Causal inference on neuroimaging data with Mendelian randomisation.

ABSTRACT: While population-scale neuroimaging studies offer the promise of discovery and characterisation of subtle risk factors, massive sample sizes increase the power for both meaningful associations and those attributable to confounds. This motivates the need for causal modelling of observational data that goes beyond statements of association and towards deeper understanding of complex relationships between individual traits and phenotypes, clinical biomarkers, genetic variation, and brain-related measures of health. Mendelian randomisation (MR) presents a way to obtain causal inference on the basis of genetic data and explicit assumptions about the relationship between genetic variables, exposure and outcome. In this work, we provide an introduction to and overview of causal inference methods

SUBMITTER: Taschler B 

PROVIDER: S-EPMC10933777 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature