Abstract
A challenge in clinical genomics is to predict whether copy number variation (CNV) affecting a gene or multiple genes will manifest as disease. Increasing recognition of gene dosage effects in neurodevelopmental disorders prompted us to develop a computational approach based on critical-exon (highly expressed in brain, highly conserved) examination for potential etiologic effects. Using a large CNV dataset, our updated analyses revealed significant (P < 1.64 × 10−15) enrichment of critical-exons within rare CNVs in cases compared to controls. Separately, we used a weighted gene co-expression network analysis (WGCNA) to construct an unbiased protein module from prenatal and adult tissues and found it significantly enriched for critical exons in prenatal (P < 1.15 × 10−50, OR = 2.11) and adult (P < 6.03 × 10−18, OR = 1.55) tissues. WGCNA yielded 1,206 proteins for which we prioritized the corresponding genes as likely to have a role in neurodevelopmental disorders. We compared the gene lists obtained from critical-exon and WGCNA analysis and found 438 candidate genes associated with CNVs annotated as pathogenic, or as variants of uncertain significance (VOUS), from among 10,619 developmental delay cases. We identified genes containing CNVs previously considered to be VOUS to be new candidate genes for neurodevelopmental disorders (GIT1, MVB12B and PPP1R9A) demonstrating the utility of this strategy to index the clinical effects of CNVs.
Highlights
The broad umbrella classification of “developmental disorders” encompasses various conditions characterized by disturbance or delay of developmental milestones that appear in infancy or childhood
Through unbiased critical exon analysis coupled with weighted gene coexpression network analysis (WGCNA) we identified 1,206 candidate genes whose disruption is likely to contribute to neurodevelopmental delay (Table S5)
The brain transcriptome and proteomic method implemented here can infer candidate genes from within the boundaries of a pathogenic or VOUS copy number variation (CNV) that are likely to impact brain-related conditions. Such lists of candidate genes can be used to index the potential effect of a particular CNV or a set of CNVs in neurodevelopmental disorders
Summary
The broad umbrella classification of “developmental disorders” encompasses various conditions characterized by disturbance or delay of developmental milestones that appear in infancy or childhood. We revealed an inverse correlation between exon expression level in brain and the burden of rare missense mutations found in population controls Variants in these specific critical exons are significantly enriched among individuals with autism, relative to their unaffected siblings. Building upon this concept, we have implemented an integrated genomics approach to analyze CMA data from DNA of individuals with developmental delay, to infer biologically relevant genes at the transcriptome and proteome levels. We have implemented an integrated genomics approach to analyze CMA data from DNA of individuals with developmental delay, to infer biologically relevant genes at the transcriptome and proteome levels For this analysis, we added RNA-seq data from 388 postmortem brain tissues (prenatal to adult) and re-constructed the exon transcriptome contingency index for 226,845 exons from 19,631 genes. New candidate genes from within the pathogenic variants and VOUS were inferred from the aggregate analysis of spatio-temporal mRNA and protein expression data
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