Presented by Hongtu Zhu, Kenan Distinguished Professor of Biostatistics, Statistics, Radiology, Computer Science & Genetics, Department of Biostatistics, University of North Carolina, Gillings School of Global Public Health

AUST 202, Wednesday, April 9, 2025, 4:00 PM-5:00 PM
Webex Meeting Link

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 highly efficient imaging genetics (HEIG) framework that reduces computational time and storage burden by over 200 times. HEIG enhances statistical power by denoising images and allows for the sharing of minimal datasets of summary statistics for secondary analyses. Additionally, it introduces a unified estimator for voxel heritability, genetic correlations between voxels, and cross-trait genetic correlations. Applying HEIG to hippocampus shape and white matter microstructure in the UK Biobank (n = 33,324) reveals 94 and 540 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. HEIG replicates known genetic associations and uncovers new discoveries.