Effects of eight neuropsychiatric copy number variants on human brain structure

• Main Authors: Claudia Modenato, Kuldeep Kumar, Clara Moreau, Sandra Martin-Brevet, Guillaume Huguet, Catherine Schramm, Martineau Jean-Louis, Charles-Olivier Martin, Nadine Younis, Petra Tamer, Elise Douard, Fanny Thébault-Dagher, Valérie Côté, Audrey-Rose Charlebois, Florence Deguire, Anne M. Maillard, Borja Rodriguez-Herreros, Aurèlie Pain, Sonia Richetin, 16p11.2 European Consortium, Simons Searchlight Consortium, Lester Melie-Garcia, Leila Kushan, Ana I. Silva, Marianne B. M. van den Bree, David E. J. Linden, Michael J. Owen, Jeremy Hall, Sarah Lippé, Mallar Chakravarty, Danilo Bzdok, Carrie E. Bearden, Bogdan Draganski, Sébastien Jacquemont
• Format: Article
• Language: English
• Published: Nature Publishing Group 2021-07-01
• Series: Translational Psychiatry
• Online Access: https://doi.org/10.1038/s41398-021-01490-9

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.