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.Author summary: Copy number variants (CNVs) at 16p11.2 and 22q11.2 are associated with neurobehavioral traits including ASD, bipolar disorder, schizophrenia, BMI, and IQ). Previously, we attempted to identify individual genes within these CNVs relevant for each trait, but found that many CNV-trait pairs did not demonstrate single-gene association. Here, we use similar methodology to assess whether the effect of CNV genes on the same traits could be better explained by pairs of genes acting together. We found that in nearly all cases, pairs of genes explained trait variance better than single genes. In several cases, specific genes contributed to traits disproportionately in pairs, but not individually. Additionally, we tested for region-wide association using all genes in the region, and found that both the 16p11.2 and 22q11.2 regions had a significant effect on BMI and IQ. Our results demonstrate that the genetic architecture of CNV-trait associations is multigenic and may vary across CNVs and traits.