3.5

CiteScore

2.3

Impact Factor
  • ISSN 1674-8301
  • CN 32-1810/R
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

  • 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.
  • loading

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return