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A Haplotype-Based Permutation Approach in Gene-Based Testing

Brand, Harrison A (2013) A Haplotype-Based Permutation Approach in Gene-Based Testing. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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The soaring cost of health care is the biggest public health issue facing our country today. Development of strategies that improve the delivery of health care by identifying high risk individuals for a disease is a major approach to better utilize limited medical resources. Incorporating genomic data into risk stratification models is an essential component for creating these diagnostic and treatment strategies. Although initially applied to just small subsets of disease, advances in technology are making it economically feasible to utilize a patient's genomic data in a wider range of medical disorders. Current genetic association studies are crucial for identifying which loci to include in these models.
Genome Wide Association Studies (GWAS) are a valuable tool for identifying genetic variants associated with disease. Commonly, each SNP is initially independently tested in a GWAS with a univariate analysis. By combining the effects of multiple alleles, multivariate analysis of GWAS may increase power to detect associations and, thus, identify additional risk loci. We employ a haplotype block analysis within genes boundaries for a newly developed gene-based method, “GeneBlock”. GeneBlock is compared in a power analysis with two previously published permutation algorithms (GWiS and Fisher) and a simulation method (Vegas). All methods are tested in an Alzheimer Disease GWAS consisting of 1334 cases and 1475 controls. Results from the Alzheimer’s analysis were subsequently compared with haplotype and univariate analysis.
Power analyses shows both GeneBlock and GWiS as more powerful methods than Vegas and Fisher. A combinational approach involving the selection of the lowest p-value from Vegas, GWiS, and Geneblock has higher power than any individual method even when controlling for the additional multiple comparisons. Fisher and Vegas identify no significant genes in the Alzheimer’s GWAS, while GWiS and Geneblock identified four (PRDM16, ARHGEF16, HLA-DRA, TRAF1) and three (C17orf51, MGC29506, SLC23A1) respectively. The combination method is also most powerful in the real GWAS data; it identified all seven of the above significant genes. Comparing single, haplotype, and gene level analyses revealed that only about 1/3 of the top 100 genes are shared, indicating a large variance in results between methods.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Brand, Harrison Ahab45@pitt.eduHAB45
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Barmada, M Michaelbarmada@pitt.eduBARMADA
Ferrell, Robert Erferrell@helix.hgen.pitt.eduRFERRELL
Committee CoChairFeingold, Eleanorfeingold@pitt.eduFEINGOLD
Committee CoChairDiergaarde, Brendabbd3@pitt.eduBBD3
Date: 27 June 2013
Date Type: Publication
Defense Date: 29 January 2013
Approval Date: 27 June 2013
Submission Date: 1 February 2013
Access Restriction: 5 year -- Restrict access to University of Pittsburgh for a period of 5 years.
Number of Pages: 144
Institution: University of Pittsburgh
Schools and Programs: School of Public Health > Human Genetics
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Genetics, GWAS, Gene-Based Testing, Haplotype Analysis
Date Deposited: 27 Jun 2013 18:19
Last Modified: 01 May 2018 05:15

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