Subcortical brain alterations in carriers of genomic copy number variants.
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Kumar Kuldeep, Modenato Claudia, Moreau Clara, Ching Christopher R K, Harvey Annabelle, Martin-Brevet Sandra, Huguet Guillaume, Jean-Louis Martineau, Douard Elise, Martin Charles-Olivier, Younis Nadine, Tamer Petra, Maillard Anne M, Rodriguez-Herreros Borja, Pain Aurélie, Richetin Sonia, 16p11.2 European Consortium, Simons Searchlight Consortium, Kushan Leila, Isaev Dmitry, Alpert Kathryn, Ragothaman Anjani, Turner Jessica A, Wang Lei, Ho Tiffany C, Schmaal Lianne, Silva Ana I, van den Bree Marianne B M, Linden David E J, Owen Michael J, Hall Jeremy, Lippé Sarah, Dumas Guillaume, Draganski Bogdan, Gutman Boris A, Sønderby Ida E, Andreassen Ole A, Schultz Laura, Almasy Laura, Glahn David C, Bearden Carrie E, Thompson Paul M, Jacquemont Sébastien
Аннотация
METHODS: Subcortical structures were characterized using harmonized ENIGMA protocols in 675 CNV carriers (at the following loci: 1q21.1, TAR, 13q12.12, 15q11.2, 16p11.2, 16p13.11, and 22q11.2) and 782 controls (Male/Female: 727/730; age-range: 6-80 years) as well as ENIGMA summary-statistics for ASD, SZ, ADHD, Obsessive-Compulsive-Disorder, Bipolar-Disorder, and Major-Depression.
RESULTS: Nine of the 11 CNVs affected volume of at least one subcortical structure. The hippocampus and amygdala were affected by five CNVs. Effect sizes of CNVs on subcortical volume, thickness and local surface area were correlated with their previously reported effect sizes on cognition and risk for ASD and SZ. Shape analyses were able to identify subregional alterations that were averaged out in volume analyses. We identified a common latent dimension - characterized by opposing effects on basal ganglia and limbic structures - across CNVs and across NPDs.
CONCLUSION: Our findings demonstrate that subcortical alterations associated with CNVs show varying levels of similarities with those associated with neuropsychiatric conditions. We also observed distinct effects with some CNVs clustering with adult conditions while others clustered with ASD. This large cross-CNV and NPDs analysis provide insight into the long-standing questions of why CNVs at different genomic loci increase the risk for the same NPD, as well as why a single CNV increases the risk for a diverse set of NPDs.