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• W10590911 abstract "Genome-wide association (GWA) studies provide an extensive assessment of common genetic variants across the human genome for disease association. However, due to variation in allele frequencies and disease prevalence across populations, combining samples from different geographic or ethnic groups may lead to spurious evidence for association or diminish the true association signals. In part one of this dissertation, I propose a novel approach to correct for population stratification that makes use of the large amount of genetic information available in a GWA study. Based on allele-sharing identity-by-state (IBS) measures, I develop similarity scores that can describe genetic similarity between individuals, and match cases and controls accordingly. Association tests can then be performed conditional on the matched case-control groups. I apply our approach to the Pritzker bipolar GWA study. In part two, I extend our matching approach to families of arbitrary structure. I first apply similarity score-based matching to selected members from each family and then assign other family members to the same matched group. I modify a corrected chi-square test [Bourgain et al., 2003] following the Mantel-Haenszel procedure to account for correlations both between the family samples and between the matched cases and controls." @default.
• W10590911 created "2016-06-24" @default.
• W10590911 creator A5070015977 @default.
• W10590911 date "2010-01-01" @default.
• W10590911 modified "2023-09-27" @default.
• W10590911 title "Models and methods for genome -wide association studies" @default.
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• W10590911 hasPublicationYear "2010" @default.
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