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A dynamic model for genome-wide association studies

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Abstract

Although genome-wide association studies (GWAS) are widely used to identify the genetic and environmental etiology of a trait, several key issues related to their statistical power and biological relevance have remained unexplored. Here, we describe a novel statistical approach, called functional GWAS or fGWAS, to analyze the genetic control of traits by integrating biological principles of trait formation into the GWAS framework through mathematical and statistical bridges. fGWAS can address many fundamental questions, such as the patterns of genetic control over development, the duration of genetic effects, as well as what causes developmental trajectories to change or stop changing. In statistics, fGWAS displays increased power for gene detection by capitalizing on cumulative phenotypic variation in a longitudinal trait over time and increased robustness for manipulating sparse longitudinal data.

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Acknowledgments

This work is partially supported by grant DMS/NIGMS-0540745 to RW and NIDA, NIH grants R21 DA024260 and R21 DA024266 to RL. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIDA or the NIH.

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Authors and Affiliations

  1. Department of Statistics, The Pennsylvania State University, University Park, PA, USA

    Kiranmoy Das, Jiahan Li, Guifang Fu, Runze Li & Rongling Wu

  2. Department of Public Health Sciences, Pennsylvania State College of Medicine, Hershey, PA, USA

    Zhong Wang, Chunfa Tong, Meng Xu, Kwangmi Ahn, David Mauger, Runze Li & Rongling Wu

  3. Department of Statistics, West Virginia University, Morgantown, WV, USA

    Yao Li

Authors
  1. Kiranmoy Das
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  2. Jiahan Li
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  3. Zhong Wang
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  4. Chunfa Tong
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  5. Guifang Fu
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  6. Yao Li
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  7. Meng Xu
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  8. Kwangmi Ahn
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  9. David Mauger
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  10. Runze Li
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  11. Rongling Wu
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Corresponding author

Correspondence to Rongling Wu.

Additional information

K. Das and J. Li contributed equally to this work.

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Das, K., Li, J., Wang, Z. et al. A dynamic model for genome-wide association studies. Hum Genet 129, 629–639 (2011). https://doi.org/10.1007/s00439-011-0960-6

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  • DOI: https://doi.org/10.1007/s00439-011-0960-6

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