SUMMARY Analysis of de novo CNVs (dnCNVs) from the full Simons Simplex Collection (SSC) (N = 2,591 families) replicates prior findings of strong association with autism spectrum disorders (ASDs) and confirms six risk loci (1q21.1, 3q29, 7q11.23, 16p11.2, 15q11.2-13, and 22q11.2). The addition of published CNV data from the Autism Genome Project (AGP) and exome sequencing data from the SSC and the Autism Sequencing Consortium (ASC) shows that genes within small de novo deletions, but not within large dnCNVs, significantly overlap the high-effect risk genes identified by sequencing. Alternatively, large dnCNVs are found likely to contain multiple modest-effect risk genes. Overall, we find strong evidence that de novo mutations are associated with ASD apart from the risk for intellectual disability. Extending the transmission and de novo association test (TADA) to include small de novo deletions reveals 71 ASD risk loci, including 6 CNV regions (noted above) and 65 risk genes (FDR ≤ 0.1).

Summary Given prior evidence for the contribution of rare copy number variations (CNVs) to autism spectrum disorders (ASD), we studied these events in 4,457 individuals from 1,174 simplex families, composed of parents, a proband and, in most kindreds, an unaffected sibling. We find significant association of ASD with de novo duplications of 7q11.23, where the reciprocal deletion causes Williams-Beuren syndrome, featuring a highly social personality. We identify rare recurrent de novo CNVs at five additional regions including two novel ASD loci, 16p13.2 (including the genes USP7 and C16orf72) and Cadherin13, and implement a rigorous new approach to evaluating the statistical significance of these observations. Overall, we find large de novo CNVs carry substantial risk (OR=3.55; CI =2.16-7.46, p=6.9 × 10−6); estimate the presence of 130-234 distinct ASD-related CNV intervals across the genome; and, based on data from multiple studies, present compelling evidence for the association of rare de novo events at 7q11.23, 15q11.2-13.1, 16p11.2, and Neurexin1.

Most psychiatric disorders are moderately to highly heritable. The degree to which genetic variation is unique to individual disorders or shared across disorders is unclear. To examine shared genetic etiology, we use genome-wide genotype data from the Psychiatric Genomics Consortium (PGC) for cases and controls in schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorders (ASD) and attention-deficit/hyperactivity disorder (ADHD). We apply univariate and bivariate methods for the estimation of genetic variation within and covariation between disorders. SNPs explained 17–29% of the variance in liability. The genetic correlation calculated using common SNPs was high between schizophrenia and bipolar disorder (0.68 ± 0.04 s.e.), moderate between schizophrenia and major depressive disorder (0.43 ± 0.06 s.e.), bipolar disorder and major depressive disorder (0.47 ± 0.06 s.e.), and ADHD and major depressive disorder (0.32 ± 0.07 s.e.), low between schizophrenia and ASD (0.16 ± 0.06 s.e.) and non-significant for other pairs of disorders as well as between psychiatric disorders and the negative control of Crohn’s disease. This empirical evidence of shared genetic etiology for psychiatric disorders can inform nosology and encourages the investigation of common pathophysiologies for related disorders.

Disorders of the brain can exhibit considerable epidemiological comorbidity and often share symptoms, provoking debate about their etiologic overlap. We quantified the genetic sharing of 25 brain disorders from genome-wide association studies of 265,218 patients and 784,643 control participants and assessed their relationship to 17 phenotypes from 1,191,588 individuals. Psychiatric disorders share common variant risk, whereas neurological disorders appear more distinct from one another and from the psychiatric disorders. We also identified significant sharing between disorders and a number of brain phenotypes, including cognitive measures. Further, we conducted simulations to explore how statistical power, diagnostic misclassification, and phenotypic heterogeneity affect genetic correlations. These results highlight the importance of common genetic variation as a risk factor for brain disorders and the value of heritability-based methods in understanding their etiology.

Purpose Copy number variants (CNVs) have emerged as a major cause of human disease such as autism and intellectual disabilities. Because CNVs are common in normal individuals, determining the functional and clinical significance of rare CNVs in patients remains challenging. The adoption of whole-genome chromosomal microarray analysis (CMA) as a first-tier diagnostic test for individuals with unexplained developmental disabilities provides a unique opportunity to obtain large CNV datasets generated through routine patient care. Methods A consortium of diagnostic laboratories was established [the International Standards for Cytogenomic Arrays (ISCA) consortium] to share CNV and phenotypic data in a central, public database. We present the largest CNV case-control study to date comprising 15,749 ISCA cases and 10,118 published controls, focusing our initial analysis on recurrent deletions and duplications involving 14 CNV regions. Results Compared to controls, fourteen deletions, and seven duplications were significantly overrepresented in cases, providing a clinical diagnosis as pathogenic. Conclusion Given the rapid expansion of clinical CMA testing, very large datasets will be available to determine the functional significance of increasingly rare CNVs. This data will provide an evidenced-based guide to clinicians across many disciplines involved in the diagnosis, management, and care of these patients and their families.