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Computational methods for the interpretation of forensic DNA samples
Descriptive
TitleInfo
Title
Computational methods for the interpretation of forensic DNA samples
Name
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NamePart
(type = family)
Swaminathan
NamePart
(type = given)
Harish
NamePart
(type = date)
1989-
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Harish Swaminathan
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author
Name
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Desmond S
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Desmond S Lun
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Advisory Committee
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chair
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Piccoli
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Benedetto
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Benedetto Piccoli
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Advisory Committee
Role
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internal member
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(type = personal)
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(type = family)
Medard
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(type = given)
Muriel
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Muriel Medard
Affiliation
Advisory Committee
Role
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(authority = RULIB)
outside member
Name
(type = personal)
NamePart
(type = family)
Grgicak
NamePart
(type = given)
Catherine
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Catherine Grgicak
Affiliation
Advisory Committee
Role
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(authority = RULIB)
outside member
Name
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NamePart
Rutgers University
Role
RoleTerm
(authority = RULIB)
degree grantor
Name
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NamePart
Camden Graduate School
Role
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school
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Text
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theses
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DateCreated
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(qualifier = exact)
2015
DateOther
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(type = degree)
2015-10
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2015
Place
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xx
Language
LanguageTerm
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(type = code)
eng
Abstract
(type = abstract)
Interpretation of DNA profiles generated from STRs can be problematic because of dropout, allele overlap and artifacts like stutter. The goal of this research is to develop computational methods for the analysis of STR profiles that are robust to these phenomena and that utilize quantitative peak height information captured in profiles. These methods are expected to improve significantly on existing methods for analysis of STR profiles, particularly in cases of low amounts of template DNA or where there are many contributors. In the first part of our research, we characterized the distribution of signal, noise and stutter peak heights and studied their dependence on template DNA amount. For the second part of our project, we developed a method to identify the number of contributors to a DNA sample. Our method, NOCIt, calculates the a posteriori probability on the number of contributors to a forensic sample taking into account signal peak heights, population allele frequencies, baseline noise, allele dropout and stutter. On the experimental samples tested, NOCIt had an accuracy of 83%, while the accuracy of the best pre-existing method was 72%. The accuracies of NOCIt and the best pre-existing method on the simulated profiles were 85% and 73%, respectively. We were able to reduce the running time of NOCIt by developing a faster method based on an importance sampling algorithm. In the third and final part of our research, we developed a computational tool (MatchIt) to directly compute a continuous Likelihood Ratio (LR) for a person of interest (POI), treating other contributors (if any) as interference. MatchIt also calculates the distribution of the LR along with the p-value, which is the probability a randomly chosen individual results in a LR at least as large as the LR obtained from the POI. We observed that the amount of template DNA from the contributor impacted the LR – small LRs resulted from contributors with low template masses. Moreover, we observed a decrease of p-values as the LR increased. A p-value of 10-9, the lowest possible in our testing, was achieved in all the cases where the LR was greater than 108.
Subject
(authority = RUETD)
Topic
Computational and Integrative Biology
Subject
(authority = ETD-LCSH)
Topic
Forensic genetics
Subject
(authority = ETD-LCSH)
Topic
DNA--Analysis
RelatedItem
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TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier
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ETD
Identifier
ETD_6733
PhysicalDescription
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electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (xiii, 106 p. : ill.)
Note
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Ph.D.
Note
(type = bibliography)
Includes bibliographical references
Note
(type = vita)
Includes vita
Note
(type = statement of responsibility)
by Harish Swaminathan
RelatedItem
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TitleInfo
Title
Camden Graduate School Electronic Theses and Dissertations
Identifier
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rucore10005600001
Location
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(displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier
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doi:10.7282/T3PK0J34
Genre
(authority = ExL-Esploro)
ETD doctoral
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Rights
RightsDeclaration
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The author owns the copyright to this work.
RightsHolder
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Name
FamilyName
Swaminathan
GivenName
Harish
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime
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(point = start)
2015-09-14 13:49:27
AssociatedEntity
Name
Harish Swaminathan
Role
Copyright holder
Affiliation
Rutgers University. Camden Graduate School
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
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ETD
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windows xp
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