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.
| Identifer | oai:union.ndltd.org:GEORGIA/oai:digitalarchive.gsu.edu:cs_theses-1050 |
| Date | 06 November 2007 |
| Creators | Zhang, Jun |
| Publisher | Digital Archive @ GSU |
| Source Sets | Georgia State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Computer Science Theses |
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