Integration of gene expression network to detect important disease-related genes and SNPs

• Main Authors: Cheng-Han Hsieh, 謝承翰
• Other Authors: Wen-Yu Chung
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/42750673740673823140

碩士 === 國立高雄應用科技大學 === 資訊工程系 === 102 === Complex disease is often not affected by single genetic variants, but rather a group of related modifications. Genome-wide association study (GWAS) aims to identify such disease-associated single-nucleotide polymorphisms (SNPs) using whole genome dataset. However, traditional GWAS may overlook variations that pertain partial and not the primary disease-related alleles. Thus, I study the gene-gene interaction networks built from normal (control) and patients (case). First, the differences between control-case interaction networks were described using several parameters, including degree, clustering coefficient and distance of shortest-path. Next, I designed a scoring method which gives each gene a predicted disease-association number. SNPs within top-scoring genes were provided for further analysis. When applying this approach to analyze liver cancer, the top 10 genes are all of related functions, indicating the utility of my methods. Research on complex disease often fails to identify genes that may not be highly- significant but still involve in the biological pathway. This approach includes both the genetic information and corresponding interactions, which extends the current GWAS to a more system-wide landscape. This is the first step toward a comprehensive understanding of common and complex disease, and personal medicine.