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Statistical Issues in Family-Based Genetic Association Studies with Application to Congenital Heart Defects in Down Syndrome

Lin, Yan (2007) Statistical Issues in Family-Based Genetic Association Studies with Application to Congenital Heart Defects in Down Syndrome. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Abstract

This dissertation is motivated by data generated from a genetic association study of congenital heart defects in Down syndrome (DS). Congenital heart defects are among the most common abnormalities seen at birth. The genetic basis for most congenital heart defects is unknown. One severe form of congenital heart defect, atrioventricular septal defect (AVSD), is highly associated with DS. This makes the DS population a useful tool for discovering of genes that are associated with this specific form of congenital heart defect. Discovering genes that influence risk of AVSD will lead to a better understanding of heart development and of the etiology of these defects. This in turn can lead eventually to improved public health through better screening, prevention, and treatment strategies.Family trios were collected for the Down syndrome heart study. This dissertation discusses statistical issues raised in genetic association studies using family trio data, including the genotype calling problem (i.e. how to generate genotype data from the raw data produced by high-throughput SNP arrays) and analysis strategies. Although the motivating dataset involves trisomic individuals, we developed statistical methods both for disomic and trisomic data.For the genotype-calling problem, we generated two genotype calling methods specifically for disomic family trio data. The first method is an ad-hoc modification of the K-means clustering algorithm that incorporates family information. The second is a likelihood-based method that combines the mixture model approach with a pedigree likelihood. These two methods out-performed existing methods, which ignore the family information, both in simulation studies and a real data analysis. We also extended these two methods to trisomic trio data.With regard to analysis strategies, we discussed alternativeanalysis methods for trio designs, particularly for the combinationof case trios and control trios that we have in the Down syndromedata. We derived likelihood models that help explain the differencesamong some published methods. We also proposed an extension of acombined likelihood-based method proposed by Epstein and others foranalysis of case trios plus independent controls to our design ofcase and control trios.


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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Lin, Yanyal14@pitt.edu; yal2005@gmail.comYAL14
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairFeingold, Eleanorfeingold@pitt.eduFEINGOLD
Committee MemberTseng, Chien-Cheng (George)ctseng@pitt.eduCTSENG
Committee MemberWeeks, Daniel Edweeks@watson.hgen.pitt.eduWEEKS
Committee MemberWeissfeld, Lisalweis@pitt.eduLWEIS
Date: 25 September 2007
Date Type: Completion
Defense Date: 4 May 2007
Approval Date: 25 September 2007
Submission Date: 15 May 2007
Access Restriction: 5 year -- Restrict access to University of Pittsburgh for a period of 5 years.
Institution: University of Pittsburgh
Schools and Programs: Graduate School of Public Health > Biostatistics
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: AVSD; DS; Genotype Calling; SNP; clustering; Genetic Association Study
Other ID: http://etd.library.pitt.edu/ETD/available/etd-05152007-135940/, etd-05152007-135940
Date Deposited: 10 Nov 2011 19:44
Last Modified: 15 Nov 2016 13:43
URI: http://d-scholarship.pitt.edu/id/eprint/7877

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