Statistical Methods For High-Dimensional Genetic And Genomic Data
2018-06
Loading...
View/Download File
Persistent link to this item
Statistics
View StatisticsJournal Title
Journal ISSN
Volume Title
Title
Statistical Methods For High-Dimensional Genetic And Genomic Data
Authors
Published Date
2018-06
Publisher
Type
Thesis or Dissertation
Abstract
Modern genetics research constantly creates new types of high-dimensional genetic and genomic data and imposes new challenges in analyzing these data. This thesis deals with several important problems in analyzing high-dimensional genetic and genomic data, ranging from DNA methylation data to human microbiome data. First, we introduce a site selection and multiple imputation method to impute missing data in covariates in epigenome-wide analysis of DNA methylation data, which can help us adjust potential confounders, such as cell type composition. Second, to overcome low power issue of human microbiome association studies, we propose a powerful data-driven approach by weighting the variables (taxa) in a manner determined by the data itself. The increased power of the new test not only decreases the sample size required for a human microbiome association study but also allows for new discoveries with existing datasets. Third, we propose an adaptive test on a high-dimensional parameter of a generalized linear model (in the presence of a low-dimensional nuisance parameter). Benefiting from its adaptivity, the proposed test maintains high statistical power under various high-dimensional scenarios. We further establish its asymptotic null distribution. Finally, we propose a novel pathway-based association test by integrating gene expression, gene functional annotations, and a main genome-wide association study dataset. We applied it to a schizophrenia GWAS summary association dataset and identified 15 novel pathways associated with schizophrenia, such as GABA receptor complex (GO:1902710), which could not be uncovered by the standard single SNP-based analysis or gene-based TWAS.
Description
University of Minnesota Ph.D. dissertation.June 2018. Major: Biostatistics. Advisors: Weihua Guan, Wei Pan. 1 computer file (PDF); viii, 112 pages.
Related to
Replaces
License
Collections
Series/Report Number
Funding information
Isbn identifier
Doi identifier
Previously Published Citation
Other identifiers
Suggested citation
Wu, Chong. (2018). Statistical Methods For High-Dimensional Genetic And Genomic Data. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/200166.
Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.