Title:
Population genomics and ancestral origins for health disparities research
Population genomics and ancestral origins for health disparities research
| dc.contributor.author | Nagar, Shashwat Deepali | |
| dc.contributor.department | Biology | |
| dc.date.accessioned | 2022-01-14T16:06:54Z | |
| dc.date.available | 2022-01-14T16:06:54Z | |
| dc.date.created | 2021-12 | |
| dc.date.issued | 2021-08-25 | |
| dc.date.submitted | December 2021 | |
| dc.date.updated | 2022-01-14T16:06:54Z | |
| dc.description.abstract | Ameliorating health disparities – avoidable differences in health outcomes between population groups – is both a social imperative and a pressing scientific challenge. The relative importance of genetic versus environmental effects for health disparities, i.e. the enduring question of nature versus nurture, particularly for complex common diseases that have multifactorial etiologies, has long been debated. The importance of social and environmental determinants of health disparities is well established, whereas the role of genetics is more controversial. Nevertheless, these two classes of effects are not mutually exclusive; genes are expressed and function in the context of specific environmental conditions. Thus, it is reasonable to consider the influence of genetic and environmental factors on health disparities together. Indeed, the importance of interactions between genetic and environmental factors for shaping health outcomes has recently been recognized as a promising avenue for health disparities research. The major aim of this thesis was to investigate both genetic and environmental contributions to health disparities by leveraging population biobanks and large genomic datasets. Biobank datasets, which include collections of genetic data together with rich clinical, phenotypic, and environmental data for thousands of individuals, are ideally suited for this purpose. The thesis consists of two main parts: (1) population pharmacogenomics, and (2) complex common health disparities. The first part of the thesis investigates the partitioning of pharmacogenomic variation between populations in different geographic and socioeconomic locales (in Colombia and the US) to study differences in predicted therapeutic response among populations, and the second part of the thesis illustrates the use of a large population biobank to understand health disparities and their complex relationship to genetic, environmental, and social factors. Results from the first part of the thesis highlight how population genomics can be a powerful tool for clinical decision-making especially in settings where resources are limited (e.g. Colombia) or where resources are unequally distributed between population groups (e.g. US). These findings support the precision public health paradigm, which shifts the focus of genomic characterization efforts from individuals to populations to identify interventions that work best at the population level. This allows for uniform priors for treatment to be adjusted based on population membership. Results from the second part of the thesis demonstrate the massive potential of employing biobanks to investigate health disparities and to decompose their effects into genetic and environmental components. Interactions discovered between genetic and environmental risk factors underscore how environmental effects on disease can differ among ancestry groups, suggesting the need for group-specific interventions. Beyond these specific research advances, this thesis also takes a step towards addressing the lack of diversity in genomics research. Genomics research is currently biased towards European ancestry cohorts, and results from these studies may not transfer to more diverse ancestry groups. This genomics research gap has the potential to exacerbate existing health disparities. The focus on ancestrally diverse populations, both in developing countries and for underrepresented minority groups in the US and the UK, has the potential to support health equity through ancestrally-guided insights and interventions. | |
| dc.description.degree | Ph.D. | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | http://hdl.handle.net/1853/66056 | |
| dc.language.iso | en_US | |
| dc.publisher | Georgia Institute of Technology | |
| dc.subject | Population genomics genetic ancestry | |
| dc.subject | health disparities | |
| dc.subject | pharmacogenomics | |
| dc.subject | Type 2 diabetes | |
| dc.subject | Precision public health | |
| dc.title | Population genomics and ancestral origins for health disparities research | |
| dc.type | Text | |
| dc.type.genre | Dissertation | |
| dspace.entity.type | Publication | |
| local.contributor.corporatename | College of Sciences | |
| local.contributor.corporatename | School of Biological Sciences | |
| relation.isOrgUnitOfPublication | 85042be6-2d68-4e07-b384-e1f908fae48a | |
| relation.isOrgUnitOfPublication | c8b3bd08-9989-40d3-afe3-e0ad8d5c72b5 | |
| thesis.degree.level | Doctoral |