Statistical analysis for genome-wide association study.
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.
['Zeng|Ping|P|', 'Zhao|Yang|Y|', 'Qian|Cheng|C|', 'Zhang|Liwei|L|', 'Zhang|Ruyang|R|', 'Gou|Jianwei|J|', 'Liu|Jin|J|', 'Liu|Liya|L|', 'Chen|Feng|F|']
genome-wide association study quality control population structure multiple comparison missing heritability meta-analysis copy number variation genetic model statistical model