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A statistical method for genotypic association that is robust to sequencing misclassification
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Zhou, Lisheng. A statistical method for genotypic association that is robust to sequencing misclassification. Retrieved from https://doi.org/doi:10.7282/T3XS5Z9X

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TitleA statistical method for genotypic association that is robust to sequencing misclassification
NameZhou, Lisheng (author); Xing, Jinchuan (chair); Matise, Tara (internal member); Gordon, Derek (internal member); Buyske, Steven (outside member); Rutgers University; Graduate School - New Brunswick
Date Created2017
Other Date2017-05 (degree)
SubjectMicrobiology and Molecular Genetics, Genomics--Data processing, Computational biology
Extent1 online resource (x, 186 p. : ill.)
DescriptionIn analyzing human genetic disorders, association analysis is one of the most commonly used approaches. However, there are challenges with association analysis, including differential misclassification in data that inflates the false-positive rate. In this thesis, I present a new statistical method for testing the association between disease phenotypes and multiple single nucleotide polymorphisms (SNPs). This method uses next-generation sequencing (NGS) raw data and is robust to sequencing differential misclassification. By incorporating expectation-maximization (EM) algorithm, this method computes the test statistic and estimates important parameters of the model, including misclassification. By performing simulation studies, I report that this method maintains correct type I error rates and may obtain high statistical power.
NotePh.D.
NoteIncludes bibliographical references
Noteby Lisheng Zhou
Genretheses, ETD doctoral
Persistent URLhttps://doi.org/doi:10.7282/T3XS5Z9X
Languageeng
CollectionGraduate School - New Brunswick Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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