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School of Biological Sciences

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search.filters.applied.f.advisor: Gibson, Greg × Start date: 2000 × Has files: Yes × search.filters.applied.f.isOrgUnitOfPublicationTitle: College of Sciences × search.filters.applied.f.isOrgUnitOfPublicationTitle: School of Biological Sciences × search.filters.applied.f.resourcesubtype: Thesis ×

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Mechanisms of Coherence and Incoherence Between GWAS and Single-Cell eQTL Effects in Crohn's Disease

2023-05-02 , Collins, Jared Blake

The integration of expression quantitative trait loci with GWAS data has proven invaluable in the exploration of mechanisms through which genetic variants influence complex traits. However, it has also highlighted instances of incoherence in which the eQTL effects of GWAS risk variants seemingly contradict observed case and control expression. Patterns of incoherence may indicate variants associated with disease via protection, but due to the highly heterogenous nature of varying cell-types, may also indicate cell-type specific associations with disease that are convoluted by bulk RNA sequencing. Here, we conduct exploratory analysis integrating sceQTL and GWAS data associated with Crohn’s Disease to assess patterns of coherence and incoherence, using both bulk RNA-seq and predicted single-cell gene expression for case-control expression. We show that integration of GWAS summary statistics with single-cell eQTL data is a promising approach for uncovering cell type specific patterns of coherence and incoherence, and may suggest functional mechanisms underlying these associations.

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Functional genomics of cardiovascular disease risk

2013-07-03 , Kim, Jin Hee

Understanding variability of heath status is highly likely to be an important component of personalized medicine to predict health status of individuals and to promote personal health. Evidences of Genome Wide Association Study and gene expression study indicating that genetic factors affect the risk susceptibility of individuals have suggested adding genetic factors as a component of health status measurements. In order to validate or to predict health risk status with collected personal data such as clinical measurements or genomic data, it is important to have a well-established profile of diseases. The primary effort of this work was to find genomic evidence relevant to coronary artery disease. Two major methods of genomic analysis, gene expression profiling and GWAS on gene expression, were performed to dissect transcriptional and genotypic fingerprints of coronary artery disease. Blood-informative transcriptional Axes that can be described by 10 covariating transcripts per each Axis were utilized as a crucial measure of gene expression analysis. This study of the relationship between gene expression variation and various measurements of coronary artery disease delivered compelling results showing strong association between two transcriptional Axes and incident of myocardial infarction. 244 transcripts closely correlated with death by cardiovascular disease related events were also showing clear association with those two transcriptional Axes. These results suggest potential transcripts for use in risk prediction for the advent of myocardial infarction and cardiac death.

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