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

Claude Renaux, Laura Buzdugan, Markus Kalisch and Peter Bühlmann ()
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Claude Renaux: ETH Zürich
Laura Buzdugan: ETH Zürich
Markus Kalisch: ETH Zürich
Peter Bühlmann: ETH Zürich

Computational Statistics, 2020, vol. 35, issue 1, No 1, 40 pages

Abstract: 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.

Date: 2020
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s00180-019-00939-2

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