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A multi-marker association method for genome-wide association studies without the need for population structure correction

Author

Listed:
  • Jonas R. Klasen

    (Max Planck Institute for Plant Breeding Research (MPIPZ)
    Max Planck Institute for Plant Breeding Research (MPIPZ))

  • Elke Barbez

    (Gregor Mendel Institute (GMI), Austrian Academy of Sciences, Vienna Biocenter (VBC))

  • Lukas Meier

    (Seminar for Statistics, Eidgenssische Technische Hochschule Zurich (ETHZ))

  • Nicolai Meinshausen

    (Seminar for Statistics, Eidgenssische Technische Hochschule Zurich (ETHZ))

  • Peter Bühlmann

    (Seminar for Statistics, Eidgenssische Technische Hochschule Zurich (ETHZ))

  • Maarten Koornneef

    (Max Planck Institute for Plant Breeding Research (MPIPZ))

  • Wolfgang Busch

    (Gregor Mendel Institute (GMI), Austrian Academy of Sciences, Vienna Biocenter (VBC))

  • Korbinian Schneeberger

    (Max Planck Institute for Plant Breeding Research (MPIPZ))

Abstract

All common genome-wide association (GWA) methods rely on population structure correction, to avoid false genotype-to-phenotype associations. However, population structure correction is a stringent penalization, which also impedes identification of real associations. Using recent statistical advances, we developed a new GWA method, called Quantitative Trait Cluster Association Test (QTCAT), enabling simultaneous multi-marker associations while considering correlations between markers. With this, QTCAT overcomes the need for population structure correction and also reflects the polygenic nature of complex traits better than single-marker methods. Using simulated data, we show that QTCAT clearly outperforms linear mixed model approaches. Moreover, using QTCAT to reanalyse public human, mouse and Arabidopsis GWA data revealed nearly all known and some previously undetected associations. Following up on the most significant novel association in the Arabidopsis data allowed us to identify a so far unknown component of root growth.

Suggested Citation

  • Jonas R. Klasen & Elke Barbez & Lukas Meier & Nicolai Meinshausen & Peter Bühlmann & Maarten Koornneef & Wolfgang Busch & Korbinian Schneeberger, 2016. "A multi-marker association method for genome-wide association studies without the need for population structure correction," Nature Communications, Nature, vol. 7(1), pages 1-8, December.
    • Handle: RePEc:nat:natcom:v:7:y:2016:i:1:d:10.1038_ncomms13299
    DOI: 10.1038/ncomms13299
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    Cited by:

    1. Paulo C. Rodrigues & Vanda M. Lourenço, 2020. "Comments on: Hierarchical Inference for genome-wide association studies: a view on methodology with software by Paulo C. Rodrigues and Vanda M. Lourenço," Computational Statistics, Springer, vol. 35(1), pages 57-58, March.
    2. Claude Renaux & Laura Buzdugan & Markus Kalisch & Peter Bühlmann, 2020. "Rejoinder on: Hierarchical inference for genome-wide association studies: a view on methodology with software," Computational Statistics, Springer, vol. 35(1), pages 59-67, March.
    3. Claude Renaux & Laura Buzdugan & Markus Kalisch & Peter Bühlmann, 2020. "Hierarchical inference for genome-wide association studies: a view on methodology with software," Computational Statistics, Springer, vol. 35(1), pages 1-40, March.

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