Browsing by Author "Wadsworth, W. Duncan"

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    An integrative Bayesian Dirichlet-multinomial regression model for the analysis of taxonomic abundances in microbiome data
    (BioMed Central, 2017) Wadsworth, W. Duncan; Argiento, Raffaele; Guindani, Michele; Galloway-Pena, Jessica; Shelbourne, Samuel A.; Vannucci, Marina
    Abstract Background The Human Microbiome has been variously associated with the immune-regulatory mechanisms involved in the prevention or development of many non-infectious human diseases such as autoimmunity, allergy and cancer. Integrative approaches which aim at associating the composition of the human microbiome with other available information, such as clinical covariates and environmental predictors, are paramount to develop a more complete understanding of the role of microbiome in disease development. Results In this manuscript, we propose a Bayesian Dirichlet-Multinomial regression model which uses spike-and-slab priors for the selection of significant associations between a set of available covariates and taxa from a microbiome abundance table. The approach allows straightforward incorporation of the covariates through a log-linear regression parametrization of the parameters of the Dirichlet-Multinomial likelihood. Inference is conducted through a Markov Chain Monte Carlo algorithm, and selection of the significant covariates is based upon the assessment of posterior probabilities of inclusions and the thresholding of the Bayesian false discovery rate. We design a simulation study to evaluate the performance of the proposed method, and then apply our model on a publicly available dataset obtained from the Human Microbiome Project which associates taxa abundances with KEGG orthology pathways. The method is implemented in specifically developed R code, which has been made publicly available. Conclusions Our method compares favorably in simulations to several recently proposed approaches for similarly structured data, in terms of increased accuracy and reduced false positive as well as false negative rates. In the application to the data from the Human Microbiome Project, a close evaluation of the biological significance of our findings confirms existing associations in the literature.
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    ‘Making Babies': Religion and Moral Diversity in Views on Abortion and Human Genetic Engineering
    (Kinder Institute for Urban Research, 2013) Laws, Terri; Emerson, Michael O.; Wadsworth, W. Duncan
    This white paper using PALS data discusses how race, gender, and frequency of attendance at worship services can impact attitudes about the morality of abortion, the use of genetic engineering to guide child characteristics as well as the basis for moral views. The majority of whites and Hispanics say they base their moral views on their personal conscience. The majority of African Americans, however, say that they base their moral views on God’s law. Attitudes about the morality of abortion are influenced by frequency of religious worship. Respondents who said they attend worship services two or more times per month are most likely to believe that abortion ought to be restricted. Women were more likely than men to say that using human engineering to make a smarter baby is “always wrong.” This paper suggests that moral diversity and diverse moral messaging remain important aspects of American life. Furthermore, for some communities, religious messaging has a clear impact on their attitudes about the use of medical technologies. These influences are important to take into account in public policy debates such as accessibility to and funding for medical research.