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The Three-Way Interplay Among Early Life Exposures, The Gut Microbiome, And Outcomes In Infancy, Yuka Moroishi

Dartmouth College Ph.D Dissertations

The bidirectional relationship between the gut microbiome and immune system plays an important role in host immune status: the immune system provides the gut microbiome the optimal environment to thrive in, and the gut microbiome helps regulate the immune system. This relationship is especially important in infants, whose immune system is still premature and rely on innate immunity.

We investigated the three-way interplay among early-life exposures, the developing gut microbiome, and outcomes in infancy from the general population in New Hampshire, US. We used prospective cohort data from the New Hampshire Birth Cohort study to 1) determine whether timing of …

Validation Of A New Predictive Risk Model: Measuring The Impact Of The Major Modifiable Risks Of Death For Patients And Populations, Stephen S. Lim, Emily Carnahan, Eugene C. Nelson, Catherine W. Gillespie, Ali H. Mokdad, Christopher J. L. Murray, Elliott S. Fisher

Dartmouth Scholarship

Background: Modifiable risks account for a large fraction of disease and death, but clinicians and patients lack tools to identify high risk populations or compare the possible benefit of different interventions.

Methods: We used data on the distribution of exposure to 12 major behavioral and biometric risk factors inthe US population, mortality rates by cause, and estimates of the proportional hazards of risk factor exposure from published systematic reviews to develop a risk prediction model that estimates an adult's 10 year mortality risk compared to a population with optimum risk factors. We compared predicted risk to observed mortality in 8,241 …

Spectral Gene Set Enrichment (Sgse), H Robert Frost, Zhigang Li, Jason H. Moore

Dartmouth Scholarship

Gene set testing is typically performed in a supervised context to quantify the association between groups of genes and a clinical phenotype. In many cases, however, a gene set-based interpretation of genomic data is desired in the absence of a phenotype variable. Although methods exist for unsupervised gene set testing, they predominantly compute enrichment relative to clusters of the genomic variables with performance strongly dependent on the clustering algorithm and number of clusters. We propose a novel method, spectral gene set enrichment (SGSE), for unsupervised competitive testing of the association between gene sets and empirical data sources. SGSE first computes …

New Malignancies After Squamous Cell Carcinoma And Melanomas: A Population-Based Study From Norway, Trude E. Robsahm, Margaret R. Karagas, Judy R. Rees, Astri Syse

Dartmouth Scholarship

Skin cancer survivors experience an increased risk for subsequent malignancies but the associated risk factors are poorly understood. This study examined the risk of a new primary cancer following an initial skin cancer and assessed risk factors associated with second primary cancers.

Balancing The Presentation Of Information And Options In Patient Decision Aids: An Updated Review, Purva Abhyankar, Robert J. Volk, Jennifer Blumenthal-Barby, Paulina Bravo, Angela Buchholz, Elissa Ozanne, Dale C. Vidal, Nananda Col, Peep Stalmeier

Dartmouth Scholarship

Standards for patient decision aids require that information and options be presented in a balanced manner; this requirement is based on the argument that balanced presentation is essential to foster informed decision making. If information is presented in an incomplete/non-neutral manner, it can stimulate cognitive biases that can unduly affect individuals’ knowledge, perceptions of risks and benefits, and, ultimately, preferences. However, there is little clarity about what constitutes balance, and how it can be determined and enhanced. We conducted a literature review to examine the theoretical and empirical evidence related to balancing the presentation of information and options.

Measuring Infertility In Populations: Constructing A Standard Definition For Use With Demographic And Reproductive Health Surveys, Maya N. Mascarenhas, Hoiwan Cheung, Colin D. Mathers, Gretchen A. Stevens

Dartmouth Scholarship

Background: Infertility is a significant disability, yet there are no reliable estimates of its global prevalence. Studies on infertility prevalence define the condition inconsistently, rendering the comparison of studies or quantitative summaries of the literature difficult. This study analyzed key components of infertility to develop a definition that can be consistently applied to globally available household survey data.

Methods: We proposed a standard definition of infertility and used it to generate prevalence estimates using 53 Demographic and Health Surveys (DHS). The analysis was restricted to the subset of DHS that contained detailed fertility information collected through the reproductive health calendar. …

Dna Methylation Arrays As Surrogate Measures Of Cell Mixture Distribution, Eugene Houseman, William P. Accomando, Devin C. Koestler, Brock C. Christensen, Carmen J. Marsit

Dartmouth Scholarship

There has been a long-standing need in biomedical research for a method that quantifies the normally mixed composition of leukocytes beyond what is possible by simple histological or flow cytometric assessments. The latter is restricted by the labile nature of protein epitopes, requirements for cell processing, and timely cell analysis. In a diverse array of diseases and following numerous immune-toxic exposures, leukocyte composition will critically inform the underlying immuno-biology to most chronic medical conditions. Emerging research demonstrates that DNA methylation is responsible for cellular differentiation, and when measured in whole peripheral blood, serves to distinguish cancer cases from controls.

Bounded Search For De Novo Identification Of Degenerate Cis-Regulatory Elements, Jonathan M. Carlson, Arijit Chakravarty, Radhika S. Khetani, Robert H. Gross

Dartmouth Scholarship

The identification of statistically overrepresented sequences in the upstream regions of coregulated genes should theoretically permit the identification of potential cis-regulatory elements. However, in practice many cis-regulatory elements are highly degenerate, precluding the use of an exhaustive word-counting strategy for their identification. While numerous methods exist for inferring base distributions using a position weight matrix, recent studies suggest that the independence assumptions inherent in the model, as well as the inability to reach a global optimum, limit this approach.

Gpnn: Power Studies And Applications Of A Neural Network Method For Detecting Gene-Gene Interactions In Studies Of Human Disease, Alison A. Motsinger, Stephen L. Lee, George Mellick, Marylyn D. Ritchie

Dartmouth Scholarship

The identification and characterization of genes that influence the risk of common, complex multifactorial disease primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. We have previously introduced a genetic programming optimized neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of gene combinations associated with disease risk. The goal of this study was to evaluate the power of GPNN for identifying high-order gene-gene interactions. We were also interested in applying GPNN to a real data analysis in Parkinson's disease.

Principal Component Analysis For Predicting Transcription-Factor Binding Motifs From Array-Derived Data, Yunlong Liu, Matthew P Vincenti, Hiroki Yokota

Dartmouth Scholarship

The responses to interleukin 1 (IL-1) in human chondrocytes constitute a complex regulatory mechanism, where multiple transcription factors interact combinatorially to transcription-factor binding motifs (TFBMs). In order to select a critical set of TFBMs from genomic DNA information and an array-derived data, an efficient algorithm to solve a combinatorial optimization problem is required. Although computational approaches based on evolutionary algorithms are commonly employed, an analytical algorithm would be useful to predict TFBMs at nearly no computational cost and evaluate varying modelling conditions. Singular value decomposition (SVD) is a powerful method to derive primary components of a given matrix. Applying SVD …