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arXiv · 1805.02988

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

Claude Renaux·Laura Buzdugan·Markus Kalisch·Peter Bühlmann
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Abstract

We provide a view on high-dimensional statistical inference for genome-wide association studies (GWAS). 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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Claude Renaux, Laura Buzdugan, Markus Kalisch, Peter Bühlmann. 2019-09-12. Hierarchical inference for genome-wide association studies: a view on methodology with software. https://doi.org/10.1007/s00180-019-00939-2

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