal component analysis (PCA) is the utilization of higher order statistical dependencies, which not only yield additional information but also allow a more meaningful representation of the data with fewer components. We performed the described ICA approach on a large-scale metabolomics data set of human serum samples, comprising a total of 1764 study probands with 218 measured metabolites. Inspecting the <i>source matrix</i> of statistically independent metabolite profiles using a weighted enrichment algorithm, we observe strong enrichment of specific metabolic pathways in all components. This includes signatures from amino acid metabolism, energy-related processes, carbohydrate metabolism, and lipid metabolism. Our results imply that the human blood metabolome is composed of a distinct set of overlaying, statistically independent signals. ICA furthermore produces a <i>mixing matrix</i>, describing the strength of each independent component for each of the study probands. Correlating these values with plasma high-density lipoprotein (HDL) levels, we establish a novel association between HDL plasma levels and the branched-chain amino acid pathway. We conclude that the Bayesian ICA methodology has the power and flexibility to replace many of the nowadays common PCA and clustering-based analyses common in the research field

    Selected ethics statement (see also S1 Table) with specifying reporting on informed consent or ethics approval or both.

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    <p>Selected ethics statement (see also <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1002521#pbio.1002521.s001" target="_blank">S1 Table</a>) with specifying reporting on informed consent or ethics approval or both.</p

    Association analysis results in female gout case-control sample.

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    <p>Numbers of genotypes (11, 12, 22) according to alleles from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0007729#pone-0007729-t003" target="_blank">Table 3</a>.</p>a<p>Model including medication with diuretics, lipid lowering and antihypertensive therapy, HDL-C, type 2 diabetes, smoking, and BMI.</p

    Association analysis results in male gout case-control sample.

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    <p>Numbers of genotypes (11, 12, 22) according to alleles from <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0007729#pone-0007729-t003" target="_blank">Table 3</a>.</p>a<p>Model including medication with diuretics, lipid lowering and antihypertensive therapy, HDL-C, type 2 diabetes, smoking, and BMI.</p

    Characteristics of gout case and control study sample.

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    <p>Values denote means±standard deviations unless indicated otherwise. n. s., not significant; CAD, coronary artery disease; MI, myocardial infarction; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; BMI, body mass index.</p>a<p>At inclusion to study.</p>b<p>Defined as LDL-C ≥160 mg/dL or intake of lipid lowering medication.</p>c<p>Defined as blood pressure ≥140/90 mmHg or ongoing antihypertensive therapy.</p>d<p>Defined as history of diabetes mellitus or intake of antidiabetic medication.</p>e<p>Former or current smoking habit.</p

    Association between variations in the gene and incident type 2 diabetes is modified by the ratio of total cholesterol to HDL-cholesterol-1

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    Xis indicate TC/HDL-C concentrations of patients with at least one copy of the minor allele.<p><b>Copyright information:</b></p><p>Taken from "Association between variations in the gene and incident type 2 diabetes is modified by the ratio of total cholesterol to HDL-cholesterol"</p><p>http://www.biomedcentral.com/1471-2350/9/9</p><p>BMC Medical Genetics 2008;9():9-9.</p><p>Published online 25 Feb 2008</p><p>PMCID:PMC2292153.</p><p></p
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