Dynamic fusion of genomics and functional network connectivity in UK biobank reveals static and time-varying SNP manifolds

BackgroundMany mental disorders show strong genetic influence. In parallel, dynamic functional network connectivity (dFNC) has shown high sensitivity to brain changes related to mental disorders. However, previous studies linking dFNC to genetics largely follow a paradigm to identify associations between one set of genetic factors and multiple sets of connectivity features from different dFNC states, ignoring the potential variability in genetic correlates across states. MethodsWe propose a novel joint ICA (jICA)-based "dynamic fusion" framework to identify dynamically-tuned genetic manifolds. A sliding window approach was utilized to estimate four dFNC states and compute subject-level state-average dFNC (sa-dFNC) features. The sa-dFNC features of each state were combined with schizophrenia risk SNPs within a jICA fusion framework, resulting in four parallel fusions in 32861 individuals of the UK Biobank cohort. The extracted four sets of joint SNP-dFNC components were further validated for clinical relevance in a combined schizophrenia cohort of 1237 individuals (528 patients). The similarity of SNP-dFNC components across four parallel fusions was evaluated as a measure of state variability. ResultsWe observed a mixture of "state-invariant" and "state-variant" components for SNP and dFNC modalities. Particularly, the schizophrenia-related state-variant SNP components, or manifolds, complemented each other by capturing different genes involved in the same biological functions, revealing a partition of genomic risk particularly elicited by the dynamics of brain function. ConclusionsBy augmenting the SNP factors to state-variant manifolds, this dynamic fusion framework promises additional insights into underlying genetic risk of disease-related alterations in dynamic brain function.