Data analysis in the post-genome-wide association study era

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• 作者：Qiao-Ling Wang^a ; Wen-Le Tan^a ; Yan-Jie Zhao ; Ming-Ming Shao ; Jia-Hui Chu ; Xu-Dong Huang ; Jun Li ; Ying-Ying Luo ; Lin-Na Peng ; Qiong-Hua Cui ; Ting Feng ; Jie Yang ; Ya-Ling Han ; ^{hyl1288@aliyun.com}
• 关键词：Genome-wide association study ; Data mining ; Integrative data analysis ; Polymorphism ; Copy number variation
• 刊名：Chronic Diseases and Translational Medicine
• 出版年：2016
• 出版时间：December 2016
• 年：2016
• 卷：2
• 期：4
• 页码：231-234
• 全文大小：364 K
• 卷排序：2

文摘

Since the first report of a genome-wide association study (GWAS) on human age-related macular degeneration, GWAS has successfully been used to discover genetic variants for a variety of complex human diseases and/or traits, and thousands of associated loci have been identified. However, the underlying mechanisms for these loci remain largely unknown. To make these GWAS findings more useful, it is necessary to perform in-depth data mining. The data analysis in the post-GWAS era will include the following aspects: fine-mapping of susceptibility regions to identify susceptibility genes for elucidating the biological mechanism of action; joint analysis of susceptibility genes in different diseases; integration of GWAS, transcriptome, and epigenetic data to analyze expression and methylation quantitative trait loci at the whole-genome level, and find single-nucleotide polymorphisms that influence gene expression and DNA methylation; genome-wide association analysis of disease-related DNA copy number variations. Applying these strategies and methods will serve to strengthen GWAS data to enhance the utility and significance of GWAS in improving understanding of the genetics of complex diseases or traits and translate these findings for clinical applications.