Show simple item record

In Silico Haplotyping, Genotyping and Analysis of Resequencing Data using Markov Models.

dc.contributor.authorLi, Yunen_US
dc.date.accessioned2010-01-07T16:23:54Z
dc.date.availableNO_RESTRICTIONen_US
dc.date.available2010-01-07T16:23:54Z
dc.date.issued2009en_US
dc.date.submitteden_US
dc.identifier.urihttps://hdl.handle.net/2027.42/64640
dc.description.abstractSearches for the elusive genetic mechanisms underlying complex diseases have long challenged human geneticists. Recently, genome-wide association studies (GWAS) have successfully identified many complex disease susceptibility loci by genotyping a subset of several hundred thousand common genetic variants across many individuals. With the rapid deployment of next-generation sequencing technologies, it is anticipated that future genetic association studies will be able to more comprehensively survey genetic variation, both to identify new loci that were missed in the original round of genome-wide association studies and to finely characterize the contributions of identified loci. GWAS, whether in the current genotyping-based form or in the anticipated sequencing-based form, pose a range of computational and analytical challenges. I first propose and implement a computationally efficient hidden Markov model that can rapidly reconstruct the two chromosomes carried by each individual in a study. To achieve this goal, the methods combine partial genotype or sequence data for each individual with additional information on additional individuals. Comparisons with standard haplotypers in both simulated and real datasets show that the proposed method is at least comparable and more computational efficient. I next extend my method for imputing genotypes at untyped SNP loci. Specifically, I consider how my approach can be used to assess several million common variants that are not directly genotyped in a typical association study but for which data are available in public databases. I describe how the extended method performs in a wide range of simulated and real settings. Finally, I consider how low-depth shot-gun resequencing data on a large number of individuals can be combined to provide accurate estimates of individual sequences. This approach should speed up the advent of large-scale genome resequencing studies and facilitate the identification of rare variants that contribute to disease susceptibility and that cannot be adequately assessed with current genotyping-based GWAS approaches. My methods are flexible enough to accommodate phased haplotype data, genotype data, or re-sequencing data as input and can utilize public resources such as the HapMap consortium and the 1000 Genomes Project that now include data on several million genetic variants typed on hundreds of individuals.en_US
dc.format.extent948797 bytes
dc.format.extent1373 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.language.isoen_USen_US
dc.subjectGenotype Imputationen_US
dc.subjectHigh-throughput Sequencingen_US
dc.subjectGenome-wide Association Studies (GWAS)en_US
dc.subjectHidden Markov Modelsen_US
dc.subjectHaplotypingen_US
dc.subjectLinkage Disequilibrium (LD)en_US
dc.titleIn Silico Haplotyping, Genotyping and Analysis of Resequencing Data using Markov Models.en_US
dc.typeThesisen_US
dc.description.thesisdegreenamePhDen_US
dc.description.thesisdegreedisciplineBiostatisticsen_US
dc.description.thesisdegreegrantorUniversity of Michigan, Horace H. Rackham School of Graduate Studiesen_US
dc.contributor.committeememberAbecasis, Goncaloen_US
dc.contributor.committeememberBoehnke, Michael Leeen_US
dc.contributor.committeememberBurmeister, Margiten_US
dc.contributor.committeememberLittle, Roderick J.en_US
dc.contributor.committeememberRosenberg, Noah A.en_US
dc.subject.hlbsecondlevelGeneticsen_US
dc.subject.hlbtoplevelHealth Sciencesen_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/64640/1/ylwtx_1.pdf
dc.owningcollnameDissertations and Theses (Ph.D. and Master's)


Files in this item

Show simple item record

Remediation of Harmful Language

The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.

Accessibility

If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.