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Genome-Wide Association Studies

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Genetic Epidemiology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1793))

Abstract

Genetic association studies have made a major contribution to our understanding of the genetics of complex disorders over the last 10 years through genome-wide association studies (GWAS). In this chapter, we review the key concepts that underlie the GWAS approach. We will describe the “common disease, common variant” theory, and will review how we finally afforded to capture the common variance in genome to make GWAS possible. Finally, we will go over technical aspects of GWAS such as genotype imputation, epidemiologic designs, analysis methods, and considerations such as genomic inflation, multiple testing, and replication.

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References

  1. 1000 Genomes Project Consortium, Abecasis GR, Altshuler D et al (2010) A map of human genome variation from population-scale sequencing. Nature 467(7319):1061–1073. https://doi.org/10.1038/nature09534

    Article  CAS  Google Scholar 

  2. Hindorff LA, Sethupathy P, Junkins HA et al (2009) Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci U S A 106(23):9362–9367. https://doi.org/10.1073/pnas.0903103106

    Article  PubMed  PubMed Central  Google Scholar 

  3. Hirschhorn JN, Daly MJ (2005) Genome-wide association studies for common diseases and complex traits. Nat Rev Genet 6(2):95–108. https://doi.org/10.1038/nrg1521

    Article  PubMed  CAS  Google Scholar 

  4. Risch N, Merikangas K (1996) The future of genetic studies of complex human diseases. Science 273(5281):1516–1517

    Article  CAS  PubMed  Google Scholar 

  5. Guo SW (1997) Linkage disequilibrium measures for fine-scale mapping: a comparison. Hum Hered 47(6):301–314

    Article  CAS  PubMed  Google Scholar 

  6. International HapMap Consortium (2005) A haplotype map of the human genome. Nature 437(7063):1299–1320. https://doi.org/10.1038/nature04226

    Article  CAS  Google Scholar 

  7. Wang DG, Fan JB, Siao CJ et al (1998) Large-scale identification, mapping, and genotyping of single-nucleotide polymorphisms in the human genome. Science 280(5366):1077–1082

    Article  CAS  PubMed  Google Scholar 

  8. Li M, Li C, Guan W (2008) Evaluation of coverage variation of SNP chips for genome-wide association studies. Eur J Hum Genet 16(5):635–643. https://doi.org/10.1038/sj.ejhg.5202007

    Article  PubMed  CAS  Google Scholar 

  9. 1000 Genomes Project Consortium, Abecasis GR, Auton A et al (2012) An integrated map of genetic variation from 1,092 human genomes. Nature 491(7422):56–65. https://doi.org/10.1038/nature11632

    Article  CAS  Google Scholar 

  10. McCarthy S, Das S, Kretzschmar W et al (2016) A reference panel of 64,976 haplotypes for genotype imputation. Nat Genet 48(10):1279–1283. https://doi.org/10.1038/ng.3643

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Hill G, Connelly J, Hebert R et al (2003) Neyman's bias re-visited. J Clin Epidemiol 56(4):293–296

    Article  PubMed  Google Scholar 

  12. Lettre G, Lange C, Hirschhorn JN (2007) Genetic model testing and statistical power in population-based association studies of quantitative traits. Genet Epidemiol 31(4):358–362. https://doi.org/10.1002/gepi.20217

    Article  PubMed  Google Scholar 

  13. Yang J, Lee SH, Goddard ME et al (2011) GCTA: a tool for genome-wide complex trait analysis. Am J Hum Genet 88(1):76–82. https://doi.org/10.1016/j.ajhg.2010.11.011

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Yang J, Ferreira T, Morris AP et al (2012) Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat Genet 44(4):369–375., S361-363. https://doi.org/10.1038/ng.2213

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Pe'er I, Yelensky R, Altshuler D et al (2008) Estimation of the multiple testing burden for genomewide association studies of nearly all common variants. Genet Epidemiol 32(4):381–385. https://doi.org/10.1002/gepi.20303

    Article  PubMed  Google Scholar 

  16. van den Oord EJ (2008) Controlling false discoveries in genetic studies. American journal of medical genetics part B, neuropsychiatric genetics: the official publication of the international society of. Psychiatr Genet 147B(5):637–644. https://doi.org/10.1002/ajmg.b.30650

    Article  CAS  Google Scholar 

  17. Purcell S, Neale B, Todd-Brown K et al (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81(3):559–575. https://doi.org/10.1086/519795

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Zollner S, Pritchard JK (2007) Overcoming the winner's curse: estimating penetrance parameters from case-control data. Am J Hum Genet 80(4):605–615. https://doi.org/10.1086/512821

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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Correspondence to Abbas Dehghan .

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Dehghan, A. (2018). Genome-Wide Association Studies. In: Evangelou, E. (eds) Genetic Epidemiology. Methods in Molecular Biology, vol 1793. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7868-7_4

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  • DOI: https://doi.org/10.1007/978-1-4939-7868-7_4

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7867-0

  • Online ISBN: 978-1-4939-7868-7

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