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Tools for genetic data management and strategies for optimized imputation of missing genotypes

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TitleInfo
Title
Tools for genetic data management and strategies for optimized imputation of missing genotypes
Name (type = personal)
NamePart (type = family)
Kuo
NamePart (type = given)
Fengshen
DisplayForm
Fengshen Kuo
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Diehl
NamePart (type = given)
Scott R.
DisplayForm
Scott R. Diehl
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
chair
Name (type = personal)
NamePart (type = family)
Shibata
NamePart (type = given)
Masayuki
DisplayForm
Masayuki Shibata
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
co-chair
Name (type = personal)
NamePart (type = family)
Maes
NamePart (type = given)
Andrea Dynder
DisplayForm
Andrea Dynder Maes
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
outside member
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
School of Health Related Professions
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (qualifier = exact)
2014
DateOther (qualifier = exact); (type = degree)
2014-10
CopyrightDate (encoding = w3cdtf)
2014
Place
PlaceTerm (type = code)
xx
Language
LanguageTerm (authority = ISO639-2b); (type = code)
eng
Abstract (type = abstract)
This dissertation includes two main areas of research. The first focuses on the design and development of a genetic study data management and analysis system that aims to ease the burden of dealing with the very large amounts of genetic linkage and association study data from high throughput genotyping platforms and to facilitate the integration of data from multiple sources. The Genetic Study Database (GSD) system is designed to provide security in data transmission and user management, flexibility in study data management and simplicity in user interface operations. The second area of research focuses on the imputation of inherited genetic polymorphisms or rare variants. Since 2001, with the advent of high throughput sequencing technologies, the cost of sequencing an entire human genome has dropped from 100 million dollars to less than five thousand dollars per genome. Nevertheless, it is still too costly to obtain whole genome sequencing data for every individual in a research study involving thousands of subjects. Genotype imputation, also called in-silico genotyping, is a cost-effective and efficient way to maximize genome coverage in an association study for little or no additional cost. Depending on the type of genetic study, there are two approaches for doing genotype imputation: population-based and family-based. Both are covered in the research reported here. The population-based approach takes advantage of publicly available genotype reference panels in predicting genotypes of unobserved variants among unrelated individuals. Here, the focus will be on optimizing the post-imputation filtering strategy to find the appropriate balance in the tradeoff between accuracy and the yield of the imputation process (i.e., maximize the number of genotypes imputed). The family-based approach leverages the rich information available in a pedigree to increase power for imputing genotypes of unobserved variants among biological relatives. When performing family-based imputation, it is important to decide how many family members and which family members to select for high density variant genotyping. Their data will be used to predict genotypes of other family members. Therefore, one aim of this part of the research will be to evaluate different family-based imputation designs to identify cost-effective strategies. This dissertation includes three chapters: 1) designing and building a sophisticated web-based genetic study data management system, 2) identifying an optimized set of genotype/SNP filters for population-based imputation, and 3) discovering the most efficient family-based imputation strategies for various pedigree structures.
Subject (authority = RUETD)
Topic
Biomedical Informatics
Subject (authority = ETD-LCSH)
Topic
Genetics--Research
Subject (authority = ETD-LCSH)
Topic
Genetics--Data processing
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_5867
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (xiii, 138 p. : ill.)
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by Fengshen Kuo
RelatedItem (type = host)
TitleInfo
Title
School of Health Related Professions ETD Collection
Identifier (type = local)
rucore10007400001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier (type = doi)
doi:10.7282/T3FB54M6
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Kuo
GivenName
Fengshen
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2014-09-19 16:46:56
AssociatedEntity
Name
Fengshen Kuo
Role
Copyright holder
Affiliation
Rutgers University. School of Health Related Professions
AssociatedObject
Type
License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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Technical

RULTechMD (ID = TECHNICAL1)
ContentModel
ETD
OperatingSystem (VERSION = 5.1)
windows xp
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