Browsing by Subject "Gene Expression"

Now showing 1 - 4 of 4
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    Differentially Expressed Gene Transcripts Using RNA Sequencing from the Blood of Immunosuppressed Kidney Allograft Recipients
    (2016-02-29) Dorr, Casey; Wu, Baolin; Guan, Weihua; Muthusamy, Amutha; Sanghavi, Kinjal; Schladt, David; Maltzman, Jonathan; Scherer, Steven; Brott, Marcia; Matas, Arthur; Jacobson, Pamala; Oetting, William; Israni, Ajay; isran001@umn.edu; Israni, Ajay
    This is the FPKM and clinical covariate data from a paper in PLOS One. These data will be useful for future researchers to study gene expression patterns over time before and after immunosuppression and kidney transplantation. We removed subject names and any other identifiers in order to de-identify the subjects.
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    Expression of maize genes on a specific chromosome in the presence of a foreign genome.
    (2009-09) Cabral, Candida Braga
    Oat-maize addition (OMA) lines are derived from oat x maize sexual hybrids in which individual maize chromosomes have been retained in embryo-rescued plants containing a full complement of oat chromosomes. The maize Affymetrix microarray was used to analyze transcripts from three independently derived OMA lines containing chromosome 5 from the B73 maize inbred line in order to assess the prevalence of maize gene expression in an alien background. Despite the lack of gross morphological changes, we found that 26% of the maize genes that are normally expressed in maize seedlings are also expressed in OMA seedlings. All three OMA lines show very similar gene expression patterns. While there was evidence for expression for many of the maize genes on chromosome 5, there were also numerous chromosome 5 genes that are normally expressed in maize but expression was not detected in OMA lines. The expression, or lack thereof, of a maize chromosome 5 gene was not correlated with chromosomal position, gene annotation or DNA methylation. Although there is a correlation between expression levels of the maize chromosome 5 genes expressed in OMA and maize, the expression levels are generally lower in the OMA background. The basis for maize gene expression in the maize donor but not in the OMA lines likely reflects whether maize genes can be efficiently controlled by oat transcriptional machinery.
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    Integrating summarized imaging and genomic data with GWAS for powerful endophenotype association testing in Alzheimer’s Disease
    (2021-06) Knutson, Katherine
    Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with complex traits. However, for most diseases, individual risk variants have small effects which impact disease indirectly through upstream endophenotypes. To improve on the power and interpretability of GWAS, a number of approaches have been developed which aggregate contributions from one or multiple genetic variants to investigate the role of genetically regulated endophenotypes in complex traits. These methods include Mendelian Randomization (MR) and the Transcriptome/Imaging Wide Association Study (TWAS/IWAS, which test for associated gene expression and imaging phenotypes, respectively). In this dissertation, I will compare the performance of these approaches for detecting brain imaging derived phenotypes (IDPs) associated with Alzheimer’s Disease. I will present novel extensions to the TWAS/IWAS framework to account for key biological factors which may impact their performance in practice, namely 1) genetic pleiotropy and 2) population substructure. The first of these factors, genetic pleiotropy, describes the phenomenon in which genetic loci affect multiple intermediate risk phenotypes. The presence of pervasive pleiotropy can result in inconsistent IWAS estimates. I will present a novel extension to the IWAS model (namely, MV-IWAS) which provides consistent causal estimates of endophenotype-trait associations by directly and indirectly accounting for pleiotropic pathways. The second of these factors, population substructure, describes ancestral variation in the underlying genetic architecture of endophenotypes. This variation can lead to ancestry-specific effects of gene expression in TWAS, which go undetected in the standard TWAS framework. Here, I will present a score test to detect heterogeneity in the effects of genetically-regulated gene expression which are correlated with ancestry. By jointly analyzing samples from multiple populations, our multi-ancestry TWAS framework can improve power for detecting genes with shared expression-trait associations across populations through increased sample sizes, as compared to existing stratified TWAS approaches.
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