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© 2015 Julian Q. Zhou ALL RIGHTS RESERVED

Abstract or Description

Human geneticists in the post-genomics era are blessed with unprecedentedly powerful genomic technologies such as next-generation sequencing to uncover the mysteries of complex human diseases. On the other hand, nevertheless, new practical and analytical challenges that arise with the technological revolutions abound. Working in the context of schizophrenia, a neuropsychiatric disease with a strong genetic basis, we take advantage of genomic datasets produced by modern genomic technologies, as well as novel statistical methods developed in response to the analytical challenges. Specifically, we apply a new meta-analysis framework – Detecting Association With Network (DAWN) – to high-dimensional gene expression datasets in an attempt to identify potential risk genes and sub-networks for schizophrenia. We also address a practical measurement issue that arises with the transition between different genomic technologies. By proposing a procedure that transforms datasets measured using two different technologies to achieve comparable measurements, we combine both data sources, thereby increasing sample size. Using DAWN, we identify a set of 39 primary risk genes and 44 secondary risk genes. We conclude by visualizing the risk gene network and 6 sub-networks surrounding the primary risk genes.


Advisor: Kathryn Roeder

Department of Statistics

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