Hierarchical inference for genome-wide association studies: a view on methodology with software
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2020-02-17 v4
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.
Keywords
Cite
@article{arxiv.1805.02988,
title = {Hierarchical inference for genome-wide association studies: a view on methodology with software},
author = {Claude Renaux and Laura Buzdugan and Markus Kalisch and Peter Bühlmann},
journal= {arXiv preprint arXiv:1805.02988},
year = {2020}
}