How copy number variant gene expression contributes to neurobehavioral traits

Many neurobehavioral traits are highly heritable, yet specific genes underlying them are difficult to identify. Copy number variants – large, often multi-gene, deletions and duplications - are one category of mutation that drives traits including autism spectrum disorder (ASD), bipolar disorder, schizophrenia, obesity, and intellectual disability. A 600kb 30-gene region on chromosome 16p11.2, for example, is strongly associated with ASD when duplicated or deleted, while deletions of a 3mb 60-gene region on chromosome 22q11.2 are a well-known genetic cause of schizophrenia. Under the assumption that a subset of these 30 or 60 genes contribute to associated traits, either individually or in combination, we investigate the genic architecture of CNV association with neurobehavioral traits.We think about CNVs’ impact on traits in the context of gene expression: individuals that have genetic duplications have increased gene expression (1.5x of normal), while those with deletions have decreased gene expression (0.5x of normal). Among non-carriers, expression levels vary as well, although typically to a smaller extent. This gives rise to a hypothesis: expression variation of the genes in a CNV region may be associated with the CNV-associated (or related) traits in non-carriers. Under this hypothesis, we can separate out the association of the expression of specific CNV genes with specific traits, which cannot be done in a CNV carrier where all genes are upregulated or downregulated.We studied large populations of non-carriers with genetic data (and corresponding controls, where applicable) for five neurobehavioral traits: ASD, bipolar disorder, schizophrenia, BMI (as a proxy for obesity), and IQ (as a proxy for intellectual disability). We took an expression prediction approach which allowed us to convert GWAS-style data into imputed expression-level data which can be used for association analyses. We studied the association of individual CNV genes, pairs of CNV genes, and all genes in the region to these five traits. This study design was also used to assess association between CNV gene expression variation and clinical traits within a large biobank with genotypic and clinical information.The second chapter of this dissertation focuses on individual genes within GWAS datasets and clinical biobanks, and the third focuses on the extension of this approach to combinations of genes and the entire region. In support of our hypothesis, we were able to detect individual genes at 16p11.2 associated with neurobehavioral traits, most notably INO80E, significantly associated with schizophrenia and BMI, and nominally associated with bipolar disorder. Using the biobank, we found additional genes associated with related clinical traits including psychosis and mood disorders, with an overall over-representation of mental disorders in CNV gene associated phenotypes. We then found that variance in traits was better explained by pairs of CNV genes in nearly all instances, including those where we had identified single-gene associations. The regionwide prediction was associated with BMI and IQ at both 16p11.2 and 22q11.2, but not with any neuropsychiatric trait. The importance of the combinatorial contributions of genes did not extend to matched control regions for the same traits. In sum, our studies provide insight into the transcription-based action of CNV genes, identify potential candidate genes for further study, describe combinatorial patterns of CNV gene impacts on neurobehavior, and demonstrate the utility of integrating genetic, clinical, and transcriptomic data for in silico analyses

Combinations of genes at the 16p11.2 and 22q11.2 CNVs contribute to neurobehavioral traits.

The 16p11.2 and 22q11.2 copy number variants (CNVs) are associated with neurobehavioral traits including autism spectrum disorder (ASD), schizophrenia, bipolar disorder, obesity, and intellectual disability. Identifying specific genes contributing to each disorder and dissecting the architecture of CNV-trait association has been difficult, inspiring hypotheses of more complex models, such as multiple genes acting together. Using multi-tissue data from the GTEx consortium, we generated pairwise expression imputation models for CNV genes and then applied these elastic net models to GWAS for: ASD, bipolar disorder, schizophrenia, BMI (obesity), and IQ (intellectual disability). We compared the variance in these five traits explained by gene pairs with the variance explained by single genes and by traditional interaction models. We also modeled polygene region-wide effects using summed predicted expression ranks across many genes to create a regionwide score. We found that in all CNV-trait pairs except for bipolar disorder at 22q11.2, pairwise effects explain more variance than single genes. Pairwise model superiority was specific to the CNV region for all 16p11.2 traits and ASD at 22q11.2. We identified novel individual genes over-represented in top pairs that did not show single-gene signal. We also found that BMI and IQ have significant regionwide association with both CNV regions. Overall, we observe that genetic architecture differs by trait and region, but 9/10 CNV-trait combinations demonstrate evidence for multigene contribution, and for most of these, the importance of combinatorial models appears unique to CNV regions. Our results suggest that mechanistic insights for CNV pathology may require combinational models

Combinations of genes at the 16p11.2 and 22q11.2 CNVs contribute to neurobehavioral traits.

