  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.
