135 research outputs found

    Predictive genetic testing for the identification of high-risk groups: a simulation study on the impact of predictive ability

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    Background: Genetic risk models could potentially be useful in identifying high-risk groups for the prevention of complex diseases. We investigated the performance of this risk stratification strategy by examining epidemiological parameters that impact the predictive ability of risk models.Methods: We assessed sensitivity, specificity, and positive and negative predictive value for all possible risk thresholds that can define high-risk groups and investigated how these measures depend on the frequency of disease in the population, the frequency of the high-risk group, and the discriminative accuracy of the risk model, as assessed by the area under the receiver-operating characteristic curve (AUC). In a simulation study, we modeled genetic risk scores of 50 genes with equal odds ratios and genotype frequencies, and varied the odds ratios and the disease frequency across scenarios. We also performed a simulation of age-related macular degeneration risk prediction based on published odds ratios and frequencies for six genetic risk variants.Results: We show that when the frequency of the high-risk group was lower than the disease frequency, positive predictive value increased with the AUC but sensitivity remained low. When the frequency of the high-risk group was higher than the disease frequency, sensitivity was high but positive predictive value remained low. When both frequencies were equal, both positive predictive value and sensitivity increased with increasing AUC, but higher AUC was needed to maximize both measures.Conclusions: The performance of risk stratification is strongly determined by the frequency of the high-risk group relative to the frequency of disease in the population. The identification of high-risk groups with appreciable combinations of sensitivity and positive predictive value requires higher AUC

    Incremental value of rare genetic variants for the prediction of multifactorial diseases

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    Background: It is often assumed that rare genetic variants will improve available risk prediction scores. We aimed to estimate the added predictive ability of rare variants for risk prediction of common diseases in hypothetical scenarios.Methods: In simulated data, we constructed risk models with an area under the ROC curve (AUC) ranging between 0.50 and 0.95, to which we added a single variant representing the cumulative frequency and effect (odds ratio, OR) of multiple rare variants. The frequency of the rare variant ranged between 0.0001 and 0.01 and the OR between 2 and 10. We assessed the resulting AUC, increment in AUC, integrated discrimination improvement (IDI), net reclassification improvement (NRI(>0.01)) and categorical NRI. The analyses were illustrated by a simulation of atrial fibrillation risk prediction based on a published clinical risk model.Results: We observed minimal improvement in AUC with the addition of rare variants. All measures increased with the frequency and OR of the variant, but maximum increment in AUC remained below 0.05. Increment in AUC and NRI(>0.01) decreased with higher AUC of the baseline model, w

    PyElph - a software tool for gel images analysis and phylogenetics

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    <p>Abstract</p> <p>Background</p> <p>This paper presents PyElph, a software tool which automatically extracts data from gel images, computes the molecular weights of the analyzed molecules or fragments, compares DNA patterns which result from experiments with molecular genetic markers and, also, generates phylogenetic trees computed by five clustering methods, using the information extracted from the analyzed gel image. The software can be successfully used for population genetics, phylogenetics, taxonomic studies and other applications which require gel image analysis. Researchers and students working in molecular biology and genetics would benefit greatly from the proposed software because it is free, open source, easy to use, has a friendly Graphical User Interface and does not depend on specific image acquisition devices like other commercial programs with similar functionalities do.</p> <p>Results</p> <p>PyElph software tool is entirely implemented in Python which is a very popular programming language among the bioinformatics community. It provides a very friendly Graphical User Interface which was designed in six steps that gradually lead to the results. The user is guided through the following steps: image loading and preparation, lane detection, band detection, molecular weights computation based on a molecular weight marker, band matching and finally, the computation and visualization of phylogenetic trees. A strong point of the software is the visualization component for the processed data. The Graphical User Interface provides operations for image manipulation and highlights lanes, bands and band matching in the analyzed gel image. All the data and images generated in each step can be saved. The software has been tested on several DNA patterns obtained from experiments with different genetic markers. Examples of genetic markers which can be analyzed using PyElph are RFLP (Restriction Fragment Length Polymorphism), AFLP (Amplified Fragment Length Polymorphism), RAPD (Random Amplification of Polymorphic DNA) and STR (Short Tandem Repeat). The similarity between the DNA sequences is computed and used to generate phylogenetic trees which are very useful for population genetics studies and taxonomic classification.</p> <p>Conclusions</p> <p>PyElph decreases the effort and time spent processing data from gel images by providing an automatic step-by-step gel image analysis system with a friendly Graphical User Interface. The proposed free software tool is suitable for researchers and students which do not have access to expensive commercial software and image acquisition devices.</p

    Development of lifetime comorbidity in the world health organization world mental health surveys

