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Behavior of statistics for genetic association in a genome-wide scan context

Lin, Hui-Min (2015) Behavior of statistics for genetic association in a genome-wide scan context. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Genome-wide association studies are used to detect association between genetic variants and diseases. Hundreds of thousands to millions of SNPs are tested simultaneously. The results of the study often focus on the list of SNPs ordered according to the statistics rather than on certain p-value cutoffs. Therefore, it is important to investigate the behavior of the extreme values of the statistics rather than the behavior of the expected values. “Detection probability” and “proportion positive” have been proposed to measure the success of a genomic study when ranked lists are the primary outcome. In this dissertation, we first focused on the comparison of statistics for X-chromosome association with rare alleles. The regression with male coded as (0, 2) or adjusting for sex as a covariate is recommended. Then we evaluated statistics for detecting genetic association in the presence of an environmental covariate effect. Selecting the best statistics depends on the purpose of the study and how a researcher selects disease-associated SNPs. Studies whose goal is to find significant signal at the whole genome level should focus on which statistic can provide the highest power. Exploratory studies that look for a list of top ranking SNPs which will be further studied in the future should focus on which statistic can provide the highest detection probability. Adjusting for the environmental covariate effect or interaction effect may reduce the power, but it can help with producing more accurate ranked lists. This work will improve the statistical power of genetic association studies, which will allow us to gain a better understanding of disease processes and ultimately design better treatments and public health interventions.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Lin, Hui-Minhul27@pitt.eduHUL27
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Thesis AdvisorFeingold, Eleanorfeingold@pitt.eduFEINGOLD
Committee CoChairLin,; yal2005@gmail.comYAL14
Committee MemberWeeks, Daniel E.weeks@pitt.eduWEEKS0000-0001-9410-7228
Committee MemberDing, YingYINGDING@pitt.eduYINGDING
Date: 28 September 2015
Date Type: Publication
Defense Date: 1 May 2015
Approval Date: 28 September 2015
Submission Date: 26 May 2015
Access Restriction: 2 year -- Restrict access to University of Pittsburgh for a period of 2 years.
Number of Pages: 72
Institution: University of Pittsburgh
Schools and Programs: School of Public Health > Biostatistics
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Genome-wide association studies, X-chromosome association, environmental covariate
Date Deposited: 28 Sep 2015 15:03
Last Modified: 30 Jun 2022 15:52


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