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dc.contributor.authorJohnston, Ianen_US
dc.date.accessioned2016-05-20T17:37:52Z
dc.date.available2016-05-20T17:37:52Z
dc.date.issued2015
dc.identifier.urihttps://hdl.handle.net/2144/16345
dc.description.abstractI consider a well-known problem in the field of statistical genetics called a genome-wide association study (GWAS) where the goal is to identify a set of genetic markers that are associated to a disease. A typical GWAS data set contains, for thousands of unrelated individuals, a set of hundreds of thousands of markers, a set of other covariates such as age, gender, smoking status and other risk factors, and a response variable that indicates the presence or absence of a particular disease. Due to biological phenomena such as the recombination of DNA and linkage disequilibrium, parents are more likely to pass parts of DNA that lie close to each other on a chromosome together to their offspring; this non-random association between adjacent markers leads to strong correlation between markers in GWAS data sets. As a statistician, I reduce the complex problem of GWAS to its essentials, i.e. variable selection on a large-p-small-n data set that exhibits multicollinearity, and develop solutions that complement and advance the current state-of-the-art methods. Before outlining and explaining my contributions to the field in detail, I present a literature review that summarizes the history of GWAS and the relevant tools and techniques that researchers have developed over the years for this problem.en_US
dc.language.isoen_US
dc.rightsAttribution 4.0 Internationalen_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectStatisticsen_US
dc.titleHierarchical bayesian models for genome-wide association studiesen_US
dc.typeThesis/Dissertationen_US
dc.date.updated2016-04-08T20:32:35Z
etd.degree.nameDoctor of Philosophyen_US
etd.degree.leveldoctoralen_US
etd.degree.disciplineMathematics & Statisticsen_US
etd.degree.grantorBoston Universityen_US


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Attribution 4.0 International
Except where otherwise noted, this item's license is described as Attribution 4.0 International