Fetal Gene Regulatory Gene Deletions are Associated with Poor Cognition in Schizophrenia and Community-Based Samples

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ABSTRACT Objective Schizophrenia is a neurodevelopmental disorder involving clinical and genetic heterogeneity. Multiple recurrent copy number variants (CNVs) increase risk for schizophrenia spectrum disorders (SSD). However, how known risk CNVs and broader genome-wide CNVs influence clinical variability is unclear. Furthermore, whether biological annotation of CNV scores can improve power for patient stratification is unknown. Methods This study examined associations between severe phenotypes in 617 SSD individuals, namely, child-onset psychosis or borderline intellectual functioning (IQ), and: 1) known risk CNVs; 2) genome-wide deletion burden scores; and 3) novel scores capturing deletion burden in 18 previously validated and mutually exclusive gene-sets, representing distinct aspects of neurodevelopment. Associations with borderline IQ were assessed for replicability in 233 SSD-relatives and 581 controls, and 9,930 youth from the Adolescent Brain Cognitive Development (ABCD) Study. Results Known SSD- (odds ratios (OR)=7.07, 95%CI[1.60,31.32]) and neurodevelopmental disorder (NDD)-risk CNVs (OR=4.56, 95%CI[1.48,14.10]) were associated with borderline IQ in SSD. Furthermore, beyond effects of known NDD-risk CNVs, deletion of genes involved in regulating gene expression during fetal brain development was associated with borderline IQ across SSD cases and non-cases (OR=2.57, 95%CI[1.44,4.60]), and in the ABCD cohort (OR=1.33, 95%CI[1.00,1.76]). Exploratory structural MRI-based analyses showed associations between fetal gene regulatory gene deletions and altered gray matter volume ( b =0.09, 95%CI[0.004,0.17]) and cortical thickness ( b =0.14, 95%CI[0.05,0.24]) across SSD cases and non-cases. Conclusions Results confirm contributions of known risk CNVs to severe phenotypes in SSD, implicate disrupted fetal brain development in poor cognition, and demonstrate the utility of a neurodevelopmental framework for identifying mechanisms underlying severe SSD-relevant phenotypes.

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