Sparse Parallel Independent Component Analysis and Its Application to Identify Stable and Replicable Imaging-genomic Association Patterns in UK Biobank

Data fusion analyses of brain imaging and genomics enable the linking of genomic factors to brain patterns. Due to the small to modest effect sizes of common genetic variants, it is usually challenging to reliably identify relevant genetic factors from the rest of the genome with the typical sample size in neuroimaging studies. To alleviate this problem, we propose sparse parallel independent component analysis (spICA) to leverage the sparsity of individual genomic sources, building upon the existing parallel independent component analysis (pICA) algorithm. Sparsity is enforced by performing Hoyer projection on the estimated independent sources. Simulation results demonstrate that spICA yields improved recovery of imaging-genomic associations and sources compared to pICA. We applied spICA to whole-brain gray matter volume (GMV) and whole-genome single nucleotide polymorphisms (SNPs) data of five different sets of 24,985 discovery samples in the UK Biobank. We identified three GMV sources significantly and stably associated with one SNP source and replicated these associations. GMV sources highlighted frontal, parietal, and temporal regions. Their corresponding loadings on individuals were related to multiple cognitive measures, and the temporal region interacts with age influencing cognition. The SNP component underscored SNPs in chromosome 17 that were enriched in the inflammation response pathway and in regulation effect in the prefrontal cortex via gene expression, methylation, transcription expression, and isoform percentage. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was supported in part by the National Institutes of Health through grants 1R01MH106655 and R01MH118695 and National Science Foundation grant # 2112455 and National Natural Science Foundation of China (Grant No. 62076157 and 61703253, to YHD) ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study used openly available human data that were originally located at . I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All data produced in the present study are available at .