Browsing by Author "Shaw, Chad A."

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    A joint modeling approach for longitudinal microbiome data improves ability to detect microbiome associations with disease
    (Public Library of Science, 2020) Luna, Pamela N.; Mansbach, Jonathan M.; Shaw, Chad A.
    Changes in the composition of the microbiome over time are associated with myriad human illnesses. Unfortunately, the lack of analytic techniques has hindered researchers’ ability to quantify the association between longitudinal microbial composition and time-to-event outcomes. Prior methodological work developed the joint model for longitudinal and time-to-event data to incorporate time-dependent biomarker covariates into the hazard regression approach to disease outcomes. The original implementation of this joint modeling approach employed a linear mixed effects model to represent the time-dependent covariates. However, when the distribution of the time-dependent covariate is non-Gaussian, as is the case with microbial abundances, researchers require different statistical methodology. We present a joint modeling framework that uses a negative binomial mixed effects model to determine longitudinal taxon abundances. We incorporate these modeled microbial abundances into a hazard function with a parameterization that not only accounts for the proportional nature of microbiome data, but also generates biologically interpretable results. Herein we demonstrate the performance improvements of our approach over existing alternatives via simulation as well as a previously published longitudinal dataset studying the microbiome during pregnancy. The results demonstrate that our joint modeling framework for longitudinal microbiome count data provides a powerful methodology to uncover associations between changes in microbial abundances over time and the onset of disease. This method offers the potential to equip researchers with a deeper understanding of the associations between longitudinal microbial composition changes and disease outcomes. This new approach could potentially lead to new diagnostic biomarkers or inform clinical interventions to help prevent or treat disease.
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    A joint modeling approach for longitudinal microbiome data with time-to-event outcomes
    (2019-04-15) Luna, Pamela Nicole; Scott, David W.; Shaw, Chad A.
    Humans are living, breathing ecosystems. We share our bodies with a vast collection of microorganisms that easily outnumber the human cells in our bodies. While these microbes are mostly benign or beneficial, non-pathogenic and pathogenic microorganisms alike can be harmful in certain abundances. Microbial imbalances within anatomic sites have been linked to human illness, but the underlying mechanics of how individuals reach this state of dysbiosis are not well understood. Determining how changes in microbial abundances affect the onset of disease could lead to novel treatments that help to stabilize the microbiome. However, currently no methods appropriately quantify the associations between longitudinal microbial abundance changes and time-to-event outcomes. This lack of methodology is partially due to the inability to include time-dependent biomarker data in an event model. Though the existing joint model for longitudinal and time-to-event data addresses this issue by incorporating a linear mixed effects model into the hazard function of the Cox proportional hazards model, it does not allow for proper modeling of non-Gaussian microbiome data. In this thesis we present a joint modeling approach which uses a negative binomial mixed effects model to determine the longitudinal values included in the hazard function. We discuss how our model parameterization generates interpretable results and accounts for the underlying structure of microbiome data while still respecting its inherent compositionality. We outline how to simulate longitudinal microbiome data and associated event times to create a joint model dataset and show that our model performs better than existing alternatives. Finally, we illustrate how this methodology could be used to improve clinical diagnostics and therapeutics.
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    Variant interpretation through Bayesian fusion of frequency and genomic knowledge
    (BioMed Central, 2015) Shaw, Chad A.; Campbell, Ian M.
    Variant interpretation is a central challenge in genomic medicine. A recent study demonstrates the power of Bayesian statistical approaches to improve interpretation of variants in the context of specific genes and syndromes. Such Bayesian approaches combine frequency (in the form of observed genetic variation in cases and controls) with biological annotations to determine a probability of pathogenicity. These Bayesian approaches complement other efforts to catalog human variation.
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    A visual and curatorial approach to clinical variant prioritization and disease gene discovery in genome-wide diagnostics
    (BioMed Central, 2016) James, Regis A.; Campbell, Ian M.; Chen, Edward S.; Boone, Philip M.; Rao, Mitchell A.; Bainbridge, Matthew N.; Lupski, James R.; Yang, Yaping; Eng, Christine M.; Posey, Jennifer E.; Shaw, Chad A.
    Background: Genome-wide data are increasingly important in the clinical evaluation of human disease. However, the large number of variants observed in individual patients challenges the efficiency and accuracy of diagnostic review. Recent work has shown that systematic integration of clinical phenotype data with genotype information can improve diagnostic workflows and prioritization of filtered rare variants. We have developed visually interactive, analytically transparent analysis software that leverages existing disease catalogs, such as the Online Mendelian Inheritance in Man database (OMIM) and the Human Phenotype Ontology (HPO), to integrate patient phenotype and variant data into ranked diagnostic alternatives. Methods: Our tool, “OMIM Explorer” (http://www.omimexplorer.com), extends the biomedical application of semantic similarity methods beyond those reported in previous studies. The tool also provides a simple interface for translating free-text clinical notes into HPO terms, enabling clinical providers and geneticists to contribute phenotypes to the diagnostic process. The visual approach uses semantic similarity with multidimensional scaling to collapse high-dimensional phenotype and genotype data from an individual into a graphical format that contextualizes the patient within a low-dimensional disease map. The map proposes a differential diagnosis and algorithmically suggests potential alternatives for phenotype queries—in essence, generating a computationally assisted differential diagnosis informed by the individual’s personal genome. Visual interactivity allows the user to filter and update variant rankings by interacting with intermediate results. The tool also implements an adaptive approach for disease gene discovery based on patient phenotypes. Results: We retrospectively analyzed pilot cohort data from the Baylor Miraca Genetics Laboratory, demonstrating performance of the tool and workflow in the re-analysis of clinical exomes. Our tool assigned to clinically reported variants a median rank of 2, placing causal variants in the top 1 % of filtered candidates across the 47 cohort cases with reported molecular diagnoses of exome variants in OMIM Morbidmap genes. Our tool outperformed Phen-Gen, eXtasy, PhenIX, PHIVE, and hiPHIVE in the prioritization of these clinically reported variants. Conclusions: Our integrative paradigm can improve efficiency and, potentially, the quality of genomic medicine by more effectively utilizing available phenotype information, catalog data, and genomic knowledge.