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...

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Autores principales:	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
Formato:	article
Lenguaje:	EN
Publicado:	KeAi Communications Co., Ltd. 2016
Materias:	Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/61a7e4364c1e45bdbc6a638778249b8b
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spelling	oai:doaj.org-article:61a7e4364c1e45bdbc6a638778249b8b2021-12-02T14:20:34ZData analysis in the post-genome-wide association study era2095-882X10.1016/j.cdtm.2016.11.009https://doaj.org/article/61a7e4364c1e45bdbc6a638778249b8b2016-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2095882X16300469https://doaj.org/toc/2095-882XSince 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 variationQiao-Ling WangWen-Le TanYan-Jie ZhaoMing-Ming ShaoJia-Hui ChuXu-Dong HuangJun LiYing-Ying LuoLin-Na PengQiong-Hua CuiTing FengJie YangYa-Ling HanKeAi Communications Co., Ltd.articleMedicine (General)R5-920ENChronic Diseases and Translational Medicine, Vol 2, Iss 4, Pp 231-234 (2016)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine (General) R5-920
spellingShingle	Medicine (General) R5-920 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
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
format	article
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
title	Data analysis in the post-genome-wide association study era
title_short	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_sort	data analysis in the post-genome-wide association study era
publisher	KeAi Communications Co., Ltd.
publishDate	2016
url	https://doaj.org/article/61a7e4364c1e45bdbc6a638778249b8b
work_keys_str_mv	AT qiaolingwang dataanalysisinthepostgenomewideassociationstudyera AT wenletan dataanalysisinthepostgenomewideassociationstudyera AT yanjiezhao dataanalysisinthepostgenomewideassociationstudyera AT mingmingshao dataanalysisinthepostgenomewideassociationstudyera AT jiahuichu dataanalysisinthepostgenomewideassociationstudyera AT xudonghuang dataanalysisinthepostgenomewideassociationstudyera AT junli dataanalysisinthepostgenomewideassociationstudyera AT yingyingluo dataanalysisinthepostgenomewideassociationstudyera AT linnapeng dataanalysisinthepostgenomewideassociationstudyera AT qionghuacui dataanalysisinthepostgenomewideassociationstudyera AT tingfeng dataanalysisinthepostgenomewideassociationstudyera AT jieyang dataanalysisinthepostgenomewideassociationstudyera AT yalinghan dataanalysisinthepostgenomewideassociationstudyera
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Data analysis in the post-genome-wide association study era

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