Life Sciences Commons^™

Apply Data Clustering To Gene Expression Data, Abdullah Jameel Abualhamayl Mr.

Electronic Theses, Projects, and Dissertations

Data clustering plays an important role in effective analysis of gene expression. Although DNA microarray technology facilitates expression monitoring, several challenges arise when dealing with gene expression datasets. Some of these challenges are the enormous number of genes, the dimensionality of the data, and the change of data over time. The genetic groups which are biologically interlinked can be identified through clustering. This project aims to clarify the steps to apply clustering analysis of genes involved in a published dataset. The methodology for this project includes the selection of the dataset representation, the selection of gene datasets, Similarity Matrix Selection, …

Detection Of Genes Influencing Chronic And Mendelian Disease Via Loss-Of-Function Variation, Alexander H. Li

Dissertations & Theses (Open Access)

A typical human exome harbors dozens of loss-of-function (LOF) variants predicted to severely disrupt or abolish gene function. These variants are enriched at the extremely rare end of the allele frequency spectrum (< 0.1%), suggesting purifying selection against these sites. However, most previous population-based sequencing studies have not included analysis of genotype-phenotype relationships with LOF variants. Thus, the contribution of LOF variation to health and disease within the general population remains largely uncharacterized.

Using whole exome sequence from 8,554 participants in the Atherosclerosis Risk in Communities (ARIC) study, we explored the impact of LOF variation on a broad spectrum of human phenotypes. First, we selected 20 common chronic disease risk factor phenotypes and performed gene-based association tests. Analysis of this sample verified two relationships in well-studied genes (PCSK9 and APOC3) and identified eight new loci. Novel relationships included …