Year: 2019
Type: preprint
Abstract: Structural variants (SVs) contribute substantially to risk of many brain related disorders including autism and schizophrenia. However, annotating the potential contribution of SVs to disease remains a major challenge. Here, we integrated high resolution SV calling from genome-sequencing in 755 human post-mortem brains with dorsal lateral prefrontal cortex RNA-sequencing from a subset of 629 samples to quantify the dosage and regulatory effects of SVs. We show that genic (p = 5.44×10 −9 ) and regulatory SVs (enhancer p = 3.22×10 −23 , CTCF p = 3.86×10 −18 ) are present at significantly lower frequencies than intergenic SVs after correcting for SV length. Copy number variants (CNVs)—deletions and duplications—exhibit a significant quantitative and directional relationship between the proportion of genic and regulatory content altered and gene expression, and the size of the effect is inversely correlated with the loss-of-function intolerance of the gene. We trained a joint linear model that leverages genic and regulatory annotations to predict expression effects of rare CNVs in independent samples (R 2 = 0.21-0.41). We further developed a regulatory disruption score for each CNV that aggregates the predicted expression across all affected genes weighted by the genes’ intolerance score and applied it to an independent set of SVs from 14,891 genome-sequenced individuals. Pathogenic deletions implicated in neurodevelopmental disorders by ClinGen had significantly more extreme regulatory disruption scores than the rest of the SVs. Rank ordering based on the most extreme regulatory disruption scores prioritized pathogenic deletions that would not have been prioritized by frequency or length alone. This work points to the deleteriousness of regulatory SVs, particularly those altering CTCF sites. We further provide a simple approach for functionally annotating the regulatory effects of SVs in the human brain that has potential to be useful in larger SV studies and should improve as more regulatory annotation data is generated.
DOI: 10.1101/711754
Authors: Lide Han, Xuefang Zhao, Mary Lauren Benton, Thaneer Perumal, Ryan L. Collins, Gabriel E. Hoffman, Jessica S. Johnson, Laura Sloofman, Harold Z. Wang, Kristen J. Brennand, Harrison Brand, Solveig K. Sieberts, Stefano Marenco, Mette A. Peters, Barbara K. Lipska, Panos Roussos, John A. Capra, Michael Talkowski, Douglas M. Ruderfer
Institutions: Vanderbilt University, Vanderbilt University Medical Center, Broad Institute, Harvard University, Massachusetts General Hospital, Massachusetts Institute of Technology, Sage Bionetworks, Icahn School of Medicine at Mount Sinai, National Institutes of Health, National Institute of Mental Health, James J. Peters VA Medical Center, Stanley Center for Psychiatric Research
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: green
Awards: G1077491855, G1414860548, G148749253, G1496609009, G1589306735, G1961843657, Transcriptional consequences of structural variation in brains of schizophrenia patients, G3322638211, Risk genetic variants and cis regulation of gene expression in Bipolar Disorder, G4225100588, International Cohort Collection for Bipolar Disorder, Genetics Association in Schizophrenia and Other Disorders, G5669340364, 1/3-Networks from Multidimensional Data for Schizophrenia and Related Disorders, BIDS: Vanderbilt Training Program in BIg Biomedical Data Science, G8695630221, G9000030924, G9000045865, G9000064556, G9000072995, G9000135527, G9000152567, G9000157619, G9000190976, G9000194796, G9000247718, G9000254743, G9000335162, G9000345885
OpenAlex ID: W2962875392