Deep-learning segmentation and multi-ancestry GWAS enhance genetic discovery of the cerebellum
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The genetic architecture of cerebellar substructure volumes is crucial for understanding their functions in cognition, emotion, and neuropsychiatric disorders. We utilized deep-learning segmentation and multi-ancestry analysis to identify genetic associations with 31 cerebellar volumetric traits in 57,071 participants. We found deep learning superior to atlas-based segmentation in heritability estimation, genetic discovery, and polygenic prediction. We identified 407 new loci for these traits (241 from univariate, four from sex-stratified, and 162 from multivariate analyses), along with 19 ancestry-specific and eight sex-specific associations. We prioritized 453 causal variants and 71 relevant genes, categorizing these cerebellar substructures into nine genetically informed clusters. We linked cerebellar volumes to 20 cognitive and mental phenotypes and seven brain disorders. These findings provide an overview of the genetic architecture of cerebellar volumes.
