Cohort Identification Using Semantic Web Technologies: Ontologies and Triplestores as Engines for Complex Computable Phenotyping

Electronic health record (EHR)-based computable phenotypes are algorithms used to identify individuals or populations with clinical conditions or events of interest within a clinical data repository. Due to a lack of EHR data standardization, computable phenotypes can be semantically ambiguous and difficult to share across institutions. In this research, I propose a new computable phenotyping methodological framework based on semantic web technologies, specifically ontologies, the Resource Description Framework (RDF) data format, triplestores, and Web Ontology Language (OWL) reasoning. My hypothesis is that storing and analyzing clinical data using these technologies can begin to address the critical issues of semantic ambiguity and lack of interoperability in the context of computable phenotyping. To test this hypothesis, I compared the performance of two variants of two computable phenotypes (for depression and rheumatoid arthritis, respectively). The first variant of each phenotype used a list of ICD-10-CM codes to define the condition; the second variant used ontology concepts from SNOMED and the Human Phenotype Ontology (HPO). After executing each variant of each phenotype against a clinical data repository, I compared the patients matched in each case to see where the different variants overlapped and diverged. Both the ontologies and the clinical data were stored in an RDF triplestore to allow me to assess the interoperability advantages of the RDF format for clinical data. All tested methods successfully identified cohorts in the data store, with differing rates of overlap and divergence between variants. Depending on the phenotyping use case, SNOMED and HPO’s ability to more broadly define many conditions due to complex relationships between their concepts may be seen as an advantage or a disadvantage. I also found that RDF triplestores do indeed provide interoperability advantages, despite being far less commonly used in clinical data applications than relational databases. Despite the fact that these methods and technologies are not “one-size-fits-all,” the experimental results are encouraging enough for them to (1) be put into practice in combination with existing phenotyping methods or (2) be used on their own for particularly well-suited use cases.Doctor of Philosoph

A Systems-Level Approach to Understand The Seasonal Factors Of Early Development With Clinical and Pharmacological Applications

Major developmental defects occur in 100,000 to 200,000 children born each year in the United States of America. 97% of these defects are from unidentified causes. Many fetal outcomes (e.g., developmental defects), result from interactions between genetic and environmental factors. The lifetime effects from prenatal exposures with low impact (e.g., air pollution) are often understudied. Even when these exposures are studied, the focus is often placed on immediate effects of the exposure (e.g., fetal anomalies, miscarriage rates) leaving lifetime effects largely unexplored. This makes prolonged (or lifetime) effects of low-impact exposures an understudied research area. Included in this set of low-impact exposures is seasonal variance at birth. This thesis measures the effects of seasonal variance at birth on lifetime disease risk at both the population-level and molecular-levels. Four aims, comprising this thesis study, were conducted that utilize data from pharmacology, clinical care (Electronic Health Records) and genetics. These aims included: 1.) Development of an Algorithm to Reveal Diseases with a Prenatal/Perinatal Seasonality Component (described in chapter 2); 2.) Investigation of Climate Variables that Affect Lifetime Disease Risk By Altering Environmental Drivers (described in chapters 3 and 4); 3.) Discovery of Genes Involved in Birth Season – Disease Effects (described in chapter 5) and 4.) Investigation of Pharmacological Inhibitors As Phenocopies of the Birth Season – Disease Effect (described in chapters 6 and 7). Knowledge gained from these four areas, through seven distinct studies, establishes that birth season is a causal risk factor in a number of common diseases including cardiovascular diseases

Frameshift mutations at the C-terminus of HIST1H1E result in a specific DNA hypomethylation signature

BACKGROUND: We previously associated HIST1H1E mutations causing Rahman syndrome with a specific genome-wide methylation pattern. RESULTS: Methylome analysis from peripheral blood samples of six affected subjects led us to identify a specific hypomethylated profile. This "episignature" was enriched for genes involved in neuronal system development and function. A computational classifier yielded full sensitivity and specificity in detecting subjects with Rahman syndrome. Applying this model to a cohort of undiagnosed probands allowed us to reach diagnosis in one subject. CONCLUSIONS: We demonstrate an epigenetic signature in subjects with Rahman syndrome that can be used to reach molecular diagnosis