Thesis (Ph. D.)--University of Hawaii at Manoa, 1996. / Includes bibliographical references (leaves 185-196). / Microfiche. / xii, 196 leaves, bound ill. (some col.) 29 cm

Single nucleotide polymorphisms (SNPs) have been increasingly popular for a wide range of genetic studies. A high-throughput genotyping technologies usually involves a statistical genotype calling algorithm. Most calling algorithms in the literature, using methods such as k-means and mixturemodels, rely on elliptical structures of the genotyping data; they may fail when the minor allele homozygous cluster is small or absent, or when the data have extreme tails or linear patterns. We propose an automatic genotype calling algorithm by further developing a linear grouping algorithm (Van Aelst et al., 2006). The proposed algorithm clusters unnormalized data points around lines as against around centroids. In addition, we associate a quality value, silhouette width, with each DNA sample and a whole plate as well. This algorithm shows promise for genotyping data generated from TaqMan technology (Applied Biosystems). A key feature of the proposed algorithm is that it applies to unnormalized fluorescent signals when the TaqMan SNP assay is used. The algorithm could also be potentially adapted to other fluorescence-based SNP genotyping technologies such as Invader Assay. Motivated by the SNP genotyping problem, we propose a partial likelihood approach to linear clustering which explores potential linear clusters in a data set. Instead of fully modelling the data, we assume only the signed orthogonal distance from each data point to a hyperplane is normally distributed. Its relationships with several existing clustering methods are discussed. Some existing methods to determine the number of components in a data set are adapted to this linear clustering setting. Several simulated and real data sets are analyzed for comparison and illustration purpose. We also investigate some asymptotic properties of the partial likelihood approach. A Bayesian version of this methodology is helpful if some clusters are sparse but there is strong prior information about their approximate locations or properties. We propose a Bayesian hierarchical approach which is particularly appropriate for identifying sparse linear clusters. We show that the sparse cluster in SNP genotyping datasets can be successfully identified after a careful specification of the prior distributions.

Bibliography: leaves 147-165 / iv, 165 leaves, [31] leaves of plates : ill ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Thesis (Ph.D.)--University of Adelaide, Dept. of Biochemistry, 1986

Thesis (Ph. D.)--University of Adelaide, Dept. of Biochemistry, 1986. / Includes bibliographical references.

Techn. Univ., Habil.-Schr.--Braunschweig, 1998.

Thesis (M.Phil.)--Hong Kong University of Science and Technology, 2008. / Includes bibliographical references (leaves 144-160). Also available in electronic version.

Thesis (Ph. D.)--University of Washington, 1998. / Includes bibliographic references (leaves [97]-124).

Thesis (M.S.)--Rutgers University, 2010. / "Graduate Program in Microbiology and Molecular Genetics." Includes bibliographical references (p. 63-70).

Thesis (M. Phil.)--University of Hong Kong, 1994. / Includes bibliographical references (leaves 170-182).

Thesis (M.Sc.). / Written for the Dept. of Neurology and Neurosurgery. Title from title page of PDF (viewed 2009/06/29). Includes bibliographical references.