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Genome-Wide Association Studies

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Data Mining for Systems Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 939))

Abstract

A host of data on genetic variation from the Human Genome and International HapMap projects, and advances in high-throughput genotyping technologies, have made genome-wide association (GWA) studies technically feasible. GWA studies help in the discovery and quantification of the genetic components of disease risks, many of which have not been unveiled before and have opened a new avenue to understanding disease, treatment, and prevention.

This chapter presents an overview of GWA, an important tool for discovering regions of the genome that harbor common genetic variants to confer susceptibility for various diseases or health outcomes in the post-Human Genome Project era. A tutorial on how to conduct a GWA study and some practical challenges specifically related to the GWA design is presented, followed by a detailed GWA case study involving the identification of loci associated with glioma as an example and an illustration of current technologies.

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Notes

  1. 1.

    As a practical matter, the walk can be stopped at more stringent p values without changing the main conclusions. In particular stopping AGS at p = 10−5, and TCGA at 10−3, while requiring that p 12 pass the genomic significance level (1.7 × 10−7), loses only 2 SNPs (rs12021720 and rs2810424), neither of which adds new genomic regions.

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Authors and Affiliations

  1. Bioinformatics program, College of Engineering, Boston University, Boston, MA, USA

    Tun-Hsiang Yang

  2. Department of Mathematics and Statistics, Boston University, Boston, MA, USA

    Mark Kon

  3. College of Engineering, Boston University, Boston, MA, USA

    Charles DeLisi

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  1. Tun-Hsiang Yang
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  2. Mark Kon
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  3. Charles DeLisi
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Correspondence to Tun-Hsiang Yang .

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Editors and Affiliations

  1. , Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto, 611-0011, Japan

    Hiroshi Mamitsuka

  2. Dept. Biomedical Engineering, Boston University, Cummington St. 44, Boston, 02215, Massachusetts, USA

    Charles DeLisi

  3. Inst. Chemical Research, Bioinformatics Center, Kyoto University, Gokasho, Uji, 611-0011, Kyoto, Japan

    Minoru Kanehisa

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Yang, TH., Kon, M., DeLisi, C. (2013). Genome-Wide Association Studies. In: Mamitsuka, H., DeLisi, C., Kanehisa, M. (eds) Data Mining for Systems Biology. Methods in Molecular Biology, vol 939. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-107-3_15

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  • DOI: https://doi.org/10.1007/978-1-62703-107-3_15

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