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    CONTEXT: Although numerous studies have examined the role of latent variables in the structure of comorbidity among mental disorders, none has examined their role in the development of comorbidity. OBJECTIVE: To study the role of latent variables in the development of comorbidity among 18 lifetime DSM-IV disorders in the World Health Organization World Mental Health Surveys. DESIGN: Nationally or regionally representative community surveys. SETTING: Fourteen countries. PARTICIPANTS: A total of 21 229 survey respondents. MAIN OUTCOME MEASURES: First onset of 18 lifetime DSM-IV anxiety, mood, behavior, and substance disorders assessed retrospectively in the World Health Organization Composite International Diagnostic Interview. RESULTS: Separate internalizing (anxiety and mood disorders) and externalizing (behavior and substance disorders) factors were found in exploratory factor analysis of lifetime disorders. Consistently significant positive time-lagged associations were found in survival analyses for virtually all temporally primary lifetime disorders predicting subsequent onset of other disorders. Within-domain (ie, internalizing or externalizing) associations were generally stronger than between-domain associations. Most time-lagged associations were explained by a model that assumed the existence of mediating latent internalizing and externalizing variables. Specific phobia and obsessive-compulsive disorder (internalizing) and hyperactivity and oppositional defiant disorders (externalizing) were the most important predictors. A small number of residual associations remained significant after controlling the latent variables. CONCLUSIONS: The good fit of the latent variable model suggests that common causal pathways account for most of the comorbidity among the disorders considered herein. These common pathways should be the focus of future research on the development of comorbidity, although several important pairwise associations that cannot be accounted for by latent variables also exist that warrant further focused study

    A Methodological Perspective on Genetic Risk Prediction Studies in Type 2 Diabetes: Recommendations for Future Research

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    Fueled by the successes of genome-wide association studies, numerous studies have investigated the predictive ability of genetic risk models in type 2 diabetes. In this paper, we review these studies from a methodological perspective, focusing on the variables included in the risk models as well as the study designs and populations investigated. We argue and show that differences in study design and characteristics of the study population have an impact on the observed predictive ability of risk models. This observation emphasizes that genetic risk prediction studies should be conducted in those populations in which the prediction models will ultimately be applied, if proven useful. Of all genetic risk prediction studies to date, only a few were conducted in populations that might be relevant for targeting preventive interventions

    The sense and nonsense of direct-to-consumer genetic testing for cardiovascular disease

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    Expectations are high that increasing knowledge of the genetic basis of cardiovascular disease will eventually lead to personalised medicine—to preventive and therapeutic interventions that are targeted to at-risk individuals on the basis of their genetic profiles. Most cardiovascular diseases are caused by a complex interplay of many genetic variants interacting with many non-genetic risk factors such as diet, exercise, smoking and alcohol consumption. Since several years, genetic susceptibility testing for cardiovascular diseases is being offered via the internet directly to consumers. We discuss five reasons why these tests are not useful, namely: (1) the predictive ability is still limited; (2) the risk models used by the companies are based on assumptions that have not been verified; (3) the predicted risks keep changing when new variants are discovered and added to the test; (4) the tests do not consider non-genetic factors in the prediction of cardiovascular disease risk; and (5) the test results will not change recommendations of preventive interventions. Predictive genetic testing for multifactorial forms of cardiovascular disease clearly lacks benefits for the public. Prevention of disease should therefore remain focused on family history and on non-genetic risk factors as diet and physical activity that can have the strongest impact on disease risk, regardless of genetic susceptibility

    Structural Analysis of Biodiversity

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    Large, recently-available genomic databases cover a wide range of life forms, suggesting opportunity for insights into genetic structure of biodiversity. In this study we refine our recently-described technique using indicator vectors to analyze and visualize nucleotide sequences. The indicator vector approach generates correlation matrices, dubbed Klee diagrams, which represent a novel way of assembling and viewing large genomic datasets. To explore its potential utility, here we apply the improved algorithm to a collection of almost 17000 DNA barcode sequences covering 12 widely-separated animal taxa, demonstrating that indicator vectors for classification gave correct assignment in all 11000 test cases. Indicator vector analysis revealed discontinuities corresponding to species- and higher-level taxonomic divisions, suggesting an efficient approach to classification of organisms from poorly-studied groups. As compared to standard distance metrics, indicator vectors preserve diagnostic character probabilities, enable automated classification of test sequences, and generate high-information density single-page displays. These results support application of indicator vectors for comparative analysis of large nucleotide data sets and raise prospect of gaining insight into broad-scale patterns in the genetic structure of biodiversity

