The analyses presented here demonstrate some of the possibilities offered by the UK Biobank resource. Focused association studies may select just two variables to investigate, for example, one IDP correlated against one life factor, genetic marker, physical assay or health outcome. More complex analyses could model a larger number of variables simultaneously, for example, looking to predict health outcome from multiple linear regression against several predictor variables. Nonlinear methods (e.g. penalized regression or data-driven feature selection)22 could enable use of much larger number of predictor variables. A further extension could identify nonlinear interactions between predictor variables, for example considering an imaging measure, a life factor and an interaction term between the two as three distinct predictors. An even more complex analysis might predict multiple outcome variables, looking for “doubly-multivariate” associations between two or more sets of variables; the CCA-ICA analyses presented above are an example of this. Finally, imaging measures may in some cases be more sensitive or specific than clinical symptoms47, thereby providing proxies for healthcare outcomes and/or enabling clustering of patients that is more predictive of prognosis or therapeutic response48.
