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• W627907094 abstract "Epigenetics is the study of chemical reactions, which are orchestrated for the development and maintenance of an organism. Genetic or epigenetic variants (GEVs) encompass different types of genetic measures, such as Deoxyribonucleic acid (DNA) methylation at different CpG sites, expression level of genes, or single nucleotide polymorphisms (SNPs). With the development of technology, huge amount of genetic and epigenetic information is produced. However, the rich information potentially brings in challenges in data analyses. Thus it is necessary to reduce the dimension of data to improve efficiency. In my dissertation, I will focus on two directions of dimension reduction: variable selection and clustering. The first project on dimension reduction was motivated by an epigenetic project aiming to identifying GEVs that are associated with a health outcome. Due to the potential non-linear interaction between GEVs, we designed a backward variable selection procedure to select informative GEVs. It is built upon a reproducing kernelbased method for evaluating the joint effect of a set of GEVs, e.g, a set of CpG sites. These GEVs may interact with each other in an unknown and complex way. Simulation studies indicate that the selection method is robust to different types of interaction effects, linear or non-linear. We demonstrate the method using two data sets with the first data selecting important SNPs that are associated with lung function and the second identifying important CpG sites such that their methylation is jointly associated with active smoking measured by cotinine levels. The second project was motivated by the potential heterogeneity in clusters identified by existing methods. Traditional approaches focus on the clustering of either" @default.
• W627907094 created "2016-06-24" @default.
• W627907094 creator A5022031816 @default.
• W627907094 date "2013-01-01" @default.
• W627907094 modified "2023-09-23" @default.
• W627907094 title "Selection and Clustering for Disease Associated Genetic Variants" @default.
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