The 16p11.2 and 22q11.2 copy number variants (CNVs) are associated with neurobehavioral traits including autism spectrum disorder (ASD), schizophrenia, bipolar disorder, obesity, and intellectual disability. Identifying specific genes contributing to each disorder and dissecting the architecture of CNV-trait association has been difficult, inspiring hypotheses of more complex models, such as multiple genes acting together. Using multi-tissue data from the GTEx consortium, we generated pairwise expression imputation models for CNV genes and then applied these elastic net models to GWAS for: ASD, bipolar disorder, schizophrenia, BMI (obesity), and IQ (intellectual disability). We compared the variance in these five traits explained by gene pairs with the variance explained by single genes and by traditional interaction models. We also modeled polygene region-wide effects using summed predicted expression ranks across many genes to create a regionwide score. We found that in all CNV-trait pairs except for bipolar disorder at 22q11.2, pairwise effects explain more variance than single genes. Pairwise model superiority was specific to the CNV region for all 16p11.2 traits and ASD at 22q11.2. We identified novel individual genes over-represented in top pairs that did not show single-gene signal. We also found that BMI and IQ have significant regionwide association with both CNV regions. Overall, we observe that genetic architecture differs by trait and region, but 9/10 CNV-trait combinations demonstrate evidence for multigene contribution, and for most of these, the importance of combinatorial models appears unique to CNV regions. Our results suggest that mechanistic insights for CNV pathology may require combinational models

Integration of genetic, transcriptomic, and clinical data provides insight into 16p11.2 and 22q11.2 CNV genes.

BackgroundDeletions and duplications of the multigenic 16p11.2 and 22q11.2 copy number variant (CNV) regions are associated with brain-related disorders including schizophrenia, intellectual disability, obesity, bipolar disorder, and autism spectrum disorder (ASD). The contribution of individual CNV genes to each of these identified phenotypes is unknown, as well as the contribution of these CNV genes to other potentially subtler health implications for carriers. Hypothesizing that DNA copy number exerts most effects via impacts on RNA expression, we attempted a novel in silico fine-mapping approach in non-CNV carriers using both GWAS and biobank data.MethodsWe first asked whether gene expression level in any individual gene in the CNV region alters risk for a known CNV-associated behavioral phenotype(s). Using transcriptomic imputation, we performed association testing for CNV genes within large genotyped cohorts for schizophrenia, IQ, BMI, bipolar disorder, and ASD. Second, we used a biobank containing electronic health data to compare the medical phenome of CNV carriers to controls within 700,000 individuals in order to investigate the full spectrum of health effects of the CNVs. Third, we used genotypes for over 48,000 individuals within the biobank to perform phenome-wide association studies between imputed expressions of individual 16p11.2 and 22q11.2 genes and over 1500 health traits.ResultsUsing large genotyped cohorts, we found individual genes within 16p11.2 associated with schizophrenia (TMEM219, INO80E, YPEL3), BMI (TMEM219, SPN, TAOK2, INO80E), and IQ (SPN), using conditional analysis to identify upregulation of INO80E as the driver of schizophrenia, and downregulation of SPN and INO80E as increasing BMI. We identified both novel and previously observed over-represented traits within the electronic health records of 16p11.2 and 22q11.2 CNV carriers. In the phenome-wide association study, we found seventeen significant gene-trait pairs, including psychosis (NPIPB11, SLX1B) and mood disorders (SCARF2), and overall enrichment of mental traits.ConclusionsOur results demonstrate how integration of genetic and clinical data aids in understanding CNV gene function and implicates pleiotropy and multigenicity in CNV biology