Computation and resource efficient genome-wide association analysis for large-scale imaging studies

Abstract Imaging genetics links genetic variations to brain structures and functions, but the computational challenges posed by high-dimensional imaging and genetic data are significant. In voxel-level genome-wide association studies, we introduce a Representation learning-based Voxel-level Genetic Analysis (RVGA) framework that reduces computational time and storage burden by over 200 times. RVGA enhances statistical power by denoising images and shares minimal datasets of summary statistics for associations across the whole genome of the entire image for secondary analyses. Additionally, it introduces a unified estimator for voxel heritability, genetic correlations between voxels, and cross-trait genetic correlations between voxels and non-imaging phenotypes. Applying RVGA to hippocampus shape and white matter microstructure in the UK Biobank (n = 53,454) reveals 39 and 275 novel loci, respectively. We identify heterogeneity in genetic architecture across images and subregions that share genetic bases with 14 brain-related phenotypes, such as the genetic correlation between the hippocampus and educational attainment, and between the anterior corona radiata and schizophrenia. RVGA replicates known genetic associations and uncovers new discoveries. Competing Interest Statement The authors have declared no competing interest. Funding Statement This work was partially supported by the National Institute on Aging (NIA) of the National Institutes of Health (NIH) grant [U01AG079847, 1R01AG085581, RF1AG082938, R01AR082684 to T.L. and H.Z.], NIH grant [U01HG011720 and R01MH125236 to Y.L., and 1U01AG088667-01 to J.S.], and National Institute of Child Health and Human Development grant [P50HD103573 to Y.L.]. Additionally, this work was partially supported by the National Science Foundation (NSF) grant [DMS-2230795 and DMS-2230797 to E.F.]. 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: IRB of University of North Carolina at Chapel Hill gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the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, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability The raw data is from UK Biobank at https://www.ukbiobank.ac.uk; The shared data generated by using HEIG software is at https://zenodo.org/records/13787684.