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

Authors
  • Renaux, Claude1
  • Buzdugan, Laura1
  • Kalisch, Markus1
  • Bühlmann, Peter1
  • 1 ETH Zürich, Zürich, Switzerland , Zürich (Switzerland)
Type
Published Article
Journal
Computational Statistics
Publisher
Springer Berlin Heidelberg
Publication Date
Jan 06, 2020
Volume
35
Issue
1
Pages
1–40
Identifiers
DOI: 10.1007/s00180-019-00939-2
Source
Springer Nature
License
Yellow

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.

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