Saturday, April 27, 2024
4/27/2024
Project Abstract Copy number variants (CNVs) are associated with greatly elevated rates of neurodevelopmental psychiatric disorders (NPDs). Efforts to date have focused on a handful of the most frequent recurrent CNVs. As a result, the guiding principles underlying relationships between CNV-related variations in brain architecture and the function of genes encompassed in these CNVs are unknown. In addition, the behavioral features mediated by CNV-related brain variations, and their convergence with findings in idiopathic NPDs, are poorly understood. To address these questions, we propose to utilize existing cohorts to assemble the largest neuroimaging genomic dataset to date (n~140,000). The final aggregated dataset will include ~29,000 carriers of CNVs = 50kb, encompassing coding genes. CNVs will be identified using the same pipeline across all datasets. Multimodal neuroimaging data, including T1-weighted structural images, T1w/T2w ratio images, resting-state functional MRI, and diffusion MRI, will be processed using the same harmonized pipelines. Our team, with expertise in medical and statistical genetics, mathematical modeling, and brain imaging, will work collaboratively across 4 sites in the USA, Canada, and Norway to address the following Specific Aims: Aim 1: Characterize the effect of the most frequent and well-established recurrent NPD- associated CNVs on brain structure and function. We will also investigate if common variants (polygenic scores) modulate the effects of NPD-CNVs on neuroimaging-derived measures. Aim 2: Investigate effects on brain structure and function of global CNV burden using CNV- risk scores. A method to link functional annotations of genes to CNV-associated neuroimaging alterations. The vast majority of clinically relevant CNVs are too rare to be studied individually. Using CNV-risk scores based on gene annotations, we will investigate (in aggregate) the effects on brain architecture of all rare CNVs (n~29,000) containing coding genes. Aim 3: Linking CNV-associated neuroimaging signatures to RDoC domains and psychiatric phenotypes. We will test the relationship between CNV-neuroimaging signatures identified above and dimensional measures of cognition and psychopathology in large, deeply phenotyped, unselected population cohorts. This worldwide CNV neuroimaging initiative will boost power to identify mechanisms that mediate the effects of deletions and duplications on brain architecture. This concerted endeavor will advance our understanding of mechanisms by which genetic variants increase vulnerability for NPDs such as autism and schizophrenia.
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