Effects of eight neuropsychiatric copy number variants on human brain structure

Abstract

Many copy number variants (CNVs) confer risk for the same range of neurodevelopmental symptoms and psychiatric conditions including autism and schizophrenia. Yet, to date neuroimaging studies have typically been carried out one mutation at a time, showing that CNVs have large effects on brain anatomy. Here, we aimed to characterize and quantify the distinct brain morphometry effects and latent dimensions across 8 neuropsychiatric CNVs. We analyzed T1-weighted MRI data from clinically and non-clinically ascertained CNV carriers (deletion/duplication) at the 1q21.1 (n = 39/28), 16p11.2 (n = 87/78), 22q11.2 (n = 75/30), and 15q11.2 (n = 72/76) loci as well as 1296 non-carriers (controls). Case-control contrasts of all examined genomic loci demonstrated effects on brain anatomy, with deletions and duplications showing mirror effects at the global and regional levels. Although CNVs mainly showed distinct brain patterns, principal component analysis (PCA) loaded subsets of CNVs on two latent brain dimensions, which explained 32 and 29% of the variance of the 8 Cohen’s d maps. The cingulate gyrus, insula, supplementary motor cortex, and cerebellum were identified by PCA and multi-view pattern learning as top regions contributing to latent dimension shared across subsets of CNVs. The large proportion of distinct CNV effects on brain morphology may explain the small neuroimaging effect sizes reported in polygenic psychiatric conditions. Nevertheless, latent gene brain morphology dimensions will help subgroup the rapidly expanding landscape of neuropsychiatric variants and dissect the heterogeneity of idiopathic conditions.

Structural deviations of the posterior fossa and the cerebellum and their cognitive links in a neurodevelopmental deletion syndrome

“…Our findings indicate that 3q29Del sits at the intersection of abnormal cerebellar development and increased risk for neurodevelopmental and psychiatric disease, which converges with growing evidence implicating the cerebellum in the pathophysiology of several other genomic variants, including well-known ASD or SZ-related genes such as TSC1, FMR1, SHANK3, MECP2, and PTEN, and CNVs like 22q11.2 deletion (161)(162)(163)(164)(165)(166)(167). The explanatory gap between these loci and disease mechanism has not yet been fully bridged, however altered cerebellar development may be a shared neuroanatomical endophenotype that contributes to heightened risk for a variety of neurodevelopmental and psychiatric disorders across these variants.…”

Translational Study of Copy Number Variations in Schizophrenia

“…CNVs at proximal 16p11.2 are also associated with neurodevelopmental disorder, intellectual disability, attention-deficit hyperactivity disorder, depression, and autism [ 34 , 36 , 47 , 48 , 49 , 50 , 51 ]. Finally, CNVs at 22q11.2 are associated with congenital heart disease, neurodevelopmental disorders, intellectual disability, autism spectrum disorders, and other neuropsychiatric conditions [ 7 , 48 , 52 , 53 ]. Together, these data indicate that clinical manifestations of CNVs at the eight loci in this study are not limited to schizophrenia; they have pleiotropic clinical effects.…”

Rare CNVs and phenome-wide profiling: a tale of brain-structural divergence and phenotypical convergence

“…We analyzed a data sample of T1-weighted (T1w) images at 0.8–1 mm isotropic resolution. All T1w included in the analysis were quality checked by a domain expert (Modenato et al, 2021a). Data for Voxel-Based Morphometry were preprocessed and analyzed with SPM12 (http://www.fil.ion.ucl.ac.uk/spm/software/spm12/) (Ashburner, 2007; Ashburner and Friston, 2005; Lorio et al, 2016) running under MATLAB R2018b (https://www.mathworks.com/products/new_products/release2018b.html).…”

“…1). An extensive description of methods and analyses is available in an already published study with an identical dataset (Modenato et al, 2021a). In short, PennCNV and QuantiSNP were used, with standard quality control metrics, to identify CNVs.…”

“…below). As a data-cleaning step, derived regional brain volumes were adjusted for total grey matter, age, age 2 , and sex as fixed effects and scanning site as a random factor, following previous research on this dataset (Modenato et al, 2021a).…”