Date of Award

11-6-2007

Degree Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Alex Zelikovsky - Chair

Second Advisor

XiaoLing Hu

Third Advisor

Raj Sunderraman

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.

DOI

https://doi.org/10.57709/1059396

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