Elucidating the fundamental biological mechanisms underlying complex diseases remains, to date, a challenging task. Genetic approaches have been used to implicate genes and pathways in disease, but classical (reverse) and population (forward) genetic approaches have fallen short of bringing new understanding to multi-factorial disorders. With the emergence of rapidly advancing, new technologies and the dramatic reduction of their costs, new algorithms and analytical models that maximally leverage the data these technologies generate, are being developed to uncover novel biological insights for complex traits. Furthermore, the integration of multiple strata of biological information permits the creation of better representations of disease. This thesis aims to leverage some of these models on novel multi-scale datasets to explore the mechanisms of Alzheimer’s disease and the variability of induced pluripotent stem cells.
|Advisor:||Schadt, Eric E., Houten, Sander M.|
|Commitee:||Cho, Judy H., Ma'ayan, Avi, Mason, Christopher E., Nestler, Eric J.|
|School:||Icahn School of Medicine at Mount Sinai|
|Department:||Genetics and Genomic Sciences|
|School Location:||United States -- New York|
|Source:||DAI-B 79/05(E), Dissertation Abstracts International|
|Subjects:||Genetics, Systematic biology, Bioinformatics|
|Keywords:||Alzheimer's disease, Data integration, Modeling, Multiscale, Omics, iPSCs|
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