Statistical analysis for genome-wide association study

Ping Zeng; Yang Zhao; Cheng Qian; Liwei Zhang; Ruyang Zhang; Jianwei Gou; Jin Liu; Liya Liu; Feng Chen

doi:10.7555/JBR.29.20140007

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Volume 29 Issue 4

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Article Navigation > The Journal of Biomedical Research > 2015 > 29(4): 285-297

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

1.
Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu211166, China
2.
Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical College, Xuzhou, Jiangsu 221004,China.
3.
Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
4.
Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China
5.
Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu211166, China.

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

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.
- genome-wide association study,
- quality control,
- multiple comparison,
- population structure,
- genetic model,
- statistical model,
- missing heritability,
- meta-analysis,
- copy number variation
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通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

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Statistical analysis for genome-wide association study

doi: 10.7555/JBR.29.20140007

1. Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu211166, China

2. Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical College, Xuzhou, Jiangsu 221004,China.

3. Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China

4. Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China

5. Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu211166, China.

Funds:

Received Date: 2014-01-15
Publish Date: 2015-07-25

Key words:

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.

Citation: