Our ultimate goal is to better understand the biological mechanisms that lead to disease pathogenesis, by investigating the regulatory and mediating processes across multiple layers of biological processes. Recent advances in high-throughput technologies have generated an unprecedented amount of multi-omics data. Multi-omics measurements, including genomics data such as genotype and copy number variation (CNV), transcriptomics data such as messenger ribonucleic acid (mRNA) and microRNA expression, as well as epigenomics data such as deoxyribonucleic acid (DNA) methylation, have been traditionally analyzed separately. Multi-omics measurements profiled on different layers of biological processes are interconnected and the biological features identified on different layers influence clinical outcomes at different levels. Integrative analysis borrows strength across multiple levels of omics data by incorporating the regulatory relationship. This could substantially increase the power to reveal the underlying biology of the associations.
This dissertation includes 3 projects on multi-omic data integration. In project 1, we propose a novel approach for clustering gene expression with the information from gene regulators that are measured on different but overlapping samples. This allows integration of multiple types of omics data from different but overlapping samples. Genes with similar expression patterns under various conditions may imply co-regulation or relation in functional pathways. In project 2, we study the causal relationships between omics layers and complex traits using association summary statistics as data in multivariable Mendelian randomization (MVMR) and extend the Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO) method to detect horizontal pleiotropy in the MVMR setting. In project 3, we further optimize the MVMR-PRESSO method so that it yields higher power to detect horizontal pleiotropy under situations of directional pleiotropy – when effects of pleiotropic genetic variants on the outcome are distributed with a non-zero mean. All of these approaches will facilitate the integration of multi-omics data and lead to a better understanding of biological mechanisms underlying complex diseases.
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/46389 |
| Date | 23 June 2023 |
| Creators | Jiang, Wenqing |
| Contributors | Dupuis, Josée |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
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