    Colitis and Colon Cancer in WASP-Deficient Mice Require Helicobacter Species

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    Background: Wiskott–Aldrich syndrome protein–deficient patients and mice are immunodeficient and can develop inflammatory bowel disease. The intestinal microbiome is critical to the development of colitis in most animal models, in which Helicobacter spp. have been implicated in disease pathogenesis. We sought to determine the role of Helicobacter spp. in colitis development in Wiskott–Aldrich syndrome protein–deficient (WKO) mice. Methods: Feces from WKO mice raised under specific pathogen-free conditions were evaluated for the presence of Helicobacter spp., after which a subset of mice were rederived in Helicobacter spp.–free conditions. Helicobacter spp.–free WKO animals were subsequently infected with Helicobacter bilis. Results: Helicobacter spp. were detected in feces from WKO mice. After rederivation in Helicobacter spp.–free conditions, WKO mice did not develop spontaneous colitis but were susceptible to radiation-induced colitis. Moreover, a T-cell transfer model of colitis dependent on Wiskott–Aldrich syndrome protein–deficient innate immune cells also required Helicobacter spp. colonization. Helicobacter bilis infection of rederived WKO mice led to typhlitis and colitis. Most notably, several H. bilis–infected animals developed dysplasia with 10% demonstrating colon carcinoma, which was not observed in uninfected controls. Conclusions: Spontaneous and T-cell transfer, but not radiation-induced, colitis in WKO mice is dependent on the presence of Helicobacter spp. Furthermore, H. bilis infection is sufficient to induce typhlocolitis and colon cancer in Helicobacter spp.–free WKO mice. This animal model of a human immunodeficiency with chronic colitis and increased risk of colon cancer parallels what is seen in human colitis and implicates specific microbial constituents in promoting immune dysregulation in the intestinal mucosa.National Institutes of Health (U.S.) (R01OD011141)National Institutes of Health (U.S.) (R01CA067529)National Institutes of Health (U.S.) (P01CA026731)National Institutes of Health (U.S.) (P30ES02109

    Analysis of chemokine and chemokine receptor expression in squamous cell carcinoma of the head and neck (SCCHN) cell lines

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    The purpose of this work was to analyze chemokine and chemokine receptor expression in untreated and in irradiated squamous cell carcinoma of the head and neck (SCCHN) tumor cell lines, aiming at the establishment of assays to test for the relevance of chemokine and chemokine receptor expression in the response of SCCHN to radiotherapy and radiochemotherapy. Five low passage and 10 established SCCHN lines, as well as two normal cell lines, were irradiated at 2 Gy or sham-irradiated, and harvested between 1 and 48 h after treatment. For chemokines with CC and CXC structural motifs and their receptors, transcript levels of target and reference genes were quantified relatively by real-time PCR. In addition, CXCL1 and CXCL12 protein expression was analyzed by ELISA. A substantial variation in chemokine and chemokine receptor expression between SCCHN was detected. Practically, all cell lines expressed CCL5 and CCL20, while CCL2 was expressed in normal cells and in some of the tumor cell lines. CXCL1, CXCL2, CXCL3, CXCL10, and CXCL11 were expressed in the vast majority of the cell lines, while the expression of CXCL9 and CXCL12 was restricted to fibroblasts and few tumor cell lines. None of the analyzed cell lines expressed the chemokines CCL3, CCL4, or CCL19. Of the receptors, transcript expression of CCR1, CCR2, CCR3, CCR5, CCR7, CCXR2, and CCXR3 was not detected, and CCR6, CXCR1, and CXCR4 expression was restricted to few tumor cells. Radiation caused up- and down-regulation with respect to chemokine expressions, while for chemokine receptor expressions down-regulations were prevailing. CXCL1 and CXCL12 protein expression corresponded well with the mRNA expression. We conclude that the substantial variation in chemokine and chemokine receptor expression between SCCHN offer opportunities for the establishment of assays to test for the relevance of chemokine and chemokine receptor expression in the response of SCCHN to radiotherapy and radiochemotherapy

    The role of disease characteristics in the ethical debate on personal genome testing

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    Background: Companies are currently marketing personal genome tests directly-to-consumer that provide genetic susceptibility testing for a range of multifactorial diseases simultaneously. As these tests comprise multiple risk analyses for multiple diseases, they may be difficult to evaluate. Insight into morally relevant differences between diseases will assist researchers, healthcare professionals, policy-makers and other stakeholders in the ethical evaluation of personal genome tests. Discussion. In this paper, we identify and discuss four disease characteristics - severity, actionability, age of onset, and the somatic/psychiatric nature of disease - and show how these lead to specific ethical issues. By way of illustration, we apply this framework to genetic susceptibility testing for three diseases: type 2 diabetes, age-related macular degeneration and clinical depression. For these three diseases, we point out the ethical issues that are relevant to the question whether it is morally justifiable to offer genetic susceptibility testing to adults or to children or minors, and on what conditions. Summary. We conclude that the ethical evaluation of personal genome tests is challenging, for the ethical issues differ with the diseases tested for. An understanding of the ethical significance of disease characteristics will improve the ethical, legal and societal debate on personal genome testing
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