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Designs for Linkage Analysis and Association Studies of Complex Diseases

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Statistical Methods in Molecular Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 620))

Abstract

Genetic linkage analysis has been a traditional means for identifying regions of the genome with large genetic effects that contribute to a disease. Following linkage analysis, association studies are widely pursued to fine-tune regions with significant linkage signals. For complex diseases which often involve function of multi-genetic variants each with small or moderate effect, linkage analysis has little power compared to association studies. In this chapter, we give a brief review of design issues related to linkage analysis and association studies with human genetic data. We introduce methods commonly used for linkage and association studies and compared the relative merits of the family-based and population-based association studies. Compared to candidate gene studies, a genomewide blind searching of disease variant is proving to be a more powerful approach. We briefly review the commonly used two-stage designs in genome-wide association studies. As more and more biological evidences indicate the role of genomic imprinting in disease, identifying imprinted genes becomes critically important. Design and analysis in genetic mapping imprinted genes are introduced in this chapter. Recent efforts in integrating gene expression analysis and genetic mapping, termed expression quantitative trait loci (eQTLs) mapping or genetical genomics analysis, offer new prospect in elucidating the genetic architecture of gene expression. Designs in genetical genomics analysis are also covered in this chapter.

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Acknowledgments

This work was supported in part by NSF grants DMS-0707031 and DMS-0540745.

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Authors and Affiliations

  1. Department of Statistics and Probability, Michigan State University, East Lansing, MI, USA

    Yuehua Cui, Gengxin Li & Shaoyu Li

  2. Departments of Public Health Sciences and Statistics, Pennsylvania State University, Hershey, PA, USA

    Rongling Wu

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  4. Rongling Wu
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Editor information

Editors and Affiliations

  1. Weill Medical College, Dept. Public Health, Cornell University, East 69th St. 411, New York, 10021, New York, USA

    Heejung Bang

  2. Weill Medical College, Dept. Public Health, Cornell University, East 69th St. 411, New York, 10021, New York, USA

    Xi Kathy Zhou

  3. Journal of Experimental Medicine, Rockefeller University Press, First Ave. 1114, New York, 10021, New York, USA

    Heather L. van Epps

  4. Weill Medical College, Dept. Public Health, Cornell University, East 69th St. 411, New York, 10021, New York, USA

    Madhu Mazumdar

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© 2010 Humana Press, a part of Springer Science+Business Media, LLC

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Cui, Y., Li, G., Li, S., Wu, R. (2010). Designs for Linkage Analysis and Association Studies of Complex Diseases. In: Bang, H., Zhou, X., van Epps, H., Mazumdar, M. (eds) Statistical Methods in Molecular Biology. Methods in Molecular Biology, vol 620. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-60761-580-4_6

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  • DOI: https://doi.org/10.1007/978-1-60761-580-4_6

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  • Publisher Name: Humana Press, Totowa, NJ

  • Print ISBN: 978-1-60761-578-1

  • Online ISBN: 978-1-60761-580-4

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