Browsing by Subject "Multi-omic"

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    Multi-omics Investigation into Alzheimer's Disease: Functional Mechanism and Early Detection
    (2024-08) Pugalenthi, Pradeep Varathan; Yan, Jingwen; Janga, Sarath Chandra; Nho, Kwangsik; Wang, Juexin
    Alzheimer’s disease (AD), a multi-factorial and highly heritable condition, stands as the foremost contributor to dementia. Despite its early discovery and extensive studies, the underlying pathogenesis of AD remains incomplete. This thesis addresses critical aspects of AD through multi-omics approach for improved understanding of underlying functional mechanisms and for improved precision in early detection. Multi-omics integration allows us to explore a wide spectrum of AD-related changes at different biological levels including genomics and metabolomics and how they associate with the biomarkers. In the first aim, I performed an integrative analysis of summary statistics from genome-wide association study (GWAS) and expression quantitative trait loci (eQTL) analysis. Results of this study confirmed the potential of integrative GWAS and eQTL analysis in estimating the transcriptomic changes when lack of tissue-specific expression data, and provided important insights into tissue-specific downstream biology of observed GWAS associations in AD. In the second aim, I took a step further and hypothesized the epistatic effect of GWAS findings and neighboring variants on the downstream functional mechanism. Leveraging the recent advances in sequence-based genome annotation, I investigated the tissue-specific effects of top AD GWAS variants on the chromatin profiles. With in-silico mutagenesis, GWAS variants were found to function via either lead effect or epistatic effect, pinpointing the limitation of existing focus on single-variant-based function annotation. In the last aim, I built a comprehensive bioinformatics pipeline to investigate the potential of metabolic age as an early indicator for AD progression, in which we also observed significant difference between sex groups. We identified strong associations of metabolic age with longitudinal changes of current diagnostic metrics in the ATN framework, suggesting the potential of metabolic age as early biomarkers. Collectively, results from these aims contribute to advancing our understanding of AD and provide valuable insights for future research and clinical applications.