Large cohorts face the further challenge that statistically significant associations are identified even when their explanatory power is small. In the present dataset, significance is reached at a correlation of just r≈0.1, i.e., 1% of population variance explained41 even with multiple comparison correction. Large genome-wide association studies (GWAS) face this challenge, where it is accepted that small effect sizes can be meaningful, particularly where multiple factors combine to create a large effect. However, in GWAS, genetic variants can be interpreted as causal factors (whether direct or indirect42), whereas apparent associations across IDPs and non-imaging phenotypes could result from a shared latent (non-measured) cause. For example, education level could result in a dietary factor associating with a brain IDP, despite no direct causal connection between diet and IDP. This danger is inflated with larger subject numbers, but may here be mitigated by the rich life factor and biological variables that can be controlled for or used to match sub-groups. Population variances explained in the pairwise associations reached maxima of around 5% (Supplementary Fig 2), but these are higher with the multivariate
