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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| Published in | Journal of biomedical research Vol. 29; no. 4; pp. 285 - 297 |
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| Main Authors | , , , , , , , , |
| Format | Journal Article |
| Language | English |
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China
Editorial Department of Journal of Biomedical Research
01.01.2015
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| 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, setbased association analysis, copy number variation analysis and GWAS cohort analysis are also briefly introduced. |
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| AbstractList | 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. 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. |
| Author | Ping Zeng Yang Zhao Cheng Qian Liwei Zhang Ruyang Zhang Jianwei Gou Jin Liu Liya Liu Feng Chen |
| AuthorAffiliation | Department of Epidemiology and Biostatistics, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu211166, China Department of Eptdemtology and Biostatistics, School of Public Health, Xuzhou Medical College, Xuzhou, Jiangsu 221004, China |
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| Keywords | genetic model copy number variation population structure quality control multiple comparison genome-wide association study meta-analysis statistical model missing heritability |
| Language | English |
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| Notes | 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. |
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