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Progress and challenges in genome-wide association studies in humans

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  • Peter Donnelly

    (Peter Donnelly is director of the Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford OX3 7BN, UK.)

Abstract

After more than a decade of hope and hype, researchers are finally making inroads into understanding the genetic basis of many common human diseases. The use of genome-wide association studies has broken the logjam, enabling genetic variants at specific loci to be associated with particular diseases. Genetic association data are now providing new routes to understanding the aetiology of disease, as well as new footholds on the long and difficult path to better treatment and disease prevention.

Suggested Citation

  • Peter Donnelly, 2008. "Progress and challenges in genome-wide association studies in humans," Nature, Nature, vol. 456(7223), pages 728-731, December.
    • Handle: RePEc:nat:nature:v:456:y:2008:i:7223:d:10.1038_nature07631
    DOI: 10.1038/nature07631
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    Citations

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    Cited by:

    1. Joanna L Davies & Jean-Baptiste Cazier & Malcolm G Dunlop & Richard S Houlston & Ian P Tomlinson & Chris C Holmes, 2012. "A Novel Test for Gene-Ancestry Interactions in Genome-Wide Association Data," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-9, December.
    2. Huang, Lucy & Buzbas, Erkan O. & Rosenberg, Noah A., 2013. "Genotype imputation in a coalescent model with infinitely-many-sites mutation," Theoretical Population Biology, Elsevier, vol. 87(C), pages 62-74.
    3. Bergersen Linn Cecilie & Glad Ingrid K. & Lyng Heidi, 2011. "Weighted Lasso with Data Integration," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-29, August.
    4. Charles-Elie Rabier & Philippe Barre & Torben Asp & Gilles Charmet & Brigitte Mangin, 2016. "On the Accuracy of Genomic Selection," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-23, June.
    5. Marttinen Pekka & Gillberg Jussi & Havulinna Aki & Corander Jukka & Kaski Samuel, 2013. "Genome-wide association studies with high-dimensional phenotypes," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 12(4), pages 413-431, August.

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