Data analysis in the post-genome-wide association study era
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 underlyi...
| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2016-12-01
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| Series: | Chronic Diseases and Translational Medicine |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2095882X16300469 |
| _version_ | 1811228120506695680 |
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| author | Qiao-Ling Wang Wen-Le Tan 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 |
| author_facet | Qiao-Ling Wang Wen-Le Tan 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 |
| author_sort | Qiao-Ling Wang |
| collection | DOAJ |
| description | 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. Keywords: Genome-wide association study, Data mining, Integrative data analysis, Polymorphism, Copy number variation |
| first_indexed | 2024-04-12T09:53:13Z |
| format | Article |
| id | doaj.art-61a7e4364c1e45bdbc6a638778249b8b |
| institution | Directory Open Access Journal |
| issn | 2095-882X |
| language | English |
| last_indexed | 2024-04-12T09:53:13Z |
| publishDate | 2016-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Chronic Diseases and Translational Medicine |
| spelling | doaj.art-61a7e4364c1e45bdbc6a638778249b8b2022-12-22T03:37:46ZengWileyChronic Diseases and Translational Medicine2095-882X2016-12-0124231234Data analysis in the post-genome-wide association study eraQiao-Ling Wang0Wen-Le Tan1Yan-Jie Zhao2Ming-Ming Shao3Jia-Hui Chu4Xu-Dong Huang5Jun Li6Ying-Ying Luo7Lin-Na Peng8Qiong-Hua Cui9Ting Feng10Jie Yang11Ya-Ling Han12Department of Etiology and Carcinogenesis, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, ChinaDepartment of Etiology and Carcinogenesis, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, ChinaDepartment of Etiology and Carcinogenesis, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, ChinaDepartment of Etiology and Carcinogenesis, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, ChinaDepartment of Etiology and Carcinogenesis, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, ChinaDepartment of Etiology and Carcinogenesis, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, ChinaDepartment of Etiology and Carcinogenesis, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, ChinaDepartment of Etiology and Carcinogenesis, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, ChinaDepartment of Etiology and Carcinogenesis, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, ChinaDepartment of Etiology and Carcinogenesis, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, ChinaDepartment of Etiology and Carcinogenesis, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, ChinaDepartment of Etiology and Carcinogenesis, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, ChinaCorresponding author.; Department of Etiology and Carcinogenesis, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, ChinaSince 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. Keywords: Genome-wide association study, Data mining, Integrative data analysis, Polymorphism, Copy number variationhttp://www.sciencedirect.com/science/article/pii/S2095882X16300469 |
| spellingShingle | Qiao-Ling Wang Wen-Le Tan 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 Data analysis in the post-genome-wide association study era Chronic Diseases and Translational Medicine |
| title | Data analysis in the post-genome-wide association study era |
| title_full | Data analysis in the post-genome-wide association study era |
| title_fullStr | Data analysis in the post-genome-wide association study era |
| title_full_unstemmed | Data analysis in the post-genome-wide association study era |
| title_short | Data analysis in the post-genome-wide association study era |
| title_sort | data analysis in the post genome wide association study era |
| url | http://www.sciencedirect.com/science/article/pii/S2095882X16300469 |
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