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

TL;DR

This paper reviews and introduces new methods for high-dimensional inference in GWAS, emphasizing multivariate models and software tools to improve causal inference over traditional marginal approaches.

Contribution

It presents novel developments for meta-analysis across multiple studies and introduces the R-package hierinf for advanced high-dimensional inference in GWAS.

Findings

Enhanced software for high-dimensional GWAS analysis

Meta-analysis methods for multiple studies

Shift towards causal inference in GWAS

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.