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 ins...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, setbased association analysis, copy number variation analysis and GWAS cohort analysis are also briefly introduced.展开更多
基金supported by 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)+4 种基金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)the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)
文摘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, setbased association analysis, copy number variation analysis and GWAS cohort analysis are also briefly introduced.
