Type of Document Master's Thesis Author Zhang, Jun Author's Email Address jzhang13@student.gsu.edu URN etd-11012007-103758 Title GENOTYPE/HAPLOTYPE TAGGING METHODS AND THEIR VALIDATION Degree Master of Science Department Computer Science Advisory Committee
Advisor Name Title Alex Zelikovsky Committee Chair Raj Sunderraman Committee Member XiaoLing Hu Committee Member Keywords
- Genotype
- Haplotype
- Validation
- Tagging
- SNP
Date of Defense 2007-10-10 Availability unrestricted Abstract This study focuses how the MLR-tagging for statistical covering, i.e. either maximizing average R2 for certain number of requested tags or minimizing number of tags such that for any non-tag SNP there exists a highly correlated (squared correlation R2 > 0.8) tag SNP. We compare with tagger, a software for selecting tags in hapMap project. MLR-tagging needs less number of tags than tagger in all 6 cases of the given test sets except 2. Meanwhile, Biologists can detect or collect data only from a small set. So, this will bring a problem for scientists that the estimates accuracy of tag SNPs when constructing the complete human haplotype map. This study investigates how the MLR-tagging for statistically coverage performs under unbias study. The experiment results shows MLR-tagging still select small amount of SNPs very well even without observing the entire SNP in the sample.Files
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