This thesis explores the ethical implications of using facial recognition artificial intelligence (AI) technologies in medicine, with a focus on both the opportunities and challenges presented by the use of this technology in the diagnosis and treatment of rare genetic disorders. We highlight the positive outcomes of using AI in medicine, such as accuracy and efficiency in diagnosing rare genetic disorders, while also examining the ethical concerns including bias, misdiagnosis, the issues it may cause within patient-clinician relationships, misuses outside of medicine, and privacy. This paper draws on the opinions of medical providers and other professionals outside of medicine, which finds that while many are excited about the potential of AI to improve medicine, concerns remain about the ethical implications of these technologies. We discuss current legislation controlling the use of AI in healthcare and its ambiguity. Overall, this thesis highlights the need for further research and public discourse to address the ethical implications of using facial recognition and AI technologies in medicine, while also providing recommendations for its future use in medicine.

A mutation rate refers to the frequency at which DNA mutations occur in an organism over time. In organisms, mutations are the ultimate source of genetic variation on which selection may act. However, a large number of mutations over time can be detrimental to the cell. Mutation rates are the frequency at which these new mutations arise over time. This can give great insight into DNA repair mechanisms abilities as well as the mutagenic abilities of selected factors. CRISPR-Cas9 is a powerful tool for genome editing, but its off-target effects are not yet fully understood and studied. With its increasing implementation in science and medicine, it is crucial to understand the mutagenic potential of the tool. S. cerevisiae is a model organism for studying genetics due to its fast growth rate and eukaryotic nature. By integrating CRISPR-Cas9 systems into S. cerevisiae, the mutational burden of the technology can be measured and quantified using fluctuation assays. In this experiment, a fluctuation assay using canavanine selective plates was conducted to determine the mutational burden of CRISPR-Cas9 in S. cerevisiae. Multiple trials revealed that various strains of CRISPR-Cas9 had a mutation rate up to 3-fold higher than that of wild-type S. cerevisiae. This information is essential in improving the precision and safety of CRISPR-Cas9 editing in various applications, including gene therapy and biotechnology.

The purpose of the project is to create a survey that will be sent out to thousands of members of the Global Alliance for Genomics and Health (GA4GH) to update GA4GH's Catalogue of Genomic Data Initiatives online. GA4GH's Catalogue of Genomic Data Initiatives has not been updated in several years, leading to outdated and incorrect information. The survey will be used to gather information from genetic groups worldwide to update and increase the amount of data in the Catalogue on the GA4GH website. The questions were created in collaboration with GA4GH and the Human Pangenome Reference Consortium (HPRC). The actual survey was designed on Qualtrics.

This project is an investigation of the gene by environment (GxE) interactions’ effect on substance use outcomes among refugee communities. Substance use disorders (SUDs) are a major public health concern, affecting individuals and communities worldwide. The etiology of SUDs is complex, involving a combination of genetic, environmental, and social factors. In recent years, there has been growing interest in the role of gene by environment interactions in the development of SUDs, particularly in vulnerable populations such as refugees. Refugee populations are exposed to a range of environmental stressors that may interact with genetic factors to increase their risk of SUDs. However, a number of studies describe a “refugee paradox,” where despite having been exposed to risk factors that can lead to SUDs, they are less likely to develop SUDs. Understanding these gene by environment interactions in refugee communities is crucial for not only understanding this phenomenon, but developing effective prevention and treatment strategies for this population. This thesis aims to investigate the gene by environment interactions underlying substance use in refugee communities and to analyze different methods for gene by environment analyses, ultimately determining which method is best suited for this population.

The burden of dementia and its primary cause, Alzheimer’s disease, continue to devastate many with no available cure although present research has delivered methods for risk calculation and models of disease development that promote preventative strategies. Presently Alzheimer’s disease affects 1 in 9 people aged 65 and older amounting to a total annual healthcare cost in 2023 in the United States of $345 billion between Alzheimer’s disease and other dementias making dementia one of the costliest conditions to society (“2023 Alzheimer’s Disease Facts and Figures,” 2023). This substantial cost can be dramatically lowered in addition to a reduction in the overall burden of dementia through the help of risk prediction models, but there is still a need for models to deliver an individual’s predicted time of onset that supplements risk prediction in hopes of improving preventative care. The aim of this study is to develop a model used to predict the age of onset for all-cause dementias and Alzheimer’s disease using demographic, comorbidity, and genetic data from a cohort sample. This study creates multiple regression models with methods of ordinary least squares (OLS) and least absolute shrinkage and selection operator (LASSO) regression methods to understand the capacity of predictor variables that estimate age of onset for all-cause dementia and Alzheimer’s disease. This study is unique in its use of a diverse cohort containing 346 participants to create a predictive model that originates from the All of Us Research Program database and seeks to represent an accurate sampling of the United States population. The regression models generated had no predictive capacity for the age of onset but outline a simplified approach for integrating public health data into a predictive model. The results from the generated models suggest a need for continued research linking risk factors that estimate time of onset.

The study of macaque monkeys harbors advancements in the field of biomedical research. It is imperative to understand the genetic composition of different species of macaques to assess their accuracy as non-human primate (NHP) models for disease detection and treatment assessments. We sought to characterize the hybridization and admixture of the Southeast Asian macaques using single nucleotide polymorphism markers and analyzing the populations on the mainland and the island. Using AMOVA tests and STRUCTURE analysis, we determined that there are three distinct populations: Macaca mulatta, M. fascicularis fascicularis, and M. f. aurea. Furthermore, the island species holds an isolated population of M. f. aurea that demonstrate high inbreeding and genetic uniqueness compared to the mainland species. Findings from this study confirm that NHP models may need to be modified or updated according to changing allelic frequencies and genetic drift.