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Designs for Linkage Analysis and Association Studies of Complex Diseases

  • Yuehua Cui
  • Gengxin Li
  • Shaoyu Li
  • Rongling Wu
Part of the Methods in Molecular Biology book series (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.

Key words

Association studies complex diseases genetical genomics genome-wide association studies genomic imprinting linkage analysis 

Notes

Acknowledgments

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

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Copyright information

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

Authors and Affiliations

  • Yuehua Cui
    • 1
  • Gengxin Li
    • 1
  • Shaoyu Li
    • 1
  • Rongling Wu
    • 2
  1. 1.Department of Statistics and ProbabilityMichigan State UniversityEast LansingUSA
  2. 2.Departments of Public Health Sciences and StatisticsPennsylvania State UniversityHersheyUSA

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