The method of combination p-values from multiple tests is the foundation for some studies like meta-analysis and detection of signal. There are tremendous methods have been developed and applied like minimum p-values, Cauchy Combination, goodness-of-fit combination and Fisher’s combination. In this paper, I tested their ability to detect signals which is related to real case in biology to find out significant single-nucleotide polymorphisms (SNPs). I simulated p-values for SNPs logistics regression model and test 7 combination methods’ power performance in different setting conditions. I compared sparse or dense signals, dependent or independent and combine them in gene-level or pathway-level. One method based on Fisher’s combination called Omni-TFisher is ideal for most of the situations. Recent years, genome-wide association studies (GWASs) focused on BMD-related SNPs at gene significance level. In this paper I used Omni-TFisher to analyses real data on haplotype blocks. As a result, haplotype blocks can find more SNPs in non-coding and intergeneric regions than gene-based and save computational complexity. It finds out not only known genes, but also other genes need further verification.
| Identifer | oai:union.ndltd.org:wpi.edu/oai:digitalcommons.wpi.edu:etd-theses-2316 |
| Date | 30 May 2019 |
| Creators | Zhang, Ruosi |
| Contributors | Zheyang Wu, Advisor, Dmitry Korkin, Department Head |
| Publisher | Digital WPI |
| Source Sets | Worcester Polytechnic Institute |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Masters Theses (All Theses, All Years) |
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