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Hierarchical inference for genome-wide association studies: a view on methodology with software

Author

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  • Claude Renaux

    (ETH Zürich)

  • Laura Buzdugan

    (ETH Zürich)

  • Markus Kalisch

    (ETH Zürich)

  • Peter Bühlmann

    (ETH Zürich)

Abstract

We provide a view on high-dimensional statistical inference for genome-wide association studies. It is in part a review but covers also new developments for meta analysis with multiple studies and novel software in terms of an R-package hierinf. Inference and assessment of significance is based on very high-dimensional multivariate (generalized) linear models: in contrast to often used marginal approaches, this provides a step towards more causal-oriented inference.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:compst:v:35:y:2020:i:1:d:10.1007_s00180-019-00939-2
    DOI: 10.1007/s00180-019-00939-2
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    References listed on IDEAS

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    Cited by:

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    3. Antoine Bichat & Christophe Ambroise & Mahendra Mariadassou, 2022. "Hierarchical correction of p-values via an ultrametric tree running Ornstein-Uhlenbeck process," Computational Statistics, Springer, vol. 37(3), pages 995-1013, July.

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