• ISSN 1674-8301
  • CN 32-1810/R
Volume 29 Issue 4
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Ping Zeng, Yang Zhao, Cheng Qian, Liwei Zhang, Ruyang Zhang, Jianwei Gou, Jin Liu, Liya Liu, Feng Chen. Statistical analysis for genome-wide association study[J]. The Journal of Biomedical Research, 2015, 29(4): 285-297. doi: 10.7555/JBR.29.20140007
Citation: Ping Zeng, Yang Zhao, Cheng Qian, Liwei Zhang, Ruyang Zhang, Jianwei Gou, Jin Liu, Liya Liu, Feng Chen. Statistical analysis for genome-wide association study[J]. The Journal of Biomedical Research, 2015, 29(4): 285-297. doi: 10.7555/JBR.29.20140007

Statistical analysis for genome-wide association study

doi: 10.7555/JBR.29.20140007
Funds:

National Natural Science Foundation of China (No. 81072389, 81373102, 81473070 and 81402765), Research Found for the Doctoral Program of Higher Education of China (No. 20113234110002), Key Grant of Natural Science Foundation of the Jiangsu Higher Education Institutions of China (No. 10KJA330034), College Philosophy and Social Science Foundation from Education Department of Jiangsu Province of China (No. 2013SJB790059, 2013SJD790032), Research Foundation from Xuzhou Medical College (No. 2012KJ02), Research and Innovation Project for College Graduates of Jiangsu Province of China (No. CXLX13_574) and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).

  • Received: 15 January 2014
  • Issue Date: July 2015
  • In the past few years, genome-wide association study (GWAS) has made great successes in identifying genetie susceptibility loci underlying many complex diseases and traits. The findings provide important genetic insights into understanding pathogenesis of diseases. In this paper, we present an overview of widely used approaches and strategies for analysis of GWAS, offered a general consideration to deal with GWAS data. The issues regarding data quality control, population structure, association analysis, multiple comparison and visual presentation of GWAS results are discussed; other advanced topics including the issue of missing heritability, meta-analysis, set- based association analysis, copy number variation analysis and GWAS cohort analysis are also briefly introduced.
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Statistical analysis for genome-wide association study

doi: 10.7555/JBR.29.20140007
Funds:

National Natural Science Foundation of China (No. 81072389, 81373102, 81473070 and 81402765), Research Found for the Doctoral Program of Higher Education of China (No. 20113234110002), Key Grant of Natural Science Foundation of the Jiangsu Higher Education Institutions of China (No. 10KJA330034), College Philosophy and Social Science Foundation from Education Department of Jiangsu Province of China (No. 2013SJB790059, 2013SJD790032), Research Foundation from Xuzhou Medical College (No. 2012KJ02), Research and Innovation Project for College Graduates of Jiangsu Province of China (No. CXLX13_574) and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).

Abstract: In the past few years, genome-wide association study (GWAS) has made great successes in identifying genetie susceptibility loci underlying many complex diseases and traits. The findings provide important genetic insights into understanding pathogenesis of diseases. In this paper, we present an overview of widely used approaches and strategies for analysis of GWAS, offered a general consideration to deal with GWAS data. The issues regarding data quality control, population structure, association analysis, multiple comparison and visual presentation of GWAS results are discussed; other advanced topics including the issue of missing heritability, meta-analysis, set- based association analysis, copy number variation analysis and GWAS cohort analysis are also briefly introduced.

Ping Zeng, Yang Zhao, Cheng Qian, Liwei Zhang, Ruyang Zhang, Jianwei Gou, Jin Liu, Liya Liu, Feng Chen. Statistical analysis for genome-wide association study[J]. The Journal of Biomedical Research, 2015, 29(4): 285-297. doi: 10.7555/JBR.29.20140007
Citation: Ping Zeng, Yang Zhao, Cheng Qian, Liwei Zhang, Ruyang Zhang, Jianwei Gou, Jin Liu, Liya Liu, Feng Chen. Statistical analysis for genome-wide association study[J]. The Journal of Biomedical Research, 2015, 29(4): 285-297. doi: 10.7555/JBR.29.20140007

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