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(Unpublished)
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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| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
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| ETD Committee: |
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| 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: |
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: |
30 Jun 2022 16:21 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/7877 |
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