title: Statistical analysis for genome-wide association study.
abstract: In the past few years, genome-wide association study (GWAS) has made great successes in identifying genetic 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.
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China.|Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical College, Xuzhou, Jiangsu 221004, China.
authors.forename: Ping
authors.initials: P
authors.lastname: Zeng
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China.
authors.forename: Yang
authors.initials: Y
authors.lastname: Zhao
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China.
authors.forename: Cheng
authors.initials: C
authors.lastname: Qian
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China.
authors.forename: Liwei
authors.initials: L
authors.lastname: Zhang
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China.
authors.forename: Ruyang
authors.initials: R
authors.lastname: Zhang
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China.
authors.forename: Jianwei
authors.initials: J
authors.lastname: Gou
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China.
authors.forename: Jin
authors.initials: J
authors.lastname: Liu
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China.
authors.forename: Liya
authors.initials: L
authors.lastname: Liu
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu 211166, China. fengchen@njmu.edu.cn.
authors.forename: Feng
authors.initials: F
authors.lastname: Chen
keywords: copy number variation; genetic model; genome-wide association study; meta-analysis; missing heritability; multiple comparison; population structure; quality control; statistical model