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

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.

preprint2019arXivOpen access
Claude RenauxLaura BuzduganMarkus KalischPeter Bühlmann
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Open access4 authors1 topic
Imported metadata coverageMissing code, dataset, citation and institution fields are tracked without dominating the paper.Details
Citations: 0Reviews: 0Saves: 0Code: not linkedDataset: not linkedInstitutions: 0

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Claude RenauxLaura BuzduganMarkus KalischPeter Bühlmann

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