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 overv...

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Bibliographic Details
Published inJournal of biomedical research Vol. 29; no. 4; pp. 285 - 297
Main Authors Zeng, Ping, Zhao, Yang, Qian, Cheng, Zhang, Liwei, Zhang, Ruyang, Gou, Jianwei, Liu, Jin, Liu, Liya, Chen, Feng
Format Journal Article
LanguageEnglish
Published China Editorial Department of Journal of Biomedical Research 01.01.2015
Subjects
Review
人口结构
全基因组
关联分析
发病机制
复杂疾病
数据质量控制
统计分析
队列分析
genetic model
copy number variation
population structure
quality control
multiple comparison
genome-wide association study
meta-analysis
statistical model
missing heritability
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Summary: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.
Bibliography:genome-wide association study, quality control, multiple comparison, population structure, genetic model, statistical model, missing heritability, meta-analysis, copy number variation
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.
32-1810/R
ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-3
content type line 23
ObjectType-Review-1
CLC number: R181.2, Document code: A
The authors reported no conflict of interests.
ISSN:1674-8301
DOI:10.7555/jbr.29.20140007