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Statistical Methods for Genotype Assay Data

Cheong, Soo Yeon (2010) Statistical Methods for Genotype Assay Data. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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There are many methods to detect any relationship between genotype and phenotype. All of them need to be preceded by measuring genotypes. Genotypes are assigned at each marker for every person to be tested based on raw data from any of a number of different assays. After genotyping, association is tested with a chi-square test on a 2 x 3 table of phenotype x genotype for a simple case-control study design. Based on the chi-square test, we may infer that one of the alleles at the marker might increase risk of the disease. In this dissertation we study analysis methods for raw data from genotyping assays, with particular attention to two issues: genotype calling for trisomic individuals, and design and testing for pooled DNA studies.There are a number of statistical clustering techniques and software packages in use to call genotypes for disomic individuals. However, standard software packages cannot be used if a chromosomal abnormality exists. We used data from individuals with Down syndrome, who have an extra copy of chromosome 21. A method of calling genotypes for individuals with Down syndrome was already suggested in a previous study. In this study we propose a new method to improve the genotype calling in this situation.In most association studies, individual genotyping is used, but that approach has high cost. Pooled genotyping is a cost effective way to perform the first stage of a genetic association study. DNA pools are formed by mixing DNA samples from multiple individuals before genotyping. Pooled DNA is assayed on a standard genotyping chip, and allele frequencies are estimated from the raw intensity data for the chip. Many previous studies looked at the issue of estimating more accurate allele frequencies for pooled genotyping. In this study we consider two different issues: design of pooled studies and statistical testing methods. We consider several pooling designs with the same cost and compare to figure out the most effective design. And we also discuss the most appropriate statistics for testing each design.The two issues addressed in this study are pre-requisites to any genetic association analysis. Genetic association studies are leading to new knowledge that will eventually improve prevention and treatment options for many diseases. However, these studies cannot succeed unless we know how to design and analyze them correctly. Using incorrect genotype calls, incorrect statistics, or inefficient designs will all severely compromise the public health advances that these studies are able to make. The studies we have done will help lead to more correct and efficient genetic association studies, and thus to quicker and surer advances in prevention and treatment. Thus this work has great public health significance.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Cheong, Soo
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairFeingold, Eleanorfeingold@pitt.eduFEINGOLD
Committee MemberTseng, George Cctseng@pitt.eduCTSENG
Committee MemberBarmada, M Michaelbarmada@pitt.eduBARMADA
Committee MemberLin,; YAL14
Date: 28 June 2010
Date Type: Completion
Defense Date: 13 April 2010
Approval Date: 28 June 2010
Submission Date: 12 April 2010
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
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: genotype association; genotype calling; pooled genotyping; genotyping
Other ID:, etd-04122010-071543
Date Deposited: 10 Nov 2011 19:36
Last Modified: 15 Nov 2016 13:39


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