Physiological changes with age such as immune system decline and brain aging cause an increased risk for diseases. Age-related diseases are of major concern especially in the elderly population due to there being an increase in the average lifespan. This dissertation explores neurodegenerative and respiratory diseases and how gene expression varies due to disease status, age tissue and sex.Alzheimer's disease (AD) has been categorized by the Centers for Disease Control and Prevention (CDC) as the 6th leading cause of death in the United States. AD is a significant health-care burden because of its increased occurrence (specifically in the elderly population), and the lack of effective treatments and preventive methods. AD targets neuronal function and can cause neuronal loss due to the buildup of amyloid-beta plaques and intracellular neurofibrillary tangles.The respiratory disease, chronic obstructive pulmonary disease (COPD), was classified by the Centers for Disease Control and Prevention in 2014 as the 3rd leading cause of death in the United States. The main cause of COPD is exposure to tobacco smoke and air pollutants. In addition to exploring genetic variation due to disease state, sex and age we also explored the role of smoking status on expression profiles.Additionally, the respiratory infections, influenza and pneumonia affect thousands of people worldwide. Young children, elderly and immunocompromised individuals are at higher risk for being infected by the influenza virus and Streptococcus pneumoniae. Host responses to these pathogens and vaccinations vary by the state of one's immune system.This dissertation includes multiple meta-analyses to assess genetic variation in Alzheimer's disease, COPD and Influenza, and an assessment of pneumococcal disease and aging. To identify significant differentially expressed genes we ran an analysis of variance with a linear model with disease state, age, sex, tissue, smoking status and study as effects that also included binaryinteractions.Our meta-analysis approach effectively combined multiple publicly available microarray datasetsto identify gene expression differences across diseases including full age, sex, smoking status and tissue type considerations. Our findings provide potential gene and pathway associations that can be targeted to improve treatment and prevention of diseases.
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