ArticleNature communications2026
Computation and resource efficient genome-wide association analysis for large-scale imaging studies.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- A spatially resolved genomic-molecular atlas of human white-matter microstructure.medRxiv : the preprint server for health sciences · 2026Article
- Interpretable multimodal learning for integrating neuroimaging and genetic data in Alzheimer's disease.Frontiers in radiology · 2026Article
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7 authors.
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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 heritability within 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